Data science in drug discovery safety: Challenges and opportunities

Abstract

Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, and competitor compound intelligence to understand the potential safety risks associated with modulating a drug target. However, there is a vast amount of information, updated daily that must be considered for each TSA. We have developed a data science-based approach that allows acquisition of relevant evidence for an optimal TSA. This is built on expert-led conventional and artificial intelligence-based mining of literature and other bioinformatics databases. Potential safety risks are identified according to an evidence framework, adjusted to the degree of target novelty. Expert knowledge is necessary to interpret the evidence and to take account of the nuances of drug safety, the modality, and the intended patient population for each TSA within each project. Overall, TSAs take full advantage of the most recent developments in data science and can be used within drug projects to identify and mitigate risks, helping with informed decision-making and resource management. These approaches should be used in the earliest stages of a drug project to guide decisions such as target selection, discovery chemistry options, in vitro assay choice, and end points for investigative in vivo studies.

Keywords: Drug safety; bioinformatics; target safety.

Figure 1.

Making safety part of drug design. Safety is the main reason for failure in drug discovery and development. The development of “safer” drugs requires an in-depth knowledge of potential adverse effects related to the biology of the drug target as well as toxicity risks that are carried by the drug modality themselves.

Figure 2.

Target safety assessments (TSAs) during drug discovery and development. TSAs may be carried out throughout the process from target selection (TS) to clinical development. Source: Figure adapted from Walker et al. LG: lead generation; LO: lead optimization; CD: candidate drug; GLP: good laboratory practice.

Figure 3.

The Use of Bioinformatics to study target distribution and translatability to animal species used in toxicology studies. Plots show gene and protein expression data for PI3Kδ. (A) The expression profile of PI3Kδ was studied at both the mRNA (whole tissue and single-cell RNA-Seq) and protein level (immunohistochemistry data), aligned with key target organ systems. RNA-Seq data are curated from GTEx and HPA. Immunohistochemistry data are from all tissues assessed by HPA and scored according to the HPA methodology. The PI3Kδ antibody used by HPA was considered to be of good reliability. (B) Alignment of the PI3Kδ PI3/PI4 kinase domain was performed between human, cynomolgus monkey (MACFA), dog (CANLF), rat, and mouse using Clustal OWS. Residues are colored according to the percentage of residues in each column that agree with the consensus sequence (percentage shared identity). Only the residues that agree with the consensus residue for each column are colored. Residues bound by FDA-approved PI3Kδ inhibitors idelalisib, duvelisib, and umbralisib are highlighted in red. The motif to depict the Hidden Markov Model was derived from seed alignments curated by Pfam for the PF00454 family (41 seed sequences). (C) The mRNA expression profile of PI3Kδ was compared by RNA-Seq between human (GTEx) and the main toxicology species matched normal tissues. Expression plotted as transcripts per million (TPM), across sample types and organized by sex. Preclinical species RNA-Seq expression data were obtained from PRJNA516470.

Figure 4.

Single-cell RNA-Seq expression analysis of the human liver reveals that the target of interest expression is negligible in hepatic macrophages. (A) Expression of the target of interest in hepatic macrophages was compared using the data originally derived from MacParland et al. before curation by HPA consortium. (B) Differentially expressed genes (DEGs) with pairwise gene expression ratio were compared between inflammatory hepatic macrophage and tolerogenic macrophage clusters and (C) dimension reduction of cell distribution of the target of interest compared to markers of tolerogenic (MARCO) and inflammatory macrophages (VCAN) performed by tSNE. Distribution of hepatic cells is depicted as clusters pertaining to the principal hepatic cell types; color bar overlay represents a scale of target expression (low to high).