A review of computational drug repurposing

Uncertainty quantification in drug design

Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.

Empagliflozin and Doxorubicin Synergistically Inhibit the Survival of Triple-Negative Breast Cancer Cells via Interfering with the mTOR Pathway and Inhibition of Calmodulin: In Vitro and Molecular Docking Studies

Triple-negative breast cancers (TNBCs) comprise 10-15% of all breast cancers but with more resistance affinity against chemotherapeutics. Although doxorubicin (DOX) is the recommended first choice, it has observed cardiotoxicity together with apparent drug resistance. The anti-hyperglycemic drug, empagliflozin (EMP), was recently indicated to have in vitro anticancer potential together with its previously reported cardioprotective properties related to calmodulin inhibition. In this study, we carried out molecular docking studies which revealed the potential blocking of the calmodulin receptor by EMP through its binding with similar crucial amino acids compared to its cocrystallized inhibitor (AAA) as a proposed mechanism of action. Moreover, combination of DOX with EMP showed a slightly lower cytotoxic activity against the MDA-MB-231 cell line (IC₅₀ = 1.700 ± 0.121) compared to DOX alone (IC₅₀ = 1.230 ± 0.131), but it achieved a more characteristic arrest in the growth of cells by 4.67-fold more than DOX alone (with only 3.27-fold) in comparison to the control as determined by cell cycle analysis, and at the same time reached an increase in the total apoptosis percentage from 27.05- to 29.22-fold, compared to DOX alone as indicated by Annexin V-FITC apoptosis assay. Briefly, the aforementioned in vitro studies in addition to PCR of pro- and antiapoptotic genes (mTOR, p21, JNK, Bcl2, and MDR1) suggest the chemosensitization effect of EMP combination with DOX which can reduce the required therapeutic dose of DOX in TNBC and eventually will decrease its toxic side effects (especially cardiotoxicity), along with decreasing the chemoresistance of TNBC cells to DOX treatment.

Utilizing drug repurposing against COVID-19 - Efficacy, limitations, and challenges

The recent outbreak of Coronavirus disease (COVID-19), first in Eastern Asia and then essentially across the world has been declared a pandemic by the WHO. COVID-19 is caused by a novel virus SARS-CoV2 (2019-nCoV), against which there is currently no vaccine available; and current antiviral therapies have failed, causing a very high mortality rate. Drug repurposing i.e. utilizing an approved drug for different indication, offers a time- and cost-efficient alternative for making new therapies available to patients. Although there are several reports presenting novel approaches to treat COVID-19, still an attentive review of previous scientific literature is essential to overcome their failure to exhibit efficacy. There is an urgent need to provide a comprehensive outlook toward utilizing drug repurposing as a tool for discovery of new therapies against COVID-19. In this article, we aim to provide a to-the-point review of current literature regarding efficacy of repurposed drugs against COVID-19 and other respiratory infections caused by coronaviruses. We have briefly discussed COVID-19 epidemiology, and then have discussed drug repurposing approaches and examples, specific to respiratory viruses. Limitations of utilization of repurposed drug molecules such as dosage regimen and associated challenges such as localized delivery in respiratory tract have also been discussed in detail.

Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective, with high returns and low risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained a wider attention, as there have been several old drugs approved by FDA for new diseases. In the global emergency of COVID19 pandemic, this is one of the strategies implemented in repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economical potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.

NSAIDs as a Drug Repurposing Strategy for Biofilm Control

Persistent infections, usually associated with biofilm-producing bacteria, are challenging for both medical and scientific communities. The potential interest in drug repurposing for biofilm control is growing due to both disinvestment in antibiotic R&D and reduced efficacy of the available panel of antibiotics. In the present study, the antibacterial and antibiofilm activities of four non-steroidal anti-inflammatory drugs (NSAIDs), piroxicam (PXC), diclofenac sodium (DCF), acetylsalicylic acid (ASA) and naproxen sodium (NPX) were evaluated against Escherichia coli and Staphylococcus aureus. The minimum inhibitory/bactericidal concentrations (MICs and MBCs) and the dose–response curves from exposure to the selected NSAIDs were determined. MICs were found for PXC (800 μg/mL) and ASA (1750 μg/mL) against E. coli, and for DCF (2000 μg/mL) and ASA (2000 μg/mL) against S. aureus. No MBCs were found (>2000 μg/mL). The potential of NSAIDs to eradicate preformed biofilms was characterized in terms of biofilm mass, metabolic activity and cell culturability. Additionally, the NSAIDs were tested in combination with kanamycin (KAN) and tetracycline (TET). ASA, DCF and PXC promoted significant reductions in metabolic activity and culturability. However, only PXC promoted biofilm mass removal. Additive interactions were obtained for most of the combinations between NSAIDs and KAN or TET. In general, NSAIDs appear to be a promising strategy to control biofilms as they demonstrated to be more effective than conventional antibiotics.

