Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery

Ekins, Sean; Reynolds, Robert C.; Kim, Hiyun; Koo, Mi Sun; Ekonomidis, Marilyn; Talaue, Meliza; Paget, Steve D.; Woolhiser, Lisa K.; Lenaerts, Anne J.; Bunin, Barry A.; Connell, Nancy; Freundlich, Joel S.

doi:10.1016/j.chembiol.2013.01.011

Cited by 89 publications

(208 citation statements)

References 60 publications

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“…Specifically we have previously analyzed large datasets for Mycobacterium tuberculosis to build machine learning models that use single point data, dose-response data 43, 45 , combine bioactivity and cytotoxicity data (e.g. Vero, HepG2 or other model mammalian cells) 28, 29, 46 or combinations of these sets 47, 48 . These models in turn have been validated with additional non-overlapping datasets, demonstrating that it is possible to use publically accessible data to find novel in vitro active antituberculars.…”

Section: Discussionmentioning

confidence: 99%

“…RP Single Trees had a minimum of ten samples per node and a maximum tree depth of 20. In all cases, 5-fold cross validation or leave out 50% × 100 fold cross validation was used to calculate the Receiver Operator Curve (ROC) for the models generated 28, 29 .…”

Section: Methodsmentioning

confidence: 99%

“…We proposed that if we could learn from the many compounds already screened for anti-EBOV activity, we could more efficiently find additional compounds and perhaps understand the key molecular features needed for antiviral activity 14 . We speculated then that Laplacian-corrected Naïve Bayesian classifier models might be useful as they have been for M. tuberculosis 28, 29 and more recently for T. cruzi 30 . To our knowledge machine learning approaches to identify EBOV inhibitors have not been attempted elsewhere.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning models identify molecules active against the Ebola virus in vitro

et al. 2016

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“…The cytotoxicity of TXY436 versus Vero cells (African green monkey kidney epithelial cells; ATCC) was assessed using a 72-h continuous 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as described previously (33).…”

Section: Methodsmentioning

confidence: 99%

An FtsZ-Targeting Prodrug with Oral Antistaphylococcal Efficacy In Vivo

Kaul

Mark²,

Zhang³

et al. 2013

Antimicrob Agents Chemother

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The bacterial cell division protein FtsZ represents a novel antibiotic target that has yet to be exploited clinically. The benzamide PC190723 was among the first FtsZ-targeting compounds to exhibit in vivo efficacy in a murine infection model system. Despite its initial promise, the poor formulation properties of the compound have limited its potential for clinical development. We describe here the development of an N-Mannich base derivative of PC190723 with enhanced drug-like properties and oral in vivo efficacy. The N-Mannich base derivative (TXY436) is ϳ100-fold more soluble than PC190723 in an acidic aqueous vehicle (10 mM citrate, pH 2.6) suitable for oral in vivo administration. At physiological pH (7.4), TXY436 acts as a prodrug, converting to PC190723 with a conversion half-life of 18.2 ؎ 1.6 min. Pharmacokinetic analysis following intravenous administration of TXY436 into mice yielded elimination half-lives of 0.26 and 0.96 h for the TXY436 prodrug and its PC190723 product, respectively. In addition, TXY436 was found to be orally bioavailable and associated with significant extravascular distribution. Using a mouse model of systemic infection with methicillin-sensitive Staphylococcus aureus or methicillin-resistant S. aureus, we show that TXY436 is efficacious in vivo upon oral administration. In contrast, the oral administration of PC190723 was not efficacious. Mammalian cytotoxicity studies of TXY436 using Vero cells revealed an absence of toxicity up to compound concentrations at least 64 times greater than those associated with antistaphylococcal activity. These collective properties make TXY436 a worthy candidate for further investigation as a clinically useful agent for the treatment of staphylococcal infections.

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“…Machine learning and classification methods have been used in TB drug discovery [42], and have enabled rapid virtual screening of compound libraries for novel chemotypes [43, 44]. The use of cheminformatics for tuberculosis drug discovery has been summarized [45-47] and can be readily implemented early in the process as a means to limit the number of compounds needing to be screened, therefore saving time and money [48-52]. Recent publications in this area have hit rates >20% and focus on favorable compounds with low or no cytotoxicity [51, 52].…”

Section: Introductionmentioning

confidence: 99%

Bigger data, collaborative tools and the future of predictive drug discovery

Ekins

Clark²,

Swamidass

et al. 2014

J Comput Aided Mol Des

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Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

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Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery

Cited by 89 publications

References 60 publications

Machine learning models identify molecules active against the Ebola virus in vitro

Machine learning models identify molecules active against the Ebola virus in vitro

An FtsZ-Targeting Prodrug with Oral Antistaphylococcal Efficacy In Vivo

Bigger data, collaborative tools and the future of predictive drug discovery

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