Cilastatin for COVID-19

AbstractTo attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

Background/Objectives: The COVID-19 pandemic highlighted the critical need for drug repurposing. While computational graph-based approaches show promise, single analytical methods often fail to capture the complex pathological mechanisms needed for clinical translation. We aimed to prioritize COVID-19 repurposing candidates by combining computational prediction with orthogonal lines of evidence. Methods: We developed a Graph Attention Autoencoder (GATE) framework with multilayered evaluation integrating computational prediction, real-world data, and phenome analyses. Network-based embeddings identified candidates through latent-space similarity within biomedical knowledge graphs. Clinical relevance was assessed by disproportionality analysis (DPA) of an adverse event reporting system, and biological plausibility through gene expression and pathway profiling. Results: Our GATE framework identified 16 candidate drug entities, including 12 already used or recognized in the context of COVID-19 or its symptoms, consistent with the model recovering known drug–disease associations. The remaining four had minimal prior COVID-19 associations (cilastatin, megestrol, drotrecogin alfa, and ethacrynic acid). Of these four, the multilayered evaluation prioritized two: DPA showed significant inverse reporting associations for COVID-19-related adverse event terms for cilastatin (reporting odds ratio [ROR]: 0.19, 95% confidence interval [CI]: 0.09–0.43) and megestrol (ROR: 0.63, 95% CI: 0.42–0.94). Pathway profiling indicated that cilastatin shares molecular signatures with drugs investigated in COVID-19 clinical trials and megestrol with compounds reported as COVID-19 therapeutic candidates; gene expression analyses suggest cilastatin may exert antiviral and anti-inflammatory effects through DPEP1 inhibition. Conclusions: By integrating GATE-based prediction, real-world evidence, and phenome-level pathway profiling, this study enabled multidimensional candidate prioritization that single analytical methods do not provide. These findings, including the proposed mechanisms of action, are computational predictions that await experimental confirmation. They nonetheless provide mechanistic working hypotheses that may streamline subsequent preclinical studies. Prioritizing clinically approved drugs with established safety profiles may facilitate subsequent clinical evaluation, although the risks specific to each drug would need to be weighed. The framework offers a generalizable strategy for rapid candidate identification during pandemics and diseases with unmet therapeutic needs.