Bioorg Chem. 2021 Mar 29;111:104862. doi: 10.1016/j.bioorg.2021.104862. Online ahead of print.

ABSTRACT

For the COVID-19 pandemic caused by SARS-CoV-2, there are currently no effective drugs or vaccines to treat this coronavirus infection. In this study, we focus on the main protease enzyme of SARS-CoV-2, 3CLpro, which is critical for viral replication. We employ explicit solvent molecular dynamics simulations of about 150 compounds docked into 3CLpro's binding site and that had emerged as good main protease ligands from our previous in silico screening of over 1.2 million compounds. By incoporating protein dynamics and applying a range of structural descriptors, such as the ability to form specific contacts with the catalytic dyad residues of 3CLpro and the structural fluctuations of the ligands in the binding site, we are able to further refine our compound selection. Fourteen compounds including estradiol shown to be the most promising based on our calculations were procured and screened against recombinant 3CLpro in a fluorescence assay. Eight of these compounds have significant activity in inhibiting the SARS-CoV-2 main protease. Among these are corilagin, a gallotannin, and lurasidone, an antipsychotic drug, which emerged as the most promising natural product and drug, respectively, and might thus be candidates for drug repurposing for the treatment of COVID-19. In addition, we also tested the inhibitory activity of testosterone, and our results reveal testosterone as possessing moderate inhibitory potency against the 3CLpro enzyme, which may thus provide an explanation why older men are more severely affected by COVID-19.

PMID:33862474 | DOI:10.1016/j.bioorg.2021.104862

Comput Biol Med. 2021 Apr 2;133:104363. doi: 10.1016/j.compbiomed.2021.104363. Online ahead of print.

ABSTRACT

The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 2,000,000 deaths worldwide. Currently, vaccine development and drug repurposing have been the main strategies to find a COVID-19 treatment. However, the development of new drugs could be the solution if the main strategies fail. Here, a virtual screening of pentapeptides was applied in order to identify peptides with high affinity to SARS-CoV-2 main protease (Mpro). Over 70,000 peptides were screened employing a genetic algorithm that uses a docking score as the fitness function. The algorithm was coupled with a RESTful API to persist data and avoid redundancy. The docking exhaustiveness was adapted to the number of peptides in each virtual screening step, where the higher the number of peptides, the lower the docking exhaustiveness. Two potential peptides were selected (HHYWH and HYWWT), which have higher affinity to Mpro than to human proteases. Albeit preliminary, the data presented here provide some basis for the rational design of peptide-based drugs to treat COVID-19.

PMID:33862305 | DOI:10.1016/j.compbiomed.2021.104363

J Chem Inf Model. 2021 Apr 16. doi: 10.1021/acs.jcim.1c00279. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) is the most common and lethal brain tumor. GBM has a remarkable degree of motility and is able to infiltrate the healthy brain. In order to perform a rationale-based drug-repositioning study, we have used known inhibitors of two small Rho GTPases, Rac1 and Cdc42, which are upregulated in GBM and are involved in the signaling processes underlying the orchestration of the cytoskeleton and cellular motility. The selected inhibitors (R-ketorolac and ML141 for Cdc42 and R-ketorolac and EHT 1864 for Rac1) have been successfully employed to reduce the infiltration propensity of GBM in live cell imaging studies. Complementarily, all-atom simulations have elucidated the molecular basis of their inhibition mechanism, identifying the binding sites targeted by the inhibitors and dissecting their impact on the small Rho GTPases' function. Our results demonstrate the potential of targeting the Rac1 and Cdc42 proteins with small molecules to contrast GBM infiltration growth and supply precious information for future drug discovery studies aiming to fight GBM and other infiltrative cancer types.

PMID:33861592 | DOI:10.1021/acs.jcim.1c00279

J Biomol Struct Dyn. 2021 Apr 16:1-10. doi: 10.1080/07391102.2021.1913230. Online ahead of print.

