key: cord-0968144-zo1465i3
authors: Mirza, Muhammad Usman; Vanmeert, Michiel; Ali, Amjad; Iman, Kanzal; Froeyen, Matheus; Idrees, Muhammad
title: Perspectives towards antiviral drug discovery against Ebola virus
date: 2019-09-30
journal: J Med Virol
DOI: 10.1002/jmv.25357
sha: 8918879fa25a9d2caa472764dd3ae609deddd645
doc_id: 968144
cord_uid: zo1465i3

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

EVD outbreaks, of which the first on 8 May (May-June 2018

Equateur province DRC outbreak) and the second on 1 August (August-present, 2018, Kivu province DRC outbreak). The first outbreak resulted in 54 EVD cases (38 confirmed and 16 probable) and 33 deaths (overall case-fatality ratio of 61%) as of 24 July, 2018 (declared the end of this outbreak), 29 while the latter is ongoing, which includes 238 cases (203 confirmed and 35 probable) and 155 deaths resulting in a case-fatality rate of 65.1% (as of 21 October, 2018) . 30 Preclinical efforts toward specific EBOV countermeasures have been enduring for years with first treatment and vaccine clinical trials conducted during the 2013 to 2016 EBOV outbreak in West-Africa. [31] [32] [33] [34] [35] [36] [37] During the DRC outbreaks, the countermeasures were evaluated in West-Africa EVD outbreak and demonstrated their clinical efficacy. In addition, a small number of studies (case reports) exists for the experi countermeasures has been enduring mental use of anti-Ebola antiviral and vaccine leads. [38] [39] [40] [41] EBOV vaccine development has already been covered in previously published reviews. 34, [42] [43] [44] [45] In brief, the recent update includes the development of replication-competent rVSV-ZEBOV (rVSV) used in May-June 2018 Equateur DRC outbreak in a ring vaccination trial (Phase III; NCT03161366), which proved to be well tolerated with improved efficacy and safety. 35, 36 Currently, rVSV is being investigated along with other investigational therapeutics in the ongoing Kivu DRC outbreak (Phase II; NCT03719586). A second update includes Ad26-ZEBOV/MVA-BN-Filo prime-boost vaccine evidenced as safe and well tolerated. 46, 47 Both rVSV and Ad26-ZEBOV/MVA-BN-Filo prime-boost vaccine have been evaluated in Phases I, II, and III clinical trials. Other nonreplicating vaccines are virus-vector-based evidenced to be highly effective in non-human primates (NHPs). [48] [49] [50] [51] [52] [53] The current study is focused on the therapeutic advancement towards EVD and the challenges encountered.

