Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

Abstract

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Keywords: COVID-19; Computational sciences; Drug repurposing; Protein targets; SARS-CoV-2; Variants of concern.

Fig. 1

Various SARS-CoV-2 mutants showing the different lineages and the change in amino acids that caused the variation. The amino acid conserved among the coronaviruses is also shown. The different lineages and the mutations are also shown. B.1.1.7 has the mutations H69-V70del, P618H, and N501Y, all indicating alterations in the amino acids. Lineage B.1.351 has mutations K417N, N501Y, and E484K. Likewise, another variant of concern of lineage P.1 has mutations N501Y, K417T, and E484K. B.1.526 lineage is mutated at D614G, T95I, D253G, and S477N. B.1.427 has mutations at L452R and D614G. B.1.429 is mutated at sites D614G, S13I, W152C, and L452R. B.1.617 variant has variations P681R, E484Q, and L452R

Fig. 2

The roadmap to drug repurposing. It begins with the extraction of primary data on the drug, disease, and target. A drug-disease network is eventually constructed and its features of it are extracted to filter the data and pre-process it thoroughly. A computational-based evaluation is carried out via methods detailed in the following sections, including advanced approaches such as machine learning that entails the structural analysis of the drug candidates. The pharmacokinetic studies ensue, which are essential for obtaining an FDA approval for use by the public

Fig. 3

The latest developments and scope of computational medicinal chemistry in the treatment of COVID-19. These include molecular docking and simulations that form receptor-drug complexes. The simulation then leads to the lead design, followed by synthesis of the compound if required, primary in vitro assays, and QSAR studies. Validation of the repurposed drug can be performed via secondary bioassays and toxicity studies, using in vitro, in vivo, and in silico platforms

Fig. 4

The 2D structures of some important repurposed drugs that are FDA approved and which are in clinical phase trials for COVID-19

Fig. 5

Amino acid interactions between repurposed drug candidates and the protein targets of SARS-CoV-2 from our previous studies [105]. The targets shown are spike glycoprotein, nucleocapsid phosphoprotein, and NSP16 (middle row). The repurposed drugs that interacted with it included remdesivir, lopinavir, hydroxychloroquine, favipiravir, ritonavir, and chloroquine (top and bottom rows)