Saturday, November 23, 2024
11/23/2024
Response to National Institutes of Health Small Business Innovation Research (SBIR) NIH SBIR: PHS 2019-2 Omnibus Solicitation for SBIR/STTR Grant Applications Re : Submission of R43/44 SBIR Phase I Proposal FOA : PHS 2019-2 Omnibus Solicitation of the NIH, CDC, FDS and ACF for Small Business Innovation Research Grant Applications (Parent SBIR [R43/R44]) Institute/Division/Topic : National Institute of General Medical Sciences (NIGMS) Topic B- Pharmacological and Physiological Sciences Proposal Title : A Multiscale Toolkit for Predicting Clinical Pharmacological Response of Antibody Therapeutics ABSTRACT Antibody therapeutics (Abs) account for 80% of the best-selling drugs in the market. Their success in areas of neuroscience, oncology and autoimmune disorders infectious diseases, immuno-oncology, autoimmune diseases and rare diseases has augmented their commercial potential. Over the last three decades since the first mAb was approved by the FDA, about 60 mAbs have been marketed in the United States, and with ~350 new entities in active clinical development, the commercial potential for these therapeutic antibodies is projected to reach ~$300B by 2025. Furthermore, with two bispecific Abs (bsAbs) in the market already and approximately 85 additional bsAbs in clinical development, sales by 2023 are projected to be $4.4B. In response to this trend, a robust simulation tool, which can aid in model-based drug development for the prediction of safe and efficacious clinical dose will be valuable to the pharma industry for accelerating development regulatory approval. The overall objective is to develop a multiscale modeling/simulation toolkit for predicting the clinical pharmacology of antibody therapeutics (in collaboration with Prof. Laird Forrest at University of Kansas School of Pharmacy). During Phase I, we will develop a mechanistic physiology-based pharmacokinetic and pharmacodynamic model (PBPK/PD) of mAbs and Triomab bispecifics (bsmAbs), which are delivered intravenously or subcutaneously. We will adapt the existing human PBPK model, which was developed by the PI and team for small molecule pharmacology under prior and ongoing NIH/FDA/DoD projects. Detailed models of target organs to adequately resolve the concentrations at tissue sites, ligand types (soluble vs. membrane- bound), pH-dependent neonatal Fc receptor recycling (FcRn), binding, affinity and target suppression to better elucidate the local PK/PD interactions will be incorporated. Using the model, we will conduct predictive clinical trial simulations and validate the outcomes with available clinical data. For proof-of-concept demonstration, we will rely on clinical PK data for FDA-approved mAbs (e.g., Adalimumab, Tocilizumab, Trastuzumab, Tefibazumab and Infliximab), and Triomabs (e.g., Catumaxomab; Ertumaxomab). We believe that the predictive model developed under this project can be extended for predicting First-in-Human (FiH) doses, characterize the initial exposure-response relationships (E-R), drug-drug interactions (DDI) and bioavailability estimates. While the focus is on clinical drug development, we believe that the parametric nature of the model implementation will allow for seamless adaptation to non-human primates to facilitate mechanistic cross-species scaling. In Phase II, we will extend the scope of the PK model developed in Phase I to account for the intramuscular route, develop PK/PD modules for other bsAb variants like bi-specific T-cell engagers (BiTEs), antibody-drug conjugates (ADC), fusion proteins and bivalent and trivalent single-chain variable fragments (scFvs). We will collaborate with U.S Pharma for model-based simulations of FiH studies, DDI, E-R analysis and population PK for specific antibody formulations. 1
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