Repurposing of existing FDA approved drugs for Neprilysin inhibition: An in-silico study

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Drug repurposing of FDA approved drugs from ZINC 12 database was done using the crystal structure of extracellular domain of human NEP (PDB ID: 5JMY)

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The interactions with catalytic triad of HIS583, HIS587 and GLU646 are important for NEP inhibition.

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Based on XP molecular docking, binding energy, IFD-SP and MD simulation top 4 NEP inhibitors were identified.

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ZINC000000601283 and ZINC000003831594 were found to be stable during MD simulation and may act as NEP inhibitors.

Neprilysin (NEP) is a neutral endopeptidase with diverse physiological roles in the body. NEP's role in degradation of diverse classes of peptides such as amyloid beta, natriuretic peptide, substance P, angiotensin, endothelins, etc., is associated with pathologies of alzheimer's, kidney and heart diseases, obesity, diabetes and certain malignancies. Hence, the functional inhibition of NEP in the above systems can be a good therapeutic target. In the present study, in-silico drug repurposing approach was used to identify NEP inhibitors. Molecular docking was carried out using GLIDE tool. 2934 drugs from the ZINC12 database were screened using high throughput virtual screening (HTVS) followed by standard precision (SP) and extra precision (XP) docking. Based on the XP docking score and ligand interaction, the top 8 hits were subjected to free ligand binding energy calculation, to filter out 4 hits (ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594). Further, induced fit docking-standard precision (IFD-SP) and molecular dynamics (MD) studies were performed. The results obtained from MD studies suggest that ZINC000000601283-NEP and ZINC000003831594-NEP complexes were most stable for 20ns simulation period as compared to ZINC000001533877-NEP and ZINC000000001427-NEP complexes. Interestingly, ZINC000000601283 and ZINC000003831594 showed similarity in binding with the reported NEP inhibitor sacubitrilat. Findings from this study suggest that ZINC000000601283 and ZINC000003831594 may act as NEP inhibitors. In future studies, the role of ZINC000000601283 and ZINC000003831594 in NEP inhibition should be tested in biological systems to evaluate therapeutic effect in NEP associated pathological conditions.

Can artificial intelligence identify effective COVID-19 therapies?

In this issue of EMBO Molecular Medicine, Stebbing et al (2020b) validate an artificial intelligence-assisted prediction that a drug used to treat rheumatoid arthritis could be a potent weapon against COVID-19. Using liver organoids infected with SARS-CoV-2, they confirm dual antiviral and anti-inflammatory activities and show that its administration in four COVID-19 patients is correlated with disease improvement, paving the way for more rigorous placebo-controlled trials.

A medicinal chemistry perspective of drug repositioning: Recent advances and challenges in drug discovery

Drug repurposing is a strategy consisting of finding new indications for already known marketed drugs used in various clinical settings or highly characterized compounds despite they can be failed drugs. Recently, it emerges as an alternative approach for the rapid identification and development of new pharmaceuticals for various rare and complex diseases for which lack the effective drug treatments. The success rate of drugs repurposing approach accounts for approximately 30% of new FDA approved drugs and vaccines in recent years. This review focuses on the status of drugs repurposing approach for various diseases including skin diseases, infective, inflammatory, cancer, and neurodegenerative diseases. Efforts have been made to provide structural features and mode of actions of drugs.

An investigation into the identification of potential inhibitors of SARS-CoV-2 main protease using molecular docking study

A new strain of a novel infectious disease affecting millions of people, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has recently been declared as a pandemic by the World Health Organization (WHO). Currently, several clinical trials are underway to identify specific drugs for the treatment of this novel virus. The inhibition of the SARS-CoV-2 main protease is necessary for the blockage of the viral replication. Here, in this study, we have utilized a blind molecular docking approach to identify the possible inhibitors of the SARS-CoV-2 main protease, by screening a total of 33 molecules which includes natural products, anti-virals, anti-fungals, anti-nematodes and anti-protozoals. All the studied molecules could bind to the active site of the SARS-CoV-2 protease (PDB: 6Y84), out of which rutin (a natural compound) has the highest inhibitor efficiency among the 33 molecules studied, followed by ritonavir (control drug), emetine (anti-protozoal), hesperidin (a natural compound), lopinavir (control drug) and indinavir (anti-viral drug). All the molecules, studied out here could bind near the crucial catalytic residues, HIS41 and CYS145 of the main protease, and the molecules were surrounded by other active site residues like MET49, GLY143, HIS163, HIS164, GLU166, PRO168, and GLN189. As this study is based on molecular docking, hence being particular about the results obtained, requires extensive wet-lab experimentation and clinical trials under in vitro as well as in vivo conditions.

Communicated by Ramaswamy H. Sarma

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.