Pharmacogenetics and Variability in Drug Response

Pharmacogenetics-Pharmacogenomics-Personalized Medicine

2011-11-20T07:51:00.000-08:00

The latest plot of numbers of publications suing the terms 'Pharmacogenetics', 'Pharmacogenomics', and 'Personalized Medicine'. (see previous plot) Interestingly the term 'Pharmacogenomics' isn't replacing the term 'Pharmacogenetics' at all. This is despite what has often been expressed that the two terms are used interchangeably. Quite obviously the scientific community does make a distinction between the two terms and continue to use them in distinct ways. The number of times the P'genetics term is used still remains greater than that for P'genomics.

Perhaps P'genomics should be considered a subset of P'genetics rather than what has often conversely been suggested.

The brain as a protected efficacy and/or toxicity compartment

2011-11-18T18:05:00.000-08:00

Distribution of selected drug transporters at the BBB. ABC transporters are ATP-driven xenobiotic efflux pumps that can be highly polyspecific (P-glycoprotein is an extreme example), with overlapping substrate specificities that are briefly outlined for each in the boxes. There is considerable controversy over the localization of essentially all ABC transporters in brain capillaries [1,6]. It is likely that species differences in protein expression levels and cellular transporter distribution, as well as differences in detection techniques across laboratories, underlie many of the conflicts that permeate the literature. Nevertheless, when expressed on the luminal plasma membrane, ABC transporters provide an active, structure-sensitive barrier to drugs within the vascular space. Other transporters shown are members of the SLC superfamily and handle primarily organic anions and weak organic acids. Substrates for these transporters include some drugs, such as nonsteroidal anti-inflammatory drugs, as well as drug metabolites and waste products of normal CNS metabolism. They, along with luminal ABC transporters, provide a two-stage system for active and efficient excretion of potentially toxic chemicals and metabolites from the CNS.

(Figure taken from Trends in Pharmacological Sciences Vol.31 No.6)

The brain has for a long time been recognized as a very well protected organ. Chemically, the brain is isolated from the circulation by means of a very tight blood-brain barrier (BBB), first described by Stern in 1921. This BBB consists of very tightly stitched together endothelial junctions which restrict free movement of molecules from the circulation into the interstitial fluid of the brain. The capillaries are themselves encased by a thick basement membrane and the enveloping foot processes of the astrocytes.

It was thought for a long time that this barrier allowed small and lipophilic molecules to passively diffuse across, and that specific transporters mediated essential solutes such as glucose etc. This model however has been challenged by the recognition that few molecules, no matter how small or lipophilic, would diffuse across membranes without the assistance of some transporter protein. Even water required the assistance of aquaporins. The BBB is now recognized as a very sophisticated and metabolically dynamic membrane populated by not only transporter proteins but also CYP450 enzymes.

These functions of the BBB have recently been reviewed and it is worthwhile reading up on these. The following one is a recommended as it discusses also the regulation of transporters at the BBB:
Miller, D. Trends in Pharmacological Sciences 31 (2010) 246–254 (from where the above figure has been borrowed).

The article concludes:
It is now clear from studies with animal models and with patient samples that the expression and activity of P-glycoprotein and other ABC transporters at the BBB can be moving targets, affected by genetics, disease, pharmacotherapy and diet. Indeed, we are rapidly adding to maps of the signals and signaling pathways involved with a view to improving both CNS protection and the delivery of small-molecule drugs to the brain. Thus, an understanding of signaling could provide opportunities to both selectively fine tune barrier function up or down and to begin to identify the barrier-based and external factors that contribute to patient-to-patient variability in response to CNS-acting drugs. Although we are rapidly developing a very detailed picture of signaling to ABC transporters in animal models, it is still not clear to what extent these pathways operate in humans. An understanding of transporter function and regulation at the human BBB is critical before we can determine to what extent signaling can be manipulated to improve drug delivery to the CNS and to enhance neuroprotection.

It should however, also be noted that the BBB is not the only barrier that exists in the brain. It is probably the main barrier for drugs which act on surface receptors on neuronal membranes. But for drugs which act intracellularly, there is yet another barrier which exists at the cell (neurones or astrocytes) membranes. Furthermore, it is far from clear if the BBB is a uniform barrier throughout the brain. It is possible that the population of transporters may have regional differences.

Because of the existence of these barriers to the movement of drug molecules, we must be careful of trying to extrapolate too much from plasma concentrations of drugs acting on the CNS. There is often a significant dichotomy between the plasma pharmacokinetics of drug and what is actually happening not only in the brain but more specifically, intracellular mechanisms in the brain.

Deciphering the efficacy-toxicity profiles of CNS drugs become a complex exercise when efficacy is expected in the CNS while toxicity may be manifest in another tissue protected by a different population of receptors. It becomes even more perplexing when one considers that these populations of transporters in efficacy and toxicity compartments can be modulated differently by environmental and epigenetic mechanisms over time as well as between individuals.

Stress - Nature vs Nurture: Lessons for drug response variability

2011-10-21T18:11:00.000-07:00

This has been abstracted from Darlene Francis and Daniela Kaufer's essay in Reading Frames: The Scientist October 2011

Recent advances in neuroscience make a compelling case for finally abandoning the nature vs. nurture debate to focus on understanding the mechanisms through which genes and environments are perpetually entwined throughout an individual’s lifetime. As neurobiologists who study stress, we believe that research in this area will help reframe the study of human nature.

Researchers have historically approached the study of stress from two perspectives: 1) a physiological account of the stress response, which consists of tracking the stress hormone cortisol and its effects on metabolism, immune function, and neural processes; and 2) a psychological/cognitive focus on how the perception and experience of a stressor influences the stress response. These approaches align with the nature vs. nurture debate, pitting nature, represented by the biology of cortisol responses, against nurture, in the form of external experience influencing cognitive processing. Academic researchers typically study stress by adopting one of these perspectives. However, anyone who’s been stuck in rush hour traffic or faced a looming deadline knows that the causes and consequences of stressful experiences do not adhere to these academic divides.

Scientists and laymen alike still spend too much time and effort trying to quantify the relative importance of nature and nurture.
In the past decade, researchers have made great strides in understanding the cellular, molecular, genetic, and epigenetic processes involved in the regulation of the stress response. Surprisingly, as stress research elucidated this molecular dimension, it shed light on the powerful role of environment and experience in remodeling our molecular makeup. It became clear that the environmental effects (nurture) are modulated by genetic polymorphism and epigenetic programming of gene expression (nature) to shape development. So, as the molecular underpinnings are elucidated, the need to study the interaction between environment and our genome is highlighted, and the divide seems less relevant.

Bridging the efficacy–effectiveness gap

2011-08-22T21:54:00.000-07:00

Bridging the efficacy–effectiveness gap: a regulator's perspective on addressing variability of drug response
Hans-Georg Eichler, Eric Abadie, Alasdair Breckenridge, Bruno Flamion, Lars L. Gustafsson, Hubert Leufkens, Malcolm Rowland, Christian K. Schneider & Brigitte Bloechl-Daum

Abstract

Drug regulatory agencies should ensure that the benefits of drugs outweigh their risks, but licensed medicines sometimes do not perform as expected in everyday clinical practice. Failure may relate to lower than anticipated efficacy or a higher than anticipated incidence or severity of adverse effects. Here we show that the problem of benefit–risk is to a considerable degree a problem of variability in drug response. We describe biological and behavioural sources of variability and how these contribute to the long-known efficacy–effectiveness gap. In this context, efficacy describes how a drug performs under conditions of clinical trials, whereas effectiveness describes how it performs under conditions of everyday clinical practice. We argue that a broad range of pre- and post-licensing technologies will need to be harnessed to bridge the efficacy–effectiveness gap. Successful approaches will not be limited to the current notion of pharmacogenomics-based personalized medicines, but will also entail the wider use of electronic health-care tools to improve drug prescribing and patient adherence.

Nature Reviews Drug Discovery 10, 495-506 (July 2011)

Looking at PK data #2

2011-08-22T19:05:00.001-07:00

Here is a parallel set of data comparing young healthy adults and patients with cirrhosis. Again, this set of data is not meant to prove changes which happen in cirrhosis but more as an exercise to explore and understand PK effects.

As in the previous post, the first thing that strikes you is the massive changes in AUC seen in cirrhotics. The AUC for cirrhotics is 1.85 times that of the young. By the same logic as in the previous post this AUC increase is inversely related to a decrease in clearance and/or bioavailability. Since there is no way to assess bioavailability in this set of data, we shall leave it out of the discussion for the time being. It is not that the cirrhosis did not result in a change of bioavailability, it is just that the experimental design does not allow us to examine bioavailability effects.

Unlike the situation with the elderly, the reduction in clearance in cirrhotics is not accompanied by a fall in the unbound fraction. Instead, the unbound fraction is elevated from 3.5 to 5.3%. Changes in protein binding is not uncommon in liver cirrhosis. Often serum albumin is reduced. Here the protein binding is decreased with a corresponding increase in the free fraction. In fact the increase in free fraction has the effect of actually increasing metabolic clearance.The reduction in clearance is therefore not a result of a decrease free fraction, but primarily due to loss of enzyme activity. One can in addition, expect that the extent of degradation of enzyme activity is even greater than indicated by the extent of decrease of clearance because part of this effect is mitigated by an increased clearance caused by the increase in free fraction.

The increase in elimination halflife and corresponding fall in Kel is related to the reduction in clearance. The magnitude of the change in halflife is however larger than the fall in clearance and suggests that perhaps the Vd may have increased as well. This is expected because of the increase in free fraction.

Looking at PK data #1

2011-08-21T18:00:00.000-07:00

Here is a set of data, adapted from published information comparing pk data of orally administered Drug X between young subjects and elderly subjects. The data here just provides an opportunity to qualitatively discuss PK effects, and it is not the intention here to 'prove' any PK changes in the elderly.

In this case the most apparent difference is rather large and significant difference in AUC between young and elderly. The AUC in elderly is about 1.84 times greater than that for the young. This is not an unexpected finding. The question is what is the cause of the reduced AUC?

We know from theory that AUC is determined primarily by bioavailability and clearance. However, since we have no way to assess bioavailability here, we shall concentrate on clearance effects instead. The increase in AUC is consistent with a reduction in clearance in the elderly. Again from theory, assuming this is primarily metabolic clearance, we expect that metabolic clearance of an orally administered drug is dependent on protein binding and enzyme activity.

When we inspect the protein binding data we find that the protein binding in elderly is actually increased with the unbound fraction falling from 4.3 to 3.4%. But this magnitude of change is relatively small compared to the estimated change in clearance. Hence it is possible the the total change in clearance reflects both a reduction in unbound fraction as well as a degradation of enzyme activity.

The fall in the unbound fraction potentially also affects the Vd. We have no direct way of assessing the Vd changes here but the Cmax provides indirect (though inaccurate) look at possible Vd changes. The Cmax for the elderly is higher than in the young but marginally less (probably insignificantly less) than the magnitude of change for AUC, so the Vd effect is uncertain.

The halflife changes in the elderly are consistent with the reduction in metabolic clearance.

The uncertainty in this case study is how much any bioavailability changes play in affecting the PK data. The magnitude of halflife change is quite comparable to the magnitude of AUC and clearance changes. This suggests that if there are bioavailability effects it is probably minimal.

Phytochemicals, pharmacogenetics and pharmacokinetics

2010-11-16T18:23:00.000-08:00

When one appreciates the extensive range on interactions that exist between phytochemicals essentially entering our bodies through our diet, and the various processes involved in host protection, it becomes very clear that dietary modifications of pharmacokinetic process should be a relative given in our understanding of drug ADME. In fact it should be a central theme in our understanding of interindividual and interpopulational variability in drug behaviour, rather than just being accorded the occasional consideration as a determinant of drug behaviour.

As a corollary, pharmacogenetics cannot hope to fully explain the pharmacokinetic behaviour of any drug in an individual. Environmental chemicals, largely phytochemicals, modify pharmacokinetic processes according to the genetic constitution of the individual, while the genetic makeup of the individual can only be fully expressed in response to environmental chemical effects.

Which leads us to automatically consider what the epigenetic mechanisms might be, in shaping the PK environment of the individual. Perhaps these might be more important than the occasional loss/gain of function variants that we find in the population.

Polyphenols - another type of phytochemical

2010-11-15T19:26:00.000-08:00

Another probably more significant group of phytochemicals are the polyphenols. These are molecules of various sizes but which have more than one phenolic unit in their structure. Examples of polyphenols are flavonoids, lignins and tannins.

One of the oldest functions of polyphenols might be protection against UV damage. But polyphenols tend to have varied and complex biological roles. Some of these roles include antioxidation, cell signalling and insect/herbivore signalling.

The non-flavonoid polyphenol, curcumin, for example, is principle member of a family of co

ngeners found in turmeric.

Turmeric itself is a rhizome and a relative of the ginger. The Chinese call it the 'yellow ginger', and the Malays call it 'kunyit'. It's biological activity is recognized in in many cultures and is listed in many traditional pharmacopoieas. It is best known however for the yellow flavouring used in many curries.

Curcumin is however, actually, poorly absorbed when taken orally. The reason has been attributed to poor absorption and rapid metabolism and elimination. It has been shown to induce apoptosis of cancer cells, and thus thought to have anti-cancer properties in the colon. Some clinical studies have also shown that curcumin taken in gram amounts over a period of time can inhibit CYP1A2 while enhancing CYP2A6. Apparently, when given in these large amounts, significant absorption occurs to enable enzyme inhibition. In vitro work suggest inhibition also of CYP2B6 and CYP3A4. Curcumin is otherwise quite harmless even in large doses and therefore appear to serve only the purpose of discouraging eating of the raw rhizome. Although quite flavourful when cooked with other spices, the raw turmeric root is quite unpalatable due to the bitter and pungent taste of curcuminoids.

Similar to the alkaloids, the polyphenols interact extensively with drug metabolizing enzymes and membrane transporters. These interactions are not limited to direct interactions with the proteins, but are also mediated through interaction with regulatory processes of the various enzyme and transporter genes.

Plant alkaloids and chemodefense

2010-11-15T01:11:00.000-08:00

Although many alkaloids have toxicity which is immediate and topical so as to discourage predation, many alkaloids do get absorbed into the predator's body and can therefore produce pharmacological and toxicological effects beyond the point of exposure. Some of these effects are extreme and may cause severe reactions in the predator, again discouraging predation.

Animals learn to stay away from such plants. Alternatively, they develop protective mechanisms against the toxicological effects of the alkaloids. Apart from the biological membranes which provide an initial protective barrier against insoluble and hydrophilic chemicals, many organisms also have evolved protective mechanisms such as the cytochrome P450 enzyme systems and the efflux transport proteins to detoxify and repel the more permeable alkaloids which may be able to escape past the biological membranes. In response, plants, over time, evolve even more complex chemicals to overcome animal defense mechanisms. This plant-animal arms-race create the complex environment which now can be seen to determine pharmacokinetic behaviour of the the drugs we use.

