Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

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The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Disease-modifying treatments for osteoarthritis (DMOADs) of the knee and hip: lessons learned from failures and opportunities for the future

The accuracy and reproducibility of the endometrial receptivity array is superior to histology as a diagnostic method for endometrial receptivity.

Metastatic colorectal cancer (mCRC) that harbours a BRAF V600E mutation (BRAF MT) is associated with poorer outcomes. However, whether this mutation is predictive of treatment benefit from anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (mAbs) is uncertain. We conducted a systematic review and meta-analysis of randomised controlled trials (RCTs) published up to July 2014 that evaluated the effect of BRAF MT on the treatment benefit from anti-EGFR mAbs for mCRC. Seven RCTs met the inclusion criteria for assessment of overall survival (OS), whereas eight RCTs met the inclusion criteria for assessment of progression-free survival (PFS). For RAS WT/BRAF MT tumours, the hazard ratio for OS benefit with anti-EGFR mAbs was 0.97 (95% CI; 0.67–1.41), whereas the hazard ratio was 0.81 (95% CI; 0.70–0.95) for RAS WT/BRAF WT tumours. However, the test of interaction (P=0.43) was not statistically significant, highlighting that the observed differences in the effect of anti-EGFR mAbs on OS according to the BRAF mutation status may be due to chance alone. Regarding PFS benefit with anti-EGFR mAbs, the hazard ratio was 0.86 (95% CI; 0.61–1.21) for RAS WT/BRAF MT tumours as compared with 0.62 (95% CI; 0.50–0.77) for RAS WT/BRAF WT tumours (test of interaction, P=0.07). This meta-analysis demonstrates that there is insufficient evidence to definitively state that RAS WT/BRAF MT individuals attain a different treatment benefit from anti-EGFR mAbs for mCRC compared with RAS WT/BRAF WT individuals. As such, there are insufficient data to justify the exclusion of anti-EGFR mAb therapy for patients with RAS WT/BRAF MT mCRC.

/pdf/meta-analysis-of-braf-mutation-as-a-predictive-biomarker-of-1ky2evm8b4.pdf

Meta-analysis of BRAF mutation as a predictive biomarker of benefit from anti-EGFR monoclonal antibody therapy for RAS wild-type metastatic colorectal cancer.

Priority Medicines for Europe and the World "A Public Health Approach to Innovation"

Heterogeneity in the underlying mechanisms of disease processes and inter-patient variability in drug responses are major challenges in drug development. To address these challenges, biomarker strategies based on a range of platforms, such as microarray gene-expression technologies, are increasingly being applied to elucidate these sources of variability and thereby potentially increase drug development success rates. With the aim of enhancing understanding of the regulatory significance of such biomarker data by regulators and sponsors, the US Food and Drug Administration initiated a programme in 2004 to allow sponsors to submit exploratory genomic data voluntarily, without immediate regulatory impact. In this article, a selection of case studies from the first 5 years of this programme - which is now known as the voluntary exploratory data submission programme, and also involves collaboration with the European Medicines Agency - are discussed, and general lessons are highlighted.

Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact

One approach to delivering cost-effective healthcare requires the identification of patients as individuals or subpopulations that are more likely to respond to an appropriate dose and/or schedule of a therapeutic agent, or as subpopulations that are less likely to develop an adverse event (i.e., personalized or stratified medicine). Biomarkers that identify therapeutically relevant variations in human biology are often only uncovered in the later stage of drug development. In this article, the Industry Pharmacogenomics Working Group provides, for regulatory consideration, its perspective on the rationale for the conduct of what is commonly referred to as the prospective–retrospective analysis (PRA) of biomarkers. Reflecting on published proposals and materials presented by the US FDA, a decision tree for generating robust scientific data from samples collected from an already conducted trial to allow PRA is presented. The primary utility of the PRA is to define a process that provides robust scientific e...

Prospective-retrospective biomarker analysis for regulatory consideration: white paper from the industry pharmacogenomics working group.

Interindividual variation in pharmacodynamic (PD) response to drugs is an ongoing area of research for drugs in clinical development, pre- and postapproval. To characterize how pharmacogenomic (PG) variations can serve as a predictor of differences in PD outcomes, the pharmaceutical industry has incorporated PG/PD analysis into clinical drug development. The Pharmaceutical Research and Manufacturers of America (PhRMA) and the Industry Pharmacogenomics Working Group (I-PWG) conducted a survey of 16 pharmaceutical companies to ascertain to what extent PG/PD research is being incorporated into drug development. The survey results showed that, while the industry has made some attempt to incorporate PG/PD studies into drug development, application has been inconsistent. Nevertheless, several valid PG/PD markers have since emerged in drug labels. The I-PWG considers PG/PD research an important approach to improving success rates in drug development. This article reports the results of the survey and proposes steps toward increasing the use of PG/PD research by the industry.



Clinical Pharmacology & Therapeutics (2012); 91 6, 1035–1043. doi:10.1038/clpt.2011.334

Agnes Westelinck

Papers

Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact

Prospective-retrospective biomarker analysis for regulatory consideration: white paper from the industry pharmacogenomics working group.

PhRMA survey of pharmacogenomic and pharmacodynamic evaluations: what next?