Early characterization of drug targets associated with disease can greatly reduce clinical failures attributed to lack of safety or efficacy. As single-cell RNA sequencing (scRNA-seq) of human tissues becomes increasingly common for disease profiling, the insights obtained from this data could influence target selection strategies. Whilst the use of scRNA-seq to understand target biology is well established, the impact of single-cell data in increasing the probability of candidate therapeutic targets to successfully advance from research to clinic has not been fully characterized. Inspired by previous work on an association between genetic evidence and clinical success, we used retrospective analysis of known drug target genes to identify potential predictors of target clinical success from scRNA-seq data. Particularly, we investigated whether successful drug targets are associated with cell type specific expression in a disease-relevant tissue (cell type specificity) or cell type specific over-expression in disease patients compared to healthy controls (disease cell specificity). Analysing scRNA-seq data across 30 diseases and 13 tissues, we found that both classes of scRNA-seq support significantly increase the odds of clinical success for gene-disease pairs. We estimate that combined they could approximately triple the chances of a target reaching phase III. Importantly, scRNA-seq analysis identifies a larger and complementary target space to that of direct genetic evidence. In particular, scRNA-seq support is more likely to prioritize therapeutically tractable classes of genes such as membrane-bound proteins. Our study suggests that scRNA-seq-derived information on cell type- and disease-specific expression can be leveraged to identify tractable and disease-relevant targets, with increased probability of success in the clinic.

ED has consulted for Ensocell Therapeutics. ET, GG, FN, EdR are employees of Sanofi and own Sanofi stock. VS has been leading the application of single-cell biology for drug development at Sanofi since 2018 and owns Sanofi stock. RE is a co-founder and employee of Ensocell Therapeutics. SAT has consulted for or been a member of scientific advisory boards at Qiagen, Sanofi, GlaxoSmithKline and ForeSite Labs. She is a consultant and equity holder for TransitionBio and Ensocell Therapeutics.

ED, KBM and SAT. acknowledge Wellcome Sanger core funding (WT206194).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

https://cellxgene.cziscience.com/collections

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All scRNA-seq data analysed in this study is available via the CZ CellxGene Discover database and CxG Census API (version: 2023-07-25) available at https://chanzuckerberg.github.io/cellxgene-census/. Data on clinical precedence for known drugs for each target-disease pair and gene-disease genetic association scores was downloaded from Open Targets (version 23.02) available at https://platform.opentargets.org/downloads/data). Data on gene tolerance to loss-of-function mutations was extracted from gnomAD.v2.1 pLoF metrics by gene data (https://gnomad.broadinstitute.org/downloads). Gene sets used as universes for association analysis are available at https://github.com/emdann/sc_target_evidence/blob/master/data/universe_genes.csv. Processed datasets and analysis outputs are available as supplementary tables and via figshare (doi:10.6084/m9.figshare.25360129).

https://github.com/emdann/sc_target_evidence

doi:10.6084/m9.figshare.25360129