Digital Twins In Pharma

Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have spent over 15 years at the intersection of computational science, pharmaceutical manufacturing, and regulatory strategy. My career has spanned roles at global pharma and medtech organizations — including senior scientific positions at a top five pharmaceutical company and principal engineering roles at leading medical device firms — where I built and deployed predictive modeling platforms that directly informed product development, process scale-up, and regulatory submissions.

My technical foundation is in mechanistic modeling: computational fluid dynamics, finite element analysis, and reduced-order models that bridge first-principles physics with AI-driven surrogate layers. At a major pharma company, I led the predictive modeling program for oral solid-dose manufacturing, where we cut the experimental design-of-experiments workload by 50% and reduced process-optimization timelines by 35% — translating into over $2 million in validated cost savings.

What differentiates my perspective is the regulatory credibility dimension. I established verification and validation standards aligned with ASME V&V 40 across enterprise modeling programs, directly connecting simulation outputs to regulatory submission evidence. This experience led me to build Sigmatwin, a regulatory risk prediction platform that maps pharmaceutical …