Statistical design and analysis of pharmacogenetic trials

An Integrated Clinical and Genetic Prediction Model for Tacrolimus Levels in Pediatric Solid Organ Transplant Recipients

BACKGROUND

There are challenges in achieving and maintaining therapeutic tacrolimus levels after solid organ transplantation (SOT). The purpose of this genome-wide association study was to generate an integrated clinical and genetic prediction model for tacrolimus levels in pediatric SOT.

METHODS

In a multicenter prospective observational cohort study (2015-2018), children <18 years old at their first SOT receiving tacrolimus as maintenance immunosuppression were included (455 as discovery cohort; 322 as validation cohort). Genotyping was performed using a genome-wide single nucleotide polymorphism (SNP) array and analyzed for association with tacrolimus trough levels during 1-y follow-up.

RESULTS

Genome-wide association study adjusted for clinical factors identified 25 SNPs associated with tacrolimus levels; 8 were significant at a genome-wide level (P < 1.025 × 10-7). Nineteen SNPs were replicated in the validation cohort. After removing SNPs in strong linkage disequilibrium, 14 SNPs remained independently associated with tacrolimus levels. Both traditional and machine learning approaches selected organ type, age at transplant, rs776746, rs12333983, and rs12957142 SNPs as the top predictor variables for dose-adjusted 36- to 48-h posttacrolimus initiation (T1) levels. There was a significant interaction between age and organ type with rs776476*1 SNP (P < 0.05). The combined clinical and genetic model had lower prediction error and explained 30% of the variation in dose-adjusted T1 levels compared with 18% by the clinical and 12% by the genetic only model.

CONCLUSIONS

Our study highlights the importance of incorporating age, organ type, and genotype in predicting tacrolimus levels and lays the groundwork for developing an individualized age and organ-specific genotype-guided tacrolimus dosing algorithm.

Impact of study design and statistical model in pharmacogenetic studies with gene-treatment interaction

Gene-treatment interactions, just like drug-drug interactions, can have dramatic effects on a patient response and therefore influence the clinician decision at the patient's bedside. Crossover designs, although they are known to decrease the number of subjects in drug-interaction studies, are seldom used in pharmacogenetic studies. We propose to evaluate, via realistic clinical trial simulations, to what extent crossover designs can help quantifying the gene-treatment interaction effect. We explored different scenarios of crossover and parallel design studies comparing two symptom-modifying treatments in a chronic and stable disease accounting for the impact of a one gene and one gene-treatment interaction. We varied the number of subjects, the between and within subject variabilities, the gene polymorphism frequency and the effect sizes of the treatment, gene, and gene-treatment interaction. Each simulated dataset was analyzed using three models: (i) estimating only the treatment effect, (ii) estimating the treatment and the gene effects, and (iii) estimating the treatment, the gene, and the gene-treatment interaction effects. We showed how ignoring the gene-treatment interaction results in the wrong treatment effect estimates. We also highlighted how crossover studies are more powerful to detect a treatment effect in the presence of a gene-treatment interaction and more often lead to correct treatment attribution.

Gene-wide tagging study of the effects of common genetic polymorphisms in the α subunits of the GABA(A) receptor on epilepsy treatment response

AIM

We aimed to identify the effect of SNPs in the α-subunits of GABAA receptors on epilepsy treatment outcomes by using a gene-wide tagging method.

MATERIALS & METHODS

There were 720 epileptic patients included in the present study. A total of 136 tagging SNPs in GABRA1, GABRA2, GABRA3, GABRA4, GABRA5 and GABRA6 were genotyped by Illumina(®)GoldenGate(®) Genotyping platform. Clinical information, such as prescribed antiepileptic drugs, height, weight, epilepsy syndrome classification, etiology, number of attacks, renal function and liver function were collected. The associations between SNPs and epilepsy treatment outcomes were analyzed using SAS(®) version 9.1.3. Both multivariate logistic regression and multifactor dimensionality reduction analyses were performed.

