Computational methods for early predictive safety assessment from biological and chemical data

Abstract

Introduction: The goal of early predictive safety assessment (PSA) is to keep compounds with detectable liabilities from progressing further in the pipeline. Such compounds jeopardize the core of pharmaceutical research and development and limit the timely delivery of innovative therapeutics to the patient. Computational methods are increasingly used to help understand observed data, generate new testable hypotheses of relevance to safety pharmacology, and supplement and replace costly and time-consuming experimental procedures.

Areas covered: The authors survey methods operating on different scales of both physical extension and complexity. After discussing methods used to predict liabilities associated with structures of individual compounds, the article reviews the use of adverse event data and safety profiling panels. Finally, the authors examine the complexities of toxicology data from animal experiments and how these data can be mined.

Expert opinion: A significant obstacle for data-driven safety assessment is the absence of integrated data sets due to a lack of sharing of data and of using standard ontologies for data relevant to safety assessment. Informed decisions to derive focused sets of compounds can help to avoid compound liabilities in screening campaigns, and improved hit assessment of such campaigns can benefit the early termination of undesirable compounds.