Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Abstract

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes

“…Molecular docking represents the state-of-the-art technique for the prediction of protein-ligand complexes, thanks to the good compromise between accuracy and rapidity of execution (Pavan and Moro, 2023). Although routinely used in various structurebased drug discovery campaigns, molecular docking has been specifically designed and optimized with protein-ligand receptor in mind, due to pharmaceutical relevance and availability of experimentally determined structures to use as a benchmark for method development (Salmaso and Moro, 2018).…”

“…Although it is an appealing perspective, applying routinely adopted molecular modeling protocols to RNA systems is not trivial. Historically, these techniques have been optimized to study the recognition process between small organic molecules as ligands and proteins as receptors (Salmaso and Moro, 2018;Bassani and Moro, 2023;Pavan and Moro, 2023). Nevertheless, structural differences between proteins and RNAs, such as the peculiar surface charge properties portrayed by the polyanionic phosphate backbone, the ions' role in the structural stability and folding of RNA, the role of the solvent in mediating structural stability and forming bridged interactions, other than the intrinsic structural flexibility of ribonucleic acids, limited so far the possibility to repurpose these methodologies to the study of RNA complexes (Bissaro et al, 2020).…”

Applications of Molecular Docking Studies in SARS-CoV-2 Targeted Drug Discovery and the Gains Achieved through Molecular Docking

“…With the increasing availability of curated experimental datasets, physics-based methods and artificial intelligence techniques are expected to play a more supportive role in evaluating both the pharmacodynamic and pharmacokinetic properties of investigated compounds. Lastly, an increased probability of highly contagious disease outbreaks, underscoring the potentially significant role of successful computational strategies in combating future challenging diseases [64].…”

A functional medicine and food homology composition discovery based on disease-related target and data mining against cardiac remodeling

“…Through CADD approaches, researchers have provided useful evidence to discover anti-inflammatory drugs towards TNF-α, which is associated with immune diseases, cancer, and psychiatric disorders 19 ; Similarly, by using CADD approaches, anti-Alzheimer drugs 52 , anti-diabetic therapies 53 , and anti-cancer agents 54,55 have been rapidly screened and evaluated. Based on CADD approaches, which aims to bridge the research deficiencies by combining experimental and computational methodologies, providing a more systematic and informed exploration of the pharmacological potential of natural products, specifically in the context of cardiac remodeling and oxidative stress regulation, we selected the best promising compound in MFCD, β-ecdysterone, with high potential to combine with KEAP1-NRF2 and qualified drug-likeness.…”