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Introduction The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome‐wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2‐73), a selective sigma‐1 receptor (SIGMAR1) agonist, was studied in a 57‐week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. Methods Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini‐Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study‐Activities of Daily Living scale (ADCS‐ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. Results Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P

Next-generation deep sequencing of gene panels is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples, such as formalin-fixed, paraffin-embedded specimens, frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluate the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing, which permits the enrichment of genes and regions of interest and the identification of sequence variants from low amounts of damaged DNA. This assay utilizes a repair process adapted to clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. Our approach generates sequence libraries of high fidelity with reduced reliance on extensive PCR amplification—this facilitates the accurate assessment of copy number alterations in addition to delivering accurate single nucleotide variant and insertion/deletion detection. We apply this method to capture and sequence the exons of a panel of 130 cancer-related genes, from which we obtain high read coverage uniformity across the targeted regions at starting input DNA amounts as low as 10 ng per sample. We demonstrate the performance using a series of reference DNA samples, and by identifying sequence variants in DNA from matched clinical samples originating from different tissue types., Cancer diagnostics: Targeted DNA sequencing for low-quality tumor samples A new DNA sequencing technology enables comprehensive genetic analyses of poor-quality tumor samples. Hanlee Ji from Stanford University in California, USA, together with colleagues from a company he cofounded called TOMA Biosciences, tested the performance of a targeted sequencing assay known as oligonucleotide-selective sequencing (OS-Seq). They used the “in-solution” version of OS-Seq, which involves a pre-processing step to remove any damaged DNA and then sequences target regions of the genome to look for duplications, insertions or deletions of DNA segments. Using archival specimens (which often contain low quantities of degraded DNA) from patients with lung and colorectal cancer, the researchers showed they could detect sequence variants in a panel of 130 cancer-related genes. The findings suggest the OS-Seq assay could help inform treatment decisions for cancer patients, even with clinical specimens of low quality.

While the term ‘biomarker’ is relatively new, the concept is millennia old. However, with the introduction of new technologies to discover potential biomarkers comes the need to assess their utility and veracity for any given use. This is particularly true for the use of biomarkers to support regulatory decisions in medical product development. Hence the US Food and Drug Administration has developed processes for the qualification of biomarkers and other medical product development tools, processes that are underscored by recent legislation (i.e. the 21st Century Cures Act). In addition to these qualification processes, diagnostic tests that measure a biomarker may follow a process for regulatory decision through the processes that evaluate companion diagnostics. This mini-review provides an overview of these processes and their role in pharmaceutical development and clinical use. Impact statement This work summarizes very recent developments in the US FDA’s biomarker qualification program. Furthermore, it contrasts biomarker qualification with companion diagnostic evaluation. As such, it will be highly informative for researches considering taking a biomarker discovery farther along the road to validation.

Biomarker data are essential in the discovery and development of new drugs. However, pathways needed to make sure that biomarker data are accepted by regulatory agencies may be considered an unnecessary burden on the critical path for drug development. There is the need to consider early in discovery and development that these pathways for biomarker acceptance or qualification not only are necessary, but may also enhance the success of novel therapies through regulatory review and clinical use. There also needs to be a focus on the challenge in the application of biomarkers as these approach regulatory evaluation. Regulatory guidance is needed on how a patient population may be defined by the molecular phenotype classification associated with specific mutations in patient genomes. Enzyme replacement therapies have been implicitly approved in the past assuming a molecular phenotype of a defective enzyme, but these and other precedents have not yet been translated into regulatory guidance. A second regulatory pathway for biomarkers is a biomarker qualification process. Biomarker data may be submitted, in the context of a specific NDA, but the biomarker qualification process has added a path through which efficacy and safety biomarkers useful in product development across multiple companies may be qualified through pre-competitive collaboration between these companies.

