Doing More with Less: Accelerated Reaction Modelling in Drug Substance Development

  • Reaction
  • 26 November 2026

About the webinar

Kinetic models allow process chemists to optimize rationally, extract key insights about reactivity, and design processes in silico. Despite this, they remain underused and are often deferred to late-stage development. Two barriers account for much of this: the cost of generating representative, data-rich kinetic data, and the specialist skill required to turn that data into a model.

Recent developments have addressed both. Automated laboratory reactors with integrated sampling now deliver parallel time-course data at a few milliliters per experiment. Modern modeling packages fit a proposed mechanism directly to concentration profiles. Together they bring a kinetic model within reach of a single week of work. This presentation exemplifies this accelerated workflow in three case studies in drug substance development.

Join us on this free webinar with Alonso Arguelles, Sr. Advisor-Chemistry at Eli Lilly.

Date: Thursday, 26 November 2026
Time: 4.00 pm CEST  |  3.00 pm GMT  |  10.00 am EDT  |  7.00 am PDT  |  7.30 pm IST

Guest speaker

Alonso Arguelles

Sr. Advisor Chemistry at Eli Lilly

Hailing from Lima, Peru, Alonso joined the Synthetic Molecule Design and Development (SMDD) organization at Lilly in 2019 after completing his PhD studies in Organic Chemistry at the University of Michigan with Prof. Pavel Nagorny and Prof. Paul Zimmerman. During his graduate studies he worked on the development and application of novel acetal containing ligands for asymmetric transition metal catalysis, as well as quantum-chemical mechanistic investigations of phosphoric acid-catalyzed acetalizations. Currently a Sr. Advisor-Chemistry, Alonso has worked on the process development and optimization for several projects spanning from early development to post-approval modifications, including peptide assets as well as anti-cancer small-molecules such as selpercatinib, pirtobrutinib, imlunestrant. In parallel, Alonso leads the SMDD Data-Rich Experimentation Team and is highly enthusiastic about cultivating a data-rich and automation-centric culture in SMDD