ABSTRACT

Tuberculosis (TB) is one of the prominent cause of deaths across the world and multidrug-resistant and extensively drug-resistant TB continues to pose challenges for clinicians and public health centers. The risk of death is extremely high in individuals who have compromised immune systems, HIV infection, or diabetes. Research institutes and pharmaceutical companies have been working on repurposing existing drugs as effective therapeutic options against TB. The identification of suitable drugs with multi-target affinity profiles is a widely accepted way to combat the development of resistance. Flavin-dependent thymidylate synthase (FDTS), known as ThyX, is in the class of methyltransferases and is a possible target in the discovery of novel anti-TB drugs. In this study, we aimed to repurpose existing drugs approved by Food and Drug Administration (FDA) that could be used in the treatment of TB. An integrated screening was performed based on computational procedures: high-throughput molecular docking techniques, followed by molecular dynamics simulations of the target enzyme, ThyX. After performing in silico screening using a library of 3,967 FDA-approved drugs, the two highest-scoring drugs, Carglumic acid and Mesalazine, were selected as potential candidates that could be repurposed to treat TB.Communicated by Ramaswamy H. Sarma.

PMID:33860725 | DOI:10.1080/07391102.2021.1913230

Phytother Res. 2021 Apr 15. doi: 10.1002/ptr.7087. Online ahead of print.

ABSTRACT

Since the AKT/mammalian target of rapamycin (mTOR)/c-Myc signaling plays a pivotal role in the modulation of aerobic glycolysis and tumor growth, in the present study, the role of AKT/mTOR/c-Myc signaling in the apoptotic effect of Compound K (CK), an active ginseng saponin metabolite, was explored in HepG2 and Huh7 human hepatocellular carcinoma cells (HCCs). Here, CK exerted significant cytotoxicity, increased sub-G1, and attenuated the expression of pro-Poly (ADP-ribose) polymerase (pro-PARP) and Pro-cysteine aspartyl-specific protease (pro-caspase3) in HepG2 and Huh7 cells. Consistently, CK suppressed AKT/mTOR/c-Myc and their downstreams such as Hexokinase 2 (HK2) and pyruvate kinase isozymes M2 (PKM2) in HepG2 and Huh7 cells. Additionally, CK reduced c-Myc stability in the presence or absence of cycloheximide in HepG2 cells. Furthermore, AKT inhibitor LY294002 blocked the expression of p-AKT, c-Myc, HK2, PKM2, and pro-cas3 in HepG2 cells. Pyruvate blocked the ability of CK to inhibit p-AKT, p-mTOR, HK2, and pro-Cas3 in treated HepG2 cells. Overall, these findings provide evidence that CK induces apoptosis via inhibition of glycolysis and AKT/mTOR/c-Myc signaling in HCC cells as a potent anticancer candidate for liver cancer clinical translation.

PMID:33856720 | DOI:10.1002/ptr.7087

Sci Rep. 2021 Apr 14;11(1):8091. doi: 10.1038/s41598-021-87525-z.

ABSTRACT

Drug repositioning refers to new uses for existing drugs outside the scope of the original medical indications. This approach fastens the process of drug development allowing finding effective drugs with reduced side effects and lower costs. Colorectal cancer (CRC) is often diagnosed at advanced stages, when the probability of chemotherapy resistance is higher. Triple negative breast cancer (TNBC) is the most aggressive type of breast cancer, highly metastatic and difficult to treat. For both tumor types, available treatments are generally associated to severe side effects. In our work, we explored the effect of combining metformin and propranolol, two repositioned drugs, in both tumor types. We demonstrate that treatment affects viability, epithelial-mesenchymal transition and migratory potential of CRC cells as we described before for TNBC. We show that combined treatment affects different steps leading to metastasis in TNBC. Moreover, combined treatment is also effective preventing the development of 5-FU resistant CRC. Our data suggest that combination of metformin and propranolol could be useful as a putative adjuvant treatment for both TNBC and CRC and an alternative for chemo-resistant CRC, providing a low-cost alternative therapy without associated toxicity.

PMID:33854147 | DOI:10.1038/s41598-021-87525-z

J Orthop Surg Res. 2021 Apr 14;16(1):259. doi: 10.1186/s13018-021-02402-9.

ABSTRACT

BACKGROUND: Osteoarthritis (OA) is a worldwide musculoskeletal disorder. However, disease-modifying therapies for OA are not available. Here, we aimed to characterize the molecular signatures of OA and to identify novel therapeutic targets and strategies to improve the treatment of OA.

METHODS: We collected genome-wide transcriptome data performed on 132 OA and 74 normal human cartilage or synovium tissues from 7 independent datasets. Differential gene expression analysis and functional enrichment were performed to identify genes and pathways that were dysregulated in OA. The computational drug repurposing method was used to uncover drugs that could be repurposed to treat OA.