Drug metabolism and drug transport must therefore be seen as component parts of an integrated process to protect animals from the toxicity of plant alkaloids.

Read this interesting account of the mustard oil bomb.

A well known groups of alkaloids are the methylxanthines.

Caffeine: R₁ = R₂ = R₃ = CH₃

Theobromine: R₁ = H, R₂ = R₃ = CH₃

Theophylline: R₁ = R₂ = CH₃, R₃ = H

The three main members are caffeine (1,3,7-trimethyxanthine), theobromine (3,7-dimethylxanthine) and theophylline (1,3-diethyxanthine). Caffeine is found in tea and coffee, while theobromine is the main methylxanthine found chocolate. The methylxanthines are phospohodiesterase inhibitors.

The metabolism of caffeine is shown below. Caffeine has been use as a probe substrate to develop metabolic ratios for CYP1A2 and N-acetytransferase 2.

Plant alkaloids

2010-11-14T19:56:00.000-08:00

Alkaloids at chemicals with a nitrogen in the structure. Most are basic bit this not necessarily so as some may be neutral or weak acids. By and large the alkaloids are cyclic structures with the nitrogen as part of the ring. Exo-cyclic nitrogenous bases are just called amines.

Alkaloids can be found both in plants and animals, but for the most part we refer to plant alkaloids. For a long time, plant alkaloids were though to be incidental metabolic dead-ends which accumulated in certain plant parts, but that line of thinking has changed, and now it is thought that plant alkaloids are part of the plant defense mechanisms against insects and animals.

Alkaloids are often biologically active and tend to be bitter in taste, reflecting their role in discouraging insect and animal attacks. Their effects are often topical and immediate, and it is not necessary for them to be absorbed into the body. Many therefore are bitter tasting, emetic or intestinally toxic. They may also be directly tissue toxic, for example, producing contact dermatitis when applied to the skin.

Alkaloids are often not produced in isolation in the plant and exist as part of a family of congeners. They are found in growing parts of plants, young shoots and leaves, barks and roots. These basket of chemicals produce a variety biological effects, targeting a wide range of biological systems. Because of their versatility in interacting with biological systems, many alkaloids can produce 'beneficial' effects. For the most part, I believe these are incidental. It is therefore a

fallacy, promulgated by new age naturopaths and companies selling natural remedies, that chemicals from plants are naturally 'health giving' and safe.

The Han Chinese - fiction or biological reality?

2010-11-12T05:12:00.000-08:00

The Chinese people have been called various names. Once we were called Orientals; the word 'orient' referring to the East. The original reference to Orientals by the west was with respect to people from the middle east or asia minor- regions East of Europe. In time, the definition shifted eastwards and became more associated with East Asia. Clearly the term has no meaning today and should be completely dropped from the scientific literature. Another term, 'Asians' was sometimes used, although in the United Kingdom, this has come to refer to people from the Indian subcontinent. The term East Asians attempts to shift the definition towards people from China, Korea and Japan. For the most part, Chinese were just referred to as Chinese.

More recently however, another term 'Han Chinese' has emerged. The implication appears to be that the concept of Chinese was inadequate and that somehow a 'Han' preface was needed to distinguish Han Chinese from non-Han Chinese. I suspect this has arisen as a result of more scientific work from China, where it was necessary to distinguish the main Han ethnic group from the other 56 ethnic groups which were not 'Han'. The following chart shows the increasing use of the term 'Han Chinese' in the literature beginning from about 1980 and rising steeply over the last 10 years.

In Singapore we don't really care to be Han Chinese, other than using the term to be consistent with 1 billion other people who want to use the term. For us, we are just plain and simple Chinese.

The term Han Chinese actually takes reference to a wish to be associated with the legendary 1000 year Han dynasty which ended in 220CE. I think this is more important to the mainland Chinese, more of the northern sorts. Interestingly for many of us more southern type Chinese, of the Min-nan dialects, the term 'Tang people' '唐人' is more frequently used. This is in reference to another wonderful period of Chinese history called the Tang dynasty (618-904).

So there isn't a whole lot of consistency with respect to how Chinese people want to refer to themselves. Tracing an ancestry back to a dynastic period is not an ethnically reasonable proposition since there can be no assumption that people from that dynasty were necessarily ethnically homogeneous.

It is somewhat of a wishful thinking that the Chinese people, whether Han or Tang, are a biologically homogeneous bunch of people. For large stretches of Chinese history, the country had to deal with more belligerent northern neighbours, and had been conquered by Mongols and Manchus from the North. These people, clearly of a different ethnicity from the indigenous Chinese, not only tended to displace the populations southwards and also widely assimilated with the resident Chinese. The more southern Chinese didn't have the benefits of the Great Wall so there was really nothing to keep them from mingling with their more southern non-Han neighbours.

As a result of these admixtures, there is a north-south cline in East Asian populations. In China itself, there would appear at the very least, among the "Han Chinese", to be a distinction between northern and southern Chinese. The watershed for these two groups is not a clear one, but appear to be somewhere in the vicinity of the Yangtze or Chang Jiang river divide.

It is important to be aware of the existence of this cline because it affects the way we look at "East Asian" population data. While for the most part, Chinese data tend to cluster quite closely together (which is not surprising), we need to be careful about too readily extrapolating northern data to a southern Chinese population, and vice versa. In many situations, northern Chinese data seem to be more in alignment with data from Japan and Korea, while southern Chinese data aligns with Hong Kong, Taiwan and Singapore.

Singapore, being the most southern of the major 'Han Chinese' populations, may have some unique features potentially because of some degree of genetic admixtures with the resident austronesian populations in south east Asia.

Optimization? What's that?

2010-11-11T18:48:00.000-08:00

Optimization refers to the situation where you can adjust the inputs into a system according to output functions, and eventually arrive at the best solution. In therapeutic terms, it is the process of calibrating the dosage of a drug according to the clinical response so that the best dosage for the patient can be arrived at depending on the therapeutic targets and the patient's individualized response.

This is really no different from the engineering concept of a control system.

When there is an input into a particular process, and no feedback is received about the outcome....this is called an 'open loop control system'. This is probably the least ideal of all control systems. It fundamentally assumes you know everything there is to know and that the decision taken about the initial input is already adequate. This happens in therapeutics when you have fixed dose regiments, and there is no feedback about the outcome. Consider the situation in most cancer chemotherapeutic regiments. Dosage regiments are pretty much determined at the outset, and the only feedback received is if the patient has obvious toxicity which requires cessation of therapy, i.e. switch off the system! This is essentially the pharmacogenomic approach towards 'personalized medicine'.

In other scenarios, there is possibility for the operator to make adjustments to the original input based on feedback received, but this takes place independent of the original model which determined the input. This is called an "open-loop feedback control system'. Most therapeutic scenarios are of this sort, where an initial decision about a starting dosage regiment is made based on starting knowledge and assumptions about the patient. Subsequently minor adjustments to the dosage regiment can be made by the physician depending on feedback received about the patient's clinical drug response. Where there is poor ability to receive feedback about clinical drug response, the system starts to flounder and approximates the simple open-loop control system.

The open-loop feedback control system can operate with varying degrees of sophistication. It can be empirical, where the response to feedback received is relatively intuitive and based on clinical judgement. Or it can be highly deterministic where the response is determined by precise mathematical (PK or PK-PD) models.

A more sophisticated model takes into consideration the uncertainty in the system. This is called a stochastic approach.

The ideal system is that of a closed loop system where the original models determining the original input is linked to, and continually modified by new feedback received. The following diagram represents a closed loop control system with warfarin as an example.

Modified from Applied pharmacokinetics & pharmacodynamics: principles of therapeutic drug monitoring. 1992 Michael E. Burton et al.

At top-right there is a population PK-PD model of warfarin. This represents what is known about how warfarin behaves in the population in which the patient exists. Together with the clinical model at top-left, of what the desired or target INR response is, a decision about the starting dosage regiment can be made.

Subsequently, feedback is regularly received about warfarin pharmacokinetics (bottom right) and INR response (bottom left). These feedback into the original models at top right and left, and continually adjust the model so that decisions are continually taken about how the dosage regiment can be adjusted.

This is optimization... and represents what personalized medicine ought to be.

Can it be done for all drugs? Yes, it can. But it requires that there are good population PK-PD models for the drug, and good biomarkers of response that can be used as feedback. It requires resources and effort.

Above all, it will require that physicians be prepared to put in the extra effort to optimize their therapy according the the patient's real requirements.

Predictive pharmacogenetics

2010-10-31T21:43:00.000-07:00

Many people agonize about the predictivity of the science of pharmacogenetics. Somehow there is an expectation that pharmacogenetics should somehow lead to position where we can stop thinking.

Sounds a bit harsh but true.

Pharmacogenetics began as a science to understand the genetic basis for outlier behaviour. In much earlier experiences, outliers were characterized principally by phenotypic behaviour. As it is now, phenotypes tended to be classified in binary fashion - rapid/slow, fast/slow, extensive/poor. Such binary depictions of reality can only be predictive when the reaction or process in point is singular, critical or both. In some situations drug response can be described binarily as 'at risk for toxicity', or 'not at risk', e.g. G6PD deficiencies or HLA B*1502 for carbamazepine-SJS. For the most part however, pharmacogenetics data only helped explain genetic bases for limited aspects of drug response.

FDA classification: +, for information only; ++, recommended; +++, required

Gervasini et al, Eur J Clin Pharmacol (2010) 66:755–774

In highly controlled experiments, it can be easily shown that genetic variants can result in either loss or gain in function in specific processes related to drug response, e.g. drug metabolism and clearances. However since drug response/metabolism/

pharmacokinetics is seldom the result of a singular process, genotyping a genetic variant almost never provides a clear prediction of what the final drug response would be like.

Unfortunately the market place has misled many to believe that we can somehow construct a genetic testing panel that will predict with some degree of finality, what the patient's drug response and hence his drug dosage requirements will be. Hooray.... and we can therefore stop thinking! This line of thinking is clearly fallacious. The concept of 'personalized medicine' has been hijacked (biotech commercialism?) to refer to a one step genotyping approach to therapeutics when correctly it should refer to the ability to look at the patient in totality, i.e. the entire person - not just from the perspective of his constitutive make-up, but the totality of contributions of his altered physiology and environmental effects.

The more correct and rational approach is that of 'optimization', but the term sadly is far more mundane and less commercially sexy compared to 'personalized medicine'.

The rofecoxib (Vioxx) story - an interesting case study

2010-10-31T20:33:00.000-07:00

The selective COX-2 inhibitor rofecoxib, marketed as Vioxx, tells an interesting story. You can read more about it here at Wikipedia.

Rofecoxib was an initially successful drug that had been used as an anti-inflammatory analgesic for the management of inflammatory joint disease, acute pain and dysmenorrhoea since 1999. The selective inhibition of COX-2 meant that rofecoxib could be used effectively as an anti-inflammatory analgesic but with minimal side effects on the gastric mucosae.

In 2001, rofecoxib was proposed to be used in the prevention of adenomatous polyps in the colon. The 3-year (APPROVe) trial to investigate the prophylactic role of rofecoxib for colorectal polyps was terminated prematurely because rofecoxib was observed to be associated with increased relative risk of thrombotic cardiovascular events beginning after 18 months of treatment. Data in the first 18 months did not reveal any increased risk. In 2004, Merck voluntarily withdrew rofecoxib from the market following the report of this increased risk.

The clinical use of rofecoxib is not without variability issues with respect to its pharmacokinetics. Although it is not substantially metabolized by CYP450 enzymes, it was nevertheless glucuronidated by UGT2B7 and UGT2B15, both of which are genetically polymorphic. It is not clear to what extent these genetic variants are associated with variability in clinical response to rofecoxib.

Although variability in clinical response to NSAIDs are well known, there has not been a major concern about genetic polymorphisms affecting NSAID pharmacokinetics. This has been, I believe, largely related to the the dependence in clinical practice to adjusting dosages and drug choices to the anti-inflammatory clinical drug response. And there is currently a plethora of 'clinical joint scores' that can allow the rheumatologist to adjust for response variability. The toxicities with NSAIDs have also been largely manageable, and with the COX-2 inhibitors, GI toxicity was not perceived to be a significant problem.

The recognition that rofecoxib was associated with cardiovascular toxicity changed all that.

It is interesting that as the use of rofecoxib moved from rheumatology to polyp prevention, the clinical use of rofecoxib also lost its ability to manage any response variability. Rofecoxib use in preventing colorectal polyps was largely based on prophylactic fixed dose regiments. There was an assumption that this was relatively acceptable because rofecoxib was relatively safe.

But this was apparently not so. There was an association with cardiovascular toxicity. Eventually it was the cardiovascular risk that did rofecoxib in, and caused it to be withdrawn. It is not clear to what extent this was due to pharmacokinetic variability. Or individual susceptibilities at the vascular level, but the practical reality was the clinical use of rofecoxib for colorectal polyp prevention did not allow the clinician any means to adjust dosages for any kind of PK or PD variability. Essentially people were flying blind.

It is probably acceptable to fly blind with a drug that had a very high therapeutic index, but not when there is a risk of cardiovascular death for the drug that was being used in a prophylactic setting.

CYP2C9

2010-10-03T17:58:00.000-07:00

The CYP2C family of enzymes is the second most abundant of the CYP enzymes represented in the hepatocyte. The commonest is CYP3A4/5. Correspondingly they are also the second most important enzymes involved in drug metabolism.

There are 4 members of this family, 2C8, 2C9, 2C19 and 2C18, whose genes are all located on chromosome 10. The 2 members of this family that are of greatest importance for us are CYP2C9 and CYP2C10. CYP2C8 came into prominence initially in an association with the cerivastatin induced rhabdomyolysis and later in association with irinotecan metabolism. There has not been a lot of discussions about it lately because of the limited range of substrates linked with it. There is also little known about drug substrates associated with CYP2C18.

CYP2C9, originally known as tolbutamide hydroxylase, has been shown to be involved extensively in the metabolism of many pharmaceuticals. Of topical interest is its involvement with the metabolism with warfarin, particularly the S-isomer.

The Pharmacogenomics Journal (2005) 5, 193–202

The S:R isomeric ration of warfarin concentrations is routinely used as a convenient way of phenotyping the 2C9 activity. The common loss of function genetic variants are the *2 and *3 variants. These variants are much more common among Caucasian populations as compared to East Asians, e.g. Chinese, Japanese and Koreans. In Singapore, the ethnic group which had the highest frequencies of the *2 and *3 variants are the Indians, who had similar frequencies to the Caucasian populations.

