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FORUM |
From Pharmacokinetics, Dynamics and Metabolism (Dr Williams), Molecular Profiling (Dr Johnson), Worldwide Safety Sciences (Dr Paulauskis), Clinical Pharmacokinetics and Pharmacodynamics (Dr Cook), Pfizer Global Research and Development, Ann Arbor, Michigan.
Address for reprints: J. Andrew Williams, Pfizer Global Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, 2800 Plymouth Road, Ann Arbor, MI, 48105.
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ABSTRACT |
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 TOP ABSTRACT FUNCTIONAL RELEVANCE OF GENE...WHAT IS THE BEST...CONCLUSION AND RECOMMENDATIONREFERENCES |
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Key Words: Polymorphisms • pharmacokinetics • pharmacogenetics • drug disposition • drug-metabolizing enzymes • drug transporters
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FUNCTIONAL RELEVANCE OF GENE POLYMORPHISMS IN DRUG-METABOLIZING ENZYMES OR TRANSPORTERS: EQUIVOCAL OR EMERGING STATUS IN MANY CASES |
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TOPABSTRACTFUNCTIONAL RELEVANCE OF GENE...WHAT IS THE BEST...CONCLUSION AND RECOMMENDATIONREFERENCES |
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Our objective in this commentary is to recommend that statements incorporating the results of power analyses be included into published studies investigating polymorphism effects on pharmacokinetics, as detailed below. For the purposes of distinguishing between "clinical" and "functional" relevance of polymorphisms, the former refers to efficacy and/or safety of drugs interacting with the enzyme or transporter of interest. A better understanding of clinical relevance would also assist drug clinical development so that more efficient approaches can be taken to understand polymorphism effects at the earlier stages.
Phenotype in the following sections refers to drug exposure (area under the curve [AUC]) and clearance, two pharmacokinetic parameters that are inversely proportional to each other. Drug clearance is the sum of metabolism and excretion of unchanged drug. In descending order, the most commonly listed clearance mechanisms for the top 200 prescribed drugs in the United States in 2003 are (1) cytochrome P450 (CYP)-catalyzed metabolism, (2) renal elimination of unchanged drug, and (3) UDP-glucuronosyltransferase (UGT)-catalyzed metabolism.2 Therefore, it follows that functional polymorphisms in genes encoding CYPs, renal transporters, and UGTs are most often found to influence drug clearance.
Distribution of parent drug and metabolites between blood and solid organs (eg, brain and eye) is another process that can be influenced by functional polymorphisms in proteins influencing drug disposition, especially the drug transporters. The magnitude of change will be dependent on the degree of the polymorphism effect on transporter activity as well as the physicochemical properties of the drug in question. For example, organic anion transporter polypeptide C (encoded by OATP1B1) may influence hepatic uptake, and therefore clearance, of the hydrophilic hypocholesterolemic drug pravastatin.3 In addition, the transporter protein P-glycoprotein (encoded by ABCB1) is known to be a significant influence on brain penetration for some compounds4,5 and may thereby also influence pharmacodynamics and efficacy as well as distribution. However, transporter influence on interorgan distribution may not be readily recognized as the relevant physiologic mechanism by the typically measured pharmacokinetic parameters (eg, maximal concentrations, Cmax, AUC).6
When considering genes encoding drug metabolism enzymes or transporters, convincing evidence from consistently concordant published studies indicate functional polymorphisms in CYP2C97 and UGT1A1,8 for example. The greater challenge, however, lies in a better understanding of the equivocal evidence for functional polymorphisms in CYP3A4, CYP3A5,9-21 other UGTs, and genes encoding drug transporters such as ABCG2 (encoding Breast Cancer Resistance Protein),22-26 ABCB1,4,5 and OATP1B1.27-30 In our view, a primary reason for this conflicting information is that most studies have been conducted on too few subjects per group. Therefore, it is most often the case that there is insufficient confidence that the observed magnitude of effect (eg, difference in drug exposure between those possessing variant alleles compared to those possessing wild-type alleles) accurately reflects the true effect of the polymorphism. Our contention is not with initial published studies that test potential effects of polymorphisms in a specific gene for the first time but rather with multiple repeat publications that continue to incorporate insufficient subject numbers per study group to adequately test functional relevance, resulting in a continuing lack of clarity. We accept that in some cases, other reasons may explain a lack of clarity of polymorphism effect on pharmacokinetics, including possible misclassifications of genotype for the study. In certain studies in which patients and not healthy volunteers are incorporated into the study design, disease states and concomitant medicines may also confound data if the design does not incorporate appropriate controls. However, the focus of this commentary is on subject numbers per study group and consequences for interpreting genotype influences on pharmacokinetics.
