The outbreaks of Ebola virus (EBOV) disease (EVD) in Africa have come at great human and financial cost (1, 2) . For example, the outbreak in 2014-2016 killed over 11,000 and it is estimated that it resulted in $53bn in economic damage (3) . The most recent outbreak in the Democratic Republic of the Congo, has killed more than 2200 people (4) . Even with approval of a vaccine for prevention of EVD (5) there is still an urgent need to advance development of filovirus-specific antiviral therapeutics. A clinical trial (NCT03719586) investigated ZMapp (a monoclonal antibody cocktail) (6) ), remdesivir (a small molecule), MAb114 (a monoclonal combination) (7)) and REGN-EB3 (monoclonal antibody combination) (8) . These results showed that the antibodies REGN-EB3 and mAb114 had overall statistically similar survival rates of 71% and 66%, respectively. Unfortunately, ZMapp and remdesivir were less effective with a 51% and 47% survival rates, respectively (9) . With remdesivir showing no statistically significant clinical efficacy the search for antiviral small molecules continues.
In an effort to repurpose drugs for the treatment of EVD, we have developed a Bayesian machine learning (ML) approach with a set of 868 anti-EBOV active molecules identified in a viral pseudotype entry assay and confirmed in an EBOV replication assay (10, 11) . The EBOV ML model enabled us to virtually screen several thousand compounds and identify three active compounds against EBOV: tilorone, quinacrine and pyronaridine tetraphosphate (12) . The three molecules inhibited EBOV in HeLa cells and demonstrated significant in vivo activity in the mouse-adapted EBOV (ma-EBOV) 4 efficacy model (13) (14) (15) (16) and in the guinea pig model of EBOV infection. The compounds also inhibited replication of multiple strains of EBOV and Marburg virus (MARV) (17) .
The trend for compounds to be active against both EBOV and MARV has been demonstrated before, with analysis of previously published data revealing an in vitro inhibition (IC 50 's) correlation ((10, 18) , Figure S1 ).
To date, we have not determined the mechanism of the antiviral compounds we have identified. Previously we evaluated pyronaridine, tilorone and quinacrine in vitro for its anti-EBOV activity (Zaire strain) in the type I IFN-deficient Vero 76 cell line (19, 20) and no antiviral activity was observed at any concentration below the 50% cytotoxicity concentration. In HeLa cells all three drugs demonstrated selectivity (12, 14) . These observations support the hypothesis that their antiviral activity could be partially acting through or on the type I IFN-related innate immunity pathway (15) . We also tested a combination of pyronaridine with tilorone in HeLa cells and evaluated the data with the BRAID model which suggested they are likely synergistic (21) . Based on published data for tilorone and quinacrine, which are well known to be lysosomotropic agents, it was suspected that this may also be important. In addition, pyronaridine is used as an antimalarial in combination with artesunate (Pyramax®). We had previously determined that artesunate also has micromolar in vitro inhibitory activity against EBOV (22) . We now assess whether pyronaridine accumulates in lysosomes and if there is any effect with artesunate or its active metabolite dihydroartemisinin against EBOV when combined with pyronaridine in vitro. 5 
Pyronaridine tetraphosphate Sigma-Aldrich (#D7439).
Pyronaridine tetraphosphate, tilorone and quinacrine were also tested (using the NIAID DMID services) against representatives of several viruses using human cells. The general methods have been described previously (16) .
The Assay Central software has been previously described (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) which uses the source code management system Git to gather and store structure-activity datasets collated in Molecular Notebook (Molecular Materials Informatics, Inc. in Montreal, Canada). The output is a high-quality dataset and a Bayesian model using extendedconnectivity fingerprints of maximum diameter 6 (ECFP6) descriptors. Each model includes several metrics to evaluate and compare predictive performance as previously described in a relevant publication (29) , including Receiver Operator Characteristic, 6 Recall, Precision, F1 Score, Cohen's Kappa (33, 34) , and Matthews Correlation Coefficient (35) . Applicability is representative of the overlap between the training and the test set. It is the quotient of the total number of ECFP6 fingerprints of the test molecule represented in the model divided by the total number of ECFP6 fingerprints of that test molecule. Generation and interpretation of prediction scores has been previously described (36, 37) . The model consisted of curated data from a key paper from Nadanaciva et al. (38) , where their quantitative approach to measuring lysosomotropic properties allowed for a direct activity threshold cut-off and was defined as an IC 50 
A previous published lysosomotropic assay by Nadanaciva et al. was used as the basis for the following work (38) .