CYP2C19 is much more of a preoccupation for us as the *2 and *3 variants are much more common among Chinese than Caucasians. The *2 alleleic variants is particularly common at about 25%. The CYP2C19 was earlier known by the original studied substrate, mephenytoin hydroxylase. The CYP2C19 genetic polymorphism has been discussed largely because if the association of the enzyme with the metabolism of proton pump inhibitors, and lately with the

antiplatelet drug clopidogrel.

Comparison of prasugrel 60 mg and clopidogrel 600 mg loading dose exposure of active metabolite by CYP2C19 genetic classification. Box represents median, 25th, and 75th percentiles and whiskers represent the most extreme values within 1.5 times inter-quartile range of the box. AUC, area under the concentration–time curve; EM, extensive metabolizer; RM, reduced metabolizer.

Eur Heart J

(2009) 30 (14): 1744-1752

Clopidogrel - variability in response

2010-09-29T17:31:00.000-07:00

Indian Heart Journal. 2008 Nov-Dec; 60(6): 543-7

The use of clopidogrel presents another interesting challenge with respect to the variability in drug response.

Clopidogrel is a a platelet inhibitor, acting through irreversible binding to the P2Y12 purinergic receptor on the platelet membrane; though it is not clopidogrel itself that binds, but the active metabolite. The PK of clopidogrel itself is quite complex. Upon oral administration about 90% of clopidogrel is removed through the action of circulating and hepatic esterases to inactive metabolites. Only about 10-15% gets activated by CYP2C19 and CYP3A4 to the final metabolite that binds to the P2Y12 receptor. As the receptor inactivation is irreversible, the recovery of function is dependent on fresh platelet regeneration from megakaryocytes.

The way clopidogrel produces its action is therefore fraught with all kinds of problems which clearly contributes to the observed variability in therapeutic response. These are potential sources of variability:

a) high first pass and low active metabolite bioavailability

b) variability of CYP3A4 and CYP2C19 activities due to pharmacogenetics and food/drug interactions

c) irreversible binding to receptor

d) temporal delay in onset, as well as in recovery of platelet function

e] variability in rate of platelet recovery.

This extent of variability really points to a crying need for dosages of clopidogrel to be optimized according to some clinical measure of drug response. Unlike the situation with warfarin however, there isn't a universally accepted way of monitoring plate function. Nevertheless, platelet function test is shaping up to become a standard bedside test for this very reason. A recent review by Williams et al (Thromb Haemost 2010; 103: 29–33) is worth a read.

Drug level testing would clearly not be useful as it is not clopidogrel itself but the metabolite that is active. Furthermore the irreversible binding to the platelet purinergic receptor would not allow concentrations of the active metabolite to be useful in predicting the level of platelet inhibition.

Warfarin - variability in response

2010-09-28T21:07:00.000-07:00

Frequency distribution of warfarin daily dose requirement

Pharmacogenomics. 2009, 10 (12) :1955-1965

Warfarin presents a very good case study with respect to drug response variability, and the management of the uncertainty that surrounds the therapeutic use of warfarin.

Warfarin inhibits the reductase that recycles warfarin epoxide (Vit K epoxide reductase C1) so that it can be used again in the production of the Vit K dependent clotting factors. Conceptually very simple, but a number of things complicate this schematic. Firstly warfarin is optically active, and the two isomers, R and S warfarin, have different potencies and PK characteristics. The S warfarin has 5 times the potency of R warfarin and so often has been taken to represent the active ingredient of racemic warfarin. This is a convenient over-simplification, and it is by no means true that all warfarin activity is accounted for by only the S isomer. This is further

complicated by the fact that the isomers are metabolized preferentially by different CYP450 enzymes and have different elimination halflives.

S warfarin has quite a long halflife - an average of 40 hours. In some individuals it may be up to or longer than 60 hours. This means that after initiation of dosing, S warfarin doesn't achieve steady-state concentrations until about a week of dosing. Using a loading dose will get you closer to the steady-state concentrations, but will still need 5 halflives to settle into 'steady-state'. To make it worse, this does not even mean that warfarin's anticoagulant effects stabilize after one week. In fact the anticoagulant effects are not just dependent on warfarin kinetics but also on the kinetics of the clotting factors, which have their own halflives of elimination. This means that after warfarin steady-state is reached, some more time is required for the clotting factors, and consequently the fully anticoagulant effect, to settle into 'steady-state'. Simulations suggest that the whole process of anticoagulation may take up to about 2 weeks to reach steady-state.

Practically this means that the sooner you can settle into the correct maintenance dose, the sooner the patient will be at a stable level of anticoagulation. Every time you tweak the dose, it will require another 2 weeks to settle down. This makes dosage optimization particularly problematic.

The main sources of variability for warfarin response may be anticipated to relate to the following:

a] body weight

b] diet (Vit K supply, inhibitors.inducers of CYP enzymes),

c] smoking

d] genetics of CYP enzymes

-particularly CYP2C9 for S-warfarin, but cannot ignore other CYP enzymes involved with R warfarin.

e] genetics of CYP4F2 involved in breakdown of Vit K

f] genetics of Vit epoxide reductase complex 1 (VKORC1)

g] drug interaction with CYP enzymes

What saves the situation for warfarin is that it has an excellent direct measurement of drug response, - the INR (International Normalized Ratio) which directly measures the state of anticoagulation produced by warfarin. The INR allows a very convenient way to adjust warfarin dosages according to a 'target' level of response. This is called a target response strategy. For warfarin, drug level monitoring is of little use because of i) the delay in response because of the clotting factors halflives, ii) the presence of 2 active warfarin isomers, and iii) because there are different sensitivities to warfarin effects because of genetic variants affecting VKORC1.

Though the INR is a useful 'direct' measure of warfarin response, it is in reality only a 'surrogate' measure of the true warfarin efficacy, which is the eventual effect warfarin has in reducing morbidity and mortality associated with thromboembolism, strokes etc. These can only be assessed through monitoring therapeutic outcomes. However, these outcome measures, do not help us in the day to day optimization of the patient's warfarin dose.

Olanzapine and CYP1A2 genotypes

2010-09-21T17:11:00.000-07:00

Shirley et al, Neuropsychopharmacology (2003) 28, 961–966

Olanzapine, an atypical antipsychotic agent used in the treatment of schizophrenia, is metabolized to 10- and 4'-N-glucuronide, 4'-N-desmethylolanzapine via CYP1A2. Here is report demonstrating the relationship between (orally administered) olanzapine clearance/F and the metabolic ratio (PMR = 17X/137X) measured using caffeine.

Laika et al, The Pharmacogenomics Journal (2010) 10, 20–29

Notwithstanding the lack of predictive value of CYP1A2 genetics on CYP1A2 activity, here is a study looking at the effect of the presence of CYP1A2*1F allele on olanzapine steady state plasma concentrations, in the presence/absence of inducers such as smoking and carbamazepine.

While both inducers and *1F/*1F genotype showed significant effects on average olanzapine concentrations, the scatter within each group remains very large, and is clearly not explained by either effects.

CYP1A2

2010-09-20T01:00:00.000-07:00

The CYP1A gene family is a very old one, and the enzyme is found in all vertebrates. In humans there are 2 paralogous members of the family; CYP1A1 expressed predominantly in the lung, and CYP1A2 expressed in the liver.

The CYP1A enzymes very likely evolved to deal primarily with environmental polycyclic hydrocarbons. The activity of CYP1A2 varies considerably and may be easily phenotyped using caffeine as a probe substrate to generate a metabolic ratio.

The metabolic ratio in the population displays a broad unimodality which disguises the fact that the gene is highly polymorphic. The activity of the enzyme is not easily predicted by the genotype as the enzyme is very easily induced by exposure to environmental hydrocarbons. The gene haplotypes may in fact be associated with low or high inducibility of the enzyme.

Inhaled hydrocarbons through cigarette smoking is a common inducer of CYP1A2. In Singapore we are also seasonally exposed to high levels of environmental hydrocarbons as a result of forest fires in the region. The maximum exposure tends to be during dry periods, and when the prevailing winds blow in from the West. These tend to be during the July-November period. It is currently unclear to what extent this has affected our population average CYP1A2 activity.

The most comprehensive examination of CYP1A2 in Chinese is that reported by Chen et al 2005. They were able to demonstrate that haplotype pairs 10 and 13 are responsible for high CYP1A2 activity, and haplotype pairs 5, 8, 9, 12, and 15 are responsible for low CYP1A2 activity in Chinese subjects. These haplotype pairs account for approximately 6% and 25% of the population respectively.

2010-09-19T21:17:00.000-07:00

The Cytochrome p450 enzymes belong to probably the largest gene superfamilies known. Comprising more than 6500 genes, there are 57 enzymes alone in humans, involved with the metabolism of endogenous and exogenous compounds. The original CYP450 gene is a very ancient one, tracing its origins back perhaps 2 billion years. All the known members of the gene superfamily probably arose from this ancient precursor through gene duplications etc.

This vast numbers of members is required at least in part for the metabolism of endogenous substrates, but it is likely that many have developed for the purpose of dealing with environmental toxicants which enter the body through mucosal barriers of the gut, and lungs, but also the skin. As the main route for the entry of environmental chemicals is via oral ingestion, the largest amount of p450 is found in the liver, as well as the intestinal linings.

The largest amount present in the liver belong to the 3A family. However, the abundance of the enzymes does not translate directly to the relatively importance of the enzyme with respect to the metabolism of pharmaceuticals. This is likely because the enzymic isoforms have evolved primarily for environmental chemicals, while pharmaceuticals as a subset of environmental chemicals, are chemicals with a very short and recent history.

The 3 most important families for pharmaceutical detoxification are CYP3A, CYP2D6 and CYP2C.

The recognition of CYP450's role in dealing with environmental chemicals allows us to anticipate that interactions between environmental chemicals (including dietary phytochemicals) and pharmaceuticals at the level of the CYP450 enzymes may be more prevalent than we have previously anticipated.

Drug metabolism

2010-09-13T07:17:00.000-07:00

Our current ideas of drug metabolism have been shaped to a large extent by the landmark review by Richard Techwyn Williams in 1947 (see Detoxification Reactions, by RT Williams 1947). Prior to this, detoxification reactions were seen to be applicable mainly to poisons, and molecules which were structurally related to endogenous chemicals. Prof Williams himself wrote that "detoxification reactions exist for natural and structurally related foreign compounds; metabolism unlikely for totally foreign structures".

By 1959 however, the ideas had expanded to include drug metabolism.

Prof Williams went on to propose a schematic for drug metabolism that still applies today. Drug molecules were metabolized via a Phase I set of reactions which included oxidations, reductions and hydrolyses; and then a phase II synthetic reactions. These reactions were not necessarily detoxification reactions but could actually result in the formation of a toxic metabolite.

These series of reactions did however, convert molecules which were lipophilic to metabolites that were hydrophilic and more water soluble. It was also thought at that time, and for a long time after that, that these lipophilic drug molecules were freely permeable across membranes.

These ideas are however, currently being challenged as we increasingly recognize that drug molecules are not free permeable across membranes and do require the involvement of drug transporters. But this is a story for a later telling.

Arylamine N-acetyltransferase 2 (NAT2)

2010-09-12T05:55:00.001-07:00

One of the earliest demonstrations of a pharmacogenetic problem affecting therapeutic agents was that of the N-acetyltransferase enzyme. This is an enzyme that N-acetylates arylamines carcinogens and heterocyclic amines. In 1960, it was reported that the metabolism of the anti-TB drug isoniazid was bimodality distributed in the population. Patients could be distiguished into slow and rapid acetylator phenotypes by exposing them to either isoniazid or some other arylamine and measuring their acetylator status (typically the ratio of acetyl-metabolite/parent compound in either plasma or urine). The enzyme was eventually called N-acetyltransferase 2 (NAT2).

The frequency of slow acetylators varied considerably across the world. Caucasian populations generally have about 50% frequency of slow acetylators, while East Asians such as Chinese and Japanese had about 20-30% slow acetylators.

For many years after the discovery of this genetic polymorphism, drug companies avoided developing drugs which were substrates of NAT2, until it became increasingly recognized in the 1980's that NAT2 wasn't the only genetic polymorphism affecting drug metabolism. With the discovery of genetic polymorphisms affecting pretty much all drug metabolism pathways, the strategy shifted to management of pharmacogenetic problems, rather than just avoiding it.

Pharmacogenetics and genetic polymorphisms

2010-09-11T20:27:00.000-07:00

In the theoretically correct application of the term, Pharmacogenetics is the study of inherited variations in drug response. Strictly, it would apply to germ line mutations (genetic polymorphisms) and not to somatic mutations or to epigenetic mechanisms (unless these can be inherited).

Genetic polymorphism occurs when the "simultaneous occurrence in the same locality of two or more alleles is in such proportions that the rarest of them cannot be maintained just by recurrent mutation". This conventionally assumes that the rarest allele should be more than 1%.

There are two parts to this definition.

a] it must refer a population in equilibrium; although I use this term guardedly, since no population is static and is truly ever in 'equilibrium'.

b] the frequency of the rarest allele is at least 1%. This is very empirical as it is not certain what is the barest minimum frequency for the allele to be propagatable in a given population. It would seem that 1% is too high a value, and genetic polymorphisms do refer to alleles with frequencies much less than 1%. It does not however, include mutations that occur sporadically and randomly. Consequently allele frequencies for a genetic polymorphism are often tested against the Hardy-Weinberg Equilibrium. If it does not fit the HWE, it does not necessarily mean it is not a genetic polymorphism. It just cautions the investigator to look reasons why the HWE has been violated.

One particular consideration that is often overlooked in determining allele frequencies in a genetic polymorphism (which may or may not cause deviations from the HWE), is whether the population is stable and if there is a real equilibrium with respect to the transmission of alleleic variants. This is a particular problem for Singapore, whose population has been growing at a phenomenal rate through immigrations, and the expansion of our foreign worker pool. Recent additions to the population now may begin to outnumber those that are born locally. This raises questions about whether the population genetic pool is too labile to be in any sort of equilibrium. It could only be if we are able to assume that the larger population base of people of similar "race/ethnicity" are in equilibrium globally. But this is not a valid assumption.

Ibn Sina and clinical pharmacology

2010-09-09T19:13:00.000-07:00

It is probably an auspicious occasion to recognize that the preceding discussions on efficacy are not anything new, and that their very foundations were laid a thousand years ago by an amazing Persian Shia physician called Abū ‘Alī al-Ḥusayn ibn ‘Abd Allāh ibn Sīnā, or just "ibn Sina" or Avicenna.

In the second volume of his famous treatise "The Canon of Medicine", he identified 7 important principles which are still valid today, and establish the foundations of modern clinical pharmacology. He writes:

“Experimentation will bring us complete understanding of the strength of drugs; however,only if the conditions below are followed.”:

"The drug must be free from any extraneous accidental quality."

"It must be used on a simple, not a composite, disease."