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WHAT IS THE BEST WAY TO DESIGN STUDIES THAT ASK WHETHER POLYMORPHISMS ARE FUNCTIONALLY RELEVANT? |
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TOPABSTRACTFUNCTIONAL RELEVANCE OF GENE...WHAT IS THE BEST...CONCLUSION AND RECOMMENDATIONREFERENCES |
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Design of Studies to Detect Polymorphism Effects: What Can We Still Learn From Clinical Drug-Drug Pharmacokinetic Interaction Studies?
Pharmacokinetic drug-drug interaction studies are standard within the drug development paradigm and are usually triggered by in vitro evidence of possible clinically relevant metabolism by cytochrome P450 enzymes.31,32 Contrasted to some, but not all, of the emerging data from published studies designed to assess polymorphism effects on drug pharmacokinetics, the median number of patients or healthy volunteers used per pharmacokinetic drug-drug interaction study is 12 for crossover designs and 20 for parallel group designs.33 Advances from our understanding of study design for drug-drug interaction studies can be applied to investigations of polymorphism-associated effects on pharmacokinetics, as stated below.
A key design component from these pharmacokinetic drug-drug interaction studies (in academic and industrial environments) that in our opinion should be more commonly applied to studies to assess polymorphism effects on pharmacokinetics is that the numbers of subjects per group (n) must be adequately powered to be confident of detecting expected differences in measured parameter (typically drug plasma concentrations and/or drug exposure). In the cases in which small differences are expected, for example, where the exposure ratios in the presence of inhibitor compared to its absence are low, the subject number per study group would need to be significantly greater than if the expected differences are high. It therefore follows that where evidence is accumulating on genes encoding drug-metabolizing enzymes such as CYP3A5, UGT1A1, and drug transporters such as ABCB1 and OATP1B1, which suggests that the magnitude of polymorphism effect on drug exposure is also likely to be low (less than 3-fold ratio) compared to that observed for CYP2D6, the number of subjects per study group would need to be higher to confirm a polymorphism effect compared to the well-established greater effects (up to 10-fold difference in exposure) of low/null activity enzyme encoded by CYP2D6 variants. In studies in which the expected difference is unknown, power calculations can be conducted to indicate the expected performance, and publications could include a statement similar to the following: "The study was designed to look for an x% difference in a pharmacokinetic parameter, which would require y subjects per study group." In designing these studies, pharmacokinetic variability is an important consideration to detect polymorphism effects, as described below.
For drug-drug interaction studies, intrasubject variability is the variability considered in crossover clinical study designs, in which the subjects receive first one treatment then subsequently cross over to the other treatment group. However, for polymorphism effect studies that must use parallel group designs, the total variability incorporates contributions both within (intra-) and between (inter-) subjects. The contribution of intersubject variability is well exemplified from a sampling of 123 studies for 48 different drugs, in which Gaudreault et al34 found that the median intersubject coefficient of variation (CV) was nearly 80% larger than the median intrasubject CV for exposure (AUC (0-
))34. This suggests that it is likely that the observed variability for parallel group-designed polymorphism effect studies would be, on average, at least twice the intrasubject variability observed in a crossover study for the same drug.
The ratio of mean exposures for those possessing variant polymorphism(s) compared to wild-type subject means is another important determinate of study power. For the group of genes in which evidence of polymorphism effects is emerging or equivocal, it seems likely that the actual differences in exposure will be low (<3-fold), and we therefore recommend that future study designs aimed at investigating polymorphisms in these genes base sample sizes appropriately to include sufficient numbers per study group. The interrelationship between study power, variability, and mean group difference is depicted in Figure 1. It can be seen from this figure that relatively small studies have been able to robustly identify polymorphism-associated pharmacokinetic differences in clearance of CYP2D6 substrates, such as desipramine. The ratio of mean desipramine AUC values in poor versus extensive metabolizers was estimated to be 7 in one study.35 It is recognized that these are phenotypes and that individuals may not be genetically homozygous, but the case of desipramine is presented here as an example of a large difference in exposure. The CV for desipramine AUC is approximately 69%.36
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Assuming log normally distributed exposure values within each group, a common CV of 69% (for desipramine or any other probe substrate selected to detect polymorphism effects on exposure) and equal group sizes, 4 subjects per group would result in more than 80% power to detect a 7-fold difference in exposure (
= .05, 1-sided test). Assuming an equivalent CV (69%), a 10-fold difference in exposure between poor and extensive metabolizers would require only 2 subjects per group. In contrast, more subjects would be needed if the expected exposure ratios were smaller; for example, 11 and 31 subjects per group would be required to detect exposure ratios of 2 and 1.5, respectively, assuming a CV of 69% and statistical power of 80%. Although not necessarily new, the key message presented in this commentary is therefore that for cases in which polymorphism-associated effects on pharmacokinetics are emerging or equivocal, larger numbers of subjects per study group are required to provide much-needed clarity in the literature on whether specific polymorphisms are functionally relevant for marketed drugs. This would also be the case for compounds in clinical development. Adequately powered studies would also provide narrow confidence intervals in the estimates of ratios of mean exposures between study groups. For example, a claimed difference of 3-fold from an inadequately powered study may really be a 6-fold difference because of the wide confidence intervals around the estimate.
