Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises

Abstract

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Keywords: COVID-19; Cheminformatics; Clinical trials; Drug repurposing; Machine learning; Systems biology.

Graphical abstract

Fig. 1

An outline for COVID-19 investigational drug analysis summarising key analytical steps and outcomes.

Fig. 2

Summary plots for COVID-19 clinical trials data (n = 230; November 2021). Information was mined from the Open Targets COVID-19 database where duplicate entries for the same chemical entity were removed when preparing summary plots. A – a clinical phase distribution plot for COVID-19 clinical trials; B - a bar plot for the status of COVID-19 clinical trials; C – a pie chart for drug activity types; D – a pie chart for drug molecule types.

Fig. 3

A heatmap showing the size of shared gene networks for drugs (n = 230) that were used to treat or investigated for the treatment of COVID-19. Every drug used for the treatment had a main target and an extended network that consisted of protein-protein interactions or associations which were mined based on experimental, text mining, and analysis-based evidence (STRING database; threshold = 700). Each drug and its associated interactor network were cross-referenced against other drugs to establish a shared network, i.e., overlapping drug sets. This information is shown via the heatmap where diagonal entries represent the network size for a selected drug. Clusters selected for the downstream analysis are highlighted in green squares with the cluster IDs next to them. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

COVID-19 drug network enrichment for drug interactor cluster 5 (29 unique main targets, 23 drugs). A - a network plot showing genes for the top five enriched groups. B - a dot plot depicting specific functional enrichment. The size of the dots indicate the size of the cluster for the particular functional group with the probability provided through p.adj. Values.

Fig. 5

Pathway annotations for genes in cluster 5 (29 unique main targets, 23 drugs). Genes from cluster 5 (x-axis) were analysed based on their associations with specific pathways (y-axis) and the different memberships were visualised using a heatmap. Pathway data was mapped based on the Reactome database.

Fig. 6

Compound property distributions for COVID-19 drugs (n = 158) where density plots and linear regression plots are also provided with pairwise scatter plots. Abbreviations: AP - Aromatic proportion = number of aromatic atoms / number of heavy atoms; MW – molecular weight, TPSA - topological polar surface area; MolLogP – log of a partition coefficient for a molecule.

Fig. 7

A compound similarity heatmap for COVID-19 drugs (n = 158) where the legend provides information on the clusters identified through the gene network analysis for COVID-19 drugs. Similarity was assessed using the Tanimoto similarity method and compound fingerprints were calculated as Morgan fingerprints (nbits = 2048, radius = 2).