RESULTS

The results of single gene effects did not remain significant after Bonferroni's corrections. Further multivariate logistic regression and multifactor dimensionality reduction analyses of interactions between these genes showed that under adjustment of clinical factors, the epilepsy treatment outcomes were significantly associated with the genotype combinations of GABRA1 rs6883877, GABRA2 rs511310 and GABRA3 rs4828696 (p < 0.0001; adjusted r(2) = 0.149).

CONCLUSION

Our results indicated that genetic variants in the α subunits of GABA(A) receptors may interactively affect the treatment responses of antiepileptic drugs. Further replication using an independent sample collection would be essential to confirm our findings.

Effects of polymorphisms in six candidate genes on phenytoin maintenance therapy in Han Chinese patients

AIM

The present study aimed to investigate the associations between variants in pharmacokinetic- and pharmacodynamic-related genes with the dosages, concentrations and concentration-dose ratios (CDRs) of phenytoin (PHT).

METHODS & RESULTS

Eleven genetic polymorphisms in the six candidate genes were detected in 269 epileptic patients under maintenance PHT monotherapy by real-time PCR and PCR-RFLP. Results of a bivariate analysis demonstrated that among tested polymorphisms, carriers of the variant CYP2C9*3 tended to require significantly lower maintenance PHT dosages than wild-type carriers (p < 0.0001); on the other hand, carriers of the variants CYP2C9*3 or CYP2C19*3 revealed significantly higher CDRs than wild-type carriers (p < 0.004). In a further multivariate analysis, variants in SCN1A, CYP2C9, CYP2C19 and ABCB1 genes were significantly associated with CDRs of PHT under adjustment of age, gender and epilepsy classifications (adjusted r(2) = 20.07%).

CONCLUSION

The results of present study indicated that polygenic analysis may provide useful information in PHT therapy optimization.

Promises and challenges of pharmacogenetics: an overview of study design, methodological and statistical issues

Pharmacogenetics is the study of inherited variation in drug response. The goal of pharmacogenetics is to develop novel ways of maximizing drug efficacy and minimizing toxicity for individual patients. Personalized medicine has the potential to allow for a patient's genetic information to predict optimal dosage for a drug with a narrow therapeutic index, to select the most appropriate pharmacological agent for a given patient and to develop cost-effective treatments. Although there is supporting evidence in favour of pharmacogenetics, its adoption in clinical practice has been slow because of sometimes conflicting findings among studies. This failure to replicate findings may result from a lack of high-quality pharmacogenetic studies, as well as unresolved methodological and statistical issues. The objective of this review is to discuss the benefits of incorporating pharmacogenetics into clinical practice. We will also address outstanding methodological and statistical issues that may lead to heterogeneity among reported pharmacogenetic studies and how they may be addressed.

Association of polymorphisms in EPHX1, UGT2B7, ABCB1, ABCC2, SCN1A and SCN2A genes with carbamazepine therapy optimization

AIM

Carbamazepine (CBZ) is one of the most widely used antiepileptic drugs. The aim of the present study is to investigate the impacts of polymorphisms in genes related to pharmacokinetic and pharmacodynamic pathways of CBZ on the large interindividual variability in dosages and concentrations.

METHODS & RESULTS

Genetic polymorphisms in the candidate genes were detected in 234 epileptic patients under maintenance CBZ monotherapy by real-time PCR and PCR-RFLP. Results of statistical analysis demonstrated that carriers of the variant SCN1A IVS5-91G>A and EPHX1 c.337T>C allele tended to require higher CBZ dosages and lower ln(concentration-dose ratios) than noncarriers (p < 0.0001) and the homozygous carriers also seemed to require higher CBZ dosages and lower ln(concentration-dose ratios) (p < 0.0001). In addition, the multiple regression model of concentration-dose ratio of CBZ also revealed that genetic variants in SCN1A, EPHX1 and UGT2B7 genes interactively affect the concentration-dose ratio of CBZ (adjusted r(2) = 55%).

CONCLUSION

The present study identified genetic factors associated with CBZ therapy optimization and provided useful information for individualized CBZ therapy in epileptic patients. Further studies in larger populations are needed to confirm our results.

