Oak Brook, IL (USA) — In September’s SLAS Discovery cover article, “Using Physicochemical Measurements to Influence Better Compound Design,” authors Robert J. Young, Ph.D., Shenaz B. Bunally, Ph.D., and Chris N. Luscombe, Ph.D., (GlaxoSmithKline) outline commonly used physiochemical properties and how they are assessed and measured throughout the drug discovery process, while also explaining the implications of each property that have led to flawed results. This review also offers suggestions on which contemporary methods can be used to improve subpar testing outcomes.
Quantifying the physicochemical make-up of investigational molecules is fundamental to understanding the mechanisms and interactions of drug molecules. In this perspective Bunally, Luscombe and Young introduce key parameters, what they mean and how they are measured and predicted in best practice. The issues with suboptimal properties characterized by excessive lipophilicity and/or poor solubility are varied, ranging from poor outcomes in screening campaigns, promiscuity, limited and/or poorly predictable pharmacokinetic exposure and a greater chance of clinical failure.
High-throughput measurements enable data to be gathered on all experimental compounds, which are then analyzed and established as Structure Property Relationships (SPRs). This should more-rapidly identify both good and bad outliers, which enable enhanced interpretation of results and prioritization of better structures for progression. Increasingly, these data provide the basis for temporal analysis and generation of improved predictive models of the descriptors themselves or of more complex models where the descriptors have profound impact.
An introduction to the methods and techniques employed in model building provides background to the basis of modern Predict First cultures. And while the role of artificial intelligence in drug discovery is currently a hot topic and the utility of predicted physical properties is well-established, they’re not as widely utilized in drug discovery measurements as they could be. The routine generation of high-throughput measurements, the subsequent evaluation of SPRs and generation of in silico predictive models should expedite this progress through improved quality by design in experimental molecules.
Access to the complete SLAS Discovery article is available at https://journals.sagepub.com/toc/jbxb/24/8 through September 20. For more information about SLAS and its journals, visit www.slas.org/journals.
SLAS (Society for Laboratory Automation and Screening) is an international community of 19,000 professionals and students dedicated to life sciences discovery and technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.
SLAS Discovery: 2017 Impact Factor 2.355. Editor-in-Chief Robert M. Campbell, Ph.D., Eli Lilly and Company, Indianapolis, IN (USA). SLAS Discovery (Advancing Life Sciences R&D) was previously published (1996-2016) as the Journal of Biomolecular Screening (JBS).
SLAS Technology: 2017 Impact Factor 2.632. Editor-in-Chief Edward Kai-Hua Chow, Ph.D., National University of Singapore (Singapore). SLAS Technology (Translating Life Sciences Innovation) was previously published (1996-2016) as the Journal of Laboratory Automation (JALA).
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