F O C U S O N U.S. D R U G D E V E LO P M E N T CFC OO NCM SU OMSRT A O EO NI N T The Critical Path Institute: transforming competitors into collaborators Martha Brumfield The Critical Path Institute brings scientists from regulatory agencies, industry and academia together to improve drug development and regulatory processes. Founded in 2005, the Critical Path Institute (C-Path) originates from the US Food and Drug Administration (FDA)’s Critical Path Initiative, which a decade ago identified the need to connect scientific advancement with regulatory policy through public–private partnerships (PPPs)1. Today, the institute serves as a catalyst in the development of new approaches to advance medical innovation and regulatory science by leading teams that share data, knowledge and expertise to produce sound, consensus-based science. Martha Brumfield is President and CEO of the Critical Path Institute, 1730 East River Road, Tucson, Arizona 85718, USA. e‑mail: [email protected] doi:10.1038/nrd4436 Expanding the precompetitive space The rapid pace of knowledge generation, technical breakthroughs and medical advances is now such that it is impossible that any one entity has all of the information or expertise to find complete answers to key drug development challenges in isolation. Sharing knowledge and data through targeted venues is the future of developing new methods to prevent, diagnose and treat disease, especially for more complex areas such as neuro logical and psychiatric disorders. Understanding the precompetitive space and building common ground among different stakeholders is essential to a successful collaborative project. Less than a decade ago, a precompetitive project would only address in vitro or preclinical data. But the boundaries are expanding with the growing realization of the substantial benefit that can result from new discoveries (such as biomarkers, modelling tools and clinical outcome assessments (COAs)). Even clinical data, traditionally considered proprietary, are contributed to pooled databases to help solve prespecified research questions. The tools and methods created through sharing information precompetitively are changing the landscape of clinical trial design, enabling trials to be more optimally designed and executed. One such example is a simulation tool developed through a C-Path consortium — the Coalition Against Major Diseases (CAMD) — for clinical trials in mild to moderate Alzheimer’s disease2. The simulator minimizes the uncertainty regarding duration, dose and disease stage of patients to enrol in trials. The tool was created by using data from intervention trials of Alzheimer’s treatments, findings from the longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) study and academic publications. Initial interest in the tool, which received favourable regulatory designation from the FDA as well as the European Medicines Agency (EMA) in 2013, has been high. Another example of how collaboration can advance regulatory science by sharing risks and costs is the development of patient-reported outcomes (PRO) instruments through multistakeholder consortia. Working on PRO instruments in seven therapeutic areas, C-Path’s PRO Consortium develops instruments that consortium members can test in their trials and then provide the data to the consortium for regulatory qualification. The development cost of such an instrument runs into the millions of US dollars, but through collaboration the cost for each participant is lowered, and the risk is reduced owing to the active engagement of the FDA along the path to qualification. Secondary use of data collected in clinical studies is increasingly recognized as a valuable source of information that can yield unanticipated insights when curated and appropriately aggregated. Aggregation of diverse data sources is enabled by the use of global standards such as those developed through the Clinical Data Interchange Standards Consortium (CDISC). Universally accepted for use in clinical research and development, CDISC data standards also promise to streamline regulatory review. To further the opportunities from data aggregation, the institute and TransCelerate BioPharma have partnered with the CDISC to more expeditiously develop new disease area standards (see Further information). Information sharing in the precompetitive space is leading the way towards more knowledge and insight about biomarkers, disease progression trajectories, COA tools and clinical trial process improvements. For example, the institute has achieved regulatory qualification of a number of biomarkers and other tools, and has an extensive pipeline of research programmes in its seven consortia (see Supplementary information S1 (table)). NATURE REVIEWS | DRUG DISCOVERY VOLUME 13 | NOVEMBER 2014 | 785 © 2014 Macmillan Publishers Limited. All rights reserved COMMENT Addressing the challenges The challenges in building collaborative precompetitive research programmes arise from different concerns and perspectives of stakeholders. A strong collaborative programme addresses key issues at the outset: how to involve the right individuals or groups; how to reach consensus on the science; how to ensure adequate protection for intellectual property; how to enable aggregation of data by applying appropriate protective mechanisms; how to fund each research project; and how to prioritize multiple research objectives. By starting there, the independent collaborative model can factor in and resolve technical issues, provide a venue for dialogue and reduce the inherent risk in drug development. Although publications are the ‘currency’ of the acade mic world, data — especially patient-level, intervention trial data — are essential for qualifying new drug development tools (DDTs). Publications typically only present summary tables of data; consequently, the conclusions cannot