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Inverse Method-Based Kinetic Modelling and Process Optimization of Reverse-Phase Chromatography for Molnupiravir Synthesis

By Fernando Muzzio1; George Tsilomelekis1; Ravendra Singh1; Athanasios Kritikos1

1. Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical & Biochemical Engineering, Rutgers, The State University of New Jersey

Published on CMKC

Abstract

Our research addresses the shift towards continuous manufacturing in the pharmaceutical industry, focusing on optimizing chromatographic separation for the synthesis of molnupiravir. Using an inverse method with six different inlet concentrations for a single objective function, we systematically evaluated the adsorption of key intermediates, i.e., hydroxylamine and isobutyrate, in an isocratic solvent, determining the relevant isotherm constants. The study systematically evaluates the effects of operational variables, including flowrate, column geometry, dispersivity coefficient, and injection volume, on chromatographic performance. Findings reveal that specific operational adjustments, such as reducing flowrates or altering column dimensions, significantly influence retention times and peak profiles, thus potentially impacting the efficiency of molnupiravir production. Utilizing the inverse method, we efficiently determined equilibrium isotherms by integrating a nonlinear chromatography model and adjusting isotherm parameters to match the observed band profiles. Our research offers critical insights into optimizing chromatographic separation performance through precise operational control, leveraging computational tools for rapid and adaptable drug development.

Journal

Application of Process Systems Engineering in Continuous Pharmaceutical and Biopharmaceutical Manufacturing

DOI

10.3390/pr12061273

Type of publication

Peer-reviewed journal

Affiliations

  • Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemical & Biochemical Engineering, Rutgers, The State University of New Jersey

Article Classification

Peer-Reviewed Journal

Classification Areas

  • API

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