Mon
04
May
Seminars and Conferences
Computational Models to Speed-up and De-risk Biologics Developability and Formulation Development
The seminar Computational Models to Speed-up and De-risk Biologics Developability and Formulation Development, part of the Erasmus Mundus Multiphase programme, will take place on 4 May 2026 from 10:00 to 13:00 and will be delivered by Andrea Arsiccio - Coriolis Pharma.
Abstract
The development of novel therapeutic molecules is accompanied by significant financial and time investments. Advancing molecules through drug product development without proper evaluation often leads to costly failures. In some cases, these failures are related to the intrinsic instability of the candidate molecule and the difficulty of minimizing such instabilities through appropriate formulation strategies. Therefore, early characterization of a drug candidate’s formulation developability is crucial to reduce risk.
A comprehensive in vitro assessment is often constrained by the limited availability of drug substance in the early stages of development, increasing the interest in simulations and computational models. In this presentation, we discuss how various in silico tools, ranging from structure prediction and bioinformatics to machine learning, mechanistic modeling, and molecular dynamics, can be integrated to accelerate and de-risk candidate selection, lead characterization, and formulation development.
Selected case studies illustrating applications in real-world scenarios will also be presented.
Abstract
The development of novel therapeutic molecules is accompanied by significant financial and time investments. Advancing molecules through drug product development without proper evaluation often leads to costly failures. In some cases, these failures are related to the intrinsic instability of the candidate molecule and the difficulty of minimizing such instabilities through appropriate formulation strategies. Therefore, early characterization of a drug candidate’s formulation developability is crucial to reduce risk.
A comprehensive in vitro assessment is often constrained by the limited availability of drug substance in the early stages of development, increasing the interest in simulations and computational models. In this presentation, we discuss how various in silico tools, ranging from structure prediction and bioinformatics to machine learning, mechanistic modeling, and molecular dynamics, can be integrated to accelerate and de-risk candidate selection, lead characterization, and formulation development.
Selected case studies illustrating applications in real-world scenarios will also be presented.