The Genome-Wide Association Working Group (GWAWG) is part of a large-scale effort by the MicroArray Quality Consortium (MAQC) to assess the quality of genomic experiments, technologies and analyses for genome-wide association studies (GWASs). One of the aims of the working group is to assess the variability of genotype calls within and between different genotype calling algorithms using data for coronary artery disease from the Wellcome Trust Case Control Consortium (WTCCC) and the University of Ottawa Heart Institute. Our results show that the choice of genotyping algorithm (for example, Bayesian robust linear model with Mahalanobis distance classifier (BRLMM), the corrected robust linear model with maximum-likelihood-based distances (CRLMM) and CHIAMO (developed and implemented by the WTCCC)) can introduce marked variability in the results of downstream case-control association analysis for the Affymetrix 500K array. The amount of discordance between results is influenced by how samples are combined and processed through the respective genotype calling algorithm, indicating that systematic genotype errors due to computational batch effects are propagated to the list of single-nucleotide polymorphisms found to be significantly associated with the trait of interest. Further work using HapMap samples shows that inconsistencies between Affymetrix arrays and calling algorithms can lead to genotyping errors that influence downstream analysis.

The robustness of genome-wide association study (GWAS) results depends on the genotyping algorithms used to establish the association. This paper initiated the assessment of the impact of the Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) genotyping quality on identifying real significant genes in a GWAS with large sample sizes. With microarray image data from the Wellcome Trust Case-Control Consortium (WTCCC), 1991 individuals with coronary artery disease (CAD) and 1500 controls, genetic associations were evaluated under various batch sizes and compositions. Experimental designs included different batch sizes of 250, 350, 500, 2000 samples with different distributions of cases and controls in each batch with either randomized or simply combined (4:3 case-control ratios) or separate case-control samples as well as whole 3491 samples. The separate composition could create 2-3% discordance in the single nucleotide polymorphism (SNP) results for quality control/statistical analysis and might contribute to the lack of reproducibility between GWAS. CRLMM shows high genotyping accuracy and stability to batch effects. According to the genotypic and allelic tests (P5.0 x 10(-7)), nine significant signals on chromosome 9 were found consistently in all batch sizes with combined design. Our findings are critical to optimize the reproducibility of GWAS and confirm the genetic role in the pathophysiology of CAD.

The Affymetrix GeneChip Human Mapping 500K array is common for genome-wide association studies (GWASs). Recent findings highlight the importance of accurate genotype calling algorithms to reduce the inflation in Type I and Type II error rates. Differential results due to genotype calling errors can introduce severe bias in case-control association study results. Using data from the Wellcome Trust Case Control Consortium, 1991 individuals with coronary artery disease (CAD) and 1500 controls from the UK Blood Services (NBS) were genotyped on the Affymetrix 500K array. Different batch sizes and compositions were used in the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) genotype calling algorithm to assess the batch effect on downstream association analysis. Results show that composition (cases and controls genotyped simultaneously or separate) and size (number of individuals processed by BRLMM at a time) can create 2-3% discordance in the results for quality control and statistical analysis and may contribute to the lack of reproducibility between GWASs. The changes in batch size are largely responsible for differential single-nucleotide polymorphism results, yet we observe evidence of an interactive effect of batch size and composition that contributes to discordant results in the list of significantly associated loci.

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.

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.

The first formal qualification of safety biomarkers for regulatory decision making marks a milestone in the application of biomarkers to drug development. Following submission of drug toxicity studies and analyses of biomarker performance to the Food and Drug Administration (FDA) and European Medicines Agency (EMEA) by the Predictive Safety Testing Consortium's (PSTC) Nephrotoxicity Working Group, seven renal safety biomarkers have been qualified for limited use in nonclinical and clinical drug development to help guide safety assessments. This was a pilot process, and the experience gained will both facilitate better understanding of how the qualification process will probably evolve and clarify the minimal requirements necessary to evaluate the performance of biomarkers of organ injury within specific contexts.

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.

Molecular biomarkers are used for various purposes, including disease diagnosis and prognosis, prediction and assessment of treatment response, and safety assessment. There has been a significant increase in the number of US FDA-approved drug labels containing information on molecular biomarkers over the last decade. Almost every pharmaceutical company has been developing molecular biomarker programs, either alone, through partnerships or other ventures. More molecular biomarkers are expected to be identified and validated in drug development, and used to support approval of drug products. This article summarizes the current status of molecular biomarkers used for FDA-approved drug products, and discusses the challenges and future perspectives for the identification and qualification of molecular biomarkers. Specific FDA programs and research projects related to molecular biomarkers are also discussed for supporting regulatory review in the future.

