Large‐Scale Synthesis

Abstract

Large‐scale synthesis enables drug discovery, clinical development, and commercialization for new small molecule medicines. Several key disciplines collaborate to develop and define chemical processes, and ensure that they are suitable for large‐scale manufacturing activities. This article will discuss how new drugs transition from the medicinal chemistry laboratory, to kilo‐scale facilities, to pilot, and eventually to commercial scale. Important topics along the way include route selection, process safety, environmental considerations, equipment and facility types, scale‐dependent phenomenon, continuous processing, outsourcing, development models, and regulatory considerations. The article concludes with recent two case studies that highlight some of the topics covered and exemplify the power of process development and large‐scale production for modern small molecule drugs.

Process Development and Scale-Up of a Novel Atypical DAT Inhibitor (S)-CE-123

“…4 During this process of drug transition, from the medicinal chemistry laboratory to kilo-scale facilities, large-scale synthesis plays a crucial role. 5 Hence, kilo laboratories are rather important tools that support process development in the pharmaceutical industry with the main objective to manage a dynamic portfolio of potential products while meeting all environmental, health, and safety aspects, ensuring product quality and enabling business requirements. 6 (S)-5-((Benzhydrylsulfinyl)methyl)thiazole, (S)-CE-123, is a novel modafinil analogue (Figure 1) with improved specificity and efficacy for dopamine transporter inhibition that improves cognitive and motivational processes in experimental animals.…”

“…Upon identification of a lead candidate, a typical preclinical development program aims to fulfill the following six goals: manufacture of the drug substance/active pharmaceutical ingredient; drug formulation (dosage design); analytical and bioanalytical method development and validation; metabolism and pharmacokinetics; safety and genetic toxicology and possibly safety pharmacology; and good manufacturing practice (GMP) manufacture and documentation of the drug product for use in clinical trials . During this process of drug transition, from the medicinal chemistry laboratory to kilo-scale facilities, large-scale synthesis plays a crucial role . Hence, kilo laboratories are rather important tools that support process development in the pharmaceutical industry with the main objective to manage a dynamic portfolio of potential products while meeting all environmental, health, and safety aspects, ensuring product quality and enabling business requirements …”

“… 4 During this process of drug transition, from the medicinal chemistry laboratory to kilo-scale facilities, large-scale synthesis plays a crucial role. 5 Hence, kilo laboratories are rather important tools that support process development in the pharmaceutical industry with the main objective to manage a dynamic portfolio of potential products while meeting all environmental, health, and safety aspects, ensuring product quality and enabling business requirements. 6 …”

Opportunities for Machine Learning and Artificial Intelligence to Advance Synthetic Drug Substance Process Development

“…In a previous article, we reviewed the DMTA drug discovery cycle and discussed areas in which artificial intelligence (AI) might be beneficially applied to medicinal chemistry, e.g., with computer-aided synthesis planning (CASP). In this Perspective, we focus on developing a suitable synthetic manufacturing process for clinical and commercial production of drug substance …”

“…In this Perspective, we focus on developing a suitable synthetic manufacturing process for clinical and commercial production of drug substance. 3 In our experience and our reading of the relevant literature over the last 15 years, we see significant unrealized opportunities for machine learning (ML) and AI to enable more efficient drug substance process development. We suspect that these opportunities have not been in focus because of the complex, multifaceted, and iterative nature of synthetic drug substance process development; such tasks, in their vast formulation, are hard to pin down into confined supervised learning problems that can be tackled with ML or narrow AI.…”