RESULTS: We identified several pathways associated with the development of OA, such as extracellular matrix organization, inflammation, bone development, and ossification. By protein-protein interaction (PPI) network analysis, we prioritized several hub genes, such as JUN, CDKN1A, VEGFA, and FOXO3. Moreover, we repurposed several FDA-approved drugs, such as cardiac glycosides, that could be used in the treatment of OA.

CONCLUSIONS: We proposed that the hub genes we identified would play a role in cartilage homeostasis and could be important diagnostic and therapeutic targets. Drugs such as cardiac glycosides provided new possibilities for the treatment of OA.

PMID:33853636 | DOI:10.1186/s13018-021-02402-9

Parasite. 2021;28:38. doi: 10.1051/parasite/2021036. Epub 2021 Apr 14.

ABSTRACT

Current treatments of visceral leishmaniasis face limitations due to drug side effects and/or high cost, along with the emergence of parasite resistance. Novel and low-cost antileishmanial agents are therefore required. We report herein the antileishmanial activity of β-acetyl-digitoxin (b-AD), a cardenolide isolated from Digitalis lanata leaves, assayed in vitro and in vivo against Leishmania infantum. Results showed direct action of b-AD against parasites, as well as efficacy for the treatment of Leishmania-infected macrophages. In vivo experiments using b-AD-containing Pluronic® F127 polymeric micelles (b-AD/Mic) to treat L. infantum-infected mice showed that this composition reduced the parasite load in distinct organs in more significant levels. It also induced the development of anti-parasite Th1-type immunity, attested by high levels of IFN-γ, IL-12, TNF-α, GM-CSF, nitrite and specific IgG2a antibodies, in addition to low IL-4 and IL-10 contents, along with higher IFN-γ-producing CD4+ and CD8+ T-cell frequency. Furthermore, low toxicity was found in the organs of the treated animals. Comparing the therapeutic effect between the treatments, b-AD/Mic was the most effective in protecting animals against infection, when compared to the other groups including miltefosine used as a drug control. Data found 15 days after treatment were similar to those obtained one day post-therapy. In conclusion, the results obtained suggest that b-AD/Mic is a promising antileishmanial agent and deserves further studies to investigate its potential to treat visceral leishmaniasis.

PMID:33851916 | DOI:10.1051/parasite/2021036

medRxiv. 2021 Apr 6:2021.03.22.21254110. doi: 10.1101/2021.03.22.21254110. Preprint.

ABSTRACT

BACKGROUND: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects.

METHODS: Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect.

RESULTS: We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients.

CONCLUSIONS: Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.

PMID:33851170 | PMC:PMC8043466 | DOI:10.1101/2021.03.22.21254110

Exp Ther Med. 2021 Jun;21(6):566. doi: 10.3892/etm.2021.9998. Epub 2021 Mar 26.

ABSTRACT

Osteosarcoma is the most prevalent primary bone malignancy. Due to its high aggressiveness, novel treatment strategies are urgently required to improve survival of patients with osteosarcoma, especially those with advanced disease. Desmopressin (dDAVP) is a widely used blood-saving agent that has been repurposed as an adjuvant agent for cancer management due to its antiangiogenic and antimetastatic properties. dDAVP acts as a selective agonist of the vasopressin membrane receptor type 2 (AVPR2) present in the microvascular endothelium and in some cancer cells, including breast, lung, colorectal and neuroendocrine tumor cells. Despite the fact that dDAVP has demonstrated its antitumor efficacy in a wide variety of tumor types, exploration of its potential anti-osteosarcoma activity has, to the best of our knowledge, not yet been conducted. Therefore, the aim of the present study was to evaluate the preclinical antitumor activity of dDAVP in osteosarcoma. Human MG-63 and U-2 OS osteosarcoma cell lines were used to assess in vitro and in vivo therapeutic effects of dDAVP. At low micromolar concentrations, dDAVP reduced AVPR2-expressing MG-63 cell growth in a concentration-dependent manner. In contrast, dDAVP exhibited no direct cytostatic effect on AVPR2-negative U-2 OS cells. As it would be expected for canonical AVPR2-activation, dDAVP raised intracellular cAMP levels in osteosarcoma cells, and coincubation with phosphodiesterase-inhibitor rolipram indicated synergistic antiproliferative activity. Cytostatic effects were associated with increased apoptosis, reduced mitotic index and impairment of osteosarcoma cell chemotaxis, as evaluated by TUNEL-labeling, mitotic body count in DAPI-stained cultures and Transwell migration assays. Intravenous administration of dDAVP (12 µg/kg; three times per week) to athymic mice bearing rapidly growing MG-63 xenografts, was indicated to be capable of reducing tumor progression after a 4-week treatment. No major alterations in animal weight, biochemical or hematological parameters were associated with dDAVP treatment, confirming its good tolerability and safety. Finally, AVPR2 expression was detected by immunohistochemistry in 66% of all evaluated chemotherapy-naive human conventional osteosarcoma biopsies. Taking these findings into account, repurposed agent dDAVP may represent an interesting therapeutic tool for the management of osteosarcoma. Further preclinical exploration of dDAVP activity on orthotopic or metastatic osteosarcoma models are required.