"The drug must be tested with two contrary types of diseases, because sometimes a drug cures one disease by Its essential qualities and another by its accidental ones."

"The quality of the drug must correspond to the strength of the disease. For example, there are some drugs whose heat is less than the coldness of certain diseases, so that they would have no effect on them."

"The time of action must be observed, so that essence and accident are not confused."

"The effect of the drug must be seen to occur constantly or in many cases, for if this did not happen, it was an accidental effect."

"The experimentation must be done with the human body, for testing a drug on a lion or a horse might not prove anything about its effect on man."

Selamat Hari Raya

2010-09-09T18:39:00.001-07:00

Does body weight matter?

2010-09-08T19:42:00.001-07:00

It would appear, not very much. After all drug doses are seldom adjusted for body weight. Only in situations where the therapeutic index is very low, such as for oncologicals, is the drug dose adjusted for body weight. Clearance as a pharmacokinetic parameter, is seldom denominated by body weight.

Often when discussing differences in PK between populations, you can almost hear the sigh of relief when the differences in AUC can be discounted by body weight. Suddenly it's like the differences should not matter any more.

So should body weight matter?

The answer to my mind is, yes.

Pharmacokinetically, body weight does not matter as much to clearance as it does to distribution. For this reason Vd is often denominated by body weight. But not so clearance. The general reluctance to consider body weight as a major determinant of drug effect related to an older line of reasoning where drug response is seen primarily as a function of AUC, steady state concentrations and consequently clearance, rather than Vd. But as pointed out in previous posts, Vd changes can have considerable effects on drug PK, particularly Cmax and trough concentrations. These are less considered mainly because of their relative instability compared to steady state concentrations. It is however possible that variability in drug responses may in fact be more sensitive to changes in Cmax and troughs rather than steady state concentrations. If so, Vd effects may be potentially more profound than have been previously thought.

Why does this reality bother us?

Principally because one of the immediately noticeable differences between our Asian population and Western population is the difference in body weights. Caucasian males may have an average body weight of 85 kg as compared to age matched Chinese males of 65 kg. Chinese females would have an average of about 55 kg. If one recognizes that drug response may be affected by body weight, it would make us serious reconsider if drug dosage regiments developed from studies involving healthy Caucasian males, may be easily applied to Chinese females without adjustments to the average of 35% lesser body weight.

And this is not yet even considering differences in lean body mass, especially since Chinese/Asians have much less lean body mass for a given body weight, when compared to Caucasians.

The race-ethnicity confusion

2010-09-08T02:12:00.000-07:00

Definitions of race and/or ethnicity are spectacularly confused and lack any kind of precision whatsoever.

The FDA in its guidance, for example, make a distinction between race and ethnicity, but allow them to be combined in a one step self declaration.

Race refers to 5 categories:
American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White
Ethnicity has 2 categories, either Hispanic/Latino or not Hispanic/Latino

Such definitions do not have any scientific logic and were actually developed by the Office of Manpower and Budget.

The major problem for us is that because this guidance is used by pharmaceutical companies in drug development studies, we cannot hope to have any meaningful ethnicity data that we can use. The only data are those lumped under a generic "Asian" umbrella, which essentially denies the vast ethnic diversity in our communities.

Singapore is not any wiser and contributes to the confusion by ambiguously mixing ideas of race, ethnicity and even language into the definition. We identify 4 major categories from census definitions: Chinese, Malay, Indians and Others. The categories of Chinese, Malays and Indians are defined using fairly circuitous logic about origins which also incorporates language/dialectual characteristics. To make matters worse, race/ethnicity based medical studies often rely heavily on hospital records which for citizens are almost entirely based on recorded information from the National Registration Identification Cards. The information here do not use the same definitions as the census, but are almost completely self declarations according to self perceptions.

Race or ethnicity?

2010-09-07T18:35:00.000-07:00

Race and ethnicity are terms which are often used interchangeably. To a large part, I believe it is due to the increasing political incorrectness of using race as a descriptor, thus moving the notion of race more towards that of ethnicity.

A 1950 UNESCO declaration "The Race Question", succinctly puts it:

"National, religious, geographic, linguistic and cultural groups do not necessarily coincide with racial groups: and the cultural traits of such groups have no demonstrated genetic connection with racial traits. Because serious errors of this kind are habitually committed when the term 'race' is used in popular parlance, it would be better when speaking of human races to drop the term 'race' altogether and speak of 'ethnic groups'."

Race in its original intent referred to the "roots" of population groups (etymology: Germanic 'reiza' - bloodline). This to a large extent referred to biological characteristics and the external appearance of the population groups. The term 'race' therefore carries heavy connotations about biology and heredity.

We know now that these notions are fallacious, and believe that there are no biological bases for classification of peoples into racial groups. Race is little more than a socio-political construct that allow societies to manage resource allocations.

The UNESCO declaration was correct. The term 'race' should actually be dropped from our vocabulary altogether as it is meaningless from a biological perspective, and its continued use just continues to perpetuate the myth that people can be sub-classified according to biology.

The more correct and useful term is 'ethnicity'. Ethnicity refers to population groups that are unified by social and cultural characteristics as well as geographic origins. That different ethnic groups have differing phenotypic features do not make them definable by these characteristics.

Pharmacogenetic studies as well as studies looking at inter-populational variability in drug responses, should refer to ethnicity as a descriptor of the population rather than to race. This would then include allele frequencies, anthropometric information, environmental exposures and other societal pressures. The academic challenge presented is how do we standardize these ethnicity descriptors so that that we can understand and apply the data being generated from these studies. Correctly however, ethnicity data have limited applicability across populations. For example, data on Chinese in Singapore do not necessarily apply to Chinese populations in China, Africa, Europe or US. Likewise, we should not too readily apply Chinese data generated in Beijing to a Singapore therapeutic context.

In our lab we use the terms Chinese, Malay and Indians because these are terms used by our National Registration Office. However in our studies we recruit subjects based on self-declaration of ethnicity (Chinese, Malay and Indians) consistent over three generations. The data we generate will be correct for our population as at this time, and we make no assertions that they can be representative of Chinese, Malay and Indian populations across the globe, or even for Singapore population groups for later generations.

Biomarkers of efficacy

2010-09-07T18:29:00.000-07:00

Worthwhile pulling out for a read. Useful perspectives re approach for development of biomarkers for efficacy and disease progression.

The Pharmacogenomics Journal , (8 December 2009)

A multistep validation process of biomarkers for preclinical drug development
W M Freeman, G V Bixler, R M Brucklacher, C-M Lin, K M Patel, H D VanGuilder, K F LaNoue, S R Kimball, A J Barber, D A Antonetti, T W Gardner and S K Bronson

Abstract
Biomarkers that can be measured in preclinical models in a high-throughput, reproducible manner offer the potential to increase the speed and efficacy of drug development. Development of therapeutic agents for many conditions is hampered by the limited number of validated preclinical biomarkers available to gauge pharmacoefficacy and disease progression, but the validation process for preclinical biomarkers has received limited attention. This report defines a five-step preclinical biomarker validation process and applies the process to a case study of diabetic retinopathy. By showing that a gene expression panel is highly reproducible, coincides with disease manifestation, accurately classifies individual animals and identifies animals treated with a known therapeutic agent, a biomarker panel can be considered validated. This particular biomarker panel consisting of 14 genes (C1inh, C1s, Carhsp1, Chi3l1, Gat3, Gbp2, Hspb1, Icam1, Jak3, Kcne2, Lama5, Lgals3, Nppa, Timp1) can be used in diabetic retinopathy pharmacotherapeutic research, and the biomarker development process outlined here is applicable to drug development efforts for other diseases.

Direct, surrogates or outcomes?

2010-09-06T17:39:00.000-07:00

Students are quite often confused during discussions of these various types of efficacy measures. It is really not surprising, as many clinicians I discuss with also seem quite unclear about these concepts.

But these are to me quite important ideas; ideas which are quite often overlooked during drug development and the design of therapeutic regiments. And we do pay a price for neglecting them.

When we move from the bench to the bedside, we do lose the ability to assess drug response. Often we do not even begin to recognize just how much we have lost in our ability to do this. Yet it is vitally important for us to be able to do this because if we cannot, we will not be able to rationally manage our dosage regiments. This is one of the great difficulties in managing therapeutics.

In a relative small subset of therapeutic situations we do have direct measurements of drug effect - such as in the management of hyper/hypotension or hyper/hypoglycaemia. The blood pressure and blood sugar responses serve us well. A similar possibility exists with the management of the INR using warfarin.

In many therapeutic situations, no clear biological marker of drug response exists; or the therapeutic aims is actually far more complex compared to the immediate aspects of drug response. In these situations the desired drug response may actually be an 'outcome' measure - such as the control of epilepsy or arrhythmia, or even the management of depression and psychoses. In such situations, a surrogate for drug action should be available that can allow real time management of drug dosages. In some situations, measuring drug concentrations, can provide you with a reasonable surrogate for managing dosages. This is referred to as having a 'target concentration strategy', or more popularly called therapeutic drug monitoring.

Admittedly, good surrogate measures are often not even available. This deficiency creates a therapeutic environment where 'therapeutists' are often limited to relatively fixed, or inflexible dosage regiments, and therefore cannot deal effectively with any patient variability in drug response. This could be as comforting as having your pilot fly blind.

The recent problems with rofecoxib (Vioxx) provides an interesting example. While initially developed for the management of inflammatory joint disease, the selective Cox2 inhibitor found a new use in the prevention of intestinal polyps. When used as an anti-inflammatory analgesic, rheumatologists could manage drug dosages through assessing pain relief, joint involvement etc. When it came to the prevention of intestinal polyps, the therapeutist essentially had to 'fly blind' using fixed dose regiments, since there was neither direct nor surrogate measures of drug action. Intestinal polyposis was at best an outcome measure that could only be assessed at the end of treatment periods. Were there patients who over over-dosed or under-dosed? Very likely. Could we have better managed the dosages, and consequently the risks of cardiovascular mortality? Quite likely; but we will never know now. Rofecoxib was eventually withdrawn from the market.

A pity, perhaps.

Further thoughts about distributions.....

2010-09-05T21:34:00.000-07:00

For the most part, the volume of distribution Vd is conceptualized as little more than a proportioning factor between the administered dose of a drug and the initial plasma concentrations. While it was important in determining the C0, it had little impact on drug exposure, the AUC or steady state concentrations. These were squarely in the domain of clearance mechanisms.

We expected that while protein binding was inversely related to the Vd, the free drug concentrations would eventually equilibrate across all tissue compartments, regardless of tissue binding. What was observed in the plasma of the central compartment would represent what was happening in all tissues. Furthermore if free drug clearance remained unchanged, free concentrations would remain unchanged, regardless of protein or tissue binding.

According to these ideas, central compartment pharmacokinetics was of prime importance in understanding drug efficacy, or lack of it.

In recent years, the increasing appreciation that few molecules actually permeate across membranes with the involvement of transporter processes, have led many to question the wisdom of an approach that assumed drug molecules existed in equilibrium across membranes and consequently, tissue compartments. Depending on the expression and function of transporters at various membranes, drug concentrations can vary independently of central compartment concentrations. Hence, in some individuals, the plasma concentrations may mirror concentrations in any tissue compartments if the transporters involved are ubiquitous.

Conversely, in some individuals, the concentrations in tissues 'compartments' may be very different if specific transporters are differentially expressed, leading to widely different efficacy-toxicity profiles even though central compartment concentrations appear invariate.

Do changes in Vd have any impact on clinical drug effects?

2010-08-27T23:48:00.000-07:00

This will depend entirely on which drug concentration measurements have the greatest impact on drug effects. And here is where our relative ignorance of this particular aspect of drug pharmacology constrains our ability to use PK as a predictor of drug effects.

If for example our understanding of what best predicts drug effects is limited to considerations of 'average' or 'steady-state' drug concentrations, then we are compelled to expect that Vd changes will have little or negligible impact on clinical drug effects. This is because Vd changes do not alter the area-under-the-curve (AUC) of a drug's pharmacokinetics (since AUC is determined primarily by clearance and bioavailability). Remember (F x Dose)/AUC = Clearance?

This line of reasoning has dominated and shaped our thinking for the last few decades as evidenced by the large number of studies looking at clearance or AUC changes as predictors of drug effects. Correspondingly, we have tended to minimize the effect of distributional changes as a predictor.

Is this line of reasoning correct?

Only to a limited extent. We have seen in the previous post, that the immediate and most obvious effect of Vd changes is on the C0. Consequently if a drug's effect is dependent on peak concentrations, Vd changes will have inversely related effects on peak concentrations of the drug profile, and by extension, any clinical effect, be it efficacy or toxicity, associated with the peak.

We see this also with multiple dose regiments.

With a multiple dose regiment, even though changes in Vd are not expected to change the AUC or steady state concentrations, you can see that the fluctuations over the dosage interval are greater if the Vd is smaller (in the above case 115L compared to 230L). With the increased fluctuations, one should note that the peak and trough concentration actually move in different directions, i.e. with a smaller Vd, the peak increases but the trough decreases, while the AUC remains unchanged.

Going back to the original question....do Vd changes have an impact on clinical drug effect? It will depend on whether drug effects relate best to steady-state concentration, the AUC, peak or trough concentrations. And this is poorly understood at the moment.

For the moment however, being over-focused on the AUC therefore limits our ability to see potential causes of variability in drug response.

Even more problematic ideas of distribution.....

2010-08-27T18:13:00.000-07:00

The simplest idea of the volume of distribution (Vd) of a drug, is to think of it as the volume that a drug distributes into when that drug is administered into the body, but before any elimination has taken place. The simplest approach therefore is to divide the dose of the drug (intravenously administered) by the observed plasma concentration at zero time, C0, i.e. before any elimination has had a chance to occur.

Vd = Dose/C0

Not all of any drug, however, is uniformly distributed throughout the body; and what is seen in the plasma only represents a fraction of all of the drug molecules distributed throughout the body. If more is distributed outside of the plasma 'compartment', the C0 will be smaller for a given dose of drug, and the Vd will appear correspondingly higher. The converse is also true, that if less is distributed outside of the plasma compartment, the Vd will appear lower.

The original ideas of the process of distribution, was that drug molecules were mostly freely permeable entities, and found a 'distributional equilibrium' across cell and tissue membranes. Binding to proteins or other large molecules prevented their effective permeation across membranes, and therefore 'trapped' these drug molecules into various 'compartments'.

Conceptually therefore, the Vd of a drug may be seen to be 'governed' by an expression relating body volumes (presumably determined by body weight), plasma protein binding and tissue binding:

Vd = Volume of central compartment + Volume of peripheral tissue x (fu/fut),

where central compartment referred to plasma volume, tissue referred to undefined number of tissues outside of plasma, fu is unbound or free fraction of drug in plasma, and fut is the unbound fraction of drug in the tissues.