Novel biomarkers of kidney toxicity are powerful tools not only with respect to their clinical applications but also because of their impact on drug development. These biomarkers can influence the assessment of efficacy of new drugs for kidney diseases as well as the risk management for new drugs. The science behind these novel biomarkers reflects the evolution over the past decade of genomic and proteomic platforms that have transformed the discovery and development of new biomarkers for preclinical and clinical applications in drug development. Several of these biomarkers are in use as transcriptomic biomarkers in animal models as well as translational proteomic biomarkers in animal models and in humans. Their ability to detect kidney damage earlier than is possible with currently accessible biomarkers is being given qualification through regulatory biomarker-qualification programs, which will help establish consensus for their widespread use.

The 4th US FDA/Industry Workshop on Pharmacogenomics in Drug Development and Regulatory Decision Making, was held in MD, USA, on December 10–12, 2007. One of the breakout sessions of the workshop focused on the regulatory issues around codevelopment of drugs and companion diagnostics. This session used hypothetical case studies as focal points for discussion of current thought and critical issues for both industry and the FDA in this evolving field. The panel and the audience discussed the evolution of the FDA’s thinking on the regulatory path for companion diagnostics since the release of the April 2005 draft Drug Test Codevelopment Concept Paper and the issues faced by industry in attempting codevelopment efforts. This session provided an opportunity to allow an exchange of ideas between the FDA and industry and to identify critical issues that need further discussion in this important and evolving field.

This is a historical moment on the path to genomic medicine - the point at which theory is about to be translated into practice. We have previously described human genome variation studies taking place in Mexico, India, Thailand, and South Africa. Such investments into science and technology will enable these countries to embark on the path to the medical and health applications of genomics, and to benefit economically. Here we provide a perspective on the challenges and opportunities facing these and other countries in the developing world as they begin to harness genomics for the benefit of their populations.

Histopathological and immunohistochemical studies were conducted to characterize vascular injuries in rats treated with phosphodiesterase (PDE) IV inhibitors SCH 351591 or SCH 534385. Sprague-Dawley rats were administered PDE IV inhibitors by gavage at a range of doses and times. The two PDE IV inhibitors induced comparable levels of vascular injury, primarily in the mesentery and to a lesser extent in the pancreas, kidney, liver, small intestine, and stomach. Mesenteric vascular changes occurred as early as one hour, progressively developed over twenty-four to forty-eight hours, peaked at seventy-two hours, and gradually subsided from seven to nine days. The typical morphology of the vascular toxicity consisted of hemorrhage and necrosis of arterioles and arteries, microvascular injury, fibrin deposition, and perivascular inflammation of a variety of blood vessels. The incidence and severity of mesenteric vascular injury increased in a time- and dose-dependent manner in SCH 351591- or SCH 534385-treated rats. Mesenteric vascular injury was frequently associated with activation of mast cells (MC), endothelial cells (EC), and macrophages (MØ). Immunohistochemical studies showed increases in CD63 immunoreactivity of mesenteric MC and in nitrotyrosine immunoreactivity of mesenteric EC and MØ. The present study also provides a morphological and cellular basis for evaluating candidate biomarkers of drug-induced vascular injury.

This article summarizes the motivation for, and the proceedings of, the first ISA-TAB workshop held December 6–8, 2007, at the EBI, Cambridge, UK. This exploratory workshop, organized by members of the Microarray Gene Expression Data (MGED) Society's Reporting Structure for Biological Investigations (RSBI) working group, brought together a group of developers of a range of collaborative systems to discuss the use of a common format to address the pressing need of reporting and communicating data and metadata from biological, biomedical, and environmental studies employing combinations of genomics, transcriptomics, proteomics, and metabolomics technologies along with more conventional methodologies. The expertise of the participants comprised database development, data management, and hands-on experience in the development of data communication standards. The workshop's outcomes are set to help formalize the proposed Investigation, Study, Assay (ISA)-TAB tab-delimited format for representing and c...