Identification of suitable biological drug targets is the primary step towards therapeutic development. 54, 55 Validated through a combination of genetic, 56 biochemical, 57, 58 structural, and computational strategies, 54 ,55 a variety of drug targets have been identified in both host and pathogen. 59 Encased in a lipid envelope, filoviruses are filamentous in shape. 60 Linear, nonsegmented, negative-sense single-stranded RNA encodes a 19-kb genome containing the genetic information for seven structural proteins and considered as potential drug targets, namely transcription activator VP30, polymerase cofactor VP35, matrix proteins VP40 and VP24, nucleoprotein (NP), glycoprotein (GP), and RNA-dependent RNA polymerase (L). 61, 62 The structure and function of these proteins aided in deciphering the molecular mechanisms of filovirus lifecycle and have been explanatorily reviewed in a series of three reviews by Martin et al., describing aspects of filovirus entry, 63 replication cycle, 64 assembly, and budding. 65 Briefly, GP, a heterodimeric complex of GP 1,2 surface protein, orchestrates viral entry into the host cell and participates in virus egress. 66 Due to the major role of GP 1,2 in viral entry, numerous approaches targeting the entry process have been explored to block EBOV replication at an early stage, namely immune-based therapies, [67] [68] [69] [70] peptide-based antiviral molecules, a broad range of small molecules, reviewed by Rhein and Maury, 71 and more specific entry inhibitors targeting the fusion events characterized by the GP 2 /NPC1 (Niemann-Pick C1) interaction. 72, 73 After entry by macropinocytosis, replication and transcription cycles involve the releasing of viral nucleocapsids into the host cell cytoplasm, resulting in the synthesis of new viral proteins and genomes. The whole process of assembly and budding is coordinated by NP, VP24, and VP40, and enhanced by GP. 74 NP binds to the viral RNA and creates RNP complex with polymerase L and viral proteins VP24, VP30, and VP35. 75 VP30 and NP play an important role in the RNA-binding activity. 75 The multifunctional protein, VP35, suppresses host-innate immunity and has been found to be involved in transcription/replication processes together with nucleocapsid assembly. 76, 77 The interactions between these proteins (ie, NP-NP and NP-VP35) are essential to regulate the formation of the EBOV replication complex for efficient transcription/replication. Blocking this nucleocapsid formation by proteinprotein interaction inhibitors can lead to potential inhibition of EBOV. for example, VP35-derived peptide (first identified NP ligand) that specifically binds to NP, blocks NP oligomerization and causes the release of RNA from NP-RNA complex. 78, 79 Other studies identified 18β-glycyrrhetinic acid and licochalcone-A 80 to potentially disrupt the association of NP-RNA complexes, aptamers, 81 pyrrolidinone compounds, 82 and recently MCCB4 (ene-thiazolidinedione group-containing compound), 83 that specifically lead to specific VP35-NP interaction. VP24 functions as nucleocapsid maturation factor 84 and transcription/replication modulator. 85 Additionally, it has been identified as a target for protein-protein interaction inhibitors, namely for a macrocyclic peptide inhibitor VP24-KPNA5 (karyopherin α5) which specifically disrupt VP24-KPNA interaction. 86 VP40 is a matrix protein which coordinates with VP24 toward viral assembly, budding, 87 and regulation of viral transcription. 88 All structural/functional features and recent advances for these crucial regulatory proteins, essential in viral messenger RNA (mRNA) synthesis and genome replication, have been critically reviewed 64 and therefore constitute key targets for designing EBOV-specific drugs.

Key strategies recognized to combat EBOV include: (i) directly targeting the virus, (ii) modulating the host factors or immune response, and (iii) disease management. Critically targeting the viral lifecycle is among the most popular strategies for EBOV therapeutics. 61, 63, [89] [90] [91] This is done either by targeting the initial binding and/or entry of the virus into the host cells or the later viral replication and packaging. EBOV antiviral compounds mainly encompass small molecules, antisense therapies, and immunotherapeutic drugs. Current treatment of EVD is centered upon modulating coagulation and maintaining oxygen levels in Ebola patients. 13, [92] [93] [94] Because of these therapeutic interventions, sustenance and recovery have been reported.

However, significant improvement might be observed as a result of an adequate level of support care. 27, 92, 93, 95, 96 To date, no commercial vaccines or specific therapies are available for EBOV. However, various studies have been reported that explain the comprehensive development of vaccines, 11, 42, 43, 61 small-molecule inhibitors, 90,91,97-103 and repurposed Food and Drug Administration (FDA) approved drugs against Ebola. 90, 100, [104] [105] [106] [107] [108] [109] [110] Table 1 provides an overview of anti-EBOV compounds and their level of efficacy, reported in either IC 50 or EC 50 , in vitro assays against EBOV, clinical status, and observational studies. By the end of the West-Africa EBOV outbreak, GS-5734 clearly indicated 100% protection of rhesus monkeys following lethal EBOV challenge 113 and an improved highly potent in vitro efficacy against Mayinga and Makona strains as compared to favipiravir. 112 Moreover, GS-5734 has been recently administered for the first time to a newborn baby. 127 BCX4430 has been tested against for its broad-spectrum inhibition of various viruses including, arenaviruses, bunyaviruses, coronaviruses, paramyxoviruses, and flaviviruses. 89, 128, 129 Additionally, animal survival efficacy accounted for 90% to 100% in mice 128 and 67% to 100% in rhesus monkeys using increased doses of BCX4430. 111 Favipiravir (T-705) remained a potential anti-EBOV candidate during the West-Africa outbreak 94, 130 and has reported up to 100% survival rate in EBOV-infected mice even with the lowest oral dose of 37.5 mg/ kg once daily. In comparison, NHP resulted in 17% to 50% survival rate. 94, 115 Among various FDA screens, multiple compounds including amodiaquine, diphenylpyraline, ketotifen, diphenoxylate were reported to inhibit EBOV replication, while others including, verapamil, dronedarone, sertraline, toremifene, chloroquine, teicoplanin, and amiodarone were considered EBOV entry inhibitors. 90, 100, 104, 110 Very recently, tilorone (EC 50 = 0.23 μM) was reported to be the most potent small-molecule inhibitor with a single-daily dose of 25 and 50 mg/kg intraperitoneal and proved efficacious in protecting 90% of mice from a lethal EBOV challenge. 116 Some of these inhibitors are shown in Figure 1 .