By this expression one can see that the Vd can be reasonably expected to be related directly to the extent of tissue binding, and inversely related to the extent of plasma protein binding.

The Vd of a drug can also be seen to have direct and immediate effects on the starting concentrations of any drug administered, particularly if administered intravenously. And if the starting concentrations were responsible for efficacy or toxicity, the Vd may be expected to be a major determinant of efficacy or toxicity.

Knowing the Vd also helps us estimate the starting dose of a drug if we have a target drug concentration in mind. For this reason, dosage regiments of drugs with large Vds often incorporate a loading dose regiment before settling into a lower maintenance dose regiment.

The enigmatic AUC (area-under-the-curve)

2010-08-22T01:51:00.000-07:00

The area under the plasma concentration-time curve (AUC) is an easily measurable pharmacokinetic parameter. It is used extensively in clinical pharmacokinetic studies, but students very often have a poor idea of what to make of the AUC.

Mathematically, the AUC is obtained by integrating the mathematical function that describes the plasma concentration-time profile. Practically however, it is estimated by summing all the small trapezoids that can be constructed under the concentration-time plot, using what is well known as the "trapezoidal rule".

Since it is mathematically the sum of all the plasma concentrations over the dose interval, it is often taken to represent clinical drug 'exposure'.

Pharmacokinetically however, the AUC is used to estimate drug clearance,

Clearance = Dose/AUC ...................(1)

After an oral dose, the equation is,

Clearance = (Bioavailability x Dose)/AUC ................(2)

A corollary of the above statements is that, since AUC is assumed to represent clinical drug exposure, and since AUC is determined principally by Bioavailability and Clearance, clinical drug exposure can be assumed to be determined primarily by clearance and bioavailability.

This concept has shaped our thinking for many decades, and it has closed our minds to distribution being perhaps an equal if not more important determinant of drug effects (more about this later!).

One particular area of confusion for students is that they too readily associate the AUC with bioavailability. When asked why the AUC changes for a particular drug, their first response is often that the bioavailability has changed. This is only half right.....since the AUC is determined by both clearance and bioavailability. In a situation where there are no bioavailability issues, AUC is determined primarily by clearance. AUC is only reflective of bioavailability when the clearance remains stable.

Experimentally, bioavailability is determined by measuring the AUC under oral and intravenous administrations. The clearance of the drug, measured under intravenous administration allows calculation of the clearance, which can then be used to estimate the bioavailability from the oral experiment.

Implications of the Effect-Time relationship

2010-08-19T18:08:00.000-07:00

The relationship as plotted in the earlier post assumes the concentration move from an initial position when it associated with maximum pharmacological effect.

In the first phase, even when concentrations are declining at an exponential rate, effect hardly changes. Not until the effect reaches about 85% of Emax does the rate of decrease in the effect assumes some linearity with respect to time. Between 85% and 15% of Emax, the rate of change of effect is linear when the concentrations are expected to be falling exponentially. Only when the effect is less than 15% of Emax is the rate of decrease exponential. Only then is the rate of change of effect parallel to the change in concentrations.

Implications:

a] When using plasma concentrations as a surrogate for pharmacological effect, a linear relationship between plasma concentration and effect is only seen at low effect levels (less than 15% Emax)

b] At toxicological levels (>85% Emax) as might occur in poisonings, concentrations may fall exponentially with any improve in patient's condition. Some drugs are actually dosed under Emax or near Emax conditions. In such situations one should expect that fluctuations in concentrations, or small adjustments to doses will not be associated with any significant effect changes.

c] For most drugs, presumably the concentrations operate somewhere between 85-15% of Emax. Under such conditions, the effect falls linearly with time, but it is important to note that the effects fall linearly even though concentrations are falling exponentially.

d] Few drugs operate throughout the entire range of concentration-effect curve, hence the effect time relationship is entirely dependent on the concentrations ranges the drug exhibits under a certain dosing regiment. It is important to know where any drug is operating to appreciate how changes of concentrations over time will determine effect variability.

Generating an Effect-Time plot

2010-08-18T19:09:00.000-07:00

First step is to generate a Concentration - Time plot. We know that in its simplest form, an IV dose of a drug will be eliminated by a mono-exponential process. The plamsa concentration at any time, C, can be described by an equation using the starting concentration C0, elimination rate constant, k, and the time, t.

C = C0exp-kt

Secondly, we know that the effect at any concentration can be described by an 'Emax model', which is similar to a Michaelis Menten equation, using Emax (max effect), ED50 (concentration producing 50%) effect, and E0 (residual effect when there is no drug).

Effect = E0 + Emax. C/(ED50+ C)

It is then a simple matter to link those the above two relationships in an EXCEL spreadsheet and generate a plot of Effect vs Time. You can arbitrarily use the following constants: C0=100, k=0.015, E0=10, Emax=100, ED50=5

Here is the output using the above method. This plot assumes the concentrations begin at a point associated with Emax and reduces down to zero.

Temporal considerations in understanding efficacy

2010-08-15T20:22:00.001-07:00

This is one of the overlook areas for lab-based pharmacologists.

It is easy enough to think of a concentration-response relationship based on static concentrations of active drug in an incubation medium but drug concentrations in clinical practice are almost never static (unless administered as a fixed infusion regiment). Concentrations fluctuate over a dosage interval, and even in a pseudo-steady state situation simulated through multiple dosing, concentrations continue to fluctuate. Even 'average' concentration are seldom stable because patient's compliance vary dose to dose and over days and weeks. It is therefore almost never possible to identify drug effects with any drug concentration. For convenience, we refer to average (randomly obtained) concentrations, peak concentrations, trough concentrations or even areas-under-the-curve (AUC) if multiple sampling has been done over a dosage interval, but in reality we seldom know which concentration measurements best reflect the observed drug effects.

Different aspects of the drug effects may in fact to different types of concentration measurements. Toxicity effects may in fact relate more to peak concentrations while therapeutic effects may related better to trough concentrations. In some situations the converse may even be true. In various other situations drug efficacy may better relate to an index of exposure such as the AUC, or even the amount of time exposed to concentrations above a certain threshold concentration value.

Another aspect of the temporality of drug effects may be related to the involvement of down-stream effects of any drug action. Significant delays in drug effects 'coming on' and 'going off' will make the association between drug effects and concentrations less obvious.

Here's an interesting exercise for you:

We know that there is a sigmoidal log concentration-effect relationship. There is also a log decline of concentrations (assume simplest one compartmental IV model) over time. How would the Effect - Time relationship look like? Email me your answer.

Mechanisms of drug action - we need to rethink our models

2009-08-16T20:53:00.000-07:00

When we think about the mechanism of drug action we almost always look to the traditional sigmoidal log dose or log concentration response relationship.

Students are almost always befuddled by the fact that once you go into the clinics, hardly anyone ever refers to this fundamentally important relationship. It seems to be important only at lab benches and do not seem to apply in the clinical context. Part of the reason for this is few drugs exhibit a range of actions that span the entire range of that sigmoidal curve. Many drugs either just operate close to the Emax or are limited in getting close to Emax because of toxicity, or compensatory mechanisms. One other constraint is that the estimates of concentrations are poorly representative of the actual concentrations at the effector site. So often we are reduced to just looking at circulating (fluctuating) plasma concentrations (which are very distant from the effector site) or a very crude estimate of the administered dose.

One other problem is that our ideas of drug response mechanisms are heavily influenced by receptor binding models shaped by earlier studies of G-protein type membrane receptors. The simplistic model assumes easily reversible competitive binding to a receptor with almost immediate effects. More and more drugs nowadays do not operate that way.

Here is a list of the top 50 prescribed drugs (as listed by IMS in 2007).

Of these only a minority can be regarded as operating according to that model of drug action. If you consider that many clinically useful drugs (antiinfectives, anticancer, etc) work through mechanisms more related to irreversible cell kill type models, I think you can readily appreciate the inadequacy of that simplistic traditional concentration-response model of drug action.

Measures of efficacy

2009-08-16T06:43:00.000-07:00

In the previous posts we had considered the optimization of warfarin dosages. The genotyping offers an added dimension to the optimization process, but may not be really cost effective. The warfarin problem is probably not a good example for predictive genotyping. This is because it already has a very good biomarker of clinical response - the INR (International Normalized Ratio). Most other drugs do not have good measures of clinical response. The absence of such measures of efficacy makes the optimization much more challenging.

Some drugs like warfarin allow direct measurement of the therapeutic efficacy. So for warfarin, it is the INR. For antihypertensive drugs, it could be the blood pressure response. For an antiasthmatic bronchodilator, it could be the airway relaxation, FEV1 for example. For an antidiabetic drug, it could easily be the blood glucose response. There are many other examples.

But for many therapeutic areas, the clinical response is much less directly quantifiable. For example, in the use of an antiepileptic drug, without a good surrogate measure of response, one is never really sure whether appropriate amounts of drug have been used. Likewise, for an antibiotic or a chemotherapeutic agent, the inappropriateness of the dosing regiment may not be recognized until it is too late.

For such situations, the availability of a 'surrogate' measure of response may be critical in patient care. One such surrogate measure is the measurement of circulating drug concentrations. Crude though it may be, getting the patient into a 'therapeutic range' may provide some guidance as to whether or not appropriate dosages have been used. Such an approach is sometimes referred to as the 'target concentration strategy'. There are however limitations to this method and it is not always applicable.

For the target concentration strategy to work, there must be evidence that circulating concentrations bear a good relationship to therapeutic outcome.

CYP4F2

2009-06-15T01:19:00.000-07:00

The CYP4F2 is involved in the synthesis of a number of sterols and lipids. Because it was involved in the production of 20-hydroxyeicosatetraenoic acid (20-HETE), it was postulated to be involved in the pathophysiology of hypertension. It is now thought to be also involved in the breakdown of Vit K1. Correspondingly, the V433M variant is thought to result in decreased breakdown of VitK1 and an leading an excess of VitK1, leading to relative warfarin resistance. Recent studies have suggested that the polymorphism contributes 1-2% of the variability in warfarin dosage requirements.

The frequency of the polymorphism in Caucasians and 'Asians' is approximately 30%. There isn't any more specific population frequencies than these, and I have no breakdown of Chinese, Malay and Indian frequncies.

Manny Pacquiao (a.k.a. Pac-man)

2009-05-06T22:39:00.000-07:00

Sorry for the long silence.I haven't been sleeping .... just pretty busy thinking about lots of other things. Here's something for you to think about.

Manny Pacquiao (a.k.a. Pac-man). Small man punching above his weight and reach to victory against Ricky Hatton.

Much as I don't really like boxing as a sport because of the physical violence, when you watch a great boxer at his craft, it's really poetry in motion. Yes, there is beauty, even in violence.

But the real reason why I am posting about this here is to point this out as a dramatic example of human diversity. If you haven't watched the video, do try and watch it somewhere where it hasn't been taken down yet. :) Watch the man's hands. Literally fast as lightning. Hatton didn't have a chance.

Makes you wonder about what makes the muscles move so fast for this man.

Grapefruits and ....Pomelos

2009-04-02T22:32:00.000-07:00

Few people in the west will know about pomelos. Apart from being a really yummy fruit, it is as potent as grapefruit in inhibiting CYP3A4, and MDR1 as well.

Here's my interpretation of the family tree of the pomelo and how it relates to the grapefruit.

Grapefruit, CYP3A4 and deep vein thrombosis

2009-04-02T08:38:00.001-07:00

Here's an interesting story off the news:

Grapefruit diet almost cost woman her leg

PARIS, April 3, 2009 (AFP) - A woman who ate a grapefruit each day almost had to have her leg amputated because of a dangerous blood clot, according to an unusual case study reported in the Lancet.

Emergency doctors in Olympia, in the US Pacific coast state of Washington, treated the 42-year-old woman in November 2008 after she was admitted with shortness of breath, dizziness and difficulty walking. An ultrasound scan found she had a large clot blocking the veins of her left leg.

She was in imminent danger of losing the limb to gangrene, but doctors administered a clot-busting drug directly into the blockage and safely dissolved it.

The physicians found she had taken a relatively long car journey, of about an hour and a half, the day before; took a daily dose of oestrogen oral contraceptives; and had a genetic variant, called the factor V Leiden mutation, which is linked to a blood-clot disorder.

All are well-established factors for causing deep vein thrombosis (DVT), as these dangerous events are called.

But what "may well have tipped the balance" is that she had been eating a grapefruit every morning under a weight-loss diet begun three days earlier, the report said.

Grapefruit juice is known to block the action of an enzyme called CYP3A4 which breaks down the contraceptive hormone oestrogen.

This in turn boosts levels of coagulability - the tendency of blood to clot.

Grapefruit juice is broken down only very slowly, which means that it has a cumulative effect if taken daily. Thus, on the third day of her diet, the patient's oestrogen levels would have been many times above normal, helping the clot to form.

DVT has been popularly termed "economy-class syndrome," as it is associated with passengers hunched up on cramped seats in long-haul flights.

But experts say DVT can be inflicted by any kind of immobility - in cars, the office or at home - that causes the leg to be bent for long periods and prevents blood from flowing. The clotting risk is amplified by oral contraceptives and heritability.

Transporters in erythrocytes

2009-03-30T16:52:00.000-07:00

Here's something to think about.

The erythrocyte is unique among cells because it lacks a nucleus. At some point in its development, it jetisons the nucleus and and other organelles including mitochondria and lives out the rest of its lifespan circulating as a membrane enclosed sac of haemoglobin and various enzymes.

It is an interesting situation because the erythrocyte, despite having no nucleus survives for an estimated 120 days fulfilling some of most important functions in the body. In the course of its work, as it circulates around the body, it is exposed to a wide variety of endogenous and exogenous chemicals.

The question is 'How does the erythrocyte membrane deal with these chemicals?'. More specifically, what transporters are expressed on the erythrocyte membranes, and what role(s) do they play in health, disease and therapeutics?

Genes vs environment

2009-03-28T00:39:00.000-07:00

Not an easy topic, but here is an interesting recent guest column by Sandra Aamodt and Sam Wang, in the New York Times discussing the complex interactions between genes and the environment. Although they discuss this from a largely neuropsychiatric perspective the lessons are widely applicable to therapeutics. We have far too many champions of the genetic approach who push ideas that genetic variability underlie everything that determines drug efficacy and toxicity. The underfunded environmental approaches go largely ignored because they use rather unexciting mundane technology, and produce results that tend not to generate patents.

IMO the gene only approach is clearly not valid. The challenge is how to tease out the various gene-environment interactions and to define them clearly so that they can eventually help us optimize our therapeutic regiments.

Boyanese (Baweanese) in Singapore

2009-03-25T18:04:00.000-07:00

Here's an interesting aside following from our previous discussions on ear wax and ethnicities.

A casual discussion with a Boyanese lady revealed that she had wet ear wax. Just an interesting association despite n= 1.

