Targeting PD-1: A Computational Approach to Discover Small Molecule Inhibitors for Cancer Treatment

Abstract

Globally, cancer is a major burden of disease threatening human health. To date, immune checkpoint inhibitors, which are monoclonal antibodies used as monotherapy or in combination, have revealed remarkable clinical success in a wide range of solid tumors and hematologic malignancies. Given the limitations of antibody therapies, orally bioavailable small-molecule inhibitors present a viable alternative. The discovery of new therapeutic drugs is complex, costly, and time-consuming.  Leveraging a combination of computational methods can significantly accelerate the drug discovery process, enabling the identification of promising drug candidates from the large compound libraries. In the Silico Computational study, 30 hit compounds were initially retrieved by pharmacophore-based virtual screening. Thereafter, 5 compounds with lowest Gibb's free energy (ΔG) values, namely ZINC85867378, ZINC16267039, ZINC64219346, ZINC68604154 and ZINC20576138, have been chosen for further evaluation. This study establishes the workflow combining pharmacophore virtual screening, molecular docking, and absorption, distribution, metabolism, excretion - toxicity (ADMET) prediction to identify possible small molecules that can interact with PD-1. The identified compounds might serve as starting points to design potential safe and efficacious molecules in cancer Immunotherapy. Further evaluation is necessary to optimize drug properties.