Other studies include the identification of coumarin-based antihistamine-like molecules, 131 68 To identify the most protective combination, ZMapp was introduced as a combination of three EBOV-GP mAbs (13C6, 2G4, and 4G7) and demonstrated 100% protection for rhesus monkeys. 70 The functional mechanism of binding of ZMapp has been described for its neutralizing activity targeting the GP base and glycan cap. 137 Further studies reported the further optimization of ZMapp against NHPs by using two chimeric mAbs (13C6 and 2G4), designated as MIL77, which protected all EBOV (Makona strain) infected rhesus monkeys. 138 Very recently, adeno-associated virus-mediated mAb 5D2 or 7C9 delivers 100% protection against mouse-adapted EBOV infection, whereas neutralizing mAb 2G4 revealed 83% protection. 139 Also, a coformulated cocktails REGN3470-3471-3479 (three human 3-mAb cocktails REGN3470, REGN3471, and REGN3479 in 1:1:1 ratio) is currently being evaluated and proved safe and well tolerated with no observed immunogenicity after a randomized, first-in-human Phase I clinical trial (NCT002777151). 37 Other therapeutics include the nucleic acid-based inhibitors with phosphorodiamidate morpholino oligomers (PMOs) and small-interference RNAs (siRNAs), involved in promoting degradation of mRNA transcripts and constitute two essential classes of antisense therapies. 102, 140, 141 Both compounds target vital proteins involved in EBOV transcription/translation processes VP24, VP35, and viral polymerase L. The development underlying the modification of PMOs and siRNA has been elaborated 142 and recently reviewed. 143 

In silico methods in drug discovery hold great potential and may prove beneficial at any stage in the preclinical development of drug candidates. 168 Especially, areas like target validation, the design of compound libraries, 169 

Protein modeling plays a significant role in the drug discovery process. The goal of homology modeling is structure prediction from a known sequence with accuracy comparable to experimentally resolved structures. [176] [177] [178] Restrictions linked with this technique are the presence of inserts and loop sequences, which cannot be accurately predicted in the absence of a three-dimensional (3D) crystal structure. 179 The gap between known protein sequences and identified protein structures is significantly growing. Given an enormous amount of data through a vast array of DNA sequencing MIRZA ET AL.

techniques available, experimental structure identification techniques require attention. 180 Computational techniques are actively exploited in the pharmaceutical industry for the prediction of 3D protein models. 124, 168 To expand the scope of computational methods and to improve model accuracy, efforts are being made continuously. These approaches help to predict the tertiary structure of a protein through its amino acid sequence to combat this issue. 176 Depending on the available information, these methods can be characterized as either de novo or homology modeling. Templatebased modeling also referred to as homology modeling or comparative modeling, is the most trusted method for model design. 176 Similar folding properties of the members of a protein family with the core structure unaffected by modifications in the sequence are fundamental criteria governing homology modeling. 181 Contrary to these predictions, the experiments demonstrated lower activity and binding potential of these compounds, therefore, negating the prediction made. Later, the in silico refinements of these compounds using MD revealed the interruption of key

Stepwise drug discovery process from target identification, hit-to-lead optimization, and clinical trials along with the progress in the development of small-molecule inhibitors against EBOV. ADMET, absorption, distribution, metabolism, excretion, and toxicity; HTS, high through-put screen; PDB, protein data bank; PK, pharmacokinetics; QSAR, quantitative structure-activity relationship; R&D, research and development; SAR, structure-activity relationship interactions between sulphonamide ligands and the receptor due to an additional water molecule. The migration of this water molecule from outside explained the reduced activity of these compounds when tested experimentally. 196 In another study by Cavalli et al, 197 MD simulations were used as a platform to discern several different docked complexes of propidium and human acetylcholinesterase and most stable structures identified which correlated with the experimentally verified binding modes.