The other interesting nugget of information was that apparently the Boyanese do not classify themselves as Malays on their NRIC (National Registration Identify Card). They are either listed as 'Boyanese' or 'Others'. This is of interest because when we are collecting ethnicity/race information for purposes of medical case studies etc, any 'Malay' data may be under-represented.

The Boyanese originate from a small island off the north coast of East Java. It is of interest to us because many Singaporean Malays trace their heritage back to the original Boyanese settlers. Ethnically they are austronesians like the other Malays. If hospital records use NRIC classifications for ethnicity, and if the Boyanese do not classify themselves on the NRIC as Malays, it may be expected the the Malay data from hospital records will not be very representative.

Aldehyde Dehydrogenase polymorphism

2009-03-24T05:17:00.000-07:00

We had previously discussed the alcohol dehydrogenase genetic polymorphism, and had pointed out that among the Han Chinese, there is a high frequency of a genetic variant of alcohol dehydrogenase that allowed a faster conversion of ethanol to acetaldehyde. Acetaldehyde is the chemical that is thought to be responsible for not only the unpleasant effects of alcohol consumption (headaches, flushing etc) but is also thought to be the cause of tissue damage.

There is another enzyme that is responsible for the conversion of aldehyde dehydrogenase to acetic acid. This is called aldehyde dehydrogenase (ALDH). ALDH itself is subject to a genetic polymorphism where the genetic variant ALDH2*2 produces a slower enzyme. Among Han Chinese, the frequency of the ALDH2*2 variant is about 30%.

Among Han Chinese therefore, there is a significant number of individuals who will convert alcohol very quickly to acetaldehyde, and then have a slower removal of acetaldehyde. These individuals build up acetaldehyde concentrations in the blood very rapidly after consumption of alcohol. These individuals are the ones we recognize at drinking parties, who turn red very quickly after low consumption of alcohol.

A recent editorial in Human Genomics 3(2) 2009 highlights the risk this polymorphism poses with respect to the development of esophageal cancers.

So, who are the Thais anyway?

2009-03-23T22:02:00.000-07:00

I was thinking about this when I was in Khon Kaen.

It is not an easy question to resolve, as despite the Thais now being numerically so much larger than the Khmers, the Khmers were so much more dominant historically because of the Angkorean civilization. The earliest recognition of a Thai entity only surfaced when the Angkor civilization started to decline.

In early prehistory the region was populated by a Mon language speaking people. Who were they? Most likely people of a Sino-Tibetan stock. On top of this was a distinct amount of 'indianization' as evidenced by civilizations like the Dvararati (pre-angkorean). What 'indianization' really means is not clear, and it is not certain if this was a cultural thing or there was actually an influx of Indian genes as had happened in Cambodia (through the Kambujas from India). In any case, during the Angkor period, the region was 'Khmer-ized', so I am sure there was a substantial of genetic admixture. (See 'So, who are the Khmers anyway?')

As the Khmer civilization ebbed, the Thai people emerged as a distinct entity through the Lavo and Sukhothai kingdoms. A large part of this may have resulted from an influx of Southern Chinese people from Yunnan, fleeing the Mongol invasions.

Thailand now does not recognize differnt ethnicities within the country and everyone is regarded as Thai, although unofficially different ethnic groups are apparent. In a very broad sense, indigenous Thais are generally a sino-tibetan people with a variable amount of Indian admixture. There may be some degree of contribution from the austronesian gene pool, especially in Southern Thailand. On top of these are more recent contributions from Southern China.

So how do we regard pharmacogenetic data from Thailand? I think it becomes important for us to evaluate the source of the data. If it is generated in large urban centres, the contribution of Chinese genes is quite substantial. Indigenous Thai data is best seen in studies conducted in rural communities. In Southern Thailand, one must expect a significant amount of similarity to 'Malay' genetics. In the southern provinces, there still remain pockets of negrito peoples.

This is so far my limited understanding of the situation. I may be wrong. But this is how the current understanding appear to leading us. Perhaps there may be others with a better understanding of Thai ethnicity who can share their experiences and understanding with us?

Hardy & Weinberg

2009-03-23T18:51:00.000-07:00

The Hardy Weinberg Equilibrium (HWE) is such a fundamental 'law' in Mendellian genetics. Wikipedia has done a fairly good job summarizing so I am just going to shamelessly copy-paste from there. :) Essentially the principle is based on the random pairing of genetic material during mating. In a system where there is true randomness, at 'steady state' the genotypic frequencies are in 'equilibrium' and will remain relatively stable year after year. In pharmacogenetics, when we are looking at population frequencies of genetic variants, it is always useful to establish if the observed frequencies are consistent with the HWE.

From Wikipedia:
In the simplest case of a single locus with two alleles: the dominant allele is denoted A and the recessive a and their frequencies are denoted by p and q; freq(A) = p; freq(a) = q; p + q = 1. If the population is in equilibrium, then we will have freq(AA) = p² for the AA homozygotes in the population, freq(aa) = q² for the aa homozygotes, and freq(Aa) = 2pq for the heterozygotes.

The HWE is named after G. H. Hardy and Wilhelm Weinberg.

From Wikipedia:

Mendelian genetics were rediscovered in 1900. However, it remained somewhat controversial for several years as it was not then known how it could cause continuous characteristics. Udny Yule (1902) argued against Mendelism because he thought that dominant alleles would increase in the population. The American William E. Castle (1903) showed that without selection, the genotype frequencies would remain stable. Karl Pearson (1903) found one equilibrium position with values of p = q = 0.5. Reginald Punnett, unable to counter Yule's point, introduced the problem to G. H. Hardy, a British mathematician, with whom he played cricket. Hardy was a pure mathematician and held applied mathematics in some contempt; his view of biologists' use of mathematics comes across in his 1908 paper where he describes this as "very simple".

To the Editor of Science: I am reluctant to intrude in a discussion concerning matters of which I have no expert knowledge, and I should have expected the very simple point which I wish to make to have been familiar to biologists. However, some remarks of Mr. Udny Yule, to which Mr. R. C. Punnett has called my attention, suggest that it may still be worth making...

Suppose that Aa is a pair of Mendelian characters, A being dominant, and that in any given generation the number of pure dominants (AA), heterozygotes (Aa), and pure recessives (aa) are as p:2q:r. Finally, suppose that the numbers are fairly large, so that mating may be regarded as random, that the sexes are evenly distributed among the three varieties, and that all are equally fertile. A little mathematics of the multiplication-table type is enough to show that in the next generation the numbers will be as (p+q)²:2(p+q)(q+r):(q+r)², or as p₁:2q₁:r₁, say.

The interesting question is — in what circumstances will this distribution be the same as that in the generation before? It is easy to see that the condition for this is q² = pr. And since q₁² = p₁r₁, whatever the values of p, q, and r may be, the distribution will in any case continue unchanged after the second generation

The principle was thus known as Hardy's law in the English-speaking world until Curt Stern (1943) pointed out that it had first been formulated independently in 1908 by the German physician Wilhelm Weinberg (see Crow 1999). Others have tried to associate Castle's name with the Law because of his work in 1903, but it is only rarely seen as the Hardy–Weinberg–Castle Law.

The Great Ear Wax Poll outcome

2009-03-23T08:39:00.000-07:00

We don't know the ethnicity of the pollsters (n=47), nor their country of origin, but with the eye of faith, the outcome shows a nice histogram that is consistent with the Hardy-Weinberg Equilibrium (A=0.73, a=0.27). This is assuming the the three categories represents the three genotypes. This assumption may not be true. Nevertheless the outcome is interesting.

The ABC Transporters

2009-03-23T05:14:00.000-07:00

For the longest time we all worked on the premise that drug molecules moved across the cell membranes passively, and by virtue of their lipophilicity. In technical terms, this was determined by the water:octanol partition coefficient. But increasingly we are seeing that this is not all there is to the story. It would seem that few drug molecules actually traverse the lipid bilayer by passive diffusion. Few would cross the membrane without engaging the transport process in some way or other.

One important and very fundamental part of this process is mediated by a superfamily of drug transporters called the ABC transporters. The 'ABC' stands for ATP-Binding Cassette. Apart from having at least one ATP-binding domain, these transporters are characterized by a signature sequence of amino acid residues within the nucleotide binding domain - an LSGGQ motif.

In contrast to prokaryotes, the ABC transporters function as efflux pumps, which together with the detoxification enzymes constitute a complex integrated 'chemo-immunological defense' system against drugs and other xenobiotics.

In human beings, this superfamily of transporters comprises 48 members grouped into 7 families. Just how fundamental these transporters is evidenced by the ubiquity in all living systems be they eukaryotes or prokaryotes. Even mitochondria carry at least 4 of their own ABC transporters.

Prokaryotes
Agrobacterium tumefaciens - 135
Fungi/yeast
Saccharomyces cerevisiae - 22
Plasmodium - 15
Mitochondria - 4
Human - 48

The ABC transporters and their SLC (solute carrier) counterparts are fast shaping up to be probably the most important determinants of interindividual differences in drug efficacy/toxicity.

31st Pharmacological and Therapeutic Society of Thailand Meeting

2009-03-19T17:11:00.000-07:00

I just spent 3 wonderful days in Khon Kaen, Thailand at the 31st Pharmacological and Therapeutic Society of Thailand Meeting. I was there as the honoured guest of the Society to deliver the 'Chiravat Sadavongvivad Memorial Lecture was on " The ABC Transporters: Their role in determining drug resistance and drug response'. The proceedings are published in the Thai Journal of Pharmacology. For Khon Kaen University, see here.

Apart from enjoying the characteristically warm and wonderful hospitality of the hosts, it was a great time to catch up with old friendships and also on an academic front, to try and understand a bit more about Thai ideas of ethnicity issues.

ABCC11 Transporter

2009-03-17T03:07:00.000-07:00

The ABCC11 gene encodes for a little known member of the ABC (ATP binding cassette) superfamily of membrane transporters. The protein is called MRP8 or multi-drug resistance protein 8. Not much is yet known about its function. It became 'famous' largely because of its association with the dry-wet ear wax phenotype. Additionally, it has been shown to transport cyclic nucleotides e.g. cAMP and cGMP, and consequently has been implicated in resistance to a number of nucleotidic anticancer drugs used for breast cancers.

Other interesting associations other than ear wax....is the association with the production of colustrum by nursing mothers (early milk).

The main genetic polymorphism associated with ABCC11 is the 538G-A transition in exon 4 giving rise to a substitution of glycine to arginine. The frequency of this polymorphism is unknown among our local ethnic groups is Singapore.

What's with the Japanese - wet or dry ear wax??

2009-03-16T08:08:00.001-07:00

Another interesting bit of trivia I discovered when I visited RIKEN. There is a genetic polymorphism affecting the ABC (ATP-Binding Cassette) transporter ABC11, that determines if your ear wax comes out dry or wet. This was discovered by Yoshiura et al in 2006. See also report in the New York Times. Apparently the Japanese and Koreans (and Northern Chinese) exclusively have the 'dry ear wax' variant while African and West Europeans have the "wet ear wax" phenotype. The rest of the world have mixed proportions of dry and wet waxes. In Southern China and South East Asia, the frequency of wet wax increases. It is thought that the dry wax allele originated in Northern Asia, thus giving rise to an exclusive distribution of the allele in North China, Korea and Japan.

Wet is black, dry is white

The Japanese use the dry wax phenotype to distinguish the Yamato Japanese from the other Japanese arising from Hokkaido (Ainus) and Ryukyu (Okinawans).

Why not take the ear wax poll?

Who are the Japanese, anyway?

2009-03-14T20:21:00.000-07:00

The Japanese ethnic group is deceptively homogeneous.The dominant ethnic group however is recognizable as the Yamato people. These are whom we have come to commonly recognize as 'Japanese'. However, some minor ethnic groups do find their home in Japan. Apart from the obvious transplants such as Koreans, Chinese, Taiwanese etc, these are the Ainus, Ryukyuans and the Nivkhs.

The Yamatos are people who derive originally from the North Chinese mainland, having crossed over through the Korean peninsula. Consequently, there is a genetic thread that runs through and links the Northern Han Chinese and Koreans with the Yamato Japanese. By contrast the Ainu and Ryukyuans are earlier peoples who antedate the arrival of the Yamato people. RIKEN has data that show the genetic similarity between the Ainu and the Ryukuans (Okinawans). It is thought that the arrival of Yamato cleaved the distribution of the original Austronesian population to create a northern Ainu group and a southern Okinawan group. The Nivkhs are a small minority who derive from Siberia.

That said, the Japanese are pretty much of the Yamato group (>98% of toal 130 million). The Ryukuans probably account for about 1% and the Ainu even less (0.3%).

From a practical perspective, when one examines Japanese PGx data we should make mental links with data originating from Northern China and Korea. Accordingly, some caution is necessary when we try and connect the dots between Japanese and essentially Southern Chinese and South East Asian data.

The following excerpted from Wikipedia (Japanese_people):

A recent study for the origins of Japanese people is based on the "dual structure model" proposed by Hanihara in 1991. He concludes that modern Japanese lineages consist of the original Jōmon people and immigrants from the Yayoi period. The Jōmon people originated in southeast Asia, moving to the Japanese Archipelago in the Palaeolithic period. In past several decades, the Japanese people was proposed to relate to Yi, Hani and Dai people based on folk customs or genetic evidences.

Another southeast Asian groupmoved to northeastern Asia. The population of this group increased in the Neolithic period and some moved to the archipelago during the Yayoi period. The miscegenation prevailed in Kyūshū, Shikoku and Honshū islands but not in Okinawa and Hokkaido, respectively represented by the Ryukyuan and Ainu people. This theory was based on the study of the development of human bones and teeth. The comparison of mitochondrial DNA between Jōmon people and medieval Ainu also supports the theory.

Personalized medicine vs Genome-based medicine

2009-03-13T16:37:00.000-07:00

I have a very good series of meeting as part of the Health Sciences Authority, Singapore (HSA) team with the Japanese Pharmaceutical and Medical Devices Agency (PMDA), Ministry of Health, Labour and Welfare (MHLW), and the National Institute of Health Sciences (NIHS), as well as the RIKEN Center for Genomics Medicine (CGM).

I learnt a lot just talking the the various agencies. But the highlight for the meeting for me was catching that fleeting comment by Prof Yusuke Nakamura, Director of Riken CGM, that there was a difference between "personalized medicine" and genome-based medicine", and that RIKEN had more of a focus on "genome-based medicine". I thought that was incredibly insightful. So many of the luminaries in PGx toss around the term "personalized medicine" almost as a justification for spending their multimillion $$ budgets but totally missing the point that what they are after really isn't personalizing therapeutics.

In our analogy of the F1 race car driver, it's really no different from developing better and more precise fuel injection systems, or tires that are better suited for the road surface. Technology is great. But after all that we musn't forget that one still needs to navigate and drive fast to get to the finish line, ... with the best time. That is personalized medicine.

