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Genotype-based risk and pharmacogenetic sampling in clinical trials.

ABSTRACT: A number of recent genome-wide association (GWA) studies have identified unequivocal statistical associations between inherited genetic variations, mostly single-nucleotide polymorphisms (SNPs), and common complex diseases such as diabetes, cardiovascular disease, and obesity. Genotyping individuals for these variations has the potential to help redefine how pharmacologic agents undergo clinical development. By identifying carriers of known genomic variants that contribute to susceptibility, a high-risk population can be defined, as well as individuals with potential for a better response to a drug. We evaluated the potential utility that selecting individuals for a trial on the basis of genotypes identified in contemporary GWA studies would have had on recently described clinical trials. We pursued this by constraining both the risks of a disease outcome associated with particular genotypes and overall drug responses to those actually observed in genetic association and clinical trial studies, respectively. We pursued these evaluations in the context of clinical trials investigating drugs for macular degeneration, obesity, heart disease, type II diabetes, prostate cancer, and Alzheimer's disease. We show that the increase in incidence of outcomes in trials restricted to individuals with specific genotypic profiles can result in substantial reductions in requisite sample sizes for such trials. In addition, we also derive realistic bounds for samples sizes for clinical trials investigating pharmacogenetic effects that leverage genetic variations identified in recent association studies.

SUBMITTER: Schork NJ

PROVIDER: S-EPMC2892229 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Genotype-based risk and pharmacogenetic sampling in clinical trials.

Schork Nicholas J NJ Topol Eric J EJ

Journal of biopharmaceutical statistics 20100301 2

A number of recent genome-wide association (GWA) studies have identified unequivocal statistical associations between inherited genetic variations, mostly single-nucleotide polymorphisms (SNPs), and common complex diseases such as diabetes, cardiovascular disease, and obesity. Genotyping individuals for these variations has the potential to help redefine how pharmacologic agents undergo clinical development. By identifying carriers of known genomic variants that contribute to susceptibility, a h ...[more]

PMID: 20309761

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Project description:AimAnalysis of distribution of p-values of continuous differences between test and controls after randomization provides evidence of unintentional error, non-random sampling, or data fabrication in randomized controlled trials (RCTs). We assessed evidence of highly unusual distributions of baseline characteristics of subjects enrolled in clinical trials in implant dentistry.Materials and methodsRCTs published between 2005 and 2020 were systematically searched in Pubmed, Embase, and Cochrane databases. Baseline patient data were extracted from full text articles by two independent assessors. The hypothesis of non-random sampling was tested by comparing the expected and the observed distribution of the p-values of differences between test and controls after randomization.ResultsOne-thousand five-hundred and thirty-eight unique RCTs were identified, of which 409 (26.6%) did not report baseline characteristics of the population, and 671 (43.6%) reported data in forms other than mean and standard deviation and could not be used to assess their random sampling. Four-hundred and fifty-eight trials with 1449 baseline variables in the form of mean and standard deviation were assessed. The study observed an over-representation of very small p-values [<.001, 1.38%, 95% confidence interval (CI) 0.85-2.12 compared to the expected 0.10%, 95% CI 0.00-0.26]. No evidence of over-representation of larger p-values was observed. Unusual distributions were present in 2.38% of RCTs and more frequent in non-registered trials, in studies supported by non-industry funding, and in multi-centre RCTs.ConclusionsThe inability to assess random sampling due to insufficient reporting in 26.6% of trials requires attention. In trials reporting suitable baseline data, unusual distributions were uncommon, and no evidence of data fabrication was detected, but there was evidence of non-random sampling. Continued efforts are necessary to ensure high integrity and trust in the evidence base of the field.

Dataset Information

Genotype-based risk and pharmacogenetic sampling in clinical trials.

Publications

Genotype-based risk and pharmacogenetic sampling in clinical trials.

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