Prioritizing target-disease associations with novel safety and efficacy scoring methods

Abstract

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

Large‐scale production, purification, and function of a tumor multi‐epitope vaccine: Peptibody with bFGF/VEGFA

“…For biological agents from gene engineering, majority of fundings will sink into failures if safety findings occur [26]. Thereby, the purity and residues which produce side effects need further detection.…”

Translational Scoring of Candidate Treatments for Alzheimer’s Disease: A Systematic Approach

“…Excellence in drug development will lead to effective therapies only if the target of the therapeutic modulation is meaningfully related to altering the disease course. Bioinformatics and quantitative pharmacology present a means of scoring disease targets to assist with target choices [93]. These approaches can lead to scores that allow the ranking and prioritizing of candidate targets.…”

“…These approaches can lead to scores that allow the ranking and prioritizing of candidate targets. The proposed scores utilize gene expression data, disease-specific network information, and drug-gene interaction data to improve the inference of target-disease associations and potentially efficacious therapeutic interventions [93, 94].…”

Network Insights into Improving Drug Target Inference Algorithms

“…Network-based algorithms predict drug or disease targets by combining network information and transcriptomic data [14, 21-27]. Two recent representatives, DeMAND [22] and ProTINA [14], model the systemic dysregulation of regulatory network caused by a drug treatment, connecting molecular interactions with differential expression (DE).…”

“…For target inference algorithms, it remains an open question as to which kind of biological data most affects the accuracy. Furthermore, algorithms can infer drug targets in a cell/tissue type-specific manner [14, 22, 27], and it is unknown how efficient or meaningful cell/tissue type-specific network data is for target inference. Answering these questions can provide us with insights into future algorithm improvement.…”