PMID:33850538 | PMC:PMC8027742 | DOI:10.3892/etm.2021.9998

Pharmacology. 2021 Apr 13:1-9. doi: 10.1159/000514843. Online ahead of print.

ABSTRACT

INTRODUCTION: The presence of mercury in the environment is a worldwide concern. Inorganic mercury is present in industrial materials, is employed in medical devices, is widely used in batteries, is a component of fluorescent light bulbs, and it has been associated with human poisoning in gold mining areas. The nephrotoxicity induced by inorganic mercury is a relevant health problem mainly in developing countries. The primary mechanism of mercury toxicity is oxidative stress. Trimetazidine (TMZ) is an anti-ischemic drug, which inhibits cellular oxidative stress, eliminates oxygen-free radicals, and improves lipid metabolism. The aim of this study was to evaluate whether the administration of TMZ protects against mercuric chloride (HgCl2) kidney damage.

METHODS: Adult male Wistar rats received only HgCl2 (4 mg/kg bw, sc) (Hg group, n = 5) or TMZ (3 mg/kg bw, ip) 30 min before HgCl2 administration (4 mg/kg bw, sc) (TMZHg group, n = 7). Simultaneously, a control group of rats (n = 4) was studied. After 4 days of HgCl2 injection, urinary flow, urea and creatinine (Cr) plasma levels, Cr clearance, urinary glucose, and sodium-dicarboxylate cotransporter 1 (NaDC1) in urine were determined. Lipid peroxidation (MDA) and glutathione (GSH) levels were measured in kidney homogenates.

RESULTS: Rats only treated with HgCl2 showed an increase in urea and Cr plasma levels, urinary flow, fractional excretion of water, glucosuria, and NaDC1 urinary excretion as compared with the control group and a decrease in Cr clearance. TMZHg group showed a decrease in urea and Cr plasma levels, urinary flow, fractional excretion of water, glucosuria, NaDC1 urinary excretion, and an increase in Cr clearance when compared to the Hg group. Moreover, MDA and GSH levels observed in Hg groups were decreased and increased, respectively, by TMZ pretreatment.

CONCLUSION: TMZ exerted a renoprotective action against HgCl2-induced renal injury, which might be mediated by the reduction of oxidative stress. Considering the absence of toxicity of TMZ, its clinical application against oxidative damage due to HgCl2-induced renal injury should be considered. The fact that TMZ is commercially available should simplify and accelerate the translation of the present data "from bench to bedside." In this context, TMZ become an interesting new example of drug repurposing.

PMID:33849026 | DOI:10.1159/000514843

J Biomol Struct Dyn. 2021 Apr 13:1-16. doi: 10.1080/07391102.2021.1910571. Online ahead of print.

