The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less
Harvey Wong, Stephen E Gould · 2016 · Drug discovery today. Technologies · Elsevier · added 2026-04-20
Translational pharmacokinetic/pharmacodynamic (PK/PD) analysis is becoming an increasingly important tool for the identification and selection of new anticancer agents. There are two important element Show more
Translational pharmacokinetic/pharmacodynamic (PK/PD) analysis is becoming an increasingly important tool for the identification and selection of new anticancer agents. There are two important elements of effectively using PK/PD analysis to translate preclinical antitumor efficacy from tumor bearing mice (xenografts and allografts) to cancer patients. These two sometimes overlapping elements are termed translation 'WITHIN' and 'ACROSS' species. Translating 'WITHIN' species refers to the quantitative characterization of drug action and disease behavior within tumor bearing mice using PK/PD modeling in order to use this information to make predictions of drug response in humans. Translating 'ACROSS' species refers to use of PK/PD modeling to quantify species similarities and differences in drug response in order to understand the clinical relevance of preclinical efficacy data. Show less