Interestingly, MD simulation assisted in the discovery and development of antiviral drugs. 198 

Hit-to-lead optimization is the most essential phase to closely examine the chemical scaffold concerning its absorption, distribution, metabolism, and excretion (ADME) challenges in the drug discovery process. In silico ligand-profiling 210,211 benefited from the boost of repurposing drugs 91, 104, 108, 194 and the notion of designing drugs with controlled selectivity profiles. 212 This approach is aimed at: (i) The utilization of phenotypic screening hits to predict potential targets and their mechanism of action, (ii) identifying off-targets potentially responsible for adverse reactions and side effects, and (iii) careful analysis of ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters to propose potential hits.

Another useful in silico method, particularly beneficial for lead optimization, is quantitative structure-property relationship (QSPR) modeling which is useful for the identification of key structural features responsible for interacting with the target protein. For many ADME endpoints measured in the pharmaceutical industry, QSPR models have prospectively shown their ability to extract knowledge from a wide variety of chemical scaffolds proving their utility as predictive models. 213 QSPR models, based on machine learning techniques, are desirable to achieve the optimal potency and ADME

properties. To reduce the risk of failure in trials, a useful QSAR/QSPR model is necessary to accurately predict the activity of a compound for each drug discovery project. However, these models do not provide adequate information about the modifications that should be made to the tested compound in the next cycle of drug design. To address this issue, the matched molecular pair analysis technique is another promising approach. This method assesses the mean effect of different substituents on various ADME parameters, such as: (i) permeability, 214 (ii) solubility, 215 (iii) clearance, 214 and (iv) cytochrome P450 inhibition. 147 The design of a new scaffold that interacts with the desired pharmacological target can be benefitted based on these findings. Molecular substitutions that are closely linked with the molecular properties can guide the design of such scaffolds. Several studies are reporting the use of quantitative structure-activity relationship modeling in lead optimization. [216] [217] [218] [219] Computational drug discovery has proven to accelerate the challenging process of designing and optimizing new drug candidates.

Hierarchical virtual screening of ligand-based and structure-based methods delineated their validity in finding potential hits, even in the early phase of drug discovery. Because of the increased efficiency on

Ebola hit-to-lead optimization, an interplay between the several stages of in silico drug design has been depicted in Figure 3 . Because of the rapid development of faster architectures and comprehensive algorithms for high-level computations, the impact of computational structure-based drug design on antiviral drug discovery and lead optimization will have a more profound impact in future years. Not only hit identification but also elucidation of its biological target has provided information for use in drug discovery research. In this perspective, Perilla et al, 220 been developed that show to the ability to reach millisecond timescales that represent interesting biological processes. 238, 239 Efforts being made in quantum computing offer an exciting outlook. 240 To date, actual quantum computers are still in their initial stage of development with quantum computational operations executed only experimentally on a very small number of quantum bits. With these timewise developments, it might become possible to simulate large biological systems and determine, computationally, the 3D folding of proteins starting from their amino acid sequence. 241 In drug discovery processes the applicability is very promising, especially in target identification and interaction analysis.

The success rates for drug discovery pipeline involving target identification, screening, hit-to-lead optimization, and preclinical candidate selection are within the range of 69% to 85%. 242 With advancement in understanding the mechanism and mode of action of EBOV, future in silico work will have an essential role in the development of drug candidates against the devastating EVD.

