Bioinformatics Approaches for Anti-cancer Drug Discovery

Abstract

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Drugging multiple same-allele driver mutations in cancer

“…However, as we discuss below, the double/multiple co-occurring mutations in cis scenarios also indicate a likelihood of conformational and dynamic alterations which can be exploited in drug optimization. At the same time, the problem of drug resistance through alterations in the expression and mutations in other proteinsdownstream in the same pathway or in parallel pathwaysremains imminent [28].…”

Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics

“…In addition, most of the existing drug combination predictive models are based on omics and drug response data. There has been lack of sufficient data to model the unique characteristics of patients (Kening et al, 2020), which is a major limitation of current researches, including our study. Overcoming these limitations will further increase the value of combination therapies, which requires the joint efforts of researchers across various disciplines, such as biology, chemistry, medicine, and computer science.…”

Triorganotin complexes in cancer chemotherapy: Mechanistic insights and future perspectives

“…Henceforward, this approach could be combined with high-throughput "omics" technologies (transcriptomics, metabolomics, proteomics, and genomics) to allow the use of systematic and comprehensive methodologies in identifying the mechanism of action, putative targets, and the nonspecific toxicity of the optimized active templates of the triorganotin complexes. [180][181][182] The time and costs required for the drug discovery and development process can be reduced, therefore enabling quicker penetration of new drugs into clinical trials. [183] 6 | CONCLUSION…”