Industrializing AI/ML during the end-to-end drug discovery process.

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

Process Management Framework: Guidance to Successful Implementation of Processes in Clinical Development.

Clearly defined, documented, and managed processes form the foundation for how we effectively develop medicines for our patients. For this reason, process has been identified as a primary "element" of an effective quality management system (QMS) as described in the TransCelerate clinical quality management system (CQMS) conceptual framework. The importance of identifying and effectively managing processes is also emphasized in ICH GCP E6 (R2) in the new Section 5.0 Quality Management. An effective process management framework is fundamental to ensure the efficient and effective delivery of clinical development programs, enhance quality and productivity, and ultimately benefit our ability to deliver needed treatments to patients. The aim of this paper is to provide a conceptual process management framework to be used as guidance for effective process mapping, process documentation, implementation of optimal learning methods, and ensuring ongoing process performance evaluation and continuous improvement.