The human metastatic mammary gland cell line MCF7 was obtained from American 
The BRAID analysis (21) The SynergyFinder analysis service (41) similarly calculates the degree of combination, synergy or antagonism by comparing the observed drug combination response against the expected response, while assuming there is no interaction between the two drugs.
These scores were calculated using the Loewe reference additivity mode (42) . The threshold to define a good synergy score is variable, but the program developers suggest that synergy scores near 0 gives limited confidence on synergy or antagonism and a score < -10 or >10 are expected to be antagonist or synergistic, respectively.
Pyronaridine, quinacrine and tilorone were previously demonstrated to be active against EBOV in HeLa cell but not Vero cells ( showed selectivity in the cell lines and concentrations tested, but this may be due to high cytotoxicity and or the insufficient range of concentrations tested in these cell lines (Table S1A- 
A Bayesian machine learning model with 52 compounds (23 were classed as lysosomotropic) was generated from published data using Assay Central TM with 5-fold cross validation ROC = 0.765 ( Figure 1 ). Additional model statistics suggest that the model is potentially useful for scoring compounds to predict lysosomal accumulation (Table 1) . Tilorone, pyronaridine and artesunate were used as a prospective test set. All compounds were correctly predicted, with tilorone and pyronaridine predicted to be lysosomotropic, while artesunate was predicted to not be lysosomotropic.
Pyronaridine tetraphosphate was found to be a potent inhibitor of Lysotracker accumulation in MCF7 lysosomes in vitro (IC 50 = 0.56 μM). In contrast, artesunate showed no appreciable inhibition of Lysotracker ( Figure 2 ). Tilorone (IC 50 = 3.09 μM), chloroquine (IC 50 = 6.21 μM) and finally quinacrine (IC 50 = 7.31 μM) were less potent inhibitors of Lysotracker accumulation in MCF7 lysosomes in vitro ( Figure S2 ).
The BRAID analysis (Figure 3 ) of pyronaridine and artesunate in vitro inhibition data from the checkerboard assay indicates additivity of these molecules in HeLa cells.
Artesunate ameliorates the toxicity of pyronaridine in the checkboard assay and therefore indirectly potentiates pyronaridine. The non-linear regression, 4-parameter curve fit (Hill equation) for the artesunate control in Huh 7 cells suggested a plateau at ~60% inhibition ( Figure S3 ), therefore the combination data for this cell-line and pyronaridine/artesunate pair was not included in the analysis. Pyronaridine and DHA similarly shows an additive effect in HeLa and Huh 7 cells, both with a parallel reduction in toxicity based on the BRAID analysis ( Figure S4) . A secondary analysis using Synergyfinder ( Figures S5-S6 ) also suggests that this combination indirectly potentiates pyronaridine in HeLa cells, but in Huh 7 cells these interpretations are ambiguous ( Figure S7 ).
Within the last 5 years we have seen two major EVD outbreaks in Africa. These led to renewed efforts to develop treatments for this virus. The actives and inactives of several in vitro high throughput drug screens (10, 18, 43) have been used to develop computational models for predicting anti-EBOV activity of compounds. More recently, combinations of approved drugs found in these and other studies have suggested synergistic combinations (44) (45) (46) (47) . To date, none of these many efforts for EBOV have resulted in a clinical antiviral candidate. Several small molecule antivirals were felled at the hurdle of animal models, specifically the transition from mouse to the guinea pig model. Compounds that have failed to show in vivo efficacy against EBOV following this well-trodden route include chloroquine (18, 48) , azithromycin (18), amiodarone (49),
BGB324 (50), NCK8 (50) and 17-DMAG (50) . As discussed previously (17) , this may be due to differences in drug metabolism making the model inappropriate for EBOV.
