Biostatistic tools in pharmacogenomics - advances, challenges, potential

Yasunori Sato, Nan M. Laird, Teruhiko Yoshida

Research output: Contribution to journalReview article

7 Citations (Scopus)

Abstract

Inter-individual variations in drug response are all-too common and, throughout medical history have often posed problems, many of them serious ones. The variations could stem from multiple factors, which include those of both the host (age, genetic and environmental factors) and disease (pathophysiological phenotypes, somatic mutations in case of cancers). The complex interplay of these factors can influence pharmacodynamic responses, such as adverse effects and efficacy, as well as pharmacokinetic manifestations through variability in drug absorption, distribution, metabolism and excretion. Recently, several potentially powerful tools to decipher such intricacies are emerging in various fields of science, and the translation of such knowledge to personalized medicine, called, in general, pharmacogenomics, has been promoted and has occasioned strong expectations from almost every sector of health care. However, at present, few biomarkers can predict which group of patients will respond positively, which will be non-responders and who might experience adverse reactions from the same medication and dosage. This review highlights several important aspects related to the design and statistical analysis for pharmacogenomics studies or clinical trials, which incorporate biomarkers. First, we review biomarker development: how biomarkers may be used as targets and the difference between prognostic and predictive markers. Second, in confirmatory clinical trials, we focus on issues related to study design for evaluating biomarkers and how they can be used to determine which patients might optimally benefit from a specific therapy. Finally, we review exploratory statistical screening techniques for detecting biomarkers in Phase I or pharmacokinetics studies.

Original languageEnglish
Pages (from-to)2232-2240
Number of pages9
JournalCurrent Pharmaceutical Design
Volume16
Issue number20
DOIs
Publication statusPublished - 2010 Aug 20
Externally publishedYes

Fingerprint

Biostatistics
Pharmacogenetics
Biomarkers
Pharmacokinetics
Clinical Trials
Precision Medicine
Health Care Sector
Translational Medical Research
Pharmaceutical Preparations
Phenotype
Mutation

Keywords

  • Biomarker
  • Clinical trials
  • Drug development
  • Genome-wide study
  • Optimal design
  • Personalized medicine
  • Pharmacogenomics

ASJC Scopus subject areas

  • Pharmacology
  • Drug Discovery

Cite this

Biostatistic tools in pharmacogenomics - advances, challenges, potential. / Sato, Yasunori; Laird, Nan M.; Yoshida, Teruhiko.

In: Current Pharmaceutical Design, Vol. 16, No. 20, 20.08.2010, p. 2232-2240.

Research output: Contribution to journalReview article

Sato, Yasunori ; Laird, Nan M. ; Yoshida, Teruhiko. / Biostatistic tools in pharmacogenomics - advances, challenges, potential. In: Current Pharmaceutical Design. 2010 ; Vol. 16, No. 20. pp. 2232-2240.
@article{d7b1f42d212e4034a642d03d00cf07c9,
title = "Biostatistic tools in pharmacogenomics - advances, challenges, potential",
abstract = "Inter-individual variations in drug response are all-too common and, throughout medical history have often posed problems, many of them serious ones. The variations could stem from multiple factors, which include those of both the host (age, genetic and environmental factors) and disease (pathophysiological phenotypes, somatic mutations in case of cancers). The complex interplay of these factors can influence pharmacodynamic responses, such as adverse effects and efficacy, as well as pharmacokinetic manifestations through variability in drug absorption, distribution, metabolism and excretion. Recently, several potentially powerful tools to decipher such intricacies are emerging in various fields of science, and the translation of such knowledge to personalized medicine, called, in general, pharmacogenomics, has been promoted and has occasioned strong expectations from almost every sector of health care. However, at present, few biomarkers can predict which group of patients will respond positively, which will be non-responders and who might experience adverse reactions from the same medication and dosage. This review highlights several important aspects related to the design and statistical analysis for pharmacogenomics studies or clinical trials, which incorporate biomarkers. First, we review biomarker development: how biomarkers may be used as targets and the difference between prognostic and predictive markers. Second, in confirmatory clinical trials, we focus on issues related to study design for evaluating biomarkers and how they can be used to determine which patients might optimally benefit from a specific therapy. Finally, we review exploratory statistical screening techniques for detecting biomarkers in Phase I or pharmacokinetics studies.",
keywords = "Biomarker, Clinical trials, Drug development, Genome-wide study, Optimal design, Personalized medicine, Pharmacogenomics",
author = "Yasunori Sato and Laird, {Nan M.} and Teruhiko Yoshida",
year = "2010",
month = "8",
day = "20",
doi = "10.2174/138161210791792886",
language = "English",
volume = "16",
pages = "2232--2240",
journal = "Current Pharmaceutical Design",
issn = "1381-6128",
publisher = "Bentham Science Publishers B.V.",
number = "20",

