Jean-René Authelin, Sanofi-Aventis - Qepler Summits And Conferences

Jean-René Authelin

Global Head of Pharmaceutical Engineering
Discover Sanofi: a global biopharmaceutical company focused on human health

Jean René Authelin has an Engineer degree in chemical Engineering from ENSIC (Nancy France), and a PhD from The Institut National Polytechnique de Lorraine (France). He joined Rhone Poulenc in 1988 as a Chemical Engineer. In the 90’s he founded the Physical Quality function, dedicated to the API crystallization, drying, polymorphism… for which he was for 10 years Global Head in Rhone Poulenc Rorer, Aventis and finally sanofi. In, 1988 JR Authelin was nominated Global Head of Pharmaceutical Engineering. Domains of interest of JRA include: thermodynamics of hydrates, drug polymorphism, amorphous solids physics, drug stability, crystallization, nanoparticles engineering and processing, drying, milling; spray drying, fluid bed granulation, roller compaction, freeze drying.

Jean René Authelin is the author or co-author of 20 publications or book chapters and the co-inventor of 9 patents.

Related Sessions:

3rd Annual Pharmaceutical
Lyophilization Summit 2021

Discuss best practices in tech & regulatory updates, process, formulation, testing, monitoring, new products development.
  • 29 Jul 2021
  • Virtual,
  • Pharma
Day 1: Thursday, 29 July 2021
CASE STUDY: Taking into account Kv distribution in freeze drying simulation.

Theory of freeze drying was set up in the 80’s by pioneering work of Pikal. Although very simple (1D, quasi steady state), the model provides very good representation of the reality, the only limitation is that it uses average value. However it is well known that the edge vials are much hotter than center vials, due to radiation impact. This difference in temperature may result in heterogeneities in the product : if the cycle is too aggressive, edge vials may collapse, or if it the primary drying is too short some residual ice may still be present when the cycle is moving to secondary drying, leading to melt back. We will present a new process simulation methodology, based on the distribution of Kv’s which allows top predict the variability of product temperature and primary drying time. Real life examples will be discussed.

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