be directly verified through re-analysis. Moreover, a single publication usually summarizes a single clinical trial, which would probably not satisfy the data demands for tools intended to be broadly applied across multiple development programmes. Individual patient-level data from multiple trials is the currency for DDT qualification, which requires not only sharing data but also standardizing that data in order to pool individual patient data from multiple trials. Pooling data avoids the inevitable complications of meta-analysis, and the rigour enforced through standardization adds to the integrity of the process. The evaluation of the performance of any DDT depends on aggregated data, but sharing data collected during clinical trials runs counter to the established system. Difficulties arise when: the study’s informed consent document precludes secondary use of data; data were not collected in a standardized manner; and stakeholders have concerns about proprietary information. Solutions to some of these challenges can be addressed within a consortium. Legacy data can be remapped to a common standard, which may necessitate creating data and measurement standards to facilitate data aggregation and analysis. Data contributors can also specify who is allowed access to the data. Concerns about proprietary information may be set aside when access to large-scale, integrated data sources is agreed upon and clearly defined upfront with input from all data contributors. The one issue that cannot be addressed retrospectively is informed consent; however, most companies now use consent documents that allow more flexibility in the secondary use of data. The inclusion of patient groups within the consortium is a key success factor as they represent the position of multiple patients, many of whom wish for their individual data to be used for the greater good of science and society. Ensuring collaboration among multiple PPPs and alliances can prevent or at least minimize duplication of efforts. C-Path’s Predictive Safety Testing Consortium (PSTC) directly engages with the Innovative Medicines Initiative’s Safer and Faster Evidence-Based Translation (SAFE-T) consortium through shared work plans, as both are evaluating biomarkers to assess organ-specific toxicity. Another strategy for coordinating consortia projects is to enlist participants of related consortia, as has been done by the CAMD, where members coordinate with the ADNI3. Funding is another challenge faced by consortia, partly because regulatory science is still in its infancy. Also, government funding traditionally has not favoured PPPs. Some forward-thinking private foundations have recognized that it is not enough to fund bench science or even clinical trials without addressing the critical requirement to validate the measurement science that enables utility in a clinical setting, especially in a development programme intended for regulatory assessment. For example, the National Multiple Sclerosis Society is providing research support to develop a new COA for use as a primary or secondary end point in clinical trials of patients with relapsing–remitting or progressive forms of multiple sclerosis4, the Polycystic Kidney Disease Foundation is funding the development of an imaging biomarker, and the Bill and Melinda Gates Foundation funds the development of tools for tuberculosis therapies through the Critical Path to TB Drug Regimens Initiative. The recently formed Accelerating Medicines Partnership, a PPP established to validate disease targets, indicates that the US government could adopt the European strategy and support regulatory science consortia through equivalent contributions from government and industry. The next generation of partnerships To move into the era of precision medicine, where treatment is tailored both to the genotype and phenotype of each individual patient as well as to the specific character istics of the disease, each piece of collected data needs to be utilized effectively. Building on the lessons learned, new collaborative efforts are tapping into the burgeoning clinical data in a fashion that protects patient confidentiality, recognizes stakeholder contributions and maximizes the ability of researchers to address the complexities of disease5. As third-party conveners increase collaboration between competitors so that it becomes the norm instead of the exception, the ability to develop innovative treatments more efficiently and effectively will improve. 1. 2. 3. 4. 5. Woodcock, J. & Woosley, R. The FDA Critical Path Initiative and its influence on new drug development. Annu. Rev. Med. 59,1–12 (2008). Rogers, J. A. et al. Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression metaanalysis. J. Pharmacokinet. Pharmacodyn. 39, 479–498 (2012). Yu, P. et al. Operationalizing hippocampal volume as an enrichment biomarker for amnestic mild cognitive impairment trials: effect of algorithm, test-retest variability, and cut point on trial cost, duration, and sample size. Neurobiol. Aging 35, 808–818 (2014). Rudick, R. A. et al. Multiple Sclerosis Outcome Assessments Consortium: genesis and initial project plan. Mult. Scler. 20, 12–17 (2014). Walker, E. G. et al. Evolving global regulatory science through the voluntary submission of data: a 2013 assessment. Ther. Innovation Regul. Sci. 48, 236–245 (2014). Acknowledgements M.B. would like to thank L. Hudson for her contributions to this article. Competing interests statement The author declares no competing interests. FURTHER INFORMATION CDISC: http://www.cdisc.org/ Coalition For Accelerating Standards and Therapies (CFAST): http://c-path.org/programs/cfast/ SUPPLEMENTARY INFORMATION See online article: S1 (table) ALL LINKS ARE ACTIVE IN THE ONLINE PDF 786 | NOVEMBER 2014 | VOLUME 13 www.nature.com/reviews/drugdisc © 2014 Macmillan Publishers Limited. All rights reserved