A further extension of the approach described above, posing the question of whether polymorphism-associated exposure differences are detectable, would be to design studies to confirm that polymorphism-associated differences in exposure are functionally irrelevant. In this case, one would consider the boundary for judging clinical relevance to be typically greater than a 2-fold difference in the measured pharmacokinetic parameter, in addition to the expected ratio and variability. This is analogous to using the ratio boundaries of 0.8:1 and 1.25:1 for the procedure used to establish bioequivalence of different drug formulations.37
Subject Selection by Genotype for Genes in Which Functional Relevance Is Emerging or Equivocal
Depending on the phenotype exhibited as a result of a polymorphism, the associated change in activity of the encoded protein will range from minimal to severe. If the genetic variant results in absence of encoded protein, heterozygous subjects may readily demonstrate a phenotype that is measurably different to the phenotype exhibited by homozygous wild types. However, it has been argued that comparison of homozygous wild types and homozygous variants is likely to yield the greatest observed difference in drug exposures.38 Foti and Fisher presented a valid case for this latter approach to appropriately investigate the influence of CYP3A5 polymorphisms on the pharmacokinetics of CYP3A substrates.38
Allele frequency for the gene of interest will significantly affect efforts to select appropriate subjects for comparison of disposition. Multiple variant alleles may exist for some genes (for the best characterized gene—CYP2D6, there are approximately 160 proposed variants—see http://www.imm.ki.se/CYP alleles) and would implicate multiple subject groups in the study. If the variant allele is frequent in the test population, only a relatively small group of subjects will need to be prescreened to reach the required number per group (see below). An example of a variant allele frequent in the population for a gene with equivocal evidence of functional relevance is that of UGT2B7*2, which had a frequency of 0.55 in a bank of human livers predominantly from whites (the wild-type allele, UGT2B7*1, had a frequency of 0.45).39 Conversely, if the variant allele is rare, a larger number of subjects would need to be prescreened. An important consideration is that the frequency of variant alleles is often dependent on ethnicity. For example, the CYP2D6*10 allele, encoding an unstable enzyme with decreased activity relative to wild-type protein, has a frequency of 1% to 2% in whites and 51% in Asians.40 The cited Web site (http://www.imm.ki.se/CYP alleles) is available to aid readers in finding up-to-date information on nomenclature and can provide direction to further information such as allele frequencies.
For each candidate gene for investigation, there may be multiple single nucleotide polymorphisms in the coding and/or promoter regions. It may be that efforts are unknowingly focused on "silent" genetic variants, thus leading to the inappropriate conclusion that functional polymorphisms do not exist in the particular gene of interest. It therefore makes best sense to assess all known relevant single nucleotide polymorphisms and/or haplotypes for confirmation of whether polymorphisms are functional.
Probe Substrate Selection and Magnitude of Polymorphism Effect
The optimal substrates are those that are selective for the enzyme or transporter being studied. However, identification of selective substrates can present a significant challenge in that there is often overlap of substrate selectivity for members of the same gene family, for example, pitivastatin is a substrate of OATP1B1 and OATP1B3.41 Substrate dependency of polymorphism effects is likely to be observed in situations in which the genetic variant is in a substrate recognition site, as with CYP2C9, in which warfarin pharmacokinetics is very sensitive to CYP2C9 polymorphisms.42
The direction, if not the magnitude, of polymorphism effects would be predicted to be substrate independent if the polymorphism significantly influences levels of active protein by altering levels of transcription, translation, protein stability, or turnover. However, based on current evidence, this does not appear to be the case for CYP3A5 polymorphisms (see previous references), where in general, midazolam pharmacokinetics appears not be influenced by CYP3A5 polymorphisms,11,14,19 but there is consistent evidence for influence on tacrolimus drug dosage requirements.9,10,16 A proven explanation for these diverse observations is yet to be provided.
