Ruth March

Rosuvastatin pharmacokinetics and pharmacogenetics in white and Asian subjects residing in the same environment

Systemic exposure to rosuvastatin had been observed to be approximately 2‐fold higher in Japanese subjects living in Japan compared with white subjects in Western Europe or the United States. The organic anion transporting polypeptide 1B1 contributes to the hepatic uptake of rosuvastatin. Polymorphisms in the SLCO1B1 gene can lead to reduced transport function in vitro (T521>C). This study was conducted to determine whether the pharmacokinetic differences between Japanese and white subjects extended to other Asian ethnic groups and to determine whether polymorphisms in the SLCO1B1 gene contribute to any pharmacokinetic differences observed.

Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis

One of the major goals of pharmacogenetics is to elucidate mechanisms and identify patients at increased risk of adverse events (AEs). To date, however, there have been only a few successful examples of this type of approach. In this paper, we describe a retrospective case–control pharmacogenetic study of an AE of unknown mechanism, characterized by elevated levels of serum alanine aminotransferase (ALAT) during long-term treatment with the oral direct thrombin inhibitor ximelagatran. The study was based on 74 cases and 130 treated controls and included both a genome-wide tag single nucleotide polymorphism and large-scale candidate gene analysis. A strong genetic association between elevated ALAT and the MHC alleles DRB1*07 and DQA1*02 was discovered and replicated, suggesting a possible immune pathogenesis. Consistent with this hypothesis, immunological studies suggest that ximelagatran may have the ability to act as a contact sensitizer, and hence be able to stimulate an adaptive immune response.

Pharmacogenomics: The Genomics of Drug Response

Pharmacogenomics is defined as the study of the association between genetics and drug response. This is a rapidly expanding field with the hope that, within a few years, prospective genotyping will lead to patients being prescribed drugs which are both safer and more effective (‘the right drug for the right patient’, or personalized medicine). There are many existing examples in the literature of strong associations between genetic variation and drug response, and some of these even form the basis of accepted clinical tests. The molecular basis for some of these associations is described, and includes examples of variation in genes responsible for absorption and metabolism of the drug, and in target and disease genes. However, there are many issues surrounding the legal, regulatory and ethical framework to these studies that remain unanswered, and a huge amount of education both for the public and healthcare professionals will be needed before the results of this new medicine can be widely accepted. Copyright

Gene mapping by linkage and association analysis

Genetic analysis is used to map genes, including disease loci, to positions within the human genome. Linkage analysis depends on the co-segregation of a gene (locus) and a phenotype through a pedigree, while association analysis, or linkage disequilibrium mapping, depends on measuring deviation from the random occurrence of alleles in a haplotype in unrelated individuals or nuclear families. Complex computer programs may be used in both forms of analysis. In recent years most interest has focused on identifying genes involved in common, multifactorial diseases. Here I review some current and developing techniques of genetic analysis and give references to where further information can be obtained.

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.

Novel technology and the development of pharmacogenetics within the pharmaceutical industry

This article focuses on the role of pharmacogenetics (PGx) technology across the drug development pipeline. Recent technology developments in three main areas are discussed: the discovery of polymorphisms or other variants in genes of interest; genotyping technologies used in PGx research (both for candidate gene analyses and for a whole-genome association approach); and the use of genotyping in patients prior to prescription (diagnostics). Finally, the associated issues of genetic data management and analysis are addressed, and the challenges facing the pharmaceutical industry in storing, manipulating and exploiting the large and complex data sets that will be generated from emerging PGx platforms are discussed. In conclusion, it is demonstrated that, despite the failures of some technology development programs and the slow rate of progress of others, there has, in fact, been steady progress toward the implementation of PGx within the pharmaceutical industry.

Selecting cases from nuclear families for case-control association analysis

We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AFaff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AFL). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.

Application of Translational Science to Clinical Development

Despite the large and ever-growing investment in pharmaceutical R&D, the number of innovative new medicines that meet significant unmet medical needs has been stagnant, if not declining. There are many potential reasons for this low return on pharmaceutical R&D investment, but one likely cause is the low probability of the success of clinical trials, particularly in early clinical development. Translational science, which we define as identifying the ‘right’ patient for the ‘right’ drug at the ‘right’ dose, promises to improve not only the odds of success of clinical development, but perhaps more importantly, to get the right drug to the right patient, thereby sparing those patients who may be less likely to benefit from a new therapeutic. We believe that this goal can be achieved by putting the patient first, i.e., by investing in understanding of disease heterogeneity at the molecular level, and then tailoring new therapeutics to subsets of patients. Using examples from the literature and our own experience, we describe current and emerging translational approaches that employ genomic and genetic methods in the areas of cancer, inflammation, and metabolic and infectious disease to this end. We use simple simulations to demonstrate how such translational strategies can significantly reduce the size of clinical trials or increase the likelihood of success of early phase trials. We end by discussing genomic approaches to understand adverse drug reactions.

Abstract C196: EGFR mutation detection in ctDNA isolated from NSCLC patient plasma; a cross-platform comparison of leading technologies.

Emerging evidence suggests that circulating tumor DNA (ctDNA) provides an alternative diagnostic sample when surgical biopsies are inaccessible, and could enable disease diagnosis, frequent assessment of disease progression, and almost real-time monitoring of the emergence of resistance mutations. We hypothesized that novel highly sensitive technologies enable EGFR T790M mutation detection in ctDNA from patients that had previously been treated with and become resistant to EGFR-TKIs. We have used 3 technologies to assess T790M mutation detection rate. Samples comprised patients with sensitizing EGFR mutations and wild type EGFR patients who had been previously treated with EGFR TKI therapies. In approximately 50% of EGFR mutation positive cases, acquired resistance to EGFR-TKIs is mediated by an EGFR T790M mutation. Reference data for T790M mutations from tumor material was unavailable.We undertook a comprehensive cross-technology comparison of three technology platforms: ARMS based detection using the Roche cobas EGFR mutation detection kit; digital droplet PCR using the BioRad ddPCR instrument (by MolecularMD) and bead based digital PCR using the Inostics BEAMing technology for the detection of T790M mutations. In total, 135 frozen plasma samples, obtained from 2 clinical studies, were used to estimate T790M detection rates in ctDNA. Within the study group, 72 samples were taken from EGFR mutation positive patients, of which around 36 (expected range 28-44) would be expected to have T790M mutations, the remaining samples would be expected to be T790M negative. Each individual patient plasma sample was split and evaluated across multiple platforms. In addition, the suitability of 3 ctDNA preparation kits (cobas Circulating DNA Isolation kit, QIAamp Circulating Nucleic Acid kit and QIAamp DNA Mini kit) in terms of DNA yield was evaluated. The QIAamp Circulating Nucleic Acid kit and cobas Circulating DNA Isolation kit were equally effective in preparing ctDNA from plasma samples. Overall, concordance of EGFR T790M mutation status was high between all 3 methods. In addition, the false positive rate in the known EGFR mutation negative cases was low for all 3 methods. However, differences were seen in the rate of false negative results (assay sensitivity) between methods. DNA preparation kits specifically designed for use with ctDNA samples are preferable to other methods. Current technologies for the detection of T790M mutations in ctDNA, where patient tumor material is not available, still require further development to increase detection rates in these samples. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C196. Citation Format: Helen Brown, Roz Brant, Simon Dearden, Suzanne Jenkins, Kenneth Thress, Alain Horvais, Ruth March, J. Carl Barrett. EGFR mutation detection in ctDNA isolated from NSCLC patient plasma; a cross-platform comparison of leading technologies. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C196.

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.: