Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection

Veljko Veljkovic; Philippe M. Loiseau; Bruno Figadere; Sanja Glisic; Nevena Veljkovic; Vladimir R. Perovic; David P. Cavanaugh; Donald R. Branch

doi:10.12688/f1000research.6110.2

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Research Article

Revised

Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection

[version 2; peer review: 2 approved]

Veljko Veljkovic¹, Philippe M. Loiseau², Bruno Figadere², [...] Sanja Glisic¹, Nevena Veljkovic¹, Vladimir R. Perovic¹, David P. Cavanaugh³, Donald R. Branch⁴

Veljko Veljkovic¹, Philippe M. Loiseau², [...] Bruno Figadere², Sanja Glisic¹, Nevena Veljkovic¹, Vladimir R. Perovic¹, David P. Cavanaugh³, Donald R. Branch⁴

PUBLISHED 16 Feb 2015

Author details Author details

¹ Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia
² Antiparasitic Chemotherapy, UMR 8076 CNRS BioCIS, Faculty of Pharmacy Université Paris-Sud, Rue Jean-Baptiste Clément, F 92290- Chatenay-Malabry, France
³ Bench Electronics, Bradford Dr., Huntsville, AL, 35801, USA
⁴ Canadian Blood Services, Center for Innovation, 67 College Street, Toronto, Ontario, M5G 2M1, Canada

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Ebola Virus collection.

Abstract

The ongoing Ebola virus epidemic has presented numerous challenges with respect to control and treatment because there are no approved drugs or vaccines for the Ebola virus disease (EVD). Herein is proposed simple theoretical criterion for fast virtual screening of molecular libraries for candidate inhibitors of Ebola virus infection. We performed a repurposing screen of 6438 drugs from DrugBank using this criterion and selected 267 approved and 382 experimental drugs as candidates for treatment of EVD including 15 anti-malarial drugs and 32 antibiotics. An open source Web server allowing screening of molecular libraries for candidate drugs for treatment of EVD was also established.

Keywords

Ebola virus, drug candidates, entry inhibitors, virtual screening

Corresponding author: Veljko Veljkovic

Competing interests: No competing interests were disclosed.

Grant information: This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant no. 173001).

Copyright: © 2015 Veljkovic V et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

How to cite: Veljkovic V, Loiseau PM, Figadere B et al. Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection [version 2; peer review: 2 approved]. F1000Research 2015, 4:34 (https://doi.org/10.12688/f1000research.6110.2) First published: 02 Feb 2015, 4:34 (https://doi.org/10.12688/f1000research.6110.1) Latest published: 16 Feb 2015, 4:34 (https://doi.org/10.12688/f1000research.6110.2)

Revised Amendments from Version 1

According to Referees’ suggestions we have made the following changes: (i) the explanation why some compounds from Ref. 3 are not included in Dataset1 was added in Material and methods, (ii) the explanation of the AQVN/ISM concept is moved from the Material and methods to Introduction. (iii) Results and discussion is divided in two separate sections.

See the authors' detailed response to the review by Bruno Botta
See the authors' detailed response to the review by Patrick Butaye

Introduction

The current Ebola virus outbreak is one of the largest outbreaks of its kind in history and the first in West Africa. By January 14, 2015, a total of 21296 probable and confirmed cases, including 8429 deaths from Ebola virus disease (EVD), had been reported from five countries in West Africa - Guinea, Liberia, Nigeria, Senegal, and Sierra Leone (http://apps.who.int/iris/bitstream/10665/148237/2/roadmapsitrep_14Jan2015_eng.pdf?ua=1). EVD with a high case-fatality rate of 40% and with currently no approved vaccine or therapy, represents a major public health threat.

In response to the current Ebola virus outbreak, the international community has urged for accelerated development of drugs against EVD but also has endorsed the clinical use of unregistered treatments for Ebola¹. Conventional time and the money consuming approach of drug development (> 10 years; > 2 billions $) does not meet the current urgent need for anti-Ebola drugs. Repurposing or repositioning of existing drugs could overcome some of these obstacles and help in the rapid discovery and development of therapeutics for EVD, although this approach does not negate the need for some preclinical studies and clinical trials for validation of the proposed indications. Recently, results of two large repurposing screenings of Food and Drug Administration (FDA)-approved drugs have been reported. In the first study, Madrid and co-workers performed in vitro and in vivo (in mice) screening of 1012 FDA-approved drugs and selected 24 candidate entry inhibitors for Ebola virus². In the second study, 53 inhibitors of Ebola virus infection with IC₅₀ < 10 µM and selectivity index SI > 10-fold have been identified by in vitro screening of 2816 FDA-approved drugs³. In the same study, an additional 95 drugs which are active against Ebola virus infection with IC₅₀ > 10 µM and SI <10-fold were also reported.

Although in vitro and in vivo screening for repurposing/repositioning of existing drugs could significantly accelerate discovery of new drugs these approaches are time-consuming and costly for screening of large drug libraries. Recently, we proposed a novel approach for in silico screening of molecular libraries for drug candidates^4–8. This approach, which uses the average quasi valence number (AQVN) and the electron-ion interaction potential (EIIP), parameters determining long-range interaction between biological molecules, might hold a key to overcoming some of these obstacles in experimental screening by significantly reducing the number of compounds which should be in vitro and in vivo tested⁹. Among 3300 currently used molecular descriptors, AQVN and EIIP represent the unique physical properties which characterize the long-range interactions between biological molecules⁴³. Small molecules with similar AQVN and EIIP values interact with the common therapeutic target, which allow establish criterions for virtual screening of molecular libraries for compounds with similar therapeutic properties^4–9.

Here we develop the EIIP/AQVN-based criterion for virtual screening of molecular libraries for candidate drugs against Ebola virus infection. Using this criterion we screened DrugBank (http://www.drugbank.ca) and selected 267 approved and 382 experimental drugs as candidates for treatment of EVD. Anopen access portal allowing screening of molecular libraries for candidate drugs for treatment of EVD was established.

Material and methods

Molecular libraries

For screening of drugs for repurposing to select candidates for Ebola virus entry inhibitors, 1463 approved and 4975 experimental drugs from DrugBank (http://www.drugbank.ca) were screened. For development of the predictive criterion used in this analysis, the learning set (Dataset 1) encompassing 152 drugs which are selected as inhibitors of Ebola virus infection by in vitro and in vivo screening of 3828 FDA-approved drugs^2,3, was established. From this Dataset are excluded microtubule modulators from Ref. 3, which do not directly block the entry of Ebola virus, as well as compounds which previously were reported in Ref. 2. As control data sets 45,010,644 compounds from PubChem (http://www.ncbi.nlm.nih.gov/pccompound) and 49 Ebola virus entry inhibitors collected by data mining of literature and patents, were used. For screening of literature data the NCBI literature database PubMed (http://www.ncbi.nlm.nih.gov/pubmed) was used. For search of patents and patent applications we used the Free Patent Online browser (http://www.freepatentsonline.com).

Calculation of AQVN and EIIP parameters of organic molecules

Specific recognition and targeting between interacting biological molecules at distances > 5Å are determined by the average AQVN and the EIIP¹⁰, which are derived from the general model pseudopotential^11,12. These parameters for organic molecules are determined by the following simple equations¹⁰:

E I I P = 0.25 \frac{Z^{*} sin (1.04 π Z^{*})}{2 π} (1)

Where Z* is the average quasi-valence number (AQVN) determined by

Z^{*} = \frac{1}{N} \sum_{i = 1}^{m} n_{i} Z_{i} (2)

where Z_i is the valence number of the i-th atomic component, n_i is the number of atoms of the i-th component, m is the number of atomic components in the molecule, and N is the total number of atoms. EIIP values calculated according to equation 1 and equation 2 are expressed in Rydberg units (Ry).

Results

Previously, analyses of the EIIP/AQVN distribution of 45,010,644 compounds from the PubChem database (http://www.ncbi.nlm.nih.gov/pccompound) revealed that 92.5% of presented compounds are homogenously distributed within EIIP and AQVN intervals (0.00 – 0.11 Ry) and (2.4 – 3.3), respectively). This domain of the EIIP/AQVN space, encompassing the majority of known chemical compounds, is referred to as the “basic EIIP/AQVN chemical space” (BCS)⁶. Analysis of the molecular training set (Dataset 1), encompassing 152 small molecule inhibitors of Ebola virus infection selected by in vitro screening of 3828 FDA approved drugs^2,3, show that 79% of these compounds are placed within AQVN and EIIP region (2.3 – 2.7) and (0.0829 – 0.0954 Ry), respectively (“Ebola Virus Infection Inhibitors Space”, EVIIS). The AQVN region (2.36 – 2.54) and the EIIP region (0.0912 – 0.0924 Ry) form the part of EVIIS which encompasses 55.5% of all drugs from the learning set (core EVIIS, cEVIS). Literature data mining reveals 49 compounds with experimentally proved activity against Ebola virus infection (Table 1)^13–29. Most of these compounds 47 (95.9%) are placed within EVIIS (Table 1). Of note is that EVIIS and cEVIIS domains contain only 14.6% and 6.5% of compounds from PubChem, respectively. This confirms high specificity of clustering of Ebola virus infection inhibitors within the EIIP/AQVN space. Comparison of distributions of Ebola virus infection inhibitors and compounds from PubMed is given in Figure 1.

Table 1. Small-molecule entry inhibitors for Ebola virus.

Compound	Formula	AQVN	EIIP [Ry]	Reference
Chloroquine	C18H26ClN3	2.375	0.0941	16
Bafilomycin A1	C35H58O9	2.471	0.0960	17
Cytochalasin B	C29H37NO5	2.611	0.0810	17
Cytochalasin D	C30H37NO6	2.676	0.0672	17
Latruculion A	C22H31NO5S	2.667	0.0693	17
Jasplakinolide	C36H45BrN4O6	2.674	0.0676	18
Clomiphene	C26H28ClNO	2.526	0.0926	18
Toremifene	C26H28ClNO	2.526	0.0926	18
Chlorpromazine	C17H19ClN2S	2.600	0.0829	19
Amiodarone	C25H29I2NO3	2.567	0.0880	20
Dronedarone	C31H44N2O5S	2.578	0.0864	20
Verapamil	C27H38N2O4	2.535	0.0917	20
Clomiphene	C26H28ClNO	2.526	0.0926	21
AY-9944	C22H28Cl2N2	2.370	0.0938	21
Ro 48-8071	C23H27BrFNO2	2.505	0.0940	21
U18666A	C25H41NO2	2.290	0.0849	21
Terconazole	C26H31Cl2N5O3	2.687	0.0644	21
Triparanol	C27H32ClNO2	2.508	0.0941	21
Impramine	C19H24N2	2.444	0.0964	22
3.47	C34H43N3O5	2.635	0.0763	23
Cytochalasin B	C29H37NO5	2.611	0.0810	24
Cytochalasin D	C30H37NO6	2.676	0.0672	24
Latrunculin A	C22H31NO5S	2.667	0.0693	24
Jasplakinolide	C36H45BrN4O6	2.674	0.0676	24
NSC62914	C31H40O3	2.460	0.0962	25
Compound 1	C30H38N6O2	2.632	0.0770	26
Compound 2	C32H46N6	2.429	0.0963	26
Compound 3	C28H34N6O2	2.686	0.0635	26
Compound 5	C42H58N10O6	2.690	0.0635	27
Compound 8a	C17H23N3O3	2.695	0.0621	27
Compound 8b	C17H23N3O3	2.695	0.0621	27
Compound 8y	C16H20BrNO2	2.610	0.0812	27
Compound 15h	C15H20JN5O	2.667	0.0693	27
Compound 15k	C15H128Br3N5O	2.667	0.0693	27
Retinazone	C38H56Na3N5S2	2.385	0.0947	28
Compound 7	C17H12F4N2	2.467	0.0647	29
Brincidofovir*	C27H52N3O7P	2.467	0.0961	30
Hit compound 3	C25H35N3O2	2.494	0.0950	31
Hit compound 3.1	C21H24ClN3O2	2.667	0.0693	31
Hit compound 3.2	C20H29N3O2	2.518	0.0933	31
Hit compound 3.3	C30H35N3O2	2.556	0.0894	31
Hit compound 3.4	C25H32N4O3	2.656	0.0717	31
Hit compound 3.5	C20H23N3O2	2.708	0.0587	31
Hit compound 3.6	C25H33N3O2	2.540	0.09913	31
Hit compound 3.7	C22H27N3O3	2.691	0.0633	31
Hit compound 3.18	C26H37N3O2	2.471	0.0471	31
Hit compound 3.48	C34H43N3O5	2.635	0.0763	31
Hit compound 3.105	C34H40N6O2	2.658	0.0712	31
NSC 62914	C31H39O3	2.480	0.0957	32

*Experimental drug applied for treatment of Ebola patients in Liberia (http://www.ox.ac.uk/news/2014-11-13-oxford-lead-trial-experimental-drug-ebola-patients)

Figure 1. Distribution of compounds according to their average quasivalence number (AQVN) and electron-ion interaction potential (EIIP) values.

(A) 45010644 compounds from the PubChem database (http://www.ncbi.nlm.nih.gov/pccompound); (B) FDA-approved drugs which are active against Ebola virus infection (Dataset 1)^2,3; (C) Entry inhibitors of Ebola virus (Table 1).

Drug Name	Formula	AQVN	EIIP [Ry]	Approved Indication
Perhexiline	C19H35N	2.109	0.0479	Antianginal
Methylbenzethonium	C28H44ClNO2	2.29	0.0848	Anti-Ulcerogenic
Trihexyphenidyl	C20H31NO	2.302	0.0866	Antiparkinsonian
Trihexyphenidyl	C20H31NO	2.302	0.0866	Antiparkinsonian
Drofenine	C20H32ClNO2	2.321	0.089	NA
Alverine	C20H27N	2.333	0.0904	antispasmoic
Penbutolol	C18H29NO2	2.36	0.0929	beta blocker
Butriptyline	C21H27N	2.367	0.0935	Antidepressant
Aprindine	C22H30N2	2.37	0.0938	antiarrhythmic
Halofantrine	C26H30Cl2F3NO	2.381	0.0945	Antimalaria
Biperiden	C21H29NO	2.385	0.0947	anti-cholinergic anti-parkinsonian
Bepridil	C24H34N2O	2.393	0.0952	Anti-Anginal
Ipenoxazone	C22H34N2O2	2.4	0.0955	NA
Vanoxeamine	C28H34Cl2F2N2O	2.406	0.0957	Cardiac Arrhythmias
Proadifen	C23H32ClNO2	2.407	0.0958	Inhibitor Of Cytochrome P450 Enzymes
Trimipramine	C20H26N2	2.417	0.0961	Antidepressant
Carbetapentane	C20H31NO3	2.436	0.0964	Antitussive
Thenalidine	C17H22N2	2.439	0.0964	Antihistamine
Clemastine	C21H26ClNO	2.44	0.0964	antiallergic
Methadone	C21H27NO	2.44	0.0964	Anti-Addictive
Diphenidol	C21H27NO	2.44	0.0964	Antiemetic
Cyclomethycaine	C22H33NO3	2.441	0.0964	anestethic
Clomipramine	C19H23ClN2	2.444	0.0964	antidepressant
Homochlorcyclizine	C19H23ClN2	2.444	0.0964	Antihistamine
Bamipine	C19H24N2	2.444	0.09964	Antihistamine
Dopexamine	C22H32N2O2	2.448	0.0964	Adrenergic Beta-Agonists
Amitriptyline	C20H23N	2.454	0.0963	Antidepressant
Estradiol	C18H24O2	2.455	0.0963	estrogen
Maprotiline	C20H23N	2.455	0.0963	antidepressant
Dimethisoquin	C17H24N2O	2.455	0.0963	Antipruritic
Octylonium	C29H43N2O4Br	2.456	0.0963	Antimuscarinic
Cinacalcet	C22H22F3N	2.458	0.0927	Hyperparathyroidism
Bifemelane	C18H23NO	2.465	0.0961	antidepressant
Orphenadrine	C18H23NO	2.465	0.0961	Parkinsonism
Zanapezil	C25H32N2O	2.467	0.09961	NA
Azithromycin	C38H72N2O12	2.468	0.0961	antimicrobial
Rimcazole	C21H27N3	2.471	0.096	Antipsychotic
Dexbrompheniramine	C16H19BrN2	2.474	0.0959	antihistaminic
Salmeterol	C25H37NO4	2.478	0.0957	antiasthma
Clocapramine	C28H37ClN4O	2.479	0.0957	Antipsychotic
(S)-(+)-Dimethindene	C20H24N2	2.479	0.0957	Antihistamine / Anticholinergic
Oxethazaine	C28H41N3O3	2.48	0.0956	Anesthetic
Buprenorphine	C29H41NO4	2.48	0.0956	Anti-Addictive
Mycophenolate mofetil	C22H29NO2	2.481	0.0956	immunosuppressant
Levopropoxyphene	C22H29NO2	2.482	0.0956	antitussive
Fluoperazine	C21H25ClF3N3S	2.482	0.0956	Antipsychotic
L-Alpha-Acetyl-N-Normethadol	C22H29NO2	2.482	0.0956	NA
Ebastine	C32H39NO2	2.486	0.0954	Antihistamine
Protriptyline	C19H21N	2.488	0.0953	antidepressant
Fendiline	C23H25N	2.49	0.0952	Calcium Channel Blocker
Protryptyline	C19H21N	2.488	0.0953	Antidepressant
Amindocate	C19H29N3O2	2.491	0.0951	NA
Diphenylpyraline	C19H23NO	2.5	0.0946	antihistaminic
Dirithromycin	C42H78N2O14	2.5	0.0946	antibacterial
Fluoxetine	C17H18F3NO	2.5	0.0946	antidepressant
Benztropine	C21H25NO	2.5	0.0946	anticholinergic
Tomoxetine	C17H21NO	2.5	0.0946	Attention Deficit-Hyperactivity Disorder
Lidoflazine	C30H35F2N3O	2.507	0.0941	Antiarrhythmic
Triparanol	C27H32ClNO2	2.508	0.0941	Antilipemic
Malachite Green	C23H25ClN2	2.51	0.0939	Antiinfective Agent
Clarithromycin	C38H69NO13	2.512	0.0937	antimicrobiall
Premethadone	C19H22N2	2.512	0.0938	NA
Propiverine	C23H30ClNO3	2.517	0.0934	Anticholinergic
Dibucaine	C20H29N3O2	2.518	0.0933	local anesthetic
Mosapramine	C28H35ClN4O	2.522	0.03	Antipsychotic
Mefloquine	C17H16F6N2O	2.524	0.0928	antimalarial
Erythromycin	C37H67NO13	2.525	0.0927	antibacterial
Tamoxifen	C26H29NO	2.526	0.0926	anticancer
Tilorone	C25H34N2O3	2.531	0.0921	antiviral
Metixene	C20H23NS	2.533	0.0919	Anticholinergic
Trifluoperazine	C21H24F3N3S	2.538	0.0914	antipsychotic
Mibefradil	C29H38FN3O3	2.54	0.0912	antihypertensive
Spiramycin	C43H74N2O14	2.556	0.0893	antimicrobial
Triflupromazine	C18H19F3N2S	2.558	0.0891	Antipsychotic
Cyproheptadine	C21H21N	2.558	0.0891	Antihistamine
Difeterol	C25H29NO2	2.561	0.0887	NA
Oxyphencyclimine	C20H28N2O3	2.566	0.0881	anti-cholinergic
Maduramicin	C47H80O17	2.569	0.0876	antimicrobial
Prochlorperazine	C20H24ClN3S	2.571	0.0874	antiemetic
Bromperidol	C21H23BrFNO2	2.571	0.0874	Antipsychotic
Fluspirilene	C29H31F2N3O	2.576	0.0867	Antipsychotic
Propafenone	C21H27NO3	2.577	0.0866	antiarrhythmic
Sertraline	C17H17Cl2N	2.577	0.0866	antidepressant
Roxindole	C23H26N2O	2.577	0.0866	Schizophrenia
Desloratadine	C19H19ClN2	2.585	0.0853	Allergies
Thioridazine	C21H26N2S2	2.588	0.0848	Antipsychotic
Octoclothepin	C19H21ClN2S	2.591	0.0844	NA
Dipivefrin	C19H29NO5	2.592	0.0842	glaucoma
Promazine	C17H20N2S	2.6	0.0829	Antipsychotic
Pimozide	C28H29F2N3O	2.603	0.0824	Antipsychotic
Perphenazine	C21H26ClN3OS	2.604	0.0823	Antipsychotic
Carfilzomib	C40H57N5O7	2.605	0.082	anticancer
Benzydamine	C19H23N3O	2.609	0.0814	Anti-Inflammatory
Piperacetazine	C24H30N2O2S	2.61	0.0811	antipsychotic
Indocate	C22H26N2O2	2.615	0.0802	NA
Astemizole	C28H31FN4O	2.615	0.0802	Antihistamine
Chlorprothixene	C18H18ClNS	2.615	0.0802	Antipsychotic
Digoxin	C41H64O14	2.622	0.079	antiarrhythmic
Mequitazine	C20H22N2S	2.622	0.0789	Antihistamine / Anticholinergic
Methiothepin	C20H24N2S2	2.625	0.0784	Antipsychotic
Proscillaridin A	C30H42O8	2.625	0.0784	Cardiac Arrhythmia
Ami-193	C22H26FN3O2	2.63	0.0774	5-Ht2A 5-Ht1A and D2 Receptor Antagonist
Sertindole	C24H26ClFN4O	2.632	0.077	Antipsychotic
Diphenoxylate	C30H32N2O2	2.636	0.0761	anti-peristaltic
Amodiaquine	C20H22ClN3O	2.638	0.0756	anti-malarial
Bazedoxifene	C30H34N2O3	2.638	0.0758	postmenopausal osteoporosis
Amodiaquine	C20H22ClN3O	2.638	0.0756	antimalarial
Sunitinib	C22H27FN4O2	2.643	0.0747	anticancer
Metergoline	C25H29N3O2	2.644	0.0744	Antipsychoactive
Pimethixene	C19H19NS	2.65	0.0731	Antihistamine / Anticholinergic
Tandutinib	C31H42N6O4	2.651	0.073	Anticancer (Experimental)
Sarpogrelate	C24H32ClNO6	2.656	0.0717	Diabetes Mellitus
Monatepil	C28H30FN3OS	2.656	0.0717	Antihypertensive
Ftormetazine	C21H22F3N3OS	2.667	0.0693	NA
Nebivolol	C22H25F2NO4	2.667	0.0693	Hypertension
Proglumetacin	C46H58ClN5O8	2.678	0.0666	anti-inflammatory
Deslanoside	C47H74O19	2.686	0.0647	antiarrhythmic
Azaclorzine	C22H24ClN3OS	2.692	0.063	antianginal
Odanacatib	C25H27F4N3O3S	2.698	0.0614	Osteoporosis / Bone Metastasis
Thioproperazine	C22H30N4O2S2	2.7	0.0609	antipsychotic
Duloxetine	C18H19NOS	2.7	0.0609	Antidepressant
Thiothixene	C23H29N3O2S2	2.712	0.0577	antipsychotic
Sulconazole	C18H15Cl3N2S	2.718	0.056	Antifungal
Paroxetine	C19H20FNO3	2.727	0.0534	anti-depressant
Ketotifen	C19H19NOS	2.732	0.0521	anti-histaminic
Bosutinib	C26H29Cl2N5O3	2.738	0.0501	anticancer
Bitolterol	C28H31NO5	2.738	0.0501	bronchodilatator
Posaconazole	C37H42F2N8O4	2.753	0.0458	antifungal
Amlodipine	C20H25ClN2O5	2.755	0.0451	anti-hypertensive
Gefitinib	C22H24ClFN4O3	2.764	0.0423	anticancer
(S)-(+)-Niguldipine	C36H39N3O6	2.786	0.0352	Calcium Channel Blocker
Carvedilol	C24H26N2O4	2.786	0.0352	Adrenergic Alpha-1 Receptor Antagonists
Amoxapine	C17H16ClN3O	2.79	0.034	Antidepressant
Cepharanthine	C37H38N2O6	2.795	0.032	anti-inflammatory/antineoplastic
Rescimetol	C33H38N2O8	2.815	0.0253	NA
Mesoridazine	C27H32N2O4S3	2.824	0.0223	Phenothiazine
Dasatinib	C22H26ClN7O2S	2.848	0.0137	Anticancer
Raloxifene	C28H27NO4S	2.852	0.0119	anticancer
Tioconazole	C16H13Cl3N2OS	2.883	0.0188	Antifungal
Carprofen	C15H12ClNO2	2.903	0.007	anti-inflammatory
Oxibendazole	C12H15N3O3	2.909	0.0092	anthelmintic
Mk-2206	C25H21N5O	2.923	0.0146	NA
Cantharidin	C10H12O4	2.923	0.0146	Blister Agent
Lapatinib	C29H26ClFN4O4S	2.939	0.0208	Anticancer
Topotecan	C23H23N3O5	2.963	0.0298	anticancer
Axitinib	C22H18N4OS	3	0.0439	Anticancer
Daunomycin	C27H29NO10	3.015	0.0495	anticancer
Daunorubicin	C27H29NO10	3.015	0.0495	antimicrobial/anticancer
Niclosamide	C13H8Cl2N2O4	3.31	0.1296	antihelmintic
Dipyrithione	C10H8N2O2S2	3.417	0.134	Fungicide
Nitrovin	C14H12N6O6	3.526	0.1214	antimicrobial

Dataset 1.FDA-approved drugs which are active against Ebola virus infection2,3.

AQVN: average quasivalence number; EIIP: electron-ion interaction potential

It was shown that Ebola virus glycoprotein (GP)-mediated entry and infection is subordinated with a membrane-trafficking event that translocates a GP binding partner to the cell surface, which depends on microtubules^30,31. Consistently, microtubule inhibitors which block this trafficking process could decrease infection without interfering with the direct binding and translocation of the Ebola virus into cells. AQVN and EIIP values of microtubule modulators and transcription inhibitors with reported anti-Ebola virus activity are given in Table 2. As can be seen, all these compounds, which do not directly affect binding and internalization of Ebola virus, are located outside of EVIIS. This additionally confirms the specificity of the EVIS domain.

Table 2. Viral transcription inhibitors and microtubule modulators with anti-Ebola virus activity.

Compound	Formula	AQVN	EIIP [Ry]
Viral transcription inhibitors
BCX4430	C11H15N5O3	3.000	0.0439
Favipiravir	C5H4FN3O2	3.467	0.1304
C-c3Ado	C12H16N4O3	2.914	0.0112
c3Nep	C12H14N4O3	3.030	0.0552
“D-like” 1’-6’-isoneplanocin	C11H12N5O3	3.194	0.1076
“L-like” 1’-6’-isoneplanocin	C11H12N5O3	3.194	0.1076
CMLDBU3402	C30H26BrN3O7	3.045	0.1343
Microtubule modulators
Vinblastine	C13H8Cl2N2O4	3.310	0.0130
Vinorelbine	C45H54N4O8	2.721	0.0552
Vincristine	C46H56N4O10	2.759	0.0439
Colchicine	C22H25NO6	2.852	0.0121
Nocodazole	C14H11N3O3S	3.312	0.1298
Mebendazole	C16H13N3O3	3.143	0.0934
Albendazole	C12H15N3O2S	2.909	0.0092

In further analysis we used EVIIS as a filter for virtual screening for candidate Ebola virus infection inhibitors. In Dataset 2 622 approved and 1089 experimental drugs in Dataset 3 selected by EVIIS screening of 6532 drugs from DrugBank are reported. Using cEVIIS, we located 267 approved and 382 experimental drugs. This small molecular library represents a source of candidate drugs for treatment of Ebola virus disease (EVD), which can be further experimentally tested.

Database ID	Generic Name	Formula	AQVN	EIIP [Ry]
DB00376	Trihexyphenidyl	C20H31NO	2.301887	0.086554
DB01022	Phylloquinone	C31H46O2	2.303797	0.086811
DB00191	Phentermine	C10H15N	2.307692	0.087326
DB00313	Valproic Acid	C8H16O2	2.307692	0.087326
DB01577	Methamphetamine	C10H15N	2.307692	0.087326
DB06204	Tapentadol	C14H23NO	2.307692	0.087326
DB06709	Methacholine	C8H18NO2	2.310345	0.087669
DB08887	Icosapent ethyl	C22H34O2	2.310345	0.087669
DB01187	Iophendylate	C19H29IO2	2.313725	0.088098
DB01337	Pancuronium	C35H60N2O4	2.316832	0.088483
DB00947	Fulvestrant	C32H47F5O3S	2.318182	0.088648
DB01083	Orlistat	C29H53NO5	2.318182	0.088648
DB00387	Procyclidine	C19H29NO	2.32	0.088868
DB00942	Cycrimine	C19H29NO	2.32	0.088868
DB08804	Nandrolone decanoate	C28H44O3	2.32	0.088868
DB00818	Propofol	C12H18O	2.322581	0.089174
DB01463	Fencamfamine	C15H21N	2.324324	0.089378
DB00137	Xanthophyll	C40H56O2	2.326531	0.089632
DB01616	Alverine	C20H27N	2.333333	0.090387
DB06694	Xylometazoline	C16H24N2	2.333333	0.090387
DB06439	Tyloxapol	C51H80O6	2.335766	0.090648
DB00304	Desogestrel	C22H30O	2.339623	0.091049
DB01339	Vecuronium	C34H57N2O4	2.340206	0.091108
DB00711	Diethylcarbamazine	C10H21N3O	2.342857	0.091375
DB01338	Pipecuronium	C35H62N4O4	2.342857	0.091375
DB00159	Icosapent	C20H30O2	2.346154	0.091697
DB06710	Methyltestosterone	C20H30O2	2.346154	0.091697
DB00182	Amphetamine	C9H13N	2.347826	0.091857
DB01576	Dextroamphetamine	C9H13N	2.347826	0.091857
DB01586	Ursodeoxycholic acid	C24H40O4	2.352941	0.092329
DB06777	Chenodeoxycholic acid	C24H40O4	2.352941	0.092329
DB00230	Pregabalin	C8H17NO2	2.357143	0.092698
DB06718	Stanozolol	C21H32N2O	2.357143	0.092698
DB01359	Penbutolol	C18H29NO2	2.36	0.09294
DB01599	Probucol	C31H48O2S2	2.361446	0.093059
DB00728	Rocuronium	C32H53N2O4	2.362637	0.093156
DB00847	Cysteamine	C2H7NS	2.363636	0.093236
DB01036	Tolterodine	C22H31NO	2.363636	0.093236
DB00297	Bupivacaine	C18H28N2O	2.367347	0.093525
DB00464	Sodium Tetradecyl Sulfate	C14H30O4S	2.367347	0.093525
DB00624	Testosterone	C19H28O2	2.367347	0.093525
DB01002	Levobupivacaine	C18H28N2O	2.367347	0.093525
DB01429	Aprindine	C22H30N2	2.37037	0.09375
DB08924	Amfecloral	C11H12Cl3N	2.37037	0.09375
DB02300	Calcipotriol	C27H40O3	2.371429	0.093827
DB00608	Chloroquine	C18H26ClN3	2.375	0.094079
DB00652	Pentazocine	C19H27NO	2.375	0.094079
DB00396	Progesterone	C21H30O2	2.377358	0.094238
DB00470	Dronabinol	C21H30O2	2.377358	0.094238
DB00486	Nabilone	C24H36O3	2.380952	0.09447
DB01216	Finasteride	C23H36N2O2	2.380952	0.09447
DB01218	Halofantrine	C26H30Cl2F3NO	2.380952	0.09447
DB00285	Venlafaxine	C17H27NO2	2.382979	0.094595
DB00411	Carbachol	C6H14N2O2.ClH	2.384615	0.094694
DB00621	Oxandrolone	C19H30O3	2.384615	0.094694
DB00810	Biperiden	C21H29NO	2.384615	0.094694
DB00941	Hexafluronium	C36H42N2.C2H6	2.386364	0.094796
DB01037	Selegiline	C13H17N	2.387097	0.094837
DB00219	Oxyphenonium	C21H34NO3	2.389831	0.094989
DB01209	Dezocine	C16H23NO	2.390244	0.095011
DB00296	Ropivacaine	C17H26N2O	2.391304	0.095067
DB01244	Bepridil	C24H34N2O	2.393443	0.095177
DB00202	Succinylcholine	C14H30N2O4	2.4	0.095485
DB00379	Mexiletine	C11H17NO	2.4	0.095485
DB00514	Dextromethorphan	C18H25NO	2.4	0.095485
DB00523	Alitretinoin	C20H28O2	2.4	0.095485
DB00755	Tretinoin	C20H28O2	2.4	0.095485
DB00929	Misoprostol	C22H38O5	2.4	0.095485
DB00930	Colesevelam	C22H38O5	2.4	0.095485
DB00982	Isotretinoin	C20H28O2	2.4	0.095485
DB01407	Clenbuterol	C12H18Cl2N2O	2.4	0.095485
DB08808	Bupranolol	C14H22ClNO2	2.4	0.095485
DB00193	Tramadol	C16H25NO2	2.409091	0.095841
DB01255	Lisdexamfetamine	C15H25N3O	2.409091	0.095841
DB06700	Desvenlafaxine	C16H25NO2	2.409091	0.095841
DB00281	Lidocaine	C14H22N2O	2.410256	0.09588
DB00865	Benzphetamine	C17H21N	2.410256	0.09588
DB00332	Ipratropium bromide	C20H30NO3.BrH	2.410714	0.095895
DB00937	Diethylpropion	C13H19NO	2.411765	0.095929
DB01156	Bupropion	C13H18ClNO	2.411765	0.095929
DB00996	Gabapentin	C9H17NO2	2.413793	0.095991
DB01625	Isopropamide	C23H32N2O	2.413793	0.095991
DB01185	Fluoxymesterone	C20H29FO3	2.415094	0.096029
DB00645	Dyclonine	C18H27NO2	2.416667	0.096072
DB00726	Trimipramine	C20H26N2	2.416667	0.096072
DB00268	Ropinirole	C16H24N2O	2.418605	0.096121
DB00935	Oxymetazoline	C16H24N2O	2.418605	0.096121
DB00308	Ibutilide	C20H36N2O3S	2.419355	0.09614
DB00253	Medrysone	C22H32O3	2.421053	0.096178
DB01420	Testosterone Propionate	C22H32O3	2.421053	0.096178
DB05812	Abiraterone	C24H31NO	2.421053	0.096178
DB00857	Terbinafine	C21H25N	2.425532	0.096267
DB00896	Rimexolone	C24H34O3	2.42623	0.096279
DB00854	Levorphanol	C17H23NO	2.428571	0.096315
DB00195	Betaxolol	C18H29NO3	2.431373	0.09635
DB00367	Levonorgestrel	C21H28O2	2.431373	0.09635
DB00378	Dydrogesterone	C21H28O2	2.431373	0.09635
DB01091	Butenafine	C23H27N	2.431373	0.09635
DB01256	Retapamulin	C30H47NO4S	2.433735	0.096374
DB00504	Levallorphan	C19H25NO	2.434783	0.096382
DB00944	Demecarium	C32H52N4O4	2.434783	0.096382
DB01258	Aliskiren	C30H53N3O6	2.434783	0.096382
DB04835	Maraviroc	C29H41F2N5O	2.435897	0.09639
DB00354	Buclizine	C28H33ClN2	2.4375	0.096399
DB00866	Alprenolol	C15H23NO2	2.439024	0.096405
DB00283	Clemastine	C21H26ClNO	2.44	0.096408
DB00333	Methadone	C21H27NO	2.44	0.096408
DB01231	Diphenidol	C21H27NO	2.44	0.096408
DB00770	Alprostadil	C20H34O5	2.440678	0.096409
DB01046	Lubiprostone	C20H32F2O5	2.440678	0.096409
DB04575	Quinestrol	C25H32O2	2.440678	0.096409
DB00189	Ethchlorvynol	C7H9ClO	2.444444	0.096407
DB00458	Imipramine	C19H24N2	2.444444	0.096407
DB00612	Bisoprolol	C18H31NO4	2.444444	0.096407
DB00750	Prilocaine	C13H20N2O	2.444444	0.096407
DB00852	Pseudoephedrine	C10H15NO	2.444444	0.096407
DB01010	Edrophonium	C10H15NO	2.444444	0.096407
DB01019	Bethanechol	C7H16N2O2	2.444444	0.096407
DB01242	Clomipramine	C19H23ClN2	2.444444	0.096407
DB01364	Ephedrine	C10H15NO	2.444444	0.096407
DB06804	Nonoxynol-9	C33H60O10	2.446602	0.096399
DB02703	Fusidic Acid	C31H48O6	2.447059	0.096397
DB01057	Echothiophate	C9H23NO3PS	2.447368	0.096395
DB01122	Ambenonium	C28H40Cl2N4O2	2.447368	0.096395
DB06702	Fesoterodine	C26H37NO3	2.447761	0.096393
DB01611	Hydroxychloroquine	C18H26ClN3O	2.44898	0.096384
DB00961	Mepivacaine	C15H22N2O	2.45	0.096376
DB04896	Milnacipran	C15H22N2O	2.45	0.096376
DB08918	Levomilnacipran	C15H22N2O	2.45	0.096376
DB00654	Latanoprost	C26H40O5	2.450704	0.09637
DB01579	Phendimetrazine	C12H17NO	2.451613	0.096361
DB00611	Butorphanol	C21H29NO2	2.45283	0.096348
DB06713	Norelgestromin	C21H29NO2	2.45283	0.096348
DB00149	L-Leucine	C6H13NO2	2.454545	0.096326
DB00167	L-Isoleucine	C6H13NO2	2.454545	0.096326
DB00264	Metoprolol	C15H25NO3	2.454545	0.096326
DB00321	Amitriptyline	C20H23N	2.454545	0.096326
DB00513	Aminocaproic Acid	C6H13NO2	2.454545	0.096326
DB00780	Phenelzine	C8H12N2	2.454545	0.096326
DB00783	Estradiol	C18H24O2	2.454545	0.096326
DB00934	Maprotiline	C20H23N	2.454545	0.096326
DB06698	Betahistine	C8H12N2	2.454545	0.096326
DB01227	Levomethadyl Acetate	C23H31NO2	2.45614	0.096304
DB01433	Methadyl Acetate	C23H31NO2	2.45614	0.096304
DB00717	Norethindrone	C20H26O2	2.458333	0.096268
DB01012	Cinacalcet	C22H22F3N	2.458333	0.096268
DB01186	Pergolide	C19H26N2S	2.458333	0.096268
DB00184	Nicotine	C10H14N2	2.461538	0.096207
DB00294	Etonogestrel	C22H28O2	2.461538	0.096207
DB00302	Tranexamic Acid	C8H15NO2	2.461538	0.096207
DB00176	Fluvoxamine	C15H21F3N2O2	2.465116	0.096125
DB00925	Phenoxybenzamine	C18H22ClNO	2.465116	0.096125
DB01173	Orphenadrine	C18H23NO	2.465116	0.096125
DB00185	Cevimeline	C10H17NOS	2.466667	0.096085
DB01107	Methyprylon	C10H17NO2	2.466667	0.096085
DB01196	Estramustine	C23H31Cl2NO3	2.466667	0.096085
DB01550	Fenproporex	C12H16N2	2.466667	0.096085
DB00207	Azithromycin	C38H72N2O12	2.467742	0.096056
DB00429	Carboprost Tromethamine	C21H36O5.C4H11NO3	2.469136	0.096017
DB00641	Simvastatin	C25H38O5	2.470588	0.095974
DB08823	Spinosad	C42H67NO9	2.470588	0.095974
DB00291	Chlorambucil	C14H19Cl2NO2	2.473684	0.095874
DB00405	Dexbrompheniramine	C16H19BrN2	2.473684	0.095874
DB00835	Brompheniramine	C16H19BrN2	2.473684	0.095874
DB01035	Procainamide	C13H21N3O	2.473684	0.095874
DB01114	Chlorphenamine	C16H19ClN2	2.473684	0.095874
DB01620	Pheniramine	C16H20N2	2.473684	0.095874
DB00473	Hexylcaine	C16H23NO2	2.47619	0.095785
DB00752	Tranylcypromine	C9H11N	2.47619	0.095785
DB01151	Desipramine	C18H22N2	2.47619	0.095785
DB01176	Cyclizine	C18H22N2	2.47619	0.095785
DB08936	Chlorcyclizine	C18H21ClN2	2.47619	0.095785
DB00905	Bimatoprost	C25H37NO4	2.477612	0.095732
DB00938	Salmeterol	C25H37NO4	2.477612	0.095732
DB01126	Dutasteride	C27H30F6N2O2	2.477612	0.095732
DB06708	Lumefantrine	C30H32Cl3NO	2.477612	0.095732
DB08801	Dimetindene	C20H24N2	2.478261	0.095707
DB08824	Ioflupane I 123	C18H23FINO2	2.478261	0.095707
DB00091	Cyclosporine	C62H111N11O12	2.479592	0.095654
DB00677	Isoflurophate	C6H14FO3P	2.48	0.095637
DB00892	Oxybuprocaine	C17H28N2O3	2.48	0.095637
DB00921	Buprenorphine	C29H41NO4	2.48	0.095637
DB01626	Pargyline	C11H13N	2.48	0.095637
DB08834	Tauroursodeoxycholic acid	C26H45NO6S	2.481013	0.095595
DB00280	Disopyramide	C21H29N3O	2.481481	0.095576
DB00647	Dextropropoxyphene	C22H29NO2	2.481481	0.095576
DB00531	Cyclophosphamide	C7H15Cl2N2O2P	2.482759	0.095521
DB01103	Quinacrine	C23H30ClN3O	2.482759	0.095521
DB01181	Ifosfamide	C7H15Cl2N2O2P	2.482759	0.095521
DB00603	Medroxyprogesterone Acetate	C24H34O4	2.483871	0.095471
DB01050	Ibuprofen	C13H18O2	2.484848	0.095427
DB01104	Sertraline	C17H17Cl2N	2.486486	0.09535
DB08803	Tymazoline	C14H20N2O	2.486486	0.09535
DB01160	Dinoprost Tromethamine	C20H34O5.C4H11NO3	2.487179	0.095316
DB00344	Protriptyline	C19H21N	2.487805	0.095286
DB00540	Nortriptyline	C19H21N	2.487805	0.095286
DB00725	Homatropine Methylbromide	C17H24NO3.BrH	2.489362	0.095208
DB01357	Mestranol	C21H26O2	2.489796	0.095185
DB06730	Gestodene	C21H26O2	2.489796	0.095185
This is a portion of the data; to view all the data, please download the file.

Dataset 2.Approved and experimental drugs selected as candidate for treatment of EVD.

AQVN: average quasivalence number; EIIP: electron-ion interaction potential

Dataset 3.Experimental drugs selected as candidate for treatment of EVD.

AQVN: average quasivalence number; EIIP: electron-ion interaction potential

Discussion

Madrid and co-workers selected 24 drugs by in vitro screening of 1012 FDA-approved drugs, which are effective against Ebola virus infection². They also showed that among these compounds, four antimalarial drugs (chloroquine, hydroxychloroquine, amodiaquine and aminoquinoline-13) also are effective against Ebola virus infection in vivo². Among 53 compounds which effectively inhibit Ebola virus infection in vitro, which Kouznetsova and co-workers selected from 2816 approved drugs, are also three anti-malarial drugs (mefloquione, chloroquine, amodiaquine)³. It was also suggested that application of chloroquine for prevention of virus transmission should be considered because this compound significantly inhibits Ebola virus infection¹³. Our analysis showed that 15 of 22 approved ant-malarial drugs (http://en.wikipedia.org/wiki/Antimalarial_medication) are located in EVIIS (Table 3). Six 2-alkylquinolines have been also included in this study. This chemical series is promising as some derivatives exhibited antiviral activity such as 2PQ, and 2QQ^32,33 antimalarial activity such as 2PQ and 2PentQ2³⁴, antileishmanial activity such as 2PQ^35,36 and neurotrophin-like activity on dopaminergic neurons such as 2QI15³⁷. These compounds exhibit some advantages in regard to their chemical synthesis with few steps and good yields as well as their chemical stability in tropical conditions of storage. Their combined effects against virus and Leishmania parasites suggested they could be an advantage for the treatment of Leishmania/HIV co-infections and they were considered as attractive enough to enter the pipeline of DNDi on 2010.

All these data strongly suggest that this class of drugs should be further investigated as a promising source of therapeutics for treatment of EVD. Anti-malarial drugs with dual activity should be of special interest because malaria represents the highest health-related disease in African countries with EVD.

Among 3828 FDA-approved drugs screened for anti-Ebola activity were six antibiotics which inhibit Ebola virus infection (azthromycin, erythromycin, spiramycin, dirithromycin, maduramicin, charitromycin)^2,3. All these antibiotics are within EVIIS and four of them are in cEVIIS. Analysis of 184 approved antibiotics (Dataset 4) showed that only 32 (17.4%) have AQVN and EIIP values in EVIIS, and that 11 of them are located within cEVIIS. Previously we reported domains of AQVN and EIIP which characterize different classes of antibiotics (Table 4)⁶. According to these data, among antibiotics some macrolides, pleuromutilins and aminoglycosides have the highest chance for inhibition of Ebola virus infection. Of note is that five of six antibiotics with experimentally proved activity against Ebola virus infection (azthromycin, erythromycin, spiramycin, dirithromycin, charitromycin) are macrolides. Antibiotics representing candidate Ebola virus infection inhibitors selected by EIIP/AQVN criterion are given in Table 5.

Table 3. Approved anti-malarial drugs selected as candidate drugs for EVD.

Compound	Formula	AQVN	EIIP [Ry]
Quinine	C20H24N2O2	2.625	0.0784
Chloroquinine	C18H26ClN3	2.375	0.0941
Amodiquinine	C20H22ClN3O	2.638	0.0756
Proguanil	C11H16ClN5	2.606	0.0819
Mefloquine	C17H16F6N2O	2.524	0.0928
Primaquine	C15H21NO3	2.600	0.0829
Halofantrine	C26H30Cl2F3NO	2.381	0.0945
Clindamycin	C18H33ClN2O5S	2.533	0.0919
Artemether	C16H26O5	2.553	0.0897
Piperaquine	C29H32Cl2N6	2.609	0.0814
Artemotil	C17H28O5	2.520	0.0931
Dihydroartemisin	C15H24O5	2.591	0.0844
Quinidine	C20H24N2O2	2.625	0.0784
Cinchonidine	C19H22N2O	2.591	0.0844
Artemisin	C15H22O5	2.667	0.0693

Table 4. AQVN and EIIP range of different antibiotics classes⁶.

Antibiotic class	AQVN	EIIP [Ry]
Penicillins	2.975 - 3.180	0.035 - 0.124
Cephalosporins	3.071 - 3.473	0.070 - 0.130
Carbapenems & Penems	2.973 - 3.059	0.022 - 0.066
Monobactams	3.166 - 3.581	0.100 - 0.134
Quinolines	2.760 - 3.060	0.003 - 0.065
Aminoglycosides	2.552 - 2.820	0.024 - 0.084
Tetracyclines	2.933 - 3.111	0.018 - 0.084
Macrolides	2.467 - 2.630	0.077 - 0.096
Pleuromutilins	2.395 - 2.473	0.095 - 0.096
Nitrofurans	3.652 - 3.826	0.010 - 0.086

Antibiotics	Formula	AQVN	EIIP [Ry]
Teicoplanins	C77H77Cl2N9O13 -R	2.75	0.046
Telavancin	C80H106Cl2N11O27P	2.863	0.008
Vancomycin	C66H75Cl2N9O24	3.011	0.048
Cefpodoxime	C15H17N5O6S2	3.333	0.132
Tiamulin	C28H47NO4S	2.395	0.095
Retapamulin	C30H47NO4S	2.434	0.096
Valnemulin	C31H52N2O5S	2.440	0.096
Azithromycin	C38H72N2O12	2.468	0.096
BC-3205	C32H51N2O5S	2.472	0.096
Dirithromycin	C42H78N2O14	2.500	0.095
Clarithromycin	C38H69NO13	2.512	0.094
Surfactin	C53H93N7O13	2.518	0.093
Erythromycin	C37H67NO13	2.525	0.093
Clindamycin	C18H33ClN2O5S	2.533	0.092
Roxithromycin	C41H76N2O15	2.537	0.092
Oleandomycin	C35H61NO12	2.550	0.090
Gentamicin	C21H43N5O7	2.553	0.090
Spiramycin	C43H74N2O14	2.556	0.089
Mupirocin	C26H44O9	2.557	0.089
Lincomycin	C18H34N2O6S	2.590	0.085
Netilmicin	C21H41N5O7	2.595	0.084
Astromicin	C17H35N5O6	2.603	0.082
Tylosin	C46H77NO17	2.610	0.081
Kitasamycin	C35H59NO13	2.611	0.081
Josamycin	C42H69NO15	2.614	0.080
Telithromycin	C43H65N5O10	2.618	0.080
Telithromycin	C43H65N5O10	2.618	0.080
Verdamicin	C20H39N5O7	2.620	0.080
Midecamycin	C41H67NO15	2.629	0.078
Troleandomycin	C41H67NO15	2.629	0.078
Sisomicin	C19H37N5O7	2.647	0.074
Cethromycin	C42H59N3O10	2.649	0.073
Carbomycin A	C42H67NO16	2.667	0.069
Dibekacin	C18H37N5O8	2.676	0.067
Echinocandin B	C52H81N7O16	2.692	0.063
Rifabutin	C46H62N4O11	2.699	0.061
Arbekacin	C22H44N6O10	2.707	0.059
Rifapentine	C47H64N4O12	2.709	0.059
Herbimycin	C30H42N2O9	2.723	0.055
Tobramycin	C18H37N5O9	2.725	0.054
Grepafloxacin	C19H22FN3O3	2.750	0.047
Geldanamycin	C29H40N2O9	2.750	0.047
Rifampicin	C43H58N4O12	2.752	0.046
Sparfloxacin	C19H22F2N4O3	2.760	0.044
Balofloxacin	C20H24FN3O4	2.769	0.041
Bekanamycin	C18H37N5O10	2.771	0.040
Fleroxacin	C17H18F3N3O3	2.773	0.039
Lomefloxacin	C17H19F2N3O3	2.773	0.039
Pefloxacin	C17H20FN3O3	2.773	0.039
Isepamicin	C22H43N5O12	2.780	0.037
Paromomycin	C23H47N5O14	2.786	0.035
Quinupristin/dalfopristin	C53H67N9O10S	2.786	0.035
Moxifloxacin	C21H24FN3O4	2.792	0.033
Pristinamycin IIA	C28H35N3O7	2.794	0.032
Neomycin	C23H46N6O13	2.796	0.032
Nadifloxacin	C19H21FN2O4	2.808	0.028
Spectinomycin	C14H24N2O7	2.808	0.028
Kanamycin	C18H36N4O11	2.812	0.026
Tigecycline	C29H39N5O8	2.815	0.025
Gatifloxacin	C19H22FN3O4	2.816	0.025
Linezolid	C16H20FN3O4	2.818	0.024
Amikacin	C22H43N5O13	2.819	0.024
Meropenem	C17H25N3O5S	2.824	0.022
Sitafloxacin	C19H18ClF2N3O3	2.826	0.021
Norfloxacin	C16H18FN3O3	2.829	0.020
Pristinamycin IA	C45H54N8O10	2.855	0.011
Ciprofloxacin	C17H18FN3O3	2.857	0.010
Clinafloxacin	C17H17ClFN3O3	2.857	0.010
Eperezolid	C18H23FN4O5	2.863	0.008
Ofloxacin	C18H20FN3O4	2.870	0.006
Levofloxacin	C18H20FN3O4	2.870	0.006
Trimethoprim	C14H18N4O3	2.872	0.005
Trimethoprim	C14H18N4O3	2.872	0.005
Rolitetracycline	C27H33N3O8	2.873	0.004
Temafloxacin	C21H18F3N3O3	2.875	0.004
Hygromycin B	C20H37N3O13	2.877	0.003
Virginiamycin S1	C43H49N7O10	2.899	0.005
Enoxacin	C15H17FN4O3	2.900	0.006
Radezolid	C22H23FN6O3	2.909	0.009
Streptomycin	C21H39N7O12	2.911	0.010
Daptomycin	C72H101N17O26	2.917	0.012
Rufloxacin	C17H18FN3O3S	2.930	0.017
Minocycline	C23H27N3O7	2.933	0.018
Nimorazole	C9H14N4O3	2.933	0.019
Gemifloxacin	C18H20FN5O4	2.958	0.028
Flumequine	C14H12FNO3	2.968	0.032
Sulfadicramide	C11H14N2O3S	2.968	0.032
Imipenem	C12H17N3O4S	2.973	0.034
Piromidic acid	C14H16N4O3	2.973	0.034
Pipemidic acid	C14H17N5O3	2.974	0.034
Benzylpenicillin	C16H18N2O4S	2.976	0.035
Ampicillin	C16H19N3O4S	2.977	0.035
Nafcillin	C21H22N2O5S	2.980	0.036
Doripenem	C15H24N4O6S2	2.980	0.036
Pazufloxacin	C16H15FN2O4	3.000	0.044
Tosufloxacin	C19H15F3N4O3	3.000	0.044
Mafenide	C7H10N2O2S	3.000	0.044
Ornidazole	C7H10ClN3O3	3.000	0.044
Secnidazole	C7H11N3O3	3.000	0.044
Trovafloxacin	C20H15F3N4O3	3.022	0.052
Sulfadimidine	C12H14N4O2S	3.030	0.055
Sulfisomidine	C12H14N4O2S	3.030	0.055
Ertapenem	C22H25N3O7S	3.034	0.057
Nalidixic acid	C12H12N2O3	3.034	0.057
Tetracycline	C22H24N2O8	3.036	0.057
Chlortetracycline	C22H23ClN2O8	3.036	0.057
Doxycycline	C22H24N2O8	3.036	0.057
Lymecycline	C22H23ClN2O8	3.036	0.057
Posizolid	C21H21F2N3O7	3.037	0.058
Meticillin	C17H20N2O6S	3.043	0.060
Amoxicillin	C16H19N3O5S	3.046	0.061
Phenoxymethylpenicillin	C16H18N2O5S	3.048	0.062
Piperacillin	C23H27N5O7S	3.048	0.062
Rosoxacin	C17H14N2O3	3.056	0.064
Faropenem	C12H15NO5S	3.059	0.066
Chloramphenicol	C11H12Cl2N2O5	3.062	0.067
Cefepime	C19H24N6O5S2	3.071	0.070
Cefalexin	C16H17N3O4S	3.073	0.071
Oxytetracycline	C22H24N2O9	3.088	0.076
Azlocillin	C20H23N5O6S	3.091	0.077
Demeclocycline	C21H21ClN2O8	3.094	0.078
Sulfafurazole	C11H13N3O3S	3.097	0.079
Sulfamoxole	C11H13N3O3S	3.097	0.079
Tinidazole	C8H13N3O4S	3.103	0.081
Oxacillin	C19H19N3O5S	3.106	0.082
Cloxacillin	C19H18ClN3O5S	3.106	0.082
Dicloxacillin	C19H17Cl2N3O5S	3.106	0.082
Flucloxacillin	C19H17ClFN3O5S	3.106	0.082
Meclocycline	C22H21ClN2O8	3.111	0.084
Metacycline	C22H22N2O8	3.111	0.084
Sulfaphenazole	C15H14N4O2S	3.111	0.084
Prulifloxacin	C21H20FN3O6S	3.115	0.085
Sulfametomidine	C12H14N4O3S	3.118	0.086
Sulfaperin	C11H12N4O2S	3.133	0.090
Carbenicillin	C17H18N2O6S	3.136	0.091
Sulfapyridine	C11H11N3O2S	3.143	0.093
Metronidazole	C6H9N3O3	3.143	0.093
Torezolid	C17H15FN6O3	3.143	0.093
Prontosil	C12H13N5O2S	3.152	0.096
Cefaclor	C15H14ClN3O4S	3.158	0.098
Nocardicin A	C23H24N4O9	3.167	0.100
Sulfacetamide	C8H10N2O3S	3.167	0.100
Sulfaguanidine	C7H10N4O2S	3.167	0.100
Mezlocillin	C21H25N5O8S2	3.180	0.104
Propenidazole	C11H13N3O5	3.188	0.106
Cefpirome	C22H22N6O5S2	3.193	0.107
Sulfadimethoxine	C12H14N4O4S	3.200	0.109
Sulfaquinoxaline	C14H12N4O2S	3.212	0.112
Sulfamethoxazole	C10H11N3O3S	3.214	0.113
Sulfamazone	C23H24N6O7S2	3.226	0.115
Sulfametoxydiazine	C11H12N4O3S	3.226	0.115
Temocillin	C16H18N2O7S2	3.244	0.119
Sulfadiazine	C10H10N4O2S	3.259	0.122
Oxolinic acid	C13H11NO5	3.267	0.123
Ticarcillin	C15H16N2O6S2	3.268	0.123
Cefalotin	C16H16N2O6S2	3.286	0.126
Azanidazole	C10H10N6O2	3.286	0.126
Ceftazidime	C22H22N6O7S2	3.288	0.127
Clavulanic acid	C8H9NO5	3.304	0.129
Clavulanic acid	C8H9NO5	3.304	0.129
Sulfathiourea	C7H9N3O2S2	3.304	0.129
Cefamandole	C18H18N6O5S2	3.306	0.129
Aldesulfone	C14H16N2O6S3	3.317	0.130
Sulfamethizole	C9H10N4O2S2	3.333	0.132
Sulfathiazole	C9H9N3O2S2	3.360	0.134
Tazobactam	C10H12N4O5S	3.375	0.134
Cinoxacin	C12H10N2O5	3.379	0.134
Sulfasalazine	C18H14N4O5S	3.381	0.134
Succinylsulfathiazole	C13H13N3O5S2	3.389	0.134
Cefotaxime	C16H17N5O7S2	3.404	0.134
Cefuroxime	C16H16N4O8S	3.422	0.134
Aztreonam	C13H17N5O8S2	3.422	0.134
Phthalylsulfathiazole	C17H13N3O5S2	3.450	0.132
Ceftibuten	C15H14N4O6S2	3.463	0.131
Ceftaroline	C24H25N8O10PS4	3.472	0.130
Nifuroxazide	C12H9N3O5	3.517	0.123
Ceftriaxone	C18H18N8O7S3	3.518	0.123
Cefazolin	C14H14N8O4S3	3.535	0.120
Tigemonam	C12H15N5O9S2	3.581	0.108
Furazolidone	C8H7N3O5	3.652	0.086
Nitrofurazone	C6H6N4O4	3.700	0.068
Nifurtoinol	C9H8N4O6	3.704	0.066
Nifurzide	C12H8N4O6S	3.806	0.020

Dataset 4.Approved antibiotics screened for candidate anti-Ebola drugs.

AQVN: average quasivalence number; EIIP: electron-ion interaction potential

Previous, we determined AQVN and EIIP domains characterizing different classes of anti-HIV drugs^4–9. As can be seen in Table 6, the EIIP/AQVN domain of CCR5 HIV entry inhibitors is within EVIIS, and domains of CXCR4 HIV entry inhibitors and HIV protease inhibitors partially overlaps EVIIS. The EIIP/AQVN domains of other classes of anti-HIV agents are located outside EVIIS. This indicates that some HIV entry inhibitors and HIV protease inhibitors could also be effective drugs against Ebola virus infection.

Table 5. Antibiotics selected as candidate drugs for EVD.

Antibiotics	Formula	AQVN	EIIP [Ry]
Tiamulin	C₂₈H₄₇NO₄S	2.395	0.095
Retapamulin	C₃₀H₄₇NO₄S	2.434	0.096
Valnemulin	C₃₁H₅₂N₂O₅S	2.440	0.096
Azithromycin	C₃₈H₇₂N₂O₁₂	2.468	0.096
BC-3205	C₃₂H₅₁N₂O₅S	2.472	0.096
Dirithromycin	C₄₂H₇₈N₂O₁₄	2.500	0.095
Clarithromycin	C₃₈H₆₉NO₁₃	2.512	0.094
Surfactin	C₅₃H₉₃N₇O₁₃	2.518	0.093
Erythromycin	C₃₇H₆₇NO₁₃	2.525	0.093
Clindamycin	C₁₈H₃₃ClN₂O₅S	2.533	0.092
Roxithromycin	C₄₁H₇₆N₂O₁₅	2.537	0.092
Oleandomycin	C₃₅H₆₁NO₁₂	2.550	0.090
Gentamicin	C₂₁H₄₃N₅O₇	2.553	0.090
Spiramycin	C₄₃H₇₄N₂O₁₄	2.556	0.089
Mupirocin	C₂₆H₄₄O₉	2.557	0.089
Lincomycin	C₁₈H₃₄N₂O₆S	2.590	0.085
Netilmicin	C₂₁H₄₁N₅O₇	2.595	0.084
Astromicin	C₁₇H₃₅N₅O₆	2.603	0.082
Tylosin	C₄₆H₇₇NO₁₇	2.610	0.081
Kitasamycin	C₃₅H₅₉NO₁₃	2.611	0.081
Josamycin	C₄₂H₆₉NO₁₅	2.614	0.080
Telithromycin	C₄₃H₆₅N₅O₁₀	2.618	0.080
Telithromycin	C₄₃H₆₅N₅O₁₀	2.618	0.080
Verdamicin	C₂₀H₃₉N₅O₇	2.620	0.080
Midecamycin	C₄₁H₆₇NO₁₅	2.629	0.078
Troleandomycin	C₄₁H₆₇NO₁₅	2.629	0.078
Sisomicin	C₁₉H₃₇N₅O₇	2.647	0.074
Cethromycin	C₄₂H₅₉N₃O₁₀	2.649	0.073
Carbomycin A	C₄₂H₆₇NO₁₆	2.667	0.069
Dibekacin	C₁₈H₃₇N₅O₈	2.676	0.067
Echinocandin B	C₅₂H₈₁N₇O₁₆	2.692	0.063
Rifabutin	C₄₆H₆₂N₄O₁₁	2.699	0.061

Table 6. AQVN and EIIP range of anti-HIV drugs⁶.

Target	AQVN	EIIP [Ry]
CXCR4	2.16 - 2.53	0.062 - 0.096
CCR5	2.42 - 2.63	0.079 - 0.099
PI	2.61 - 2.78	0.040 - 0.080
NRTI/NtRTI	2.92 - 3.20	0.040 - 0.100
INI	3.00 - 3.20	0.044 - 0.116
Anti-HIV flavonoids	3.34 - 3.59	0.110 - 0.135

In conclusion, the presented results show that the EIIP/AQVN criterion can be used as an efficient filter in virtual screening of molecular libraries for candidate inhibitors of Ebola virus infection. Approved (Dataset 2) and experimental drugs (Dataset 3), anti-malarial drugs (Table 3) and antibiotics (Table 5) selected by this criterion represents a valuable source of candidate therapeutics for treatment of EVD, some of which are already approved by FDA for treatment of other diseases which can be repurposed for use in EVD. We hope that these data, obtained by an in silico drug repurposing screen, will accelerate discovery of drugs for treatment of EVD, which are necessary in this ongoing emergency situation caused by the current unprecedented Ebola virus outbreak. To enable other researchers working on online EIIP/AQVN-based screening of different sources of small molecules for candidate Ebola drugs, we established an open web server (http://www.biomedconsulting.info/ebola_screen.php).

Data availability

The virtual screen for candidate inhibitors of EBOLA virus infection web tool is available at: http://www.biomedconsulting.info/tools/ebolascreen.php. An archived version can be accessed at: http://www.webcitation.org/6Vxtuojgx³⁸

F1000Research: Dataset 1. FDA-approved drugs which are active against Ebola virus infection^2,3, 10.5256/f1000research.6110.d42876³⁹

F1000Research: Dataset 2. Approved and experimental drugs selected as candidate for treatment of EVD, 10.5256/f1000research.6110.d42877⁴⁰

F1000Research: Dataset 3. Experimental drugs selected as candidate for treatment of EVD, 10.5256/f1000research.6110.d42878⁴¹

F1000Research: Dataset 4. Approved antibiotics screened for candidate anti-Ebola drugs, 10.5256/f1000research.6110.d42879⁴²

Author contributions

Conceived and designed the study: VV SG NV. Developed the analysis tools: VP. Analyzed the data: VV SG NV DRB PPML BF DPC. Wrote the paper: VV DRB PPML.

Competing interests

No competing interests were disclosed.

Grant information

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant no. 173001).

Acknowledgments

The authors would like to gratefully acknowledge networking support by the COST Action CM1307.

Faculty Opinions recommended

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32. Fakhfakh MA, Fournet A, Prina E, et al.: Synthesis and biological evaluation of substituted quinolines: potential treatment of protozoal and retroviral co-infections. Bioorg Med Chem. 2003; 11(23): 5013–23. PubMed Abstract | Publisher Full Text
33. Fournet A, Mahieux R, Fakhfakh MA, et al.: Substituted quinolines induce inhibition of proliferation of HTLV-1 infected cells. Bioorg Med Chem Lett. 2003; 13(5): 891–4. PubMed Abstract | Publisher Full Text
34. Gantier JC, Fournet A, Munos MH, et al.: The effect of some 2-substituted quinolines isolated from Galipea longiflora on Plasmodium vinckei petteri infected mice. Planta Med. 1996; 62(3): 285–6. PubMed Abstract | Publisher Full Text
35. Campos Vieira N, Vacus J, Fournet A, et al.: Antileishmanial activity of a formulation of 2-n-propylquinoline by oral route in mice model. Parasite. 2011; 18(4): 333–6. PubMed Abstract | Publisher Full Text | Free Full Text
36. Nakayama H, Loiseau PM, Bories C, et al.: Efficacy of orally administered 2-substituted quinolines in experimental murine cutaneous and visceral leishmaniases. Antimicrob Agents Chemother. 2005; 49(12): 4950–6. PubMed Abstract | Publisher Full Text | Free Full Text
37. Schmidt F, Champy P, Séon-Méniel B, et al.: Chemicals possessing a neurotrophin-like activity on dopaminergic neurons in primary culture. PLoS One. 2009; 4(7): e6215. PubMed Abstract | Publisher Full Text | Free Full Text
38. Perovic VR: Virtual screen for candidate inhibitors of EBOLA virus infection. F1000Res. 2015. Reference Source
39. Veljkovic V, Loiseau PM, Figadère B, et al.: Dataset 1 in: Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection. F1000Research. 2015. Data Source
40. Veljkovic V, Loiseau PM, Figadère B, et al.: Dataset 2 in: Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection. F1000Research. 2015. Data Source
41. Veljkovic V, Loiseau PM, Figadère B, et al.: Dataset 3 in: Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection. F1000Research. 2015. Data Source
42. Veljkovic V, Loiseau PM, Figadère B, et al.: Dataset 4 in: Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection. F1000Research. 2015. Data Source
43. Todeschini R, Consoni V: Molecular descriptors for chemoinformatics. Weinheim: Wiley VCH Verlag. 2009. Publisher Full Text

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This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant no. 173001).

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© 2015 Veljkovic V et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

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Veljkovic V, Loiseau PM, Figadere B et al. Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection [version 2; peer review: 2 approved] F1000Research 2015, 4:34 (https://doi.org/10.12688/f1000research.6110.2)

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Patrick Butaye, School of Veterinary Medicine, Ross University, Basseterre, Saint Kitts and Nevis

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Reviewer Report 11 Feb 2015

Bruno Botta, Department of Chemistry and Pharmaceutical Technology, Sapienza University of Rome, Rome, Italy

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https://doi.org/10.5256/f1000research.6544.r7633

To identify drug candidates against Ebola virus infections is surely an urgent need, especially in light of recent virus outbreaks registered mostly in Africa. In this respect, Velijkovic's article is presented in a timely manner and offers a fast and reliable opportunity to screen among large databases to reposition old drugs against Ebola.

The experimental design relies on a consolidated methodology, developed by some of the authors and successfully applied in multiple projects. Overall, the manuscript is clear and few minor editing would be necessary, in my personal opinion, to improve its consistency.

In Materials and Methods, a dataset of 152 drugs that counteract Ebola virus in vitro and in vivo has been selected as training set. However, it seems that an inconsistency does exist within this number. As authors have reported, these anti-Ebola drugs have been identified by Madrid (24 molecules) and Kouznetsova (53 molecules and 95 weaker drugs). Accordingly, the total number of FDA-approved drugs identified in these studies is 172. Why authors used a smaller set of 152? Is there any structural redundancy? A clarification of this discrepancy would improve the reproducibility of the work.

Finally, if I understood properly authors have selected more than 500 molecules (including FDA-approved and experimental drugs) as anti-Ebola candidates by means of in silico screening and suggest that further in vitro/in vivo tests should be performed on these molecules. In my opinion, this number is still too large for enabling efficient and fast in vitro and/or in vivo assays. Experimental testing of this set would require significant efforts. Just for comparison, the number of candidates selected in silico by authors is about half of those selected by Madrid by means of HTS (ref 2). Is there a way to prioritize small molecules by using the EIIP/AQVN-based approach, and to provide a lower number of compounds to be submitted to experimental evaluation? Authors should comment on this point, because the advantage of using the EIIP/AQVN-based screening in silico appears to be limited in the current version of the manuscript.

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Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

13 Feb 2015

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The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its ... Continue reading The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its receptor. Further filtering of these candidate anti-Ebola drugs by other structural tools (pharmacophoric modeling, docking studies, etc.) will reduce the number of compounds for experimental testing.
The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its receptor. Further filtering of these candidate anti-Ebola drugs by other structural tools (pharmacophoric modeling, docking studies, etc.) will reduce the number of compounds for experimental testing.
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Author Response 13 Feb 2015

Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

13 Feb 2015

Author Response

The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its ... Continue reading The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its receptor. Further filtering of these candidate anti-Ebola drugs by other structural tools (pharmacophoric modeling, docking studies, etc.) will reduce the number of compounds for experimental testing.
The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its receptor. Further filtering of these candidate anti-Ebola drugs by other structural tools (pharmacophoric modeling, docking studies, etc.) will reduce the number of compounds for experimental testing.
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Reviewer Report 11 Feb 2015

Patrick Butaye, School of Veterinary Medicine, Ross University, Basseterre, Saint Kitts and Nevis

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https://doi.org/10.5256/f1000research.6544.r7552

This manuscript deals with the in silico analysis of molecules for their activity against Ebola Virus (EBV). They started from a reference library of compounds who have previously demonstrated in vitro and/or in vivo activity against EBV and analyzed these compounds by the determination of their EIIP and AQVN. With these data, they scanned a larger collection of compounds with unknown activity against EBV and selected possible candidates to test for their activity in vitro and/or in vivo. This is a straight forward manuscript however it may be better structured. The part of the M&M dealing with the EIIP and AQVN is more appropriate to go into the introduction since this is background information of the calculation. For clarity of the manuscript is also better to separate the results section as it is difficult to follow now. Start with the analysis of the compounds with known activity, the two datasets, and then proceed with results from the unknown dataset. Then in the discussion, the different products of interest may be evaluated. This will largely increase the readability. Upon the antibiotics, it would be good to elaborate a bit on how they work on EBV, since common sense tells that antibiotics do not work on viruses. A better explanation on how these products may interact and inhibit/kill EBV would also be good.

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Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

13 Feb 2015

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The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
Competing Interests: No competing interests were disclosed.
The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
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Author Response 13 Feb 2015

Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

13 Feb 2015

Author Response

The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
Competing Interests: No competing interests were disclosed.
The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).
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Bruno Botta, Department of Chemistry and Pharmaceutical Technology, Sapienza University of Rome, Rome, Italy

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Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

The aim of the work was not only to reduce the number of candidate drugs for EVD but to select all approved drugs which will efficiently target GP or its receptor. Further filtering of these candidate anti-Ebola drugs by other structural tools (pharmacophoric modeling, docking studies, etc.) will reduce the number of compounds for experimental testing.

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Patrick Butaye, School of Veterinary Medicine, Ross University, Basseterre, Saint Kitts and Nevis

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13 Feb 2015

Veljko Veljkovic, Center for Multidisciplinary Research, University of Belgrade, Institute of Nuclear Sciences VINCA, P.O. Box 522, 11001 Belgrade, Serbia

The antibiotics presented in this article are not selected because of their antibiotic activity but they are proposed as candidate entry inhibitors of Ebola virus (drug repurposing).

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Competing Interests

No competing interests were disclosed.

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[31] 31. Yonezawa A, Cavrois M, Greene WC: Studies of Ebola virus glycoprotein-mediated entry and fusion by using pseudotyped human immunodeficiency virus type 1 virions: involvement of cytoskeletal proteins and enhancement by tumor necrosis factor alpha. J Virol. 2005; 79(2): 918–26. PubMed Abstract | Publisher Full Text | Free Full Text

[32] 32. Fakhfakh MA, Fournet A, Prina E, et al.: Synthesis and biological evaluation of substituted quinolines: potential treatment of protozoal and retroviral co-infections. Bioorg Med Chem. 2003; 11(23): 5013–23. PubMed Abstract | Publisher Full Text

[33] 33. Fournet A, Mahieux R, Fakhfakh MA, et al.: Substituted quinolines induce inhibition of proliferation of HTLV-1 infected cells. Bioorg Med Chem Lett. 2003; 13(5): 891–4. PubMed Abstract | Publisher Full Text

[34] 34. Gantier JC, Fournet A, Munos MH, et al.: The effect of some 2-substituted quinolines isolated from Galipea longiflora on Plasmodium vinckei petteri infected mice. Planta Med. 1996; 62(3): 285–6. PubMed Abstract | Publisher Full Text

[35] 35. Campos Vieira N, Vacus J, Fournet A, et al.: Antileishmanial activity of a formulation of 2-n-propylquinoline by oral route in mice model. Parasite. 2011; 18(4): 333–6. PubMed Abstract | Publisher Full Text | Free Full Text

[36] 36. Nakayama H, Loiseau PM, Bories C, et al.: Efficacy of orally administered 2-substituted quinolines in experimental murine cutaneous and visceral leishmaniases. Antimicrob Agents Chemother. 2005; 49(12): 4950–6. PubMed Abstract | Publisher Full Text | Free Full Text

[37] 37. Schmidt F, Champy P, Séon-Méniel B, et al.: Chemicals possessing a neurotrophin-like activity on dopaminergic neurons in primary culture. PLoS One. 2009; 4(7): e6215. PubMed Abstract | Publisher Full Text | Free Full Text

[38] 38. Perovic VR: Virtual screen for candidate inhibitors of EBOLA virus infection. F1000Res. 2015. Reference Source

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[43] 43. Todeschini R, Consoni V: Molecular descriptors for chemoinformatics. Weinheim: Wiley VCH Verlag. 2009. Publisher Full Text

Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Material and methods

Molecular libraries

Calculation of AQVN and EIIP parameters of organic molecules

Results

Table 1. Small-molecule entry inhibitors for Ebola virus.

Figure 1. Distribution of compounds according to their average quasivalence number (AQVN) and electron-ion interaction potential (EIIP) values.

Table 2. Viral transcription inhibitors and microtubule modulators with anti-Ebola virus activity.

Discussion

Table 3. Approved anti-malarial drugs selected as candidate drugs for EVD.

Table 4. AQVN and EIIP range of different antibiotics classes6.

Table 5. Antibiotics selected as candidate drugs for EVD.

Table 6. AQVN and EIIP range of anti-HIV drugs6.

Data availability

Author contributions

Competing interests

Grant information

Acknowledgments

References

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Table 4. AQVN and EIIP range of different antibiotics classes⁶.

Table 6. AQVN and EIIP range of anti-HIV drugs⁶.