Impact of genetic polymorphisms in ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genes on methadone therapy in Han Chinese patients

Aim: The present study explored the integrative effect of genes encoding methadone pharmacokinetic and pharmacodynamic pathways on methadone maintenance doses in Han Chinese Patients. Materials & methods: Genomic DNA was extracted from 321 opioid-dependent patients and 202 healthy controls, and realtime-PCR and PCR-RFLP were conducted to determine the genotypes. Results: Pair-wise comparisons revealed that carriers of the variants ABCB1 3435C>T or CYP2B6 516G>T alleles were more likely to require a higher methadone dose than noncarriers (both p < 0.0001). On the other hand, carriers of the variant DRD2 -214A>G or 939C>T allele had a twofold chance of requiring a lower methadone dose than noncarriers (p = 0.001). Proportional odds regression with adjustment of cofactors demonstrated that ABCB1, CYP2B6, OPRM1, ANKK1 and DRD2 genetic variants were jointly correlated with optimal methadone dose (adjusted r2 = 53%). Conclusions: These findings provide new insight to the fact that the interindividual variability of methadone dosage requirement is polygenetic and cannot be explained by a single-gene effect.

Original submitted 4 May 2011; Revision submitted 8 July 2011

An empirical evaluation of multifarious outcomes in pharmacogenetics: beta-2 adrenoceptor gene polymorphisms in asthma treatment

OBJECTIVE

Pharmacogenetics promises to individualize therapeutics. Concerns, however, exist about the lack of replication of discoveries. Selective use of different endpoints, times of assessment, types of interventions and genetic groups across studies may lead to spurious results. Here, we examined the variability of definitions of endpoints and analyses reported across studies addressing the association of the Arg16Gly and/or Gln27Glu polymorphisms of the beta2-adrenergic receptor gene with clinical response to beta2-agonist therapy in asthma.

METHODS

We systematically calculated the number and type of endpoints and analyses reported across studies and recorded the appraisal of their statistical significance.

RESULTS

Across 21 studies, the total number of probed and reported associations was 487 when the multiple endpoints and types of comparisons presented by multiple comparisons were considered (337 for Arg16Gly, 98 for Gln27Glu and 52 for their haplotypes): 465 (95%) were probed only once; only six associations were probed twice and two associations were probed five times, for the same endpoint, time of assessment, type of interventions and genetic group. Most studies (17/21) claimed at least one significant association. Overall, however, 243/487 (49.9%) probed and reported associations were not statistically significant, 120 (24.6%) were of unspecified statistical significance, 86 (17.7%) were statistically significant only for specific selected genetic contrasts and only 38 (7.8%) were genuinely statistically significant for the comparison between all available genetic groups.

CONCLUSIONS

The multifarious outcomes in this literature are inconsistent across studies and susceptible to selective reporting. The lack of standardization hinders the evaluation of replication validity for reported discoveries.

On the utility of gene set methods in genomewide association studies of quantitative traits

In genomewide genetic association studies, prior biological knowledge may help distinguish variation that is truly associated with a quantitative trait from the vast majority of unassociated variation that may be significant in hypothesis testing due to chance. However, formal methods for integrating prior biological knowledge into association studies have only been proposed recently, and their potential utility has not been thoroughly evaluated. Herein, gene set methods from genomewide analysis of gene expression data are adapted for application to genomewide genetic analysis of quantitative traits. The proposed gene set method was tested in simulations with gene sets that included up to 500 total variants, among which up to 20 collectively explained 5% of the variance. In a population of 1,000 individuals, the gene set method was largely more efficient at detecting truly associated variants in these gene sets than a comparably calibrated conventional approach relying on P-values alone. While extremely strong associations remain best identified by conventional methods, the gene set approach may provide a complementary mode of analysis for revealing the full spectrum of genes that influence a quantitative trait.

Sequentially testing for a gene-drug interaction in a genomewide analysis

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs.

Gene-environment interactions for complex traits: definitions, methodological requirements and challenges

Genetic and environmental risk factors and their interactions contribute to the development of complex diseases. In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application. Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means. This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice. We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results. We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa. Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified. These issues as well as a critical appraisal of common methodological concerns are finally discussed.