ABSTRACT

Coronavirus disease-2019 (COVID-19) was firstly reported in Wuhan, China, towards the end of 2019, and emerged as a pandemic. The spread and lethality rates of the COVID-19 have ignited studies that focus on the development of therapeutics for either treatment or prophylaxis purposes. In parallel, drug repurposing studies have also come into prominence. Herein, we aimed at having a holistic understanding of conformational and dynamical changes induced by an experimentally characterized inhibitor on main protease (Mpro) which would enable the discovery of novel inhibitors. To this end, we performed molecular dynamics simulations using crystal structures of apo and α-ketoamide 13b-bound Mpro homodimer. Analysis of trajectories pertaining to apo Mpro revealed a new target site, which is located at the homodimer interface, next to the catalytic dyad. Thereafter, we performed ensemble-based virtual screening by exploiting the ZINC and DrugBank databases and identified three candidate molecules, namely eluxadoline, diosmin, and ZINC02948810 that could invoke local and global conformational rearrangements which were also elicited by α-ketoamide 13b on the catalytic dyad of Mpro. Furthermore, ZINC23881687 stably interacted with catalytically important residues Glu166 and Ser1 and the target site throughout the simulation. However, it gave positive binding energy, presumably, due to displaying higher flexibility that might dominate the entropic term, which is not included in the MM-PBSA method. Finally, ZINC20425029, whose mode of action was different, modulated dynamical properties of catalytically important residue, Ala285. As such, this study presents valuable findings that might be used in the development of novel therapeutics against Mpro.Communicated by Ramaswamy H. Sarma.

PMID:33847241 | DOI:10.1080/07391102.2021.1910571

Cancer Discov. 2021 Apr 12:candisc.0144.2021. doi: 10.1158/2159-8290.CD-21-0144. Online ahead of print.

ABSTRACT

In 2020, the COVID-19 pandemic led to an unprecedented destabilization of the world's health and economic systems. The rapid spread and life-threatening consequences of COVID-19 have imposed a repurposing drug testing, by investigating drugs already used in other indications, including anti-cancer drugs. Anti-cancer drug repurposing started to contour after deciphering similarities between COVID-19 and malignancies pathogenesis, including abnormal inflammatory and immunological responses. In this review we will discuss the salient positive and negative points of repurposing the main anticancer drugs to further treat COVID-19.

PMID:33846172 | DOI:10.1158/2159-8290.CD-21-0144

Parasit Vectors. 2021 Apr 12;14(1):197. doi: 10.1186/s13071-021-04691-9.

ABSTRACT

BACKGROUND: Cystic echinococcosis (CE) is a disease caused by the larval stage of Echinococcus granulosus sensu lato (s.l.). The treatment of CE mainly relies on the use of benzimidazoles, which can commonly cause adverse side effects. Therefore, more efficient treatment options are needed. Drug repurposing is a useful approach for advancing drug development. We have evaluated the in vitro protoscolicidal effects of tropisetron and granisetron in E. granulosus sensu stricto (s.s.) and assessed the expression of the calcineurin (CaN) and calmodulin (CaM) genes, both of which have been linked to cellular signaling activities and thus are potentially promising targets for the development of drugs.

METHODS: Protoscoleces (PSC) of E. granulosus (s.s.) (genotype G1) obtained from sheep hepatic hydatid cysts were exposed to tropisetron and granisetron at concentrations of 50, 150 and 250 µM for various periods of time up to 10 days. Cyclosporine A (CsA) and albendazole sulfoxide were used for comparison. Changes in the morphology of PSC were investigated by light microscopy and scanning electron microscopy. Gene expression was assessed using real-time PCR at the mRNA level for E. granulosus calcineurin subunit A (Eg-CaN-A), calcineurin subunit B (Eg-CaN-B) and calmodulin (Eg-CaM) after a 24-h exposure at 50 and 250 µM, respectively.

RESULTS: At 150 and 250 µM, tropisetron had the highest protoscolicidal effect, whereas CsA was most effective at 50 µM. Granisetron, however, was less effective than tropisetron at all three concentrations. Examination of morphological alterations revealed that the rate at which PSC were killed increased with increasing rate of PSC evagination, as observed in PSC exposed to tropisetron. Gene expression analysis revealed that tropisetron at 50 μM significantly upregulated Eg-CaN-B and Eg-CaM expression while at 250 μM it significantly downregulated both Eg-CaN-B and Eg-CaM expressions; in comparison, granisetron decreased the expression of all three genes at both concentrations.

CONCLUSIONS: Tropisetron exhibited a higher efficacy than granisetron against E. granulosus (s.s.) PSC, which is probably due to the different mechanisms of action of the two drugs. The concentration-dependent effect of tropisetron on calcineurin gene expression might reflect its dual functions, which should stimulate future research into its mechanism of action and evaluation of its potential therapeutical effect in the treatment of CE.

PMID:33845889 | DOI:10.1186/s13071-021-04691-9

Comput Biol Med. 2021 Mar 30;133:104359. doi: 10.1016/j.compbiomed.2021.104359. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.

