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.