See recent editorial: Personalized medicine: are we there yet?

Pharmacogenetics / Pharmacogenomics

2009-03-09T01:58:00.000-07:00

Here's an interesting observation I made while preparing for a presentation. This is a plot of the yearly publications where you can find the term "Pharmacogenetics" or "Pharmacogenomics". This is obtained by just searching via SCOPUS using those keywords.

"Pharmacogenetics" had a steady hit rate of about 50-60 before 1995, and publications took off after that on a steady incline. The word "Pharmacogenomics" was first used in 1997, and then really kicked in after 1999. But what really surprised me was that, unlike "Pharmacogenetics", the hit rate has since plateaued off after 4-5 years.

I am not too sure what the reason is, but I suspect there may be a bit of "Pharmacogenomics" fatigue, and the genomics people moving into broader areas of "Genomics", have stopped using the term "Pharmacogenomics". The "Pharmacogenomics" people by contrast, have tended to remain faithful to their cause and the science continues to expand.

Ethanol pharmacokinetics

2009-03-07T00:39:00.000-08:00

Ethanol is a small molecule that has a strong affinity for water. It is absorbed efficiently and rapidly after consumption. Regardless of how it is consumed and in what form of spirits, the effect of alcohol on the central nervous system is closely correlated with the circulating concentrations of alcohol in the blood. This allows enforcement authorities to set certain tolerable (legal) limits of blood alcohol concentrations (BAC) as a surrogate limit for alcoholic intoxication (Singapore's legal limit is 0.08%, or 80 milligrams of alcohol per 100 millitres of blood). As the alcohol in the blood equilibrates very rapidly with the alveolar concentrations, breath alcohol concentrations (BrAC) are often used by the traffic police as an alternative to measuring blood alcohol concentrations. In Singapore, the BrAC limit is 35 micrograms of alcohol per 100 millilitres of breath. See Singapore Traffic Police.

It is therefore of some interest to the public to know how much alcohol one can drink without exceeding the BrAC limits.

The peak (maximum) BrAC after consuming alcohol depends on a number of factors:

a] the total amount of alcohol consumed (not the volume of beverage). Obviously the larger the quantum of alcohol, the higher the BAC will be.

b] the rate of absorption of the alcohol. The rate will be faster if the stomach is empty, and if the concentrations are high but not too high. Higher concentrations of spirit (alcohol) will produce a greater concentration gradient to drive the absorption. However, if the concentrations are too high, there may be slower gastric emptying of stomach contents into the small intestines where absorption of the alcohol is faster and more complete. Men theoretically therefore absorb more alcohol then women.

c] the amount of first pass metabolism in the linings of the stomach. There is some controvery about how important this actually is. Women are said to have less alcohol dehydrogenase expressed in the stomach walls and therefore have less first pass metabolism. The less metabolism, the greater the absorption.

d] the 'volume of the body' into which the alcohol is distributed. Pharmacokinetically this is referred to as the volume of distribution. Men have a higher water content in their body compared to women, and since alcohol is distributed primarily into body water, men will weight for weight, develop lower BrACs.

e] the rate of elimination of alcohol. The ADH genetic polymorphims have been discussed elsewhere. If consumed quickly, the rate of elimination will not affect the BrAC much, but if the consumption is protracted over a period of time, long enough for elimination to occur, the rate of elimination may have a significant effect on the BrAC.

Based on the above understanding, it is a reasonable expectation (assuming that the amount of alcohol consumed is exactly the same) that a muscular Chinese man sipping several glasses of wine over the course of a 4-course meal will produce substantially lower BrACs compared to an Indian lady quaffing down a series of stiff drinks on an empty stomach.

Alcohol and the breathalyzer #1

2009-03-06T14:35:00.000-08:00

The question is: Given the genetic polymorphism affecting alcohol dehydrogenase (ADH), how much variability is there in the population, and how does this variability affect the amount of alcohol that can be consumed before one exceeds the breathalyzer legal limits?

Today we begin a study at Changi General Hospital to find out.

Keep watching this space. We'll have the results out soon....

The Great Durian Poll outcome

2009-03-05T20:20:00.000-08:00

Many thanks to all who participated. We managed a half decent 66 responses... :).

I must say I was somewhat surprised by the results. As a non-durian lover I was expecting to see a much clearer separation of lovers and haters, with perhaps a more distinct bimodality in the distribution, somewhat like the taster/non-taster distribution. Instead we had a kind of log-normal distribution, like the CYP3A4/5 one.

There are a couple of possible reasons for this. One is that there may be a sampling bias, i.e. non-durian lovers aren't that motivated to participate. Secondly, the category axis is an ordinal scale, so even though I tried to space out the responses as 'equally' as I can imagine them to be, there is no certainty that the categories have equal intervals. It could well be that there is a larger separation at the "So-so only....no big deal" category.

In any case, it was an interesting exercise. As has been shown many times before, the lack of a clear 'bi- or poly-modal' distribution does not necessarily exclude any genetic bases for the interindividual differences.

Regardless of the genetic or lack of genetic basis for the interindividual difference, (and assuming the sample represents all of Singapore) there is an interesting lesson for us here...

Firstly, because of the preponderance of durian lovers in the sample, one can say Singaporeans generally love durians...passionately...though they can mostly live without it. Secondly and perhaps more importantly, is to recognize that despite such an overwhelming support for the spikey fruit, there are regulations that protect the interests of the people represented by right tail of the distribution. You don't allow the fruit on airplanes, in cars, shopping centres and restaurents. It is such a common sense thing to do, so we kinda take it for granted. It is actually a very common phenomenon. In a classroom, for example, the (good) teacher's attention is often focused on the poor students, or the bright spark...and not on the majority of the students who (on average) do not have any problems.

We have the same situation in dealing with therapeutic problems. Most dosage regimens are designed for the average patient (central tendency, remember?), and we know (or should know) that the patients who develop problems are those at the tails of the distribution...either inadequate response, or too much response/toxicity. Our mental focus should really be on helping the patients in the tails of distribution achieve an appropriate therapeutic response. Yet physicians often forget this and assume that the recommended (average) dose will meet the needs of all the patients they treat.

The challenge for us is in helping physicians identify which of the patients reside in the tails. This is where pharmacogenetics come in.

There is a nice review, "Pharmacogenetics - Tailoring Treatment for the Outliers" in the New England Journal of Medicine by Woodcock and Lesko that deals with this specific issue. It also reminds us of what Sir William Osler had shared over a hundred years ago: "If it were not for the great variability among individuals, medicine might as well be a science and not an art." Paradoxically, medicine is now at a stage of development where dealing with this variability has become much more of a science.

Alcohol pharmacogenetics in Singapore

2009-03-04T20:49:00.000-08:00

The news about the alcohol related death at the National University of Singapore triggered some thinking about the interesting aspects about how alcohol (ethanol) is handled by the Chinese in Singapore.

Ethanol regardless of the form it comes in, is metabolized fairly efficiently in the body by means of an enzyme, alcohol hydrogenase (ADH). Although there are other enzymes that can also do this (e.g. CYP2E1), the most important pathway for alcohol degradation is really ADH. ADH itself is encoded by 7 genes but the major enzyme in the stomach mucosa and liver is the Class 1 enzyme, encoded by the ADH1B gene.

CH₃CH₂OH + NAD⁺ → CH₃CHO + NADH + H⁺

What is important for us to realize is that there is a common genetic polymorphism affecting the ADHB1 gene in Chinese. An 'atypical' ADH was first described in 1968 by Von Wartburg and Schuerch (Ann. N.Y. Acad. Sci. 151: 936-947, 1968). Subsequently Stamatoyannopoulos in 1975 found the atypical variant in 85% of Japanese. The variant is now identified as ADH1B*47 (previously ADH2*2) and exchanges a histidine for arginine in the protein, resulting in an enzyme with very much enhanced activity (100 fold difference in Vmax). A recent study (Alcohol Clin Exp Res. 2004 Jan;28(1):10-4) in Jewish carriers of this variant showed increased rate of elimination of alcohol from the blood (8.09 vs 7.14 g/hr).

This genetic variant is particularly common in South East and East Asia and is an important genetic 'protector' against alcoholism because it is thought to result in more unpleasant outcomes because of higher levels of aldehyde production . The frequency among Taiwanese Han Chinese is about 75% (Hepatology. 1997 Jan;25(1):112-7 ).

A recent report was able to show this global distribution of ALD1B*47 variant (Am J Hum Genet. 2007 Oct;81(4):842-6. Epub 2007 Aug 24). Note the interesting cluster in West Asia. Also the absence of this variant in the Indian subcontinent. More about this later, and its implications for us in Singapore.

Global contour plot of the ADH1B*47His allele frequency

Pharmacogenetics (PGt) vs Pharmacogenomics (PGx)

2009-03-02T23:41:00.000-08:00

Pharmacogenetics isn't really a new science. The term was first coined by Friedrich Vogel back in 1959 when not many people were really interested. It was really only in relatively recent time, after genetic/genomic technologies became widely available that it had a renaisance. Now it's become somewhat of a hip and much hyped up science.

Newbies often get confused by the two terms 'pharmacogenetics' and 'pharmacogenomics', often abbreviated to PGt and PGx. The two terms have overlapping characteristics and PGt is often seen nowadays as being a subset of PGx. In fact the two terms have often been used interchangeably by various publications. But for those of us working in this areas, the distinctions can be quite distinct. For us, PGt is often a study of the variations in a targeted gene, or group of functionally related genes. PGx, on the other hand is a much broader investigation of genetic variations at the level of the genome.

Here are some definitions of these terms as given by the FDA and the NCBI.

FDA Guidance for Industry (2008) E15...
PGt: The study of variations in DNA sequence as related to drug response
PGx: The study of variations of DNA and RNA characteristics as related to drug response

NCBI Factsheet
PGt: The study of inherited differences (variation) in drug metabolism and response.
PGx: The general study of all of the many different genes that determine drug behavior.

Rat poison and the F1 driver - the warfarin story

2009-03-02T16:51:00.000-08:00

The anticoagulant warfarin actually started life as rat poison. Chemically, it is derived from a natural plant product, coumarin. The way it acts is by inhibiting the enzyme Vitamin K epoxide reductase, and in so doing reduce formation of various Vit K dependent clotting factors.

So what's the deal about F1 drivers?

Well... like F1 drivers, the physician using warfarin needs to keep his eye on the road. Too little warfarin, and there is inadequate therapeutic anticoagulation; too much warfarin and the patient may suffer a catastrophic bleed. Fortunately, he has a way to do this. The Prothrombin Time and other derived measures such as the International Normalized Ratio (INR) provide a heads up to the physician about how much anticoagulation has been provided for the patient. By keeping his eye on the INR, the physician can adjust the dose of warfarin to provide just the right range on anticoagulation the patient needs. This is important, because the warfarin requirements for every patient differ, and the warfarin dose needs to be 'individualized'.

More recently, various other biomarkers enable the physician to make educated guesses about the dosage requirement for the patient. These are genetic markers related to the rate of metabolic degradation of warfarin through cytochrome P450 2C9 (not many such problems in our Chinese population) and the genetically reduced sensitivity of the Vitamin K epoxide reductase C1 subunit (VKORC1). However, using these genetic biomarkers only provide an improved starting dose. Once the race car engine starts, the F1 driver will still have to manage the therapeutic process through keeping a close eye on the INR.

There have been many discussions about the genotyping of patients prior to dosing with warfarin. I have no doubt to its usefulness in helping us to understand the patient a lot better. But becasue there is already a good efficacy marker (the INR) for us to titrate the patients dosing against, the improved starting point may only be of theoretical benefit. I think most of the benefit will come in situations when you need to deliver very fast anticoagulation. Where time is not on an essence, genotyping would likely not be a cost effective option.

Therapeutics and the F1 race.....

2009-02-27T20:21:00.000-08:00

There are quite a few variables operating in the context of an F1 race. Where the driver is concerned there are questions about his mental alertness, situational awareness, speed of reflexes, knowledge, experience and even his risk for appetite. Clearly there are also variables related to machine performance and conditions affecting road and track. All this variability create exciting conditions and an unpredictable outcome. Yet, no matter who wins eventually, most car-machine partnerships perform outstanding well.

The operation of any high performance machine system requires complex and sophisticated servo-systems operating at multiple levels. At its most simplistic level, the race car driver need to sense the speed of the car and adjust the speed through an interplay of acceleration and deceleration.

Precision therapeutics is really not any different. The physician must be able to recognize the effect of his procedures (drug regiment) and modulate these through the adjustment of his procedures (drugs dosages, for example). What is surprising is how many physicians do not recognize that they need to do this. They function like race car drivers who don't know their car, have no speedometer and are blinded. What's worse....don't even know they have brake and accelerator pedals.

For them, the patient is represented by a virtual description of a population average. Their expectation of a therapeutic effect is a relatively crude measurement of eventual outcome - cured, didn't work...died(often they don't even recognize they are driving blind). And they don't seem to realize, they can actually adjust drug doses in scientifically rational ways.

We can actually do a whole lot better than that. And many physicians have.

It's good to blog - Nature Editorial

2009-02-26T22:39:00.000-08:00

Reprinted here is an Editorial from Nature about researchers blogging.I am smugly pleased I started before they said it ......

Editorial
Nature 457, 1058 (26 February 2009) | Published online 25 February 2009

It's good to blog

More researchers should engage with the blogosphere, including authors of papers in press.

Is blogging a part of science, journalism or public discourse? In fact it may be all of these — an ambiguity that can sometimes leave scientists feeling uncertain about the rules of the game.

Imagine, for example, a case in which Nature's blog The Great Beyond highlights new scientific results presented at a conference on climate. That blog entry then stimulates an online debate, with climate sceptics interpreting the results their way, and others firing off rebuttals. Imagine also that the work is described in a paper that had been accepted, but not published, by Nature. The authors of the paper want to enter the fray, but feel inhibited from doing so because of the embargo imposed by Nature and many other journals on communication by authors to the media ahead of publication. And why was Nature blogging their work anyway, ahead of its publication?

This scenario highlights a need for clarification about Nature publications' procedures, and about how embargoes apply to blogs. It also highlights more generally the potential importance of scientists engaging in the blogosphere.

All Nature journals maintain confidentiality about submitted papers, so that only the editors directly responsible for those papers know about them. Other staff — including the various publications' journalists — are usually informed about a paper only once it has been accepted, and with the proviso that they do not disseminate any information about it to external contacts or readers. Likewise, we ask that authors refrain from actively promoting their work to the media and public ahead of its publication. This embargo policy rests on the principle that scientists' and the public's best interests are served by press coverage of work that has been peer reviewed, and is available for others to see for themselves.

