Greater Boston
Engineering leader with experience in early and late stage process development, tech transfer and commercial manufacturing of biologic and synthetic drug substance at Amgen and in innovative drug product process development at Purdue University. Proven track record in people and project management, technology development and QbD practice. Author of 14 peer-reviewed scientific publications and over 30 invited and conference presentations.
Thesis: Manufacture of Personalized Medicine: Development and Control of a Dropwise Additive Manufacturing Process for Melt-based Pharmaceuticals • Developed a prototype process for drop-wise additive manufacturing of pharmaceutical products (DAMPP) utilizing drop-on-demand (DoD) printing technology to reproducibly print solid oral dosage forms onto edible substrates enabling personalized dosing. • Developed an advanced control system for DAMPP including automation, online monitoring and closed-loop control to adjust critical process parameters (CPP) to achieve critical quality attributes (CQA). • Developed a surrogate model-based control layer to indirectly control drug dissolution online and enable real time release of solid oral dosages. • Integrated DAMPP with knowledge management system to capture data provenance and enable data analytics. • Investigated the use of different formulations, such as polymer-drug and lipid-drug melts to achieve the desired bioavailability for select drugs. • Supervised six undergraduate researchers on projects related to DoD printing of pharmaceuticals • Collaborated with researchers from four universities and mentors from industries within ERC-SOPS for formulation and characterization studies.
•Responsible of conducting quality control experiments for various kinds of cables manufactured in the facility.
•Developed a methodology to study complete cancer care trajectories using the care of pet dogs with naturally occurring cancer as a proxy to the delivery of human healthcare. •Data generated by several analytical methods (e.g. proteomics and metabolomics) are analyzed and identifying combinations of features that might serve as biomarkers for certain patient trajectories are determined. •LC/MS data of proteomes and metabolomes are analyzed by the developed MATLAB algorithm.