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.