Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

G Beis; A P Serafeim; I Papasotiriou

doi:10.1016/j.csbj.2022.11.042

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Comput Struct Biotechnol J. 2022 Nov 24:21:46-57. doi: 10.1016/j.csbj.2022.11.042. eCollection 2023.

Authors

G Beis¹, A P Serafeim¹, I Papasotiriou²

Affiliations

¹ Research Genetic Cancer Centre S.A., Industrial Area of Florina 53100, Greece.
² Research Genetic Cancer Centre International GmbH Headquarters, Baarerstrasse 95, Zug 6300, Switzerland.

Abstract

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Keywords: Differential expression; Drug discovery; Druggability assessment; Pathway analysis.

Publication types

Review