Efforts to improve the efficiency and cost effectiveness of EBOV drug discovery have involved our efforts to identify several molecules using ML which have progressed through in vitro and in vivo testing (12-15, 17, 51) . We have also previously used these ML models to predict in vitro efficacy for drugs that were then tested against EBOV (12, 22) . The mechanism for these three compounds against EBOV is unknown. Others have demonstrated that compounds with physicochemical properties such as a basic pKa (> 6.5) and cLogP of > 2 tend to be lysosomotropic (38) and they accumulate in the lysosomes. We have now taken an ML approach to predict potential for a lysosomotropic mechanism using published in vitro data (38, 39) along with ECFP6 molecular fingerprints and a Bayesian algorithm. We have now performed several in vitro studies to validate the predictive ability of this model as well as infer the potential mechanism of one of these EBOV drugs. Pyronaridine clearly is a potent lysosomotropic agent, more so than all the other molecules tested. There is a strong correlation between published anti-EBOV activity and the lysosomotropic property (Table S2 ) for a large number of drugs. All of the compounds that were considered actives in our model were researched to identify whether they had been previously tested against EBOV and or MARV either with a psuedovirus/VLP and or a competent virus inhibition assay. 21 of 23 of these compounds had been tested previously and all inhibited these viruses, with AC 50 s almost all in the nM to low µM range. This is certainly not a comprehensive list of all lysosomotropic compounds, but this strongly supports the 1 4 notion that the lysosomotropic characteristic is directly related to the antiviral activity of the compounds within this model. Artesunate which is also similarly active against EBOV was found not to share this physiological characteristic. The initial combination of these two drugs to form Pyramax® was to avoid drug resistance of Plasmodium parasites, the causative agents of Malaria and has been extensively reviewed (52) . The combinations of these two molecules are additive in inhibiting EBOV replication in vitro but with a reduced cytotoxicity as compared to the individual treatments (Figure 2 and 3). Previous work has suggested that it is possible to identify pairs of drugs that block EBOV infection in vitro via the same methodology as used here (47) This current study has implications outside of EBOV, with the increased interest in antivirals for testing against SARS-CoV-2, and in particular the heavy focus on chloroquine and hydroxychloroquine, which have shown low micromolar activity in vitro against this virus (54) (55) (56) (57) . Pyronaridine has recently also been shown to have some limited activity against SAR-CoV-2 in Vero cells (54) , but Vero cells may not be as appropriate as human cells to test compounds such as pyronaridine (low in vitro selectivity index, but high in vivo antiviral activity). Based on our previous findings, this leaves the distinct possibility that there has been an underestimate of pyronaridine's 1 5 antiviral inhibition potential. Our current data also suggests that we should be testing artesunate versus additional viruses as well as in combination with pyronaridine.
Frequently, single drugs are repurposed for new uses (58) , to our knowledge there is no precedent for a two drug combination being repurposed for the same indication. We have previously estimated that the dose used for treating malaria patients may have a beneficial effect in EBOV patients (14), further indicating the potential for direct repurposing without the need to change dose, route or formulation. From our experience and insights gained during the discovery of the antiviral properties of pyronaridine, tilorone and quinacrine we propose our ML approach could be optimized by adding the additional ML model for the lysosomotropic mechanism described here.
This would enable us to create a computational pipeline to identify new antivirals more rapidly that could have this lysosomotropic property and hence direct the antiviral mechanism of action investigations. Molecules with this mechanism may also have more utility as broad-spectrum antivirals which is needed to counter flare ups of viruses like EBOV, MARV or potential pandemics such as SARS-CoV-2. 
We kindly acknowledge NIH funding: R21TR001718 from NCATS. In addition,
1R43GM122196-01 and R44GM122196-02A1 "Centralized assay datasets for modelling support of small drug discovery organizations" from NIGMS and NIEHS for (59)) Predicted pka's (negative log of the acid dissociation constant) were obtained from drugbank, which were initially calculated using Chemaxon. AlogP (predicted log octanol-water partition coefficient was calculated via Discovery Studio). 