The FDA Guidance for Industry: Pharmacogenomics Data Submissions was issued in 2005. This guidance document covers a broad area associated with how and when to submit genomic data to the FDA. Additional tasks associated with genomic data submissions include the implementation of genomic data submissions; the process for qualification of exploratory biomarkers into valid biomarkers; and technical recommendations for the generation and submission of genomic data to the FDA. These tasks have been addressed throughout the past 2 years by a number of initiatives. These initiatives have included the development of the Interdisciplinary Pharmacogenomics Review Group for review of pharmacogenomic data submissions, the pilot process for qualification of biomarkers, and the concept paper on recommendations for the generation and submission of genomic data. These initiatives have contributed to the effective implementation of the Pharmacogenomics Guidance at the FDA.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Current advances in genomics, proteomics, and metabonomics would result in a constellation of benefits in human health. Classification applying supervised learning methods to omics data as one of the molecular classification approaches has enjoyed its growing role in clinical application. However, the utility of a molecular classifier will not be fully appreciated unless its quality is carefully validated. A clinical omics data is usually noisy with the number of independent variables far more than the number of subjects and, possibly, with a skewed subject distribution. Given that, the consensus approach holds an advantage over a single classifier. Thus, the focus of this review is mainly placed on how validating a molecular classifier using Decision Forest (DF), a robust consensus approach. We recommended that a molecular classifier has to be assessed with respect to overall prediction accuracy, prediction confidence and chance correlation, which can be readily achieved in DF. The commonalities and differences between external validation and cross-validation are also discussed for perspective use of these methods to validate a DF classifier. In addition, the advantages of using consensus approaches for identification of potential biomarkers are also rationalized. Although specific DF examples are used in this review, the provided rationales and recommendations should be equally applicable to other consensus methods.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.

Measurements of the number of copies of DNA or mRNA with the quantitative polymerase chain reaction (qPCR) have transformed the drug development process. This transformation is driven by the information these measurements have contributed for a better understanding of the molecular definition of disease and of the mechanisms of efficacy and toxicity for new drugs. As this information is translated into accurate genomic biomarkers of efficacy and toxicity, drug development processes supported by these measurements are becoming more efficient. This transformation is exemplified in the conversion of P450 enzyme activity measurements to gene expression in drug metabolism studies, the measurement of cytokine and chemokine genomic expression levels as clinical markers, and the identification and evaluation of genomic biomarkers of nephrotoxicity. A good understanding of factors affecting qPCR measurements can simplify their implementation, as will high‐throughput platforms for these assays. Drug Dev. Res. 62:151–158, 2004. © 2004 Wiley‐Liss, Inc.

