Computational-Aided Drug Discovery Approaches Exploring Therapeutic Potential of Global Health Priority Box Compounds Against Schistosoma haematobium Infection

Abstract

Background: Urinary schistosomiasis caused by Schistosoma haematobium remains a major global health burden, with nearly 800 million people at risk worldwide. The growing concern over reduced praziquantel efficacy underscores the urgent need for alternative therapeutic agents. Advances in computational drug discovery provide an efficient framework for identifying novel antischistosomal candidates targeting parasite-specific proteins.

Objective: This study employed integrative computational drug discovery approaches to evaluate Global Health Priority (GHP) Box compounds for their therapeutic potential against S. haematobium, focusing on the 23 kDa integral membrane protein (Sh23) as a putative drug target.

Methodology: The Sh23 protein was identified and structurally characterized as a potential therapeutic target. A curated library of 80 GHP Box compounds was subjected to virtual screening using PyRx, yielding  33 top-scoring candidates based on binding affinity. These candidates were further evaluated through in silico pharmacokinetic and toxicity profiling using ADMET-AI, drug-likeness assessment using MolSoft, and biological activity prediction via the PASS platform (Way2Drug).

Results: MMV1262756 emerged as the lead compound, demonstrating strong binding affinity to Sh23, low predicted clinical toxicity (0.14), high drug-likeness (0.87), and favorable predicted activity related to urological disorders.

Conclusion: This study identifies MMV1262756 as a promising antischistosomal candidate and highlights the utility of computationally driven drug discovery in accelerating the identification of novel therapeutics for S. haematobium. The findings support the potential of bioinformatics-guided strategies to address emerging challenges in praziquantel efficacy and advance schistosomiasis control.