Second regulatory workshop on utilising in silico models to expedite vaccine development, testing, and lifecycle management - an expert meeting report


Meln I., Van Molle W., Vélez M. P., Baay M., Bracewell D. G., Brusselmans K., ...More

Vaccine, vol.86, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 86
  • Publication Date: 2026
  • Doi Number: 10.1016/j.vaccine.2026.128733
  • Journal Name: Vaccine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, Environment Index, MEDLINE, Public Affairs Index
  • Keywords: Chemistry, manufacturing, and control (CMC), Downstream modelling, Mathematical models, Model risk, Regulatory science, Stability design, Stability modelling, Uncertainty, Upstream modelling, Vaccine development
  • Hacettepe University Affiliated: Yes

Abstract

The Inno4Vac consortium, funded under the Innovative Medicines Initiative 2 Joint Undertaking, was launched in September 2021. Among its four key objectives is the development of an open-source, in silico simulation platform designed to support the design, scale-up, operation, and technology transfer of vaccine manufacturing processes, including stability testing. A workshop was held on May 27, 2025, in Brussels, Belgium, which provided an opportunity for modellers to show the progress made, and have an open dialogue with regulatory health authorities' representatives.Regulators are increasingly open to advanced kinetic modelling and other modelling approaches for predicting vaccine stability and shelf-life, provided they are transparent, scientifically justified, and validated with real-time data. Initial claims can rely on accelerated studies and modelling simulating worst-case scenarios, with extensions supported by additional data. In urgent contexts, extrapolation from similar products or processes may be accepted if scientifically sound.Platform-based extrapolation requires strong similarity, but product-specific data remain critical, as small formulation changes can alter stability. Simulation studies that demonstrate robustness and predictive accuracy enhance confidence.Projects like Inno4Vac highlight Bayesian methods for formulation design, though formal guidance is limited. Regulatory scrutiny depends on model risk, with low-risk models probably still requiring justification. Transparent data practices, including justified handling of outliers, are essential.Collaboration between regulators, industry, and academia remains key to advancing science-based innovation while ensuring product quality.