Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology

Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line's and a patient's response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases. Author summary Polypharmacology has emerged as a new strategy for discovering novel therapeutics. Existing efforts in the rational design of polypharmacology have three limitations: focus on a single clinical indication, difficulties in target selection and lead identification/optimization, and ignorance of genome-wide drug-target interactions. Multi-indication therapeutics are needed for complex diseases such as cancer, which have multiple pathological manifestations. The design of multi-indication drugs requires the knowledge of chemical-protein interactions on a genome scale. To enhance our capability of identifying genome-wide chemical-protein interactions, we develop a new structural systems pharmacology platform 3D-REMAP that overcomes the limitations of existing drug-target prediction methods. We propose a strategy that uses the concept of drug repurposing to address challenges in designing dual-indication drugs that can synergistically achieve two desired clinical end points. As a proof-of-concept, we computationally predict and experimentally validate that levosimendan, a PDE inhibitor for heart failure that is associated with all existing anti-cancer therapies, is a kinase inhibitor and active against lymphoma. We further identify biomarkers that predict a patient's response to levosimendan. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. Our approach may facilitate transforming the conventional polypharmacology approach to a new one-drug-multi-target-multi-disease paradigm.