Phytochemicals, pharmacogenetics and pharmacokinetics

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

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

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

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?

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?

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

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

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

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

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

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

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

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.

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

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)

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

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

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

Does body weight matter?

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

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?

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

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?

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.....

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?

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.....

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)

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

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.