Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Gupta, Rishi; Gifford, Eric M.; Liston, Ted; Waller, Chris L.; Hohman, Moses M.; Bunin, Barry A.; Ekins, Sean

doi:10.1124/dmd.110.034918

Cited by 82 publications

(85 citation statements)

References 57 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the issues with computational models is that they are rarely accessible to others due to the commercial software licensing requirements. We have previously showed that models built with open source tools can produce validation statistics comparable to commercial modeling tools 49 . We recently made “function class fingerprints of maximum diameter 6” (FCFP6) and “extended connectivity (ECFP6) fingerprints,” open source and have described their implementation with the Chemistry Development Kit (CDK) 50 components 41 .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ADME-Tox properties may also be obtained using in silico techniques and there are several ADME-Tox methods that had been proposed [2][3][4][5]. This ADME-Tox profile is usually computed for drug candidates in the very early stage of their design [6] but it may be also substracted for other chemical compounds.…”

Section: Introductionmentioning

confidence: 99%

ADME-Tox profiles of some food additives and pesticides

Crăciun

Modra

Isvoran

2015

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. Within this study we compute the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME-Tox) profiles of several commonly used food additives and some pesticides. As expected, all the food additives considered in this study provided to be safe, their ADME-Tox profiles indicating that they have a good oral bioavailability and they do not produce phosphoslipidosis. The ADME-Tox profiles of the pesticides indicate that, with a few exceptions, they are highly toxic (some of them being not approved in the EU, but still used in other countries) and may cause many diseases. Our results are in good agreement with published data concerning the considered food additives and pesticides revealing that the ADME-Tox profiling method may be successfully used to test other chemicals than drug candidates.

show abstract

“…In some ways, there are analogous difficulties in the exchange of computational models like quantitative structure activity relationship (QSAR) datasets-while there are efforts to standardize how the data and models are stored, queried, and exchanged, there has been little consideration of licenses required to enable making the sharing of open source models a reality (Spjuth 2010;Gupta 2010). Similarly, one could consider the creation of maps of disease and how they are shared and reused [24] in the same manner (Derry et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Issues in Open Research Data

Moore

2014

View full text Add to dashboard Cite

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Abstract: ABSTRACT:Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (

Cited by 82 publications

References 57 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

ADME-Tox profiles of some food additives and pesticides

Issues in Open Research Data

Contact Info

Product

Resources

About