Association of polymorphisms in NR1I2 and ABCB1 genes with epilepsy treatment responses

OBJECTIVES

The aim of this study was to investigate whether the polymorphisms in the NR1I2 and ABCB1 genes were associated with epilepsy treatment responses.

METHODS & RESULTS

NR1I2and ABCB1 polymorphisms were genotyped in 114 drug-resistant epileptic patients, 213 seizure-free patients and 287 normal controls. Highly specific real-time PCR was applied to detect the variants by using TaqMan allelic specific probe. For a single gene test, it was demonstrated that 3435C>T in the ABCB1 gene had a significant effect on epilepsy treatment responses, but polymorphisms in the NR1I2 gene did not. Further analysis using a logistic regression model revealed that only 2677G>T and 3435C>T in the ABCB1 gene and their interaction term were associated with drug-resistant epilepsy after adjustment for etiology and epilepsy classification. In the present study, the polymorphisms in the NR1I2 gene were not significantly associated with epilepsy treatment responses.

CONCLUSION

Our results indicated that 2677G>T and 3435C > T in the ABCB1 gene contributed to drug-resistant epilepsy. Although biologically plausible, the polymorphisms in NR1I2 investigated in the present study did not play a role in epilepsy treatment responses. Other unveiled genetic variants in the NR1I2 gene that may have the potential to affect ABCB1 gene expression are worth further investigation in future studies.

Pharmacogenetic studies in depression: a proposal for methodologic guidelines

Pharmacogenetic studies in mood disorders are rapidly proliferating after the initial reports linking gene variants to treatment outcomes. However, a considerable range of methodologies has been used, making it difficult to compare results across studies and limiting the representativeness of findings. Specification of sampling source (inpatients vs outpatients, primary vs tertiary settings), standardization of diagnostic systems and treatments, adequate monitoring of compliance through plasma levels, sufficient length of observation (at least 6 weeks for acute antidepressant treatments, though 3-6 months are preferable), the use of a range of response criteria and the inclusion of possible environmental confounding variables (life events, social support, temperament) are all potentially important issues when planning pharmacogenetic studies. We reviewed the state-of-the-art methodology and suggested possible guideline for future studies.

Exploring design-related bias in clinical studies on receptor genetic polymorphism of hypertension

BACKGROUND AND OBJECTIVES

Although several candidate genes of Renin-Angiotensin-Aldosterone System (RAAS) have been investigated, the gene-drug relationship remains unclear. The objective was to appraise the elements of research methodology and explore potential biases, which may be contributing to discordant results in the gene-drug interaction assessment for RAAS.

METHODS

Systematic review of studies involving candidate polymorphisms, searching PubMed, and EMBASE.

RESULTS

Sixteen studies were identified. Nine studies had a genomic evaluation as the primary question. Six studies investigated more than one gene. A gene-drug interaction was evaluated in two studies and only one of the studies had a placebo arm for accurately exploring the interaction. Almost, 90% of the studies had sample sizes of less than 500 patients. Four studies combined the allele frequencies of the heterozygotes group with one of the homozygotes groups. Almost one quarter of the studies combined different therapeutics in one group. Five studies included patients in one group from previous studies in which selection criteria were not quite similar.

CONCLUSION

Most studies contain several methodological limitations. Also biases driven from patient selection, combining different alleles, combining different therapeutics, and combining end points may have occurred in these studies. These limitations and biases may contribute to inconsistency of the results of these studies.

Protein therapeutics: new applications for pharmacogenetics

Pharmacogenetic studies have traditionally focused on genes involved in processes that affect the pharmacokinetics of small-molecule drugs, such as drug metabolism. However, attention is shifting to the effects of genetic variations in drug targets and associated pathway components on drug responses. We describe how these variations are important for understanding differences in responses to the growing number of protein therapeutics that are entering clinical practice. Pharmacogenetic studies of these drugs are surveyed, and issues important to the success of such endeavours are discussed. As novel protein therapeutics are introduced, we anticipate that the use of pharmacogenetics will assume a key role in their development and clinical application.