A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design

Curcumin has demonstrated promising preclinical antiobesity effects, but its low bioavailability makes it difficult to exert its full effect at a suitable dose. The objective of this study was to screen curcumin derivatives with enhanced bioavailability and lipid-lowering activity under the guidance of computer-aided drug design (CADD). CAAD was used to perform virtual assays on curcumin derivatives to assess their pharmacokinetic properties and effects on pancreatic lipase activity. Subsequently, 19 curcumin derivatives containing 5 skeletons were synthesized to confirm the above virtual assay. The in vitro pancreatic lipase inhibition assay was employed to determine the half-maximal inhibitory concentration (IC50) of these 19 curcumin derivatives. Based on CADD analysis and in vitro pancreatic lipase inhibition, 2 curcumin derivatives outperformed curcumin in both aspects. Microscale thermophoresis (MST) experiments were employed to assess the binding equilibrium constants (K d) of the aforementioned 2 curcumin derivatives, curcumin, and the positive control drug with pancreatic lipase. Through virtual screening utilizing a chemoinformatics database and molecular docking, 6 derivatives of curcumin demonstrated superior solubility, absorption, and pancreatic lipase inhibitory activity compared to curcumin. The IC50 value for 1,7-bis(4-hydroxyphenyl)heptane-3,5-dione (C4), which displayed the most effective inhibitory effect, was 42.83 μM, while the IC50 value for 1,7-bis(4-hydroxy-3-methoxyphenyl)heptane-3,5-dione (C6) was 98.62 μM. On the other hand, the IC50 value for curcumin was 142.24 μM. The MST experiment results indicated that the K d values of C4, C6, and curcumin were 2.91, 18.20, and 23.53 μM, respectively. The results of the activity assays exhibited a relatively high degree of concordance with the outcomes yielded by CADD screening. Under the guidance of CADD, the targeted screening of curcumin derivatives with excellent properties in this study exhibited high-efficiency and low-cost benefits.

Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer's Disease

AIMS

In this work, we aimed to acquire the best potential small molecule for Alzheimer's disease (AD) treatment using different models in Biovia Discovery Studio to identify new potential inhibitors of acetylcholinesterase (AChE) via in silico studies.

BACKGROUND

The prevalence of cognitive impairment-related neurodegenerative disorders, such as AD, has been observed to escalate rapidly. However, we still know little about the underlying functions, outcome predictors, or intervention targets causing AD.

OBJECTIVES

The objective of the study was to optimize and identify the lead compound to target AChE against Alzheimer's disease.

METHODS

Different in silico studies were employed, including the pharmacophore model, virtual screening, molecular docking, de novo evolution model, and molecular dynamics.

RESULTS

The pharmacophoric features of AChE inhibitors were determined by ligand-based pharmacophore models and 3D QSAR pharmacophore generation. Further validation of the best pharmacophore model was done using the cost analysis method, Fischer's randomization method, and test set. The molecules that harmonized the best pharmacophore model with the estimated activity < 1 nM and ADMET parameters were filtered, and 12 molecules were subjected to molecular docking studies to obtain binding energy. 3vsp_EK8_1 secured the highest binding energy of 65.60 kcal/mol. Further optimization led to a 3v_Evo_4 molecule with a better binding energy of 70.17 kcal/mol. The molecule 3v_evo_4 was subjected to 100 ns molecular simulation compared to donepezil, which showed better stability at the binding site.

CONCLUSION

A lead compound, 3v_Evo_4 molecule, was identified to inhibit AChE, and it could be further studied to develop as a drug with better efficacy than the existing available drugs for treating AD.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Fragment tailoring strategy to design novel chemical entities as potential binders of novel corona virus main protease

The recent pandemic of severe acute respiratory syndrome-coronavirus2 (SARS-CoV-2) infection (COVID-19) has put the world on serious alert. The main protease of SARS-CoV-2 (SARS-CoV-2-MPro) cleaves the long polyprotein chains to release functional proteins required for replication of the virus and thus is a potential drug target to design new chemical entities in order to inhibit the viral replication in human cells. The current study employs state of art computational methods to design novel molecules by linking molecular fragments which specifically bind to different constituent sub-pockets of the SARS-CoV-2-MPro binding site. A huge library of 191678 fragments was screened against the binding cavity of SARS-CoV-2-MPro and high affinity fragments binding to adjacent sub-pockets were tailored to generate new molecules. These newly formed molecules were further subjected to molecular docking, ADMET filters and MM-GBSA binding energy calculations to select 17 best molecules (named as MP-In1 to MP-In17), which showed comparable binding affinities and interactions with the key binding site residues as the reference ligand. The complexes of these 17 molecules and the reference molecule with SARS-CoV-2-MPro, were subjected to molecular dynamics simulations, which assessed the stabilities of their binding with SARS-CoV-2-MPro. Fifteen molecules were found to form stable complexes with SARS-CoV-2-MPro. These novel chemical entities designed specifically according to the pharmacophoric requirements of SARS-CoV-2-MPro binding pockets showed good synthetic feasibility and returned no exact match when searched against chemical databases. Considering their interactions, binding efficiencies and novel chemotypes, they can be further evaluated as potential starting points for SARS-CoV-2 drug discovery.

GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds

The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

Targeting Ebola virus replication through pharmaceutical intervention

Introduction. The consistent emergence/reemergence of filoviruses into a world that previously lacked an approved pharmaceutical intervention parallels an experience repeatedly played-out for most other emerging pathogenic zoonotic viruses. Investment to preemptively develop effective and low-cost prophylactic and therapeutic interventions against viruses that have high potential for emergence and societal impact should be a priority.Areas covered. Candidate drugs can be characterized into those that interfere with cellular processes required for Ebola virus (EBOV) replication (host-directed), and those that directly target virally encoded functions (direct-acting). We discuss strategies to identify pharmaceutical interventions for EBOV infections. PubMed/Web of Science databases were searched to establish a detailed catalog of these interventions.Expert opinion. Many drug candidates show promising in vitro inhibitory activity, but experience with EBOV shows the general lack of translation to in vivo efficacy for host-directed repurposed drugs. Better translation is seen for direct-acting antivirals, in particular monoclonal antibodies. The FDA-approved monoclonal antibody treatment, Inmazeb™ is a success story that could be improved in terms of impact on EBOV-associated disease and mortality, possibly by combination with other direct-acting agents targeting distinct aspects of the viral replication cycle. Costs need to be addressed given EBOV emergence primarily in under-resourced countries.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites

Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.

Integrative role of traditional and modern technologies to combat COVID-19

INTRODUCTION

With the development of various branches of sciences, we will be able to resolve different clinical aspects of various diseases better. The convergence of these sciences can potentially tackle the new corona crisis.

AREAS COVERED

In this review, we attempted to explore and describe various scientific branches studying COVID-19. We have reviewed the literature focusing on the prevention, diagnosis, and treatment of COVID-19. The primary databases targeted were Science Direct, Scopus and PubMed. The most relevant reports from the recent two decades were collected utilizing keywords including SARS-CoV, MERS-CoV, COVID-19, epidemiology, therapeutics and diagnosis.

EXPERT OPINION

Based on this literature review, both traditional and emerging approaches are vital for the prevention, diagnosis and treatment of COVID-19. The traditional sciences play an essential role in the preventive and supportive care of corona infection, and modern technologies appear to be useful in the development of precise diagnosis and powerful treatment approaches for this disease. Indeed, the integration of these sciences will help us to fight COVID-19 disease more efficiently.

Understanding of Zaire ebolavirus-human protein interaction for drug repurposing

The Ebola virus is a human aggressive pathogen causes Ebola virus disease that threatens public health, for which there is no Food Drug Administration approved medication. Drug repurposing is an alternative method to find the novel indications of known drugs to treat the disease effectively at low cost. The present work focused on understanding the host-virus interaction as well as host virus drug interaction to identify the disease pathways and host-directed drug targets. Thus, existing direct physical Ebola-human protein-protein interaction (PPI) was collected from various publicly available databases and also literature through manual curation. Further, the functional and pathway enrichment analysis for the proteins were performed using database for annotation, visualization, and integrated discovery and the enriched gene ontology biological process terms includes chromatin assembly or disassembly, nucleosome organization, nucleosome assembly. Also, the enriched Kyoto Encyclopedia of Genes and Genome pathway terms includes systemic lupus erythematosus, alcoholism, and viral carcinogenesis. From the PPI network, important large histone clusters and tubulin were observed. Further, the host-virus and host-virus-drug interaction network has been generated and found that 182 drugs are associated with 45 host genes. The obtained drugs and their interacting targets could be considered for Ebola treatment.

Computational Drug Design against Ebola Virus Targeting Viral Matrix Protein VP30

Ebola viral disease (EVD) is a deadly infectious hemorrhagic viral fever caused by the Ebola virus with a high mortality rate. Until date, there is no effective drug or vaccination available to combat this condition. This study focuses on designing an effective antiviral drug for Ebola viral disease targeting viral protein 30 (VP30) of Ebola virus, highly required for transcription initiation. The lead molecules were screened for Lipinski rule of five, ADMET study following which molecular docking and bioactivity prediction was carried out. The compounds with the least binding energy were analyzed using interaction software. The results revealed that 6-Hydroxyluteolin and (-)-Arctigenin represent active lead compounds that inhibit the activity of VP30 protein and exhibits efficient pharmacokinetics. Both these compounds are plant-derived flavonoids and possess no known adverse effects on human health. In addition, they bind strongly to the predicted binding site centered on Lys180, suggesting that these two lead molecules can be imperative in designing a potential drug for EVD.

Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing

We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.

Analysis of Flavonoids Bioactivity for Cholestatic Liver Disease: Systematic Literature Search and Experimental Approaches

Flavonoids are naturally occurring compounds that show health benefits on the liver. However, there is little investigation about identification and evaluation of new flavonoid-containing drugs for cholestatic liver disease, one of the most common liver illnesses. We aimed to a systematic search regarding efficacy of flavonoids for treatment of cholestatic liver disease, and then evaluate naringenin (NG) as representative flavonoid in an obstructive cholestasis model. We searched for information of experimental and clinical studies in four major databases without time and language limits. Intervention was defined as any flavonoid derivate compared with other flavonoid, placebo, or without comparator. In addition, we evaluated NG on a bile duct-ligated model in order to contribute evidence of its actions. Eleven experimental reports that support the efficacy of flavonoids in cholestatic liver disease were identified. However, there was no homogeneity in efficacy endpoints evaluated and methodology. On the other hand, NG showed beneficial effects by improving specific metabolic (cholesterol and lipoproteins) and liver damage (bilirubin and alkaline phosphatase) biomarkers. The review lacks homogeneous evidence about efficacy of flavonoids in experimental settings, and is susceptible to risk for bias. NG only showed improvements in specific disease biomarkers. More investigation is still needed to determine its potential for drug development.

Relevance of Ebola virus VP35 homo-dimerization on the type I interferon cascade inhibition

Ebola virus high lethality relies on its ability to efficiently bypass the host innate antiviral response, which senses the viral dsRNA through the RIG-I receptor and induces type I interferon α/β production. In the bypassing action, the Ebola virus protein VP35 plays a pivotal role at multiple levels of the RIG-I cascade, masking the viral 5′-triphosphorylated dsRNA from RIG-I, and interacting with other cascade components. The VP35 type I interferon inhibition is exerted by the C-terminal domain, while the N-terminal domain, containing a coiled-coil region, is primarily required for oligomerization. However, mutations at key VP35 residues L90/93/107A (VP35-3m) in the coiled-coil region were reported to affect oligomerization and reduce type I interferon antagonism, indicating a possible but unclear role of homo-oligomerization on VP35 interaction with the RIG-I pathway components. In this work, we investigated the VP35 dimerization thermodynamics and its contribution to type I interferon antagonism by computational and biological methods. Focusing on the coiled-coil region, we combined coarse-grained and all-atom simulations on wild type VP35 and VP35-3m homo-dimerization. According to our results, wild type VP35 coiled-coil is able to self-assemble into dimers, while VP35-3m coiled-coil shows poor propensity to even dimerize. Free-energy calculations confirmed the key role of L90, L93 and L107 in stabilizing the coiled-coil homo-dimeric structure. In vitro type I interferon antagonism studies, using full-length wild type VP35 and VP35-3m, revealed that VP35 homo-dimerization is an essential preliminary step for dsRNA binding, which appears to be the main factor of the VP35 RIG-I cascade inhibition, while it is not essential to block the other steps.

Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods

Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

Advances in Designing and Developing Vaccines, Drugs, and Therapies to Counter Ebola Virus

Ebola virus (EBOV), a member of the family Filoviridae, is responsible for causing Ebola virus disease (EVD) (formerly named Ebola hemorrhagic fever). This is a severe, often fatal illness with mortality rates varying from 50 to 90% in humans. Although the virus and associated disease has been recognized since 1976, it was only when the recent outbreak of EBOV in 2014-2016 highlighted the danger and global impact of this virus, necessitating the need for coming up with the effective vaccines and drugs to counter its pandemic threat. Albeit no commercial vaccine is available so far against EBOV, a few vaccine candidates are under evaluation and clinical trials to assess their prophylactic efficacy. These include recombinant viral vector (recombinant vesicular stomatitis virus vector, chimpanzee adenovirus type 3-vector, and modified vaccinia Ankara virus), Ebola virus-like particles, virus-like replicon particles, DNA, and plant-based vaccines. Due to improvement in the field of genomics and proteomics, epitope-targeted vaccines have gained top priority. Correspondingly, several therapies have also been developed, including immunoglobulins against specific viral structures small cell-penetrating antibody fragments that target intracellular EBOV proteins. Small interfering RNAs and oligomer-mediated inhibition have also been verified for EVD treatment. Other treatment options include viral entry inhibitors, transfusion of convalescent blood/serum, neutralizing antibodies, and gene expression inhibitors. Repurposed drugs, which have proven safety profiles, can be adapted after high-throughput screening for efficacy and potency for EVD treatment. Herbal and other natural products are also being explored for EVD treatment. Further studies to better understand the pathogenesis and antigenic structures of the virus can help in developing an effective vaccine and identifying appropriate antiviral targets. This review presents the recent advances in designing and developing vaccines, drugs, and therapies to counter the EBOV threat.