Muhammad Usman Mirza http://orcid.org/0000-0001-9120-2414

Michiel Vanmeert http://orcid.org/0000-0001-5810-4935

Amjad Ali http://orcid.org/0000-0001-5858-9565

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Different molecular mechanisms of inhibition of bovine viral diarrhea virus and hepatitis C virus RNA-dependent RNA polymerases by a novel benzimidazole

Insights into saquinavir resistance in the G48V HIV-1 protease: quantum calculations and molecular dynamic simulations

HIV-1 protease molecular dynamics of a wild-type and of the V82F/I84V mutant: Possible contributions to drug resistance and a potential new target site for drugs

Structure-based approaches to target fishing and ligand profiling

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

Can we rationally design promiscuous drugs?

Computational functional group mapping for drug discovery

Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets

Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure

QSAR modeling of HIV-1 reverse transcriptase inhibitor 2-amino-6-arylsulfonylbenzonitriles and congeners using molecular connectivity and E-state parameters

IDX-184 is a superior HCV direct-acting antiviral drug: a QSAR study

Exploring the binding mechanism of Heteroaryldihydropyrimidines and Hepatitis B Virus capsid combined 3D-QSAR and molecular dynamics

Development of 3D QSAR based pharmacophore model for neuraminidase in Influenza A Virus

Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations

All-atom molecular dynamics calculation study of entire poliovirus empty capsids in solution

Molecular modeling of swine influenza A/H1N1, Spanish H1N1, and avian H5N1 flu N1 neuraminidases bound to Tamiflu and Relenza

Conformational transition paths harbor structures useful for aiding drug discovery and understanding enzymatic mechanisms in protein kinases

Computing ensembles of transitions from stable states: Dynamic importance sampling

Molecular dynamics simulation and experimental verification of the interaction between cyclin T1 and HIV-1 Tat proteins

An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine

Molecular dynamics and free energy studies on the wild-type and double mutant HIV-1 protease complexed with amprenavir and two amprenavir-related inhibitors: mechanism for binding and drug resistance

Discovery and characterization of diazenylaryl sulfonic acids as inhibitors of viral and bacterial neuraminidases

Quercetin derivatives as non-nucleoside inhibitors for dengue polymerase: molecular docking, molecular dynamics simulation, and binding free energy calculation

Exploration of binding and inhibition mechanism of a small molecule inhibitor of influenza virus H1N1 hemagglutinin by molecular dynamics simulation

Molecular dynamics simulations of human rhinovirus and an antiviral compound

Evaluation of adamantane derivatives as inhibitors of dengue virus mRNA cap methyltransferase by docking and molecular dynamics simulations

Structure of the NS5 methyltransferase from Zika virus and implications in inhibitor design

Computational study on the drug resistance mechanism of hepatitis C virus NS5B RNAdependent RNA polymerase mutants to BMS-791325 by molecular dynamics simulation and binding free energy calculations

A contribution to the drug resistance mechanism of Darunavir, Amprenavir, Indinavir, and Saquinavir complexes with HIV-1 protease due to flap mutation I50V: A systematic MM-PBSA and thermodynamic integration study

Insights into susceptibility of antiviral drugs against the E119G mutant of 2009 influenza A (H1N1) neuraminidase by molecular dynamics simulations and free energy calculations

Molecular dynamics simulation directed rational design of inhibitors targeting drug-resistant mutants of influenza A virus M2

Preliminary mapping of a putative inhibitorbinding pocket for human immunodeficiency virus type 1 integrase inhibitors

The future of molecular dynamics simulations in drug discovery

New qubit control bodes well for future of quantum computing

How robust are protein folding simulations with respect to force field parameterization?

Trends in clinical success rates

The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs?

Mutation rate and genotype variation of Ebola virus from Mali case sequences

Clinical management of HIV drug resistance

The potential for multidrug-resistant influenza

Preclinical drug trials in the mdx mouse: assessment of reliable and sensitive outcome measures

Can animal data predict human outcome? Problems and pitfalls of translational animal research

How many are enough? Statistical power analysis and sample size estimation in clinical research

Distinguishing between exploratory and confirmatory preclinical research will improve translation

Can the pharmaceutical industry reduce attrition rates?

Perspectives towards antiviral drug discovery against Ebola virus