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The selection of ideal probe substrates for assessment of polymorphism effects differs significantly from optimal probe substrates for cytochrome P450 drug-drug interaction studies. Usually, for metabolized drugs, drugs with high hepatic (and intestinal) extraction ratios are typically selected to view a significant difference in metabolic clearance, and therefore exposure, in the presence of P450 inhibitor compared to its absence. However, a major consideration for future studies investigating polymorphism effects is to consider using substrate probes with low intrinsic metabolic or transport clearance since the influence of enzymes on expression level may be easier to measure if the assumptions of the "well-stirred" model of hepatic clearance (see Figure 2) also hold true for transporters. It can be seen from the figure that according to the assumptions of the well-stirred model, when intrinsic clearance is low (<50 mL/min/kg), the relationship between intrinsic clearance and organ clearance approaches direct proportionality. Measurement of polymorphism effect on enzyme activity is therefore better suited for compounds that are more slowly turned over, and therefore have a lower intrinsic clearance, since when intrinsic clearance increases upward from 50 mL/min/kg, organ clearance becomes less dependent on intrinsic clearance and polymorphism effects become more difficult to measure.
This hypothesis described above was tested using data published for CYP2C9 polymorphism effects on clearance of 14 drugs.43 No relationship was observed between calculated intrinsic clearance (based on total clearance assuming 100% metabolism by CYP2C9) and the magnitude of reduction in clearance in CYP2C9*3 variant allele homozygotes compared to CYP2C9*1 homozygous wild types (raw data not shown). An important observation is that mean clearance values in CYP2C9*3 homozygotes were 68% ± 44% (range, 17%-143%, n = 14 drugs) of clearance in homozygous wild types, equating to a 1.5-fold difference in drug exposure. As can be seen in Figure 1, adequately powered studies to be confident of detecting a 1.5-fold difference in exposure would require somewhere between 8 and more than 50 subjects per group, depending on the CV value (30%-100%). However, most probably due to the low frequency (<1%) of the CYP2C9*3 genotype, not 1 of the 14 studies reported used more than 5 CYP2C9*3 homozygotes in their studies (mean = 2 CYP2C9*3 homozygotes). This observation reaffirms the need for sufficient numbers of subjects per study group to detect with confidence the magnitude of differences in drug exposure between individuals possessing wild-type alleles and those possessing variant alleles. Perhaps future studies on genetic polymorphisms with emerging or equivocal status could include a component of prescreening to increase numbers of subjects with less common genotypes such as CYP2C9*3 before drug dosing begins.
Ozdemir and others44 reported a repeated drug administration method to assess the relative contributions of environment versus genetics in pharmacokinetic variability for CYP3A4 substrates. It was concluded that CYP3A4 activity was primarily controlled by genetic influence. It could therefore be envisaged that for substrates of other metabolizing enzymes or transporters, this broad approach could provide an efficient assessment of the overall potential contribution of polymorphism-associated differences in pharmacokinetics before deciding on a plan of action with regard to specific single nucleotide polymorphisms or haplotypes.
Pharmacokinetic-pharmacodynamic relationships provide a further level of complexity to understanding the clinical relevance of genotype influence. A critical aspect not covered in this commentary is that of context of exposure data to clinical phenotype. A good example is that of the OATP1B1 genotype: although several studies claim an influence on pravastatin pharmacokinetics,3,27,29 a recent study incorporating 462 patients on pravastatin found no association of OATP1B1 genotype with statin efficacy.45
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CONCLUSION AND RECOMMENDATION |
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TOPABSTRACTFUNCTIONAL RELEVANCE OF GENE...WHAT IS THE BEST...CONCLUSION AND RECOMMENDATIONREFERENCES |
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Our key message is that for studies in which the objective is to assess the magnitude of polymorphism effect on drug pharmacokinetics, employing sufficient numbers of subjects per group will provide greater confidence that observed differences truly reflect the influence of genetics on drug pharmacokinetics. In addition, we suggest that advances in our ability to select (1) appropriate subjects by genotype and (2) ideal probe substrates will most likely significantly influence the magnitude of polymorphism-associated effect.
We recommend that the results of power analyses should be provided in publications describing results of prospective studies investigating genotype influence on pharmacokinetics for marketed drugs and those in clinical development. In these publications, statements should include reference to (1) subject number per study group, (2) observed variability in pharmacokinetics for the probe substrate, and (3) the minimum difference between groups in the relevant pharmacokinetic parameters (eg, exposure) that could be detected based on the relevant inputs from (1) and (2). Confidence intervals (90%) should also be incorporated when reporting the observed data.
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REFERENCES |
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TOPABSTRACTFUNCTIONAL RELEVANCE OF GENE...WHAT IS THE BEST...CONCLUSION AND RECOMMENDATIONREFERENCES |
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