Before co-founding Applied BioMath, Josh was a Principal Scientist in the Systems Biology Group of the Department of Immunology and Inflammation at Boehringer Ingelheim Pharmaceuticals. His work leveraged physics-based models to: translate in vitro and in vivo data, assess target feasibility, understand drug mechanism of action, and predict human doses. The ultimate goal of this work was to reduce late stage attrition in drug development through a deep and quantitative interrogation of drug pharmacology and disease pathophysiology.
Josh received his PhD from MIT in Biological Engineering where he worked on experiment design for Systems Biology, focusing on the identification of tractable experiments that could allow for the estimation of unknown parameters and reveal complex mechanisms in signal transduction networks. Before that Josh worked at Avaki to develop a highly scalable software platform to support High Performance Computing, and Enterprise Information Integration in the Life Sciences, and Engineering.
This is an exciting time work in biology, where our quantitative understanding of biology is reaching a point where we can apply engineering methods. I joined this field because I wanted to be part of inventing those methods.
Key Research
- Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs)
- Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions
- A next generation mathematical model for the in vitro to clinical translation of T-cell engagers
- Quantitative systems pharmacology model of GITR-mediated T cell dynamics in tumor microenvironment
- Early Feasibility Assessment: A Method for Accurately Predicting Biotherapeutic Dosing to Inform Early Drug Discovery Decisions