At the same time, however, our cardinal rule has always been to promote scientific communication. We have therefore never sought to prevent scientists from presenting their work at conferences, or from depositing first drafts of submitted papers on preprint servers. So if Nature journalists or those from any other publication should hear results presented at a meeting, or find them on a preprint server, the findings are fair game for coverage — even if that coverage is ahead of the paper's publication. This is not considered a breaking of Nature's embargo. Nor is it a violation if scientists respond to journalists' queries in ensuring that the facts are correct — so long as they don't actively promote media coverage.

The blogosphere differs from mass media and specialized media in many respects, but the same considerations apply in disseminating new scientific results there. Authors of papers in press have the right to correct misrepresentations and to point to results that will appear in a paper. But a full discussion should await the paper's publication.

Indeed, researchers would do well to blog more than they do. The experience of journals such as Cell and PLoS ONE, which allow people to comment on papers online, suggests that researchers are very reluctant to engage in such forums. But the blogosphere tends to be less inhibited, and technical discussions there seem likely to increase.

Moreover, there are societal debates that have much to gain from the uncensored voices of researchers. A good blogging website consumes much of the spare time of the one or several fully committed scientists that write and moderate it. But it can make a difference to the quality and integrity of public discussion.

Durian lovers vs durian haters

2009-02-26T07:58:00.000-08:00

Here's a slight frivolous post inspired by tonight's after dinner conversation.

The durian (Durio zibethinus), reckoned by many as the king of fruits in Asia. Others have less flattering descriptions. It's a fruit that you either love or hate.

Some descriptions of its taste (shamelessly plagiarized from Wikipedia):
Writing in 1856, the British naturalist Alfred Russel Wallace provides a much-quoted description of the flavour of the durian:
“The five cells are silky-white within, and are filled with a mass of firm, cream-coloured pulp, containing about three seeds each. This pulp is the edible part, and its consistence and flavour are indescribable. A rich custard highly flavoured with almonds gives the best general idea of it, but there are occasional wafts of flavour that call to mind cream-cheese, onion-sauce, sherry-wine, and other incongruous dishes. Then there is a rich glutinous smoothness in the pulp which nothing else possesses, but which adds to its delicacy. It is neither acid nor sweet nor juicy; yet it wants neither of these qualities, for it is in itself perfect. It produces no nausea or other bad effect, and the more you eat of it the less you feel inclined to stop. In fact, to eat Durians is a new sensation worth a voyage to the East to experience. ... as producing a food of the most exquisite flavour it is unsurpassed.”

While Wallace cautions that "the smell of the ripe fruit is certainly at first disagreeable", later descriptions by westerners are more graphic. British novelist Anthony Burgess writes that eating durian is "like eating sweet raspberry blancmange in the lavatory." Chef Andrew Zimmern compares the taste to "completely rotten, mushy onions." Anthony Bourdain, while a lover of durian, relates his encounter with the fruit as thus: "Its taste can only be described as...indescribable, something you will either love or despise. ...Your breath will smell as if you'd been French-kissing your dead grandmother." Travel and food writer Richard Sterling says:

“... its odor is best described as pig-shit, turpentine and onions, garnished with a gym sock. It can be smelled from yards away. Despite its great local popularity, the raw fruit is forbidden from some establishments such as hotels, subways and airports, including public transportation in Southeast Asia.

Other comparisons have been made with the civet, sewage, stale vomit, skunk spray and used surgical swabs.

There are clear indications that this is a pharmacogenetic differentiation of durian lovers from haters, though I do not know of any study to this effect.

Go ahead and take the Great Durian Poll!!

The not so normal distribution

2009-02-23T19:20:00.000-08:00

The normal distribution is a convenient tool when you need to describe your data. Unfortunately it introduces a blind spot when it comes to interpreting the data.

When we look at the distribution, our eye intuitively focuses on the centre of the distribution. We see the central tendency of the distribution and the variance around it. This is fine when you are describing the data. But averages don't really help you if you are the storeman who's responsible for purchasing clothes for a bunch of factory workers.

This is the odd thing about studying variability in therapeutic response to drugs. We all know variability exists. We see this in every sphere of human activity, from buying clothes and cosmetics to the ability to complete a physical fitness test. Yet inexplicably, when it comes to dosing patients, people imagine that a dosage regiment based on the mean of a relatively small unrepresentative study sample will somehow represent the dosage requirement for everyone on this planet.

Here is a series of distributions of the clearances of CYP3A5 substrates midazolam and alfentanil (Kharasch et al, Clinical Pharmacology & Therapeutics (2007) 82, 410–426). The distributions are skewed to the right and so are clearly log-normally distributed.

Here is a frequency distribution of the log-metabolic ratio for midazolam in a Chinese population (Zhu et al, Br J Clin Pharmacol. 2003 March; 55(3): 264–269).

Notice from these plots just how variable the clearances and the metabolic ratios (more about this later) are. How do we, under these conditions determine the correct doses for each patient? Clearly applying population averages will not work. Are we able to do it?

The earlier posting on wafarin show how it can be done for warfarin.More on dosage optimization issues later.

Highlighted report: Validation of VKORC1 and CYP2C9 genotypes on interindividual warfarin maintenance dose

2009-02-23T00:24:00.000-08:00

Objectives: To develop a warfarin-dosing algorithm that could be combined with pharmacogenomic and demographic factors, and to evaluate its effectiveness in a randomized prospective controlled clinical trial.

Methods: A pharmacogenetics-based dosing model was derived using retrospective data from 266 Chinese patients and multiple linear regression analysis. To prospectively validate this model, 156 patients with an operation of heart valve replacement were enrolled and randomly assigned to the group of pharmacogenetics-guided or traditional dosing for warfarin therapy. All patients were followed up for 50 days after initiation of warfarin therapy. The log-rank test was compared with the time-to-event (Kaplan-Meier) curves. Cox proportional hazards-regression model was used to assess the hazard ratio of the time to reach stable dose.

Results: The linear regression model derived from the pharmacogenomic model correlated with 54.1% of warfarin dosing variance. The final multiple linear regression model included age, body surface area, VKORC1, and CYP2C9 genotype. The study showed that the hazard ratio for the time to reach stable dose was 1.932 for the traditional dosing group versus the model-based group and a close and highly significant relationship was observed to exist between the predicted and the actual warfarin dose (R2=0.454).

Conclusion: A pharmacogenetics-based dosing algorithm has been developed for improvement in the time to reach the stable dosing of warfarin. This model may be useful in helping the clinicians to prescribe warfarin with greater safety and efficiency.

The normal distribution

2009-02-22T18:46:00.000-08:00

The normal distribution, often referred to as the Gaussian distribution, and at other times, the bell curve, takes its name from the prodigious German mathematician Carl Friedrich Gauss (1777-1855), who discovered it while studying the distribution of measurement errors in astronomy.

It is something we take very much for granted in clinical and biomedical research. It is certainly something inherently useful in being able to group our observable data, and to be able to describe a central tendency that can be used to represent groups of subjects or patients. Another way of looking at the normal distribution of data is to do a probit analysis. Here is an example of the apparent normality in the frequency distribution as applied to the CYP1A2 metabolic ratio in a Chinese population. Under these circumstances, the probit plot approximates linearity.

Frequency and probit distribution of CYP1A2 activity in a Chinese population as indicated by plasma log-transformed 1,7-dimethylxanthine/caffeine [lg(17X/137X)] ratios (n = 419).Chen et al, Clinical Pharmacology & Therapeutics 78, 249-259 (September 2005)

In this instance however, the normality of the distribution hides a plethora of heterogeneity as the CYP1A2 gene is highly polymorphic, and the authors in this study reports that the G–3113A polymorphism is associated with decreased CYP1A2 activity, haplotype pairs 10 and 13 are responsible for high CYP1A2 activity, and haplotype pairs 5, 8, 9, 12, and 15 are responsible for low CYP1A2 activity in Chinese subjects.

In understanding diversity of human drug response, understanding 'normality' is an important starting point, but we need to look beyond this. Normality can work against us.

Genetics of taste?

2009-02-19T13:59:00.001-08:00

Bimodal distribution of PTC sensitivity

Here's a a bit of an update on Arthur Fox's PTC taste blindness story from. Not a whole lot of people interested in this other than the food industry and nutritional scientists.

There is a rather dated review of this here (2001) and here (1995). Apparently the frequency of non-tasters among Caucasians is about 26-30%, but only 6-10% among Asians (whatever that means for the Americans!). Apparently tasters have a lower acceptance for bitter foodstuff such as broccoli/cruciferous vegetables, naringin, green tea and tofu. But this does not seem to be borne out in real life given the high acceptance of green tea and tofu in Asia despite a much lower incidence of non-tasters.

One of these ethnic confounders.

Carbamazepine - Stevens-Johnson Syndrome

2009-02-19T07:23:00.000-08:00

In 2004, a Taiwanese group (Chung et al, Nature 2004, 428:486) reported a very strong association between the HLA-B*1502 allele with the development of Stevens-Johnson Syndrome with the anti-epileptic drug carbamazepine.

This is actually a very interesting story about an drug SAE (serious adverse event) associated with a specific HLA genotype. Not only genetics, but also an intriguing tale about ethnicity and populations.

So far all the clinical data verify the association in Han Chinese (Taiwan, Hong Kong) and Thai. No associations have been found for Europeans....and here is what is also interesting...not for Japanese as well. Ferrel and McLeod (2008) have reviewed the data, and there have also been a number of more recent studies reporting on the HLA-B*1502 allele frequencies from different populations.

A Table of these frequencies from Ferrel and McLeod is reproduced here.
There are a couple of more recent data not only showing the same patterns but also showing how the Northern Chinese group pulls apart from the Southern Chinese. Han Chinese from Shaanxi, Koreans and Japanese have a Caucasian type frequency while Southern Chinese in Guangdong cluster with the rest of South East Asia (Philippines, Java). The Indian subcontinent occupies an intermediate position, and depending on the population sampled, with frequencies ranging between Caucasian frequencies to those similar to South East Asians and Southern Chinese. The association between HLA-B*1502 and carbamazepine SJS appears not in doubt, but apparently only for Southern Chinese (Taiwan, Hong Kong, most of Singapore) and people of South East Asian ancestry. It does not seem to apply for Caucasians, and remains doubtful for Northern Chinese, and people of a more northern ancestry such as the Japanese (and possibly the Koreans). There remains a big question mark for people from the Indian subcontinent.

The FDA did us a major injustice when in their haste they recommended that genotyping for HLA-B*1502 be done prior to dosing, for people of an "Asian ancestry". We have no idea what this means. For Singapore, it is probably reasonable for Chinese and Malays (although it may not apply to the more northern Chinese among us), but we are totally in the dark with regards to "Indians" and "Eurasians".

Pharmacogenetics in Singapore - beginnings

2009-02-18T21:16:00.000-08:00

It is easy to want to think of Pharmacogenetics as a fairly recent science in Singapore, but actually its roots go back quite far. And it is a story worth telling.

This goes back to just before when Vogel first coined the term Pharmacogenetics in 1959. In 1957, a young paediatrician, Wong Hock Boon (he was barely 34 years old then), discovered that the cases of neonatal jaundice giving rise to the devastating condition of kernicterus in Singapore was very different from those reported in the West (Wong HB, Archives of Diseases in Childhood, 32:85, 1957). Collectively, the causes of kernicterus in the West (Rhesus incompatibility, ABO incompatibility and prematurity) only accounted to about a thrid of cases in Singapore. At the time of reporting, he was not able to figure out what lay behind the remaining two thirds, but about a year previously, Carson (Carson, P. E.; Flanagan, C. L.; Ickes, C. E.; Alving, A. S. : Enzymatic deficiency in primaquine-sensitive erythrocytes. Science 124: 484-485, 1956) had described an inherited deficiency in glucose 6 phosphate dehydrogenase that predisposed to drug (primaquine) induced haemolytic anaemia. Subsequently these dots connected, and it became clear that kernicterus in Singapore was largely due to neonatal haemolytic anaemia in babies who had G6PD deficiency.

What followed was perhaps the most underrecognized, success stories in pharmacogenetics. In 1964, Singapore started its systematic neonatal screening programme for G6PD deficiency (unless I am mistaken, it may have been the first in the world to do it), and was able to bring down dramatically the incidence of kernicterus in our newborns. Taiwan and Hong Kong didn't get into it until the 80's.

And this was all due to clinical astuteness of Dr Wong Hock Boon, who in 1962 became Professor Paediatrics in the then University of Singapore. Sadly he passed away last year (December 2008) at the age of 85.

Variability in drug response - Pharmacogenetics

2009-02-18T20:37:00.000-08:00

It has long been recognized that patients differ substantially in the way they respond to their medications. These differences may relate to compliance issues, or to the way their medications interacted with other concurrent medications and chemicals in their diet. Often differences may reflect the individual genetic makeup in terms of how the body deals with drugs and exogenous chemicals. The complex interactions between one's genetic makeup, environment and culture ultimately determine the balance of health and disease, as well as one's response to drugs.

It was Friedrich Vogel of Heidelberg, Germany in 1959 who first coined the term "Pharmacogenetics" (Vogel, F. Moderne problem der humangenetik. Ergeb Inn Med U Kinderheilk. 1959;12:52–125). But in reality, the hereditability of drug response was already being recognized as far back as 1931, when a DuPont chemist Arthur Fox discovered (Fox, A. L. : Tasteblindness. Science 73: 14, 1931) that some people were actually 'blind' to the taste of PTC (phenylthiocarbamide). As has often been the case, this was a very serendipitous discovery because it resulted from a lab accident when the PTC exploded into the air. While others around him experienced the bitter taste, Fox did not. It was subsequently discovered that he lacked the N-C=S group for his taste receptor. (see Yale Scientific Magazine)

The rest, as they say, ... is history.

Genesis

2009-02-18T02:59:00.001-08:00

Always wanted to do this but never ever got around to do it. The idea really started from all the Pharmacogenetics Lab research activities and my involvement with the LSM4212 Module on "Pharmacogenetics and Drug Response Variability".

After lots of mistakes I think I finally mastered the basic tools of blogging...well, at least enough, I think, to get started. So here goes. Here's hoping we can get some sort of discussions going on all those thoughts and ideas floating around issues related to pharmacogenetics and variability in drug response.

Coincidentally, The Scientist just published a report on a blogging high school biology teacher, and how she transformed the teaching of biology. I reproduce an excerpt here, but you can go here for the real stuff (just need to register).

"High school biology teacher Stacy Baker was sick of waiting by the photocopier to make handouts for her students. So in 2006, she launched a website, missbakersbiologyclass.com, to serve as central repository for class notes, pictures, and extra credit assignments.

At first, the site was simply an online extension of her classroom, and information flowed strictly one-way: teacher to pupils. But Baker wanted the site to be more than that; she wanted to engage her students in the full interactive potential of the Internet. So she transformed the site into a participatory blog, and let her students take it over."

Cool.