Development of an <i>in Silico</i> Profiler for Mitochondrial Toxicity

Nelms, Mark; Mellor, Claire L.; Cronin, Mark T.D.; Madden, Judith C.; Enoch, Steven J.

doi:10.1021/acs.chemrestox.5b00275

Cited by 45 publications

(45 citation statements)

References 86 publications

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“…Therefore, we derived structural alerts by fragmentation of the dataset and implementation of alerts proposed by Nelms et al. and Navel et al . The fragmentation and subsequent selection of the alerts yielded 16 new alerts.…”

Section: Resultsmentioning

confidence: 99%

“…In a case study we tried to explain positive predictions by our models using the structural alert. For this we used the alert anthracenes, which is the alert number 4 and was also reported by Nelms and co‐workers, therefore we have strong evidence that this substructure is prone to cause mitochondrial toxicity. In our dataset the alert occurred 32 times with a high predictivity of 0.88.…”

Section: Resultsmentioning

confidence: 99%

“…The Tox21 dataset originates from the Tox21 challenge where the mitochondrial membrane potential was assessed in a high throughput screening assay . First efforts to characterize mitochondrial toxicity and to derive modes of action for certain structural alerts, were done by Nelms and co‐workers (based on the Zhang dataset) and by Naven and co‐workers (based on an unpublished dataset) . In this paper we present the – to our knowledge – biggest dataset for mitochondrial toxicity.…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Hemmerich

Troger

Füzi

et al. 2020

Molecular Informatics

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Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Hemmerich

Troger

Füzi

et al. 2020

Molecular Informatics

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“…Technically, these may need to extend the use of SMARTS strings into more sophisticated markup languages such as CSRML. A proposal has already been made for the incorporation of chemotypes, captured through CSRML to be integrated into KNIME Workflows for the prediction of chronic toxicity (57,65). …”

Section: An Example Of In Silico Modelling: Development Of Structuralmentioning

confidence: 99%

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

et al. 2017

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In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

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“…On the computational side, one previous predictive model of the mitochondrial toxicity of pharmaceutical drugs has focused on a cheminformatics approach (7). Here we describe the development of a computational model based upon the available crystal structure (8) that allows for the accurate prediction of relative binding affinities of NTPs to the POLRMT structure.…”

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confidence: 99%

A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase

Freedman

Winter

Tuszyński

et al. 2018

Journal of Biological Chemistry

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In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity.

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Development of an in Silico Profiler for Mitochondrial Toxicity

Cited by 45 publications

References 86 publications

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase

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