Next Generation Sequencing is increasingly implemented as a diagnostic test to identify actionable mutations in cancer patient samples. However, for routine diagnostics, tumor DNA is extracted from formalin-fixed, paraffin-embedded (FFPE) samples, which yields low quantity of damaged DNA. Inability to accurately repair the ends of these DNA fragments impairs adapter ligation by standard double stranded ligation methods. The resulting low yield of adapter-ligated DNA introduces the need for whole-genome PCR amplification prior to target capture. The drawback of such PCR amplification is the introduction of PCR biases, causing reduced sensitivity in the detection of copy number alterations (CNAs), an important biomarker for targeted therapy. To address the need for a library preparation platform that performs well with low quality and quantity DNA, and without relying on massive PCR amplification, we developed an improved, in-solution, version the OS-Seq targeted enrichment assay. OS-Seq circumvents the reliance on PCR amplification by using a single-stranded adapter ligation approach. Damaged bases induced by formalin fixation are removed by excision instead of attempting repair, and then DNA is denatured prior to adapter ligation. This method of adapter ligation result in yields of ~50% for low quality samples, eliminating the need for whole genome PCR. OS-Seq directly uses the adapter-ligated DNA in a linear targeted primer-extension, followed by low-cycle post-capture PCR expansion with Illumina bridge-PCR primers prior to library sequencing. We investigated the PCR duplication rate of the OS-Seq libraries by including an 11-mer random barcode to track unique molecules. We found that most input molecules were present in the sequencing reads at only one copy. Further, we demonstrate a linear correlation between the amount of DNA input (ranging from 1 to 600 ng) and the number of unique molecules sequenced (R2=0.94). Importantly, we show that this low PCR bias allows OS-Seq to detect CNAs in Coriell and Horizon Diagnostic cell lines highly concordant to digital PCR detection (R2=0.96). Further, we present CNA calling on cell line DNA sonicated to 200 bp fragments at 10 ng DNA input, mimicking cell-free DNA. In addition to CNA detection, OS-Seq detects SNVs with a sensitivity of 92-97% and a specificity of 100% down to 5% VAF. In conclusion, the OS-Seq library preparation method relies on single stranded adapter ligation and in-solution target capture, which generates uniform coverage with minimal PCR requirement, resulting in highly sensitive CNA calling. Note: This abstract was not presented at the meeting. Citation Format: Anna Vilborg, Yosr Bouhlal, Ryan Koheler, Daniel Mendoza, Federico Goodsaid, Yannick Pouliot, Austin So, Francisco De La Vega, Hanlee Ji. A PCR-bias free capture-based library preparation platform permitting highly accurate and sensitive CNA detection in tumor molecular profiling and liquid biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4019. doi:10.1158/1538-7445.AM2017-4019

The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.

Pharmacogenomics (PGx) is identified in the FDA Critical Path document as a major opportunity for advancing medical product development and personalized medicine. An integrated bioinformatics infrastructure for use in FDA data review is crucial to realize the benefits of PGx for public health. We have developed an integrated bioinformatics tool, called ArrayTrack, for managing, analyzing and interpreting genomic and other biomarker data (e.g. proteomic and metabolomic data). ArrayTrack is a highly flexible and robust software platform, which allows evolving with technological advances and changing user needs. ArrayTrack is used in the routine review of genomic data submitted to the FDA; here, three hypothetical examples of its use in the Voluntary eXploratory Data Submission (VXDS) program are illustrated.

By streamlining the qualification process for biomarkers, coordinated protocols recently implemented at the different regulatory agencies can facilitate progress and provide impetus to novel biomarker discovery and validation.

The FDA has developed a Pilot Process for Biomarker Qualification. Initial experience with this process has underscored the care that a long-term approach to biomarker qualification independently of development for specific drugs should have in the assembly of external industry consortia as well as the internal regulatory organization for these qualification efforts. There are complex scientific and clinical issues associated with these qualifications, and it is paramount that the expertise needed for their review be identified so that a comprehensive consensus may be reached at the end of this process. Several frequently asked questions associated with this process are presented in this paper, as well as answers reflecting the Agency's current thinking about this process.

Pharmacogenomics is changing the way drugs are being developed, approved and used. The Gurwitz et al article found in the previous issue is a timely 'call to arms' to ensure that this important field does not remain the wisdom of few, but becomes widely accepted and used. We are at a critical stage in pharmacogenomics: the science is not new, but has experienced a significant boost since the human genome project has been completed. Now is the time to capitalize on what basic science has provided and translate it into clinical practice. However, this can only happen if physicians and other health-care professionals, as well as patients, are being educated and become knowledgeable about pharmacogenomics.

There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity.This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled 'Translational Biomarkers in Toxicology.' The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process.There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process.

Despite huge investments, there are still difficulties in the development of novel therapies. This has led to a growing interest in the use of new tools, such as biomarkers, that can help overcome development hurdles while providing increased certainty about drug safety and efficacy. Until recently, no formal process has existed for qualifying biomarkers for regulatory decision making. The FDA's Center for Drug Evaluation and Research (CDER) has initiated such a process, which has led to the recent qualification of two biomarker sets for use in regulatory decisions. This article provides the reader with an overview of the CDER Biomarker Qualification Process and is shaped by the recent regulatory developments in biomarker qualification and the consideration of frequently asked questions in the area. The Biomarker Qualification Process is intended to be a mission-critical, value-added CDER program. The success of this effort will depend on the willingness of pharmaceutical and diagnostic companies, consortia, the FDA and other regulatory agencies to continue to work together, motivated by the benefits that can accrue to public health through the increasing availability of qualified biomarkers for use in drug development.

