Recommendations to Improve Collaboration and Expedite Acceptance of Animal Models

Abstract

The contents of this commentary are the sole responsibility of the authors and do not necessarily represent the official views of the Biomedical Advanced Research and Development Authority, the Office of the Assistant Secretary for Preparedness and Response, or the US Department of Health and Human Services. In our review of animal models for diseases caused by Francisella tularensis, Burkholderia mallei, or Burkholderia pseudomallei, we identified several scientific gaps that need to be filled before any one model will stand up to regulatory scrutiny (this issue). These range from a lack of well-characterized challenge material to the absence of data on appropriate treatment triggers, both of which are necessary to support regulatory qualification of a model in accordance with the US Food and Drug Administration’s (FDA’s) ‘‘Animal Rule’’ (21 CFR 314.610 and 21 CFR 601.91). The Animal Rule provides a regulatory approval pathway for candidate medical countermeasures (MCMs) when human efficacy data are impossible to obtain due to ethical reasons. This alternative regulatory pathway is especially critical for scientists and developers of MCMs for chemical, biologic, and radiologic or nuclear threats (CBRN) and emerging infectious diseases. To support the development of novel MCMs and meet the FDA’s Animal Rule requirements, the Biomedical Advanced Research and Development Authority (BARDA), a division within the US Department of Health and Human Services (HHS), established the Nonclinical Development Network (NDN) to support and accelerate animal model research and development. However, in the 2 years since the NDN was established, we and others have observed 3 areas that still need improvement—specifically, data sharing, data integration, and research coordination. Public and private entities must come together to resolve these challenges. In this commentary, we recommend several policies that could improve research coordination and enable resources to be leveraged across government agencies and their academic and industry partners. Under Federal Acquisition Regulations, the US government (USG) retains unlimited rights to data that are generated under the contracts it supports. This unfettered access to data and information should be the basis for a similarly unrestricted flow of information across the USG. Sharing data and information will allow for the timely identification of scientific and regulatory gaps, reduce the probability of redundant USG funding of studies, and ultimately reduce the number of animals that are used for experimentation. Due to the relative immaturity of animal models for Burkholderia, the USG has an opportunity to effectively implement processes for data sharing prior to engaging in a significant body of work. USG agencies that anticipate supporting Burkholderia model development could introduce common provisions in their contracts to ensure that contractors and grantees understand that their data and methods can, and will be, exchanged across agencies. The barriers to implementing such data-sharing processes should be low since this type of research and development work would be conducted independent of product development and therefore would not be deemed confidential or proprietary. BARDA and other agencies within the USG have begun to incorporate these provisions into their nonclinical development contracts. The USG has supported the development of MCMs for the prophylaxis and treatment of anthrax exposure/infection, including vaccines, antibiotics, and antitoxin therapies. Due to the diverse nature of the anthrax MCM portfolio, many animal efficacy experiments have been conducted. As one approach to addressing some of the regulatory uncertainties regarding the development of MCMs, BARDA engaged its contractors and requested the integration of data from control animals challenged with Bacillus anthracis to conduct a meta-analysis of the data. As a result, data from a larger number of animal studies were obtained and model parameters were effectively refined with greater statistical rigor. This allowed for the identification of reproducible markers of disease that have aided in developing and further characterizing an animal model of anthrax. For pathogens where there are limited animal efficacy data being generated in the context of products, it is imperative that policies and a framework be established that allow for a similar integration of experimental data. Optimally, data integration would extend to all USG agencies conducting animal model