In vitro and in vivo identification of clinically approved drugs that modify ACE2 expression.

The COVID-19 pandemic caused by SARS-CoV-2 has is a global health challenge. Angiotensin-converting enzyme 2 (ACE2) is the host receptor for SARS-CoV-2 entry. Recent studies have suggested that patients with hypertension and diabetes treated with ACE inhibitors (ACEIs) or angiotensin receptor blockers have a higher risk of COVID-19 infection as these drugs could upregulate ACE2, motivating the study of ACE2 modulation by drugs in current clinical use. Here, we mined published datasets to determine the effects of hundreds of clinically approved drugs on ACE2 expression. We find that ACEIs are enriched for ACE2-upregulating drugs, while antineoplastic agents are enriched for ACE2-downregulating drugs. Vorinostat and isotretinoin are the top ACE2 up/downregulators, respectively, in cell lines. Dexamethasone, a corticosteroid used in treating severe acute respiratory syndrome and COVID-19, significantly upregulates ACE2 both in vitro and in vivo. Further top ACE2 regulators in vivo or in primary cells include erlotinib and bleomycin in the lung and vancomycin, cisplatin, and probenecid in the kidney. Our study provides leads for future work studying ACE2 expression modulators.

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Combining drug and gene similarity measures for drug-target elucidation.

Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework--Similarity-based Inference of drug-TARgets (SITAR)--for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb.

Fair localization of function via multi-lesion analysis.

Acknowledging that causal localization of function in a processing network requires a multi-lesion analysis, this paper presents a rigorous and efficient method for defining and calculating the functional contributions of network elements as well as their interactions. The method's applicability to biological networks is demonstrated in the investigation of spatial attention in cats via lesion and reversible deactivation experiments.