PMID:33845270 | DOI:10.1016/j.compbiomed.2021.104359

Expert Rev Anticancer Ther. 2021 Apr 12:1-13. doi: 10.1080/14737140.2021.1910027. Online ahead of print.

ABSTRACT

Introduction: As a protein-based biomaterial for potential cancer targeting delivery, apoferritin has recently attracted interest.Areas covered: In this review, we discuss the development of this cage-like protein as an endogenous nanocarrier that can hold molecules in its cavity. We present the specific characterizations and formulations of apoferritin nanocarriers, and outline the recent progress of the protein as an appropriate tumor-delivery vehicle in different therapeutic strategies to treat solid tumors. Finally, we propose how the application for cancer drug repurposing delivery within apoferritin could expand cancer treatment in the future.Expert opinion: Being a ubiquitous iron storage protein that exists in many living organisms, apoferritin is promising as a cancer tumor-targeting nanocarrier. By exploiting its versatility, apoferritin could be used for cancer repurposed drug delivery and could reduce the high cost of new drug discovery development and shorten the formulation process.

PMID:33844625 | DOI:10.1080/14737140.2021.1910027

Mol Divers. 2021 Apr 12. doi: 10.1007/s11030-021-10217-3. Online ahead of print.

ABSTRACT

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

PMID:33844136 | DOI:10.1007/s11030-021-10217-3

Expert Opin Drug Discov. 2021 Apr 12:1-13. doi: 10.1080/17460441.2021.1910673. Online ahead of print.

ABSTRACT

INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation.

AREAS COVERED: In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies.

EXPERT OPINION: Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

PMID:33843398 | DOI:10.1080/17460441.2021.1910673

Front Chem. 2021 Mar 24;9:628398. doi: 10.3389/fchem.2021.628398. eCollection 2021.

ABSTRACT

Although potential anticancer activities of benzimidazole-based anthelmintic drugs have been approved by preclinical and clinical studies, modes of binding interactions have not been reported so far. Therefore, in this study, we aimed to propose binding interactions of some benzimidazole-based anthelmintics with one of the most important cancer targets (Tubulin protein). Studied drugs were selected based on their structural similarity with the cocrystallized ligand (Nocodazole) with tubulin protein. Quantum mechanics calculations were also employed for characterization of electronic configuration of studied drugs at the atomic and molecular level. Order of binding affinities of tested benzimidazole drugs toward colchicine binding site on tubulin protein is as follows: Flubendazole > Oxfendazole > Nocodazole > Mebendazole > Albendazole > Oxibendazole > Fenbendazole > Ciclobendazole > Thiabendazole > Bendazole. By analyzing binding mode and hydrogen bond length between the nine studied benzimidazole drugs and colchicine binding site, Flubendazole was found to bind more efficiently with tubulin protein than other benzimidazole derivatives. The quantum mechanics studies showed that the electron density of HOMO of Flubendazole and Mebendazole together with their MEP map are quite similar to that of Nocodazole which is also consistent with the calculated binding affinities. Our study has ramifications for considering the repurposing of Flubendazole as a promising anticancer candidate.

PMID:33842429 | PMC:PMC8024586 | DOI:10.3389/fchem.2021.628398

In Silico Pharmacol. 2021 Apr 4;9(1):27. doi: 10.1007/s40203-021-00089-8. eCollection 2021.

ABSTRACT

Coronavirus spread is an emergency reported globally, and a specific treatment strategy for this significant health issue is not yet identified. COVID-19 is a highly contagious disease and needs to be controlled promptly as millions of deaths have been reported. Due to the absence of proficient restorative alternatives and preliminary clinical restrictions, FDA-approved medications can be a decent alternative to deal with the coronavirus malady (COVID-19). The present study aims to meet the imperative necessity of effective COVID-19 drug treatment with a computational multi-target drug repurposing approach. This study focused on screening the FDA-approved drugs derived from the fungal source and its derivatives against the SARS-CoV-2 targets. All the selected drugs showed good binding affinity towards these targets, and out of them, bromocriptine was found to be the best candidate after the screening on the COVID-19 targets. Further, bromocriptine is analyzed by molecular simulation and MM-PBSA study. These studies suggested that bromocriptine can be the best candidate for TMPRSS2, Main protease, and RdRp protein.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-021-00089-8.

PMID:33842191 | PMC:PMC8019482 | DOI:10.1007/s40203-021-00089-8