Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms

• Published in: Therapeutic delivery 2021-11, Vol.12 (11), p.789-797
• Main Authors: Shah, Harsh S, Chaturvedi, Kaushalendra, Kuang, Shanming, Wang, Jian
• Format: Article
• Language: English
• Subjects: cocrystals; computational studies; crystal structure prediction; mathematical modeling; polymorphs; preformulation; salts; solid state characterization
• ISSN: 2041-5990; 2041-6008
• DOI: 10.4155/tde-2021-0043

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.