The chapters in this book share a link to two parallel experiences in the search for better tools for the development of new and better therapies. One is an outcome of the work summarized in these chapters: the current processes that lead from biomarker development to qualification for regulatory use. The other one is the path by which regulatory agencies in the US, Europe and Japan developed these processes, at once imperfect, but also essential. Both experiences have been complex and difficult.

A broad range of perspectives in this book describe how a complex regulatory process for biomarker qualification has been developed at regulatory agencies over the past decade. This process has now been used by the pharmaceutical industry for the past few years, and we can now take stock of its impact on pharmaceutical product development, and ask some questions about what the process has achieved. This chapter considers whether the Food and Drug Administration process met the goals originally proposed by the Pharmacogenomics Guidance and the Critical Path Opportunities List and Report. It also looks at the strengths and weaknesses of the different versions of biomarker qualification processes developed in each ICH region, how well the different versions of these processes have been harmonized across these regions, the opportunities and challenges for a single, universal, biomarker qualification process and ways in which metrics can be used to measure the impact of these processes on drug development and their acceptance by the pharmaceutical industry.

Cancer tumor profiling by targeted resequencing of actionable cancer genes is rapidly becoming the standard approach for selecting targeted therapies and clinical trials in refractory cancer patients. In this clinical scenario, a tumor sample is obtained from an FFPE block and sequenced by targeted next-generation sequencing (NGS) to uncover actionable somatic mutations in relevant cancer genes. Some of the challenges that arise in analyzing tumor-derived NGS data include distinguishing between somatic and germline variants in the absence of normal tissue data, recognizing pathogenic germline variants, and identifying sequencing errors (which occur at about 0.5% rate). Additional challenges arise when considering other clinical applications of NGS such as sequencing cell-free tumor DNA (cf-DNA) from plasma samples to monitor disease response or disease recurrence. Here we present a principled approach to identify both single-nucleotide and small insertion/deletion somatic mutations and germline variants from NGS data of tumor tissue that leverages the allelic fraction patterns in tumors and prior information from external databases through the use of a Bayesian Network algorithm. Our approach allows us to score each putative mutation or variant with respect to its probability of belonging to each variant class, versus classification as a sequencing error. The method enables the joint calling of related samples form the same patient, such as cases where a cf-DNA sample and primary tumor sample are both profiled improving sensitivity and specificity. We validated our method by analyzing data obtained with the TOMA OS-Seq targeted sequencing RUO assay for 98 cancer genes from a mixture of well-known genomes, patient case triads (where normal, tumor and cf-DNA are available), and a retrospective analysis of tumor patient data that underwent clinical tumor profiling for therapy selection. Citation Format: Francisco M. De La Vega, Ryan T. Koehler, Yannick Pouliot, Yosr Bouhlal, Austin So, Federico Goodsaid, Sean Irvine, Len Trigg, Lincoln Nadauld. Joint somatic mutation and germline variant identification and scoring from tumor molecular profiling and ct-DNA monitoring of cancer patients by high-throughput sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2712.

e23052Background: Circulating cell-free DNA (cfDNA) in the plasma of cancer patients carries information on tumor burden and can be a valuable tool for the detection and assessment of the disease a...

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders’ quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.

Co-developing a drug with a diagnostic to create a stratified medicine — a therapy that is targeted to a specific patient population on the basis of a clinical characteristic such as a biomarker that predicts treatment response — presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, here we present an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and developer economic value, to quantify the effects of decisions related to key issues such as the design of clinical trials. This illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the impact of the interplay of these factors on the economic feasibility of stratified medicine, which has important implications for public policy makers.