COMPUTATIONAL PREDICTIVE MODELING IN DRUG DEVELOPMENT

REQUEST FOR INFORMATION COMPUTATIONAL PREDICTIVE MODELING IN DRUG DEVELOPMENT Objective: This is a Request for Information (RFI) for planning purposes only. It is not to be construed as a commitment by the Government nor will the Government pay for the information solicited. No solicitation document exists or is guaranteed to be issued as a result of this RFI. The Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense Enabling Biotechnologies (JPEO-CBRND EB) is seeking information on the available capabilities and willingness of private entities (academic, non-profit and commercial) to collaborate with the Government in the areas listed below. Background: The discovery and development of drug candidates has traditionally relied on trial-and-error experimental processes resulting in lengthy development timelines and significant costs due to high candidate attrition rates. The relatively recent advances in computational power combined with computational applications to product development have presented an opportunity to fundamentally transform drug development. To increase the efficiency of drug development programs, the pharmaceutical industry is actively pursuing partnerships with artificial intelligence/machine learning (AI/ML) companies to strengthen and accelerate drug development via computational design of drug candidates. While the pharmaceutical industry is investigating the use of AI/ML to reduce time-to-market entry for their drug candidates, the JPEO-CBRND EB office is interested in leveraging the computational predictive modeling to enable a comprehensive strategic response capability to address chemical and biological threats and significantly reduce the timeline from threat identification to medical countermeasure (MCM) product delivery to the Warfighter. The envisioned response capability encompasses not only design of effective MCMs, but includes a coordinated and integrated computational approach to optimize a wide range of critical quality attributes. These include factors affecting product safety, manufacturability, and pharmacokinetics, as well as other developability attributes. Additionally, there is a significant interest in advanced process controls of biologics production to reduce development time and reduce risk of run failure. The goal of these approaches is to significantly reduce the risk of drug failure by computationally addressing all desired design parameters during the earliest stages of development. JPEO-CBRND EB is seeking innovative computational approaches and capabilities to reduce developmental risk via predictive modeling and to strategically accelerate MCM development. The current effort is focused primarily on design and development of large molecule drugs (e.g., vaccines, monoclonal antibodies, enzymes). Requirements: The purpose of this RFI is to solicit information on the availability and/or developmental status of computational models, tools, and approaches that have the potential to reduce risk and/or accelerate the drug development cycle. The goal is to stand up a highly responsive and fully validated computational capability that enables a broad, strategic approach to address known, emerging and unanticipated threats across a broad threat space in a cost-effective manner. Desired computational models/tools/approaches will address one or more critical drug development quality attributes or manufacturing requirements, and need not be a fully comprehensive approach to all aspects of development/design/manufacturing. Examples could include advanced protein homology modeling to enable binding prediction, a de novo capability to design high affinity monoclonal antibodies, predictive models of immunogenicity, drug product formulation prediction tools, sequence/structure-based manufacturing process prediction, prediction of potential toxicology signals, etc. Innovative computational concepts in development are of interest. Performance Objectives: The primary focus of computational models/tools/approaches is for large molecule drug development (e.g., monoclonal antibodies, vaccines, enzymes). For this specific RFI, computational models/tools/approaches with a focus on small molecule development are of interest only if there is potential applicability in the large molecule space. Computational models may address a single (e.g., discovery) and/or multiple (e.g., discovery, design, manufacturability, etc.) product development elements, or may address single or multiple critical quality attributes applicable to drug design. When multiple characteristics are computationally addressed, an integrated multi-parameter optimization approach is preferred. Computational models associated with manufacturing process control (e.g. Digital Twin) and/or real time drug substance/drug product release are of interest. Applicable technologies related to management of large molecule drug development model data as well as advanced data visualization tools that are applicable to drug development are of interest. Approaches largely dedicated to predictive pathogen characterization are of secondary interest. The Respondents shall provide the following in response to this RFI: Company Description (2 page maximum): Provide a brief description of company history, alliances and funding emphasizing experience in the application of computational models to drug development including those approved by the FDA (if applicable, please describe FDA interaction in the context of the computational model) Discuss any data rights assertions anticipated for the computational model(s)/approaches (Freedom to operate, patent application status, issued or licensed intellectual property) Computational Capability Description (5 page maximum): Description of the computational model(s)/tool(s)/approach(es) and applications to large molecule drug development Description of already demonstrated, or projected potential, of the computational model(s)/approach(es) to improve large molecule drug properties (e.g., broaden efficacy), accelerate drug development cycle via risk reduction, and/or reduction of experimental exploration that would have to be conducted in the absence of the computational modeling Description of compute and storage resources required for the computational model(s)/approach(es) Description of the current status of the model(s)/approach(es) in the context of model validation Description of the existing training and/or validation data sets, or the requirements for generating the requisite datasets for the described computational model development/validation Ability of the described computational model to be integrated into a broader modular computational design platform, as well as the private entities (academic, non-profit and commercial) willingness to collaborate or license tools as part of a larger program approach managed by the US Government. Potential risks or liabilities that are associated with computational model application and maintenance, if known Any associated intellectual property rights or patent coverage Administration: The Government will retain comments and information received in response to this RFI. Proprietary information should be identified as Company Proprietary. Do not use Government security classification markings. All written responses must be received by COB on 15 April 2022. Responses should be sent by e-mail to: contact@example.gov, and contact@example.gov, with Subject Line of Responding Organization and RFI Title. Material that is advertisement only in nature is not desired. If a solicitation is subsequently released based on the responses to this RFI the first choice for an acquisition vehicle, if appropriate, will be the Medical CBRN Defense Consortium (MCDC) Other Transaction Agreement (OTA). Respondents not already members of the consortium are encouraged to join at www.medcbrn.org. Respondents may also inquire about the MCDC at contact@example.gov and view the Medical Countermeasures Broad Agency Announcement (BAA) in SAM.gov, keywords MCS-BAA-17-01. For questions related to this RFI, please e-mail to: contact@example.gov, and contact@example.gov. The Point of Contact for this RFI is Lee A. Hess at contact@example.gov.