Novel technology and the development of pharmacogenetics within the pharmaceutical industry

DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine

In the era of personalized medicine, high-throughput technologies have allowed the investigation of genetic variations underlying the inter-individual variability in drug pharmacokinetics/pharmacodynamics. Several studies have recently moved from a candidate gene-based pharmacogenetic approach to genome-wide pharmacogenomic analyses to identify biomarkers for selection of patient-tailored therapies. In this aim, the identification of genetic variants affecting the individual drug metabolism is relevant for the definition of more active and less toxic treatments. This review focuses on the potentiality, reliability and limitations of the DMET™ (Drug Metabolism Enzymes and Transporters) Plus as pharmacogenomic drug metabolism multi-gene panel platform for selecting biomarkers in the final aim to optimize drugs use and characterize the individual genetic background.

Analytical validation of genotyping assays in the biomarker laboratory

High-throughput, whole-genome association studies conducted in various diseases and therapeutic settings are identifying an increasing number of single nucleotide polymorphisms that may predict patient responses and ultimately guide therapeutic decision-making. In order to confirm the candidate genetic markers emerging from these studies, there is a commensurate need for pharmacogenomic laboratories to design and analytically validate targeted genotyping assays capable of rapidly querying the identified individual single nucleotide polymorphisms of interest in large confirmatory clinical studies. In recent years, a number of increasingly complex technologies have been applied to the qualitative and semi-quantitative analysis of polymorphisms and mutations in DNA. The different approaches available for targeted DNA sequence analysis are characterized by various pros and cons that often present technology-specific challenges to the analytical validation of these assays prior to their use in clinical studies. Several key principles in the analytical validation of genotyping assays--including assay specificity, sensitivity, reproducibility and accuracy--are covered in this review article, with specific attention paid to three major end point detection technologies currently employed in targeted genotyping analysis: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, Pyrosequencing and Taqman-based allelic discrimination. Thorough assessment of the performance of genotyping assays during analytical validation, and careful use of quality controls during sample analysis, will help strengthen the quality of pharmacogenomic data used to ultimately confirm the validity of exploratory biomarkers in DNA.

Application of oligonucleotide arrays to high-content genetic analysis

The scope of single nucleotide polymorphism genotyping for genetic association studies has expanded recently from the use of relatively small numbers of candidate genes and markers, to include hypothesis-free, whole-genome approaches using hundreds of thousands of polymorphisms. The ability to perform such large-scale association studies has been dependant on the development of highly parallel and cost-effective genotyping platforms, of which those based on oligonucleotide arrays have proved to be the most scalable and widely adopted. It is to be expected that the new array-based genotyping methods will not only greatly expand the scope of genetic studies, but, as further content is added to arrays, will also form part of an integrated set of DNA, RNA and proteomic analyses enabling the detailed, multilayered study of complex disease-linked phenotypes.

Challenges and opportunities of pharmacogenetics in drug development

Drugs fail the regulatory process for a variety of reasons, including issues with pharmacokinetics, safety and efficacy. One of the most exciting questions in drug development today is how far the science of pharmacogenetics, the study of the genetics of drug response, can be used to address the fundamental issues that the pharmaceutical industry is facing. In particular, the question of how far it is possible to use this emerging science to deliver the right treatment, to the right patient, at the right dose, at the right time is both the challenge and opportunity of using pharmacogenetics in drug development. This review will address these questions with several real-life examples.

Gene mapping strategies for complex disease and drug response

The identification of genetic variants involved in disease susceptibility and response to drugs through the use of statistical and epidemiological approaches is a potentially powerful methodology for uncovering causal relationships in human disease and its treatment. Here we introduce and compare the application of genetics in these two fields of research.:

Automation and validation of DNA-banking systems

DNA banking is one of the central capabilities on which modern genetic research rests. The DNA-banking system plays an essential role in the flow of genetic data from patients and genetics researchers to the application of genetic research in the clinic. Until relatively recently, large collections of DNA samples were not common in human genetics. Now, collections of hundreds of thousands of samples are common in academic institutions and private companies. Automation of DNA banking can dramatically increase throughput, eliminate manual errors and improve the productivity of genetics research. An increased emphasis on pharmacogenetics and personalized medicine has highlighted the need for genetics laboratories to operate within the principles of a recognized quality system such as good laboratory practice (GLP). Automated systems are suitable for such laboratories but require a level of validation that might be unfamiliar to many genetics researchers. In this article, we use the AstraZeneca automated DNA archive and reformatting system (DART) as a case study of how such a system can be successfully developed and validated within the principles of GLP.