Faculty Directory

Prashant DograPhD, BPharm

pdogra@usc.edu

Prashant DograPhD, BPharm

Assistant Professor of Clinical Pharmacy and Associate Director of the Center for Quantitative Drug and Disease Modeling

Titus Family Department of Clinical Pharmacy

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) and National Science Foundation (NSF), 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.

Areas of Expertise

  • mathematical modeling
  • quantitative systems pharmacology
  • data science
  • translational pharmacology
  • machine learning
  • Education

    The University of New Mexico

    PhD

    Panjab University

    BPharm

  • Links
  • Research Grants

    1R01EB035545: Artificial intelligence-integrated mechanistic modeling for rational design of nanoparticles to improve organ targeting and safety

    National Institute of Biomedical Imaging and Bioengineering (NIBIB), 20240909

    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.

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    FDT-BioTech: Credible Foundations for Composable Immune System Digital Twins

    National Science Foundation (NSF), 20260901

    Modern medicine increasingly relies on computational models called digital twins to predict how patients will respond to therapies, how medical devices will perform, and how diseases will progress. These models hold promise for making healthcare more personalized and reducing risks in developing new treatments. However, most existing digital twins are built for one specific application and lack the mathematical rigor needed to verify that their predictions can be trusted, especially when clinical data are limited. This gap creates a credibility problem that prevents digital twins from fulfilling their potential in medical decision-making. This project develops foundational mathematical and computational infrastructure to make biomedical digital twins credible, composable across systems, and able to transfer knowledge from one biological context to another. The work builds tools that allow scientists to assemble digital twins from verified components, transfer knowledge across related biological systems, and produce clear documentation of how reliable the resulting predictions are. Because these tools address foundational principles rather than a single application, they can support digital twin development across a wide range of medical conditions and technologies. The project demonstrates the infrastructure using the human immune system as a proving ground, working with data from vaccination and acute infection contexts. Open-source software, benchmark test cases, and a regulatory credibility documentation template are publicly released to support broad adoption. The project provides research training opportunities for graduate students and postdoctoral scholars at the intersection of mathematical modeling, biomedical science, and regulatory science. This project develops mathematical and computational infrastructure for constructing credible biomedical digital twins under sparse clinical data, addressing three foundational challenges in the current digital twin ecosystem: principled composition of mechanistic modules with verified properties, transfer of knowledge across related biological contexts with quantified credibility, and regulatory-grade assessment of prediction reliability. Two integrated aims are developed. Aim 1 establishes an infrastructure for assembling digital twins from modular components with formal verification of mathematical properties essential for credibility (parameter estimability, dynamical soundness, appropriate resolution for available data), and produces a regulatory-aligned credibility documentation framework. Aim 2 develops a mechanistically informed hierarchy for determining which model components can be transferred across biological contexts, with quantitative criteria distinguishing knowledge that generalizes from knowledge that requires context-specific re-estimation, and complementary computational methods for scalable inference. The infrastructure is formalism-agnostic and demonstrated on two structurally distinct immune contexts. Deliverables include an open-source Python reference implementation, benchmark test cases, a regulatory credibility documentation template, and peer-reviewed publications. The work addresses core FDT-BioTech themes of computational representations, verification, validation, and uncertainty quantification, and transferability, generalizability, and robustness.

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USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences
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