Computational pharmacology and in silico clinical trials for the development of drugs from natural products: A review of advances, opportunities, and challenges.

Abstract

Natural products have inspired the discovery of several drug candidates and US Food and Drug Administration-approved drugs. However, the conventional drug development pathway has several limitations, necessitating innovative strategies. Computational pharmacology and in silico clinical trials (ISCTs) have emerged as a potential to optimize the drug discovery process. Current literature rarely discusses these 2 domains together, leaving limited guidance on how computational tools used in natural product research can complement ISCT frameworks. This review addresses this gap by summarizing key computational methods used for natural products and highlighting how their outputs can inform ISCT-based evaluation of efficacy, safety, and translational potential. Computational approaches such as molecular docking, pharmacophore modeling, quantitative structure-activity relationship analysis, absorption-distribution-metabolism-excretion-toxicity prediction, and molecular dynamics simulations have been applied in natural products-based drug discovery. The findings of these studies are promising and suggest favorable prospects for the use of computational methods in identifying, optimizing, and evaluating bioactive natural compounds. Notably, this approach minimizes the attrition rate in later clinical stages, as the most promising compounds with auspicious pharmacokinetic and pharmacodynamic profiles are chosen. ISCTs further hold considerable value in drug discovery through investigating the safety and efficacy of investigational drugs using simulated or virtual patient populations, ensuring a lower attrition rate when wet-lab clinical trials are conducted. Although the ISCTs on natural products are limited, the integration of computational pharmacology and ISCTs shows potential in the discovery of valuable natural compounds and the acceleration of drug development from natural products. Despite existing challenges in data quality and regulatory acceptance, the integration of emerging technologies such as machine learning, artificial intelligence, and hybrid platforms shows promise in advancing natural product-based therapeutics and precision medicine. SIGNIFICANCE STATEMENT: This review brings together computational pharmacology and in silico clinical trials to address persistent challenges in natural product drug development. It highlights how early computational screening, absorption-distribution-metabolism-excretion-toxicity prediction, and dynamic modeling can inform virtual clinical evaluation, reduce attrition, and accelerate translation. The paper offers a clear and practical roadmap for improving efficiency, strengthening safety assessment, and advancing precision in the development of natural product-based drugs.