(640e) Utilizing Data-Rich Experimentation to Build a Kinetic Model for Drug Substance Synthesis

Nemtabrutinib is a small molecule reversible inhibitor of Bruton’s tyrosine kinase for the treatment of several hematological malignancies. Synthesis of the active pharmaceutical ingredient (API) leverages nucleophilic aromatic substitution to combine an aryl chloride and an amino alcohol tosylate salt under basic conditions. This presentation will discuss the use of data-rich experimentation (DRE) to generate large datasets via automation and high-throughput studies to characterize the API step reaction. A key challenge in developing this reaction was determining the robust set of process parameters that would maximize the conversion of starting materials while limiting the formation of by-products. To identify these conditions more efficiently, time-series data from DRE characterization that leveraged a Design of Experiment (DoE) structure were utilized to collect reaction profiles. In addition, data-driven and knowledge-based methodologies were explored, including Dynamic Response Surface Model (DRSM) and mechanistic modeling. The results of this investigation informed process optimization for the synthesis of nemtabrutinib.