A novel in silico platform combining data-driven and physics-based models for protein formulation developability assessment

Journal of Pharmaceutical Sciences

Authors: Andrea Arsiccio, Kristian Le Vay, Andre Sao Pedro, Thomas Pabstmann, Constanze Helbig, Dominik Brandstetter, Eleonora Cerasoli, Andrea Hawe, Tim Menzen

The development of novel therapeutic proteins is accompanied by huge financial and time investments. Advancing molecules through drug product development without proper scrutiny often leads to costly failures. In some cases, such failures are related to the inherent instability of the candidate molecule, and the difficulty of minimizing these instabilities by selecting a suitable formulation. Early characterization of a protein drug candidate’s formulation developability is therefore crucial to reduce the risk. A comprehensive in vitro assessment is often constrained by the scarcity of drug substance material available in early stages of development, raising the interest in simulations and computational models. Currently, only very few in silico methods focusing on formulation development and assessing a protein drug candidate’s formulation developability are available. To address this problem, we present a novel in silico platform based on data driven and physics-based models that combines computational techniques spanning different areas, including structure prediction, bioinformatics, machine learning and molecular dynamics calculations. The platform only requires the primary sequence of a therapeutic protein as input, thus eliminating the need for physical material. We describe the computational tools that are part of the platform, and show how they can be used to identify liabilities and outlying properties of the protein drug candidate, evaluate high concentration suitability, screen the effect of formulation conditions on self-interaction propensity, and suggest formulation corridors. We additionally present two case studies that illustrate potential applications of the developed platform to real-world scenarios and prove its experimental validation.

Read the full publication: 10.1016/j.xphs.2026.104196