Integration of published information into a resistance-associated mutation database for Mycobacterium tuberculosis.

Tuberculosis remains a major global public health challenge. Although incidence is decreasing, the proportion of drug-resistant cases is increasing. Technical and operational complexities prevent Mycobacterium tuberculosis drug susceptibility phenotyping in the vast majority of new and retreatment cases. The advent of molecular technologies provides an opportunity to obtain results rapidly as compared to phenotypic culture. However, correlations between genetic mutations and resistance to multiple drugs have not been systematically evaluated. Molecular testing of M. tuberculosis sampled from a typical patient continues to provide a partial picture of drug resistance. A database of phenotypic and genotypic testing results, especially where prospectively collected, could document statistically significant associations and may reveal new, predictive molecular patterns. We examine the feasibility of integrating existing molecular and phenotypic drug susceptibility data to identify associations observed across multiple studies and demonstrate potential for well-integrated M. tuberculosis mutation data to reveal actionable findings.

Collaborative Effort for a Centralized Worldwide Tuberculosis Relational Sequencing Data Platform.

Continued progress in addressing challenges associated with detection and management of tuberculosis requires new diagnostic tools. These tools must be able to provide rapid and accurate information for detecting resistance to guide selection of the treatment regimen for each patient. To achieve this goal, globally representative genotypic, phenotypic, and clinical data are needed in a standardized and curated data platform. A global partnership of academic institutions, public health agencies, and nongovernmental organizations has been established to develop a tuberculosis relational sequencing data platform (ReSeqTB) that seeks to increase understanding of the genetic basis of resistance by correlating molecular data with results from drug susceptibility testing and, optimally, associated patient outcomes. These data will inform development of new diagnostics, facilitate clinical decision making, and improve surveillance for drug resistance. ReSeqTB offers an opportunity for collaboration to achieve improved patient outcomes and to advance efforts to prevent and control this devastating disease.

Development of a unified clinical trial database for Alzheimer's disease.

INTRODUCTION: Data obtained in completed Alzheimer's disease (AD) clinical trials can inform decision making for future trials. Recognizing the importance of sharing these data, the Coalition Against Major Diseases created an Online Data Repository for AD (CODR-AD) with the aim of supporting accelerated drug development. The aim of this study was to build an open access, standardized database from control arm data collected across many clinical trials. METHODS: Comprehensive AD-specific data standards were developed to enable the pooling of data from different sources. Nine member organizations contributed patient-level data from 24 clinical trials of AD treatments. RESULTS: CODR-AD consists of control arm pooled and standardized data from 24 trials currently numbered at 6500 subjects; Alzheimer's Disease Assessment Scale-cognitive subscale 11 is the main outcome and specific covariates are also included. DISCUSSION: CODR-AD represents a unique integrated standardized clinical trials database available to qualified researchers. The pooling of data across studies facilitates a more comprehensive understanding of disease heterogeneity.

Duchenne Regulatory Science Consortium Meeting on Disease Progression Modeling for Duchenne Muscular Dystrophy.

INTRODUCTION: The Duchenne Regulatory Science Consortium (D-RSC) was established to develop tools to accelerate drug development for DMD.  The resulting tools are anticipated to meet validity requirements outlined by qualification/endorsement pathways at both the U.S. Food and Drug Administration (FDA) and European Medicines Administration (EMA), and will be made available to the drug development community. The initial goals of the consortium include the development of a disease progression model, with the goal of creating a model that would be used to forecast changes in clinically meaningful endpoints, which would inform clinical trial protocol development and data analysis.  Methods: In April of 2016 the consortium and other experts met to formulate plans for the development of the model.  Conclusions: Here we report the results of the meeting, and discussion as to the form of the model that we plan to move forward to develop, after input from the regulatory authorities.

Precompetitive Data Sharing as a Catalyst to Address Unmet Needs in Parkinson's Disease.

Parkinson's disease is a complex heterogeneous disorder with urgent need for disease-modifying therapies. Progress in successful therapeutic approaches for PD will require an unprecedented level of collaboration. At a workshop hosted by Parkinson's UK and co-organized by Critical Path Institute's (C-Path) Coalition Against Major Diseases (CAMD) Consortiums, investigators from industry, academia, government and regulatory agencies agreed on the need for sharing of data to enable future success. Government agencies included EMA, FDA, NINDS/NIH and IMI (Innovative Medicines Initiative). Emerging discoveries in new biomarkers and genetic endophenotypes are contributing to our understanding of the underlying pathophysiology of PD. In parallel there is growing recognition that early intervention will be key for successful treatments aimed at disease modification. At present, there is a lack of a comprehensive understanding of disease progression and the many factors that contribute to disease progression heterogeneity. Novel therapeutic targets and trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination are required. The integration of robust clinical data sets is viewed as a powerful approach to hasten medical discovery and therapies, as is being realized across diverse disease conditions employing big data analytics for healthcare. The application of lessons learned from parallel efforts is critical to identify barriers and enable a viable path forward. A roadmap is presented for a regulatory, academic, industry and advocacy driven integrated initiative that aims to facilitate and streamline new drug trials and registrations in Parkinson's disease.

Coalition Against Major Diseases: Precompetitive Collaborations and Regulatory Paths to Accelerating Drug Development for Neurodegenerative Diseases.

Precompetitive collaborations have been successful in several disease areas and industries. Such collaborations are critical to address the gaps and challenges in therapeutic development for chronic neurodegenerative diseases. On November 5, 2012, members of the scientific community, advocates, regulators, industry, and government officials met at the US Food and Drug Administration to develop tools to expedite drug development and maximize the potential for success in future drug trials for Alzheimer disease and Parkinson disease. The meeting established that multiple collaborative approaches are essential for accelerating drug development. Such approaches include precompetitive data sharing, regulatory qualification of biomarkers and clinical outcome assessments, implementation of data standards, and development of quantitative drug disease trial models. While challenges to collaboration among industry partners are formidable, they are not insurmountable. The Coalition Against Major Diseases (CAMD) has several positive examples to highlight. This review represents proceedings from CAMD's annual conference and discusses the key themes that are being advanced by the Critical Path Institute.