Prashant Dogra is an Assistant Professor in the Titus Family Department of Clinical Pharmacy and Associate Director of the Center for Quantitative Drug and Disease Modeling at the USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences. With a broad background in pharmaceutical and biomedical sciences, expertise in mathematical modeling, and data science, he has led collaborative translational research with clinicians, engineers, and scientists. His lab develops New Approach Methodologies (NAMs) that combine multiscale mechanistic modeling, quantitative systems pharmacology (QSP), and artificial intelligence (AI) to address challenges in drug and vaccine development. Supported by funding from the National Institutes of Health (NIH), his current projects include AI-guided rational nanoparticle design for improved safety and targeting, systems modeling for experimental cancer therapeutics and personalized oncology, and modeling-based strategies to optimize vaccination for enhanced immunogenicity. Dr. Dogra welcomes interdisciplinary collaborations and encourages students and trainees from diverse backgrounds with an interest in computational biology and medicine to reach out for opportunities in his lab.
1R01EB035545: Artificial intelligence-integrated mechanistic modeling for rational design of nanoparticles to improve organ targeting and safety
PROJECT SUMMARY. Nanoparticles (NPs) hold great promise as targeted drug delivery systems but tailoring their pharmacokinetics (PK) to specifically target regions of interest remains a challenge. This limits the clinical translation of NPs due to poor efficacy and safety concerns associated with off-target accumulation of NP-based formulations. Due to the interactions of NPs with biological components, driven by their structural properties, customizing the pharmacokinetics (PK) of NPs requires a quantitative understanding of the effect of NP structural properties on their whole-body biodistribution, which in turn also governs their safety profile. Therefore, to enable rational design of NPs to achieve organ targeting and safety, we propose to leverage artificial intelligence to develop a toxicology-integrated physiologically-based pharmacokinetic model (PBPK-Tox) capable of accurately predicting the whole-body exposure and safety of novel nanomaterials, based solely on their structural properties, dose, and route of administration. For this, we will (1) develop the PBPK-Tox model based on diverse datasets from literature, (2) establish the quantitative relationship between NP properties, exposure, and toxicity, and (3) experimentally test the model predictions of rational design to target one or more organs. Our proposed modeling framework will enable efficient preclinical development of novel nanomaterials (and accelerate their clinical translation) by providing rational design guidelines through in-depth computational investigation of biological and physicochemical variability on biodistribution and safety of NPs.