}

TY - JOUR

T1 - Biostatistic tools in pharmacogenomics - advances, challenges, potential

AU - Sato, Yasunori

AU - Laird, Nan M.

AU - Yoshida, Teruhiko

PY - 2010/8/20

Y1 - 2010/8/20

N2 - Inter-individual variations in drug response are all-too common and, throughout medical history have often posed problems, many of them serious ones. The variations could stem from multiple factors, which include those of both the host (age, genetic and environmental factors) and disease (pathophysiological phenotypes, somatic mutations in case of cancers). The complex interplay of these factors can influence pharmacodynamic responses, such as adverse effects and efficacy, as well as pharmacokinetic manifestations through variability in drug absorption, distribution, metabolism and excretion. Recently, several potentially powerful tools to decipher such intricacies are emerging in various fields of science, and the translation of such knowledge to personalized medicine, called, in general, pharmacogenomics, has been promoted and has occasioned strong expectations from almost every sector of health care. However, at present, few biomarkers can predict which group of patients will respond positively, which will be non-responders and who might experience adverse reactions from the same medication and dosage. This review highlights several important aspects related to the design and statistical analysis for pharmacogenomics studies or clinical trials, which incorporate biomarkers. First, we review biomarker development: how biomarkers may be used as targets and the difference between prognostic and predictive markers. Second, in confirmatory clinical trials, we focus on issues related to study design for evaluating biomarkers and how they can be used to determine which patients might optimally benefit from a specific therapy. Finally, we review exploratory statistical screening techniques for detecting biomarkers in Phase I or pharmacokinetics studies.

AB - Inter-individual variations in drug response are all-too common and, throughout medical history have often posed problems, many of them serious ones. The variations could stem from multiple factors, which include those of both the host (age, genetic and environmental factors) and disease (pathophysiological phenotypes, somatic mutations in case of cancers). The complex interplay of these factors can influence pharmacodynamic responses, such as adverse effects and efficacy, as well as pharmacokinetic manifestations through variability in drug absorption, distribution, metabolism and excretion. Recently, several potentially powerful tools to decipher such intricacies are emerging in various fields of science, and the translation of such knowledge to personalized medicine, called, in general, pharmacogenomics, has been promoted and has occasioned strong expectations from almost every sector of health care. However, at present, few biomarkers can predict which group of patients will respond positively, which will be non-responders and who might experience adverse reactions from the same medication and dosage. This review highlights several important aspects related to the design and statistical analysis for pharmacogenomics studies or clinical trials, which incorporate biomarkers. First, we review biomarker development: how biomarkers may be used as targets and the difference between prognostic and predictive markers. Second, in confirmatory clinical trials, we focus on issues related to study design for evaluating biomarkers and how they can be used to determine which patients might optimally benefit from a specific therapy. Finally, we review exploratory statistical screening techniques for detecting biomarkers in Phase I or pharmacokinetics studies.

KW - Biomarker

KW - Clinical trials

KW - Drug development

KW - Genome-wide study

KW - Optimal design

KW - Personalized medicine

KW - Pharmacogenomics

UR - http://www.scopus.com/inward/record.url?scp=77955599966&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955599966&partnerID=8YFLogxK

U2 - 10.2174/138161210791792886

DO - 10.2174/138161210791792886

M3 - Review article

C2 - 20459388

AN - SCOPUS:77955599966

VL - 16

SP - 2232

EP - 2240

JO - Current Pharmaceutical Design

JF - Current Pharmaceutical Design

SN - 1381-6128

IS - 20

ER -