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

Hemmerich, Jennifer; Troger, Florentina; Füzi, Barbara; Ecker, Gerhard

doi:10.1002/minf.202000005

Cited by 47 publications

(44 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of this study was to predict mitochondrial toxicity of chemicals. Other researchers (Hemmerich, Troger, Füzi, Ecker, & G., 2020; Tang, Chen, & Hong, 2020) collected compounds that were inactive in mitochondrial membrane potential assay as negative chemicals. However, in this study, considering that mitochondrial functions include ATP production, ROS generation, material metabolism, cell proliferation and apoptosis, and so forth, so single indicator that shows negative cannot be used to determine that the compound will not affect mitochondrial function (Massart, Borgne‐Sanchez, & Fromenty, 2018; Wills, 2017).…”

Section: Discussionmentioning

confidence: 99%

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

Zhao

Peng

et al. 2021

J of Applied Toxicology

View full text Add to dashboard Cite

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.

show abstract

Section: Discussionmentioning

confidence: 99%

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

Zhao

Peng

et al. 2021

J of Applied Toxicology

View full text Add to dashboard Cite

show abstract

“…An important area here was the extrapolation of NAM concentrations to in vivo doses (IVIVE, PBPK) (Fisher et al 2019;Simeon et al 2020;Toma et al 2018). Additionally, novel QSAR tools and machine learning approaches were put in place to assess chemical similarity among test compounds (Gadaleta et al 2018a, b;Hemmerich et al 2020;Luechtefeld et al 2018;Toropova et al 2018;Toropov and Toropova 2017;Troger et al 2020) and to predict toxicological properties from their structure.…”

Section: The Repository Of Nams Available For Nam-enhanced Raxmentioning

confidence: 99%

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

et al. 2020

View full text Add to dashboard Cite

In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

show abstract

“…The cell death mechanism presumably involves mitochondrial membrane permeabilization, independently of mPTPM. In this sense, a recent chemoinformatic study shows that molecules causing mitochondrial toxicity can be characterized by having relatively restricted values of physicochemical properties such as log P , molecular weight, and surface area [52] …”

Section: Resultsmentioning

confidence: 99%

Beyond Pseudo‐natural Products: Sequential Ugi/Pictet‐Spengler Reactions Leading to Steroidal Pyrazinoisoquinolines That Trigger Caspase‐Independent Death in HepG2 Cells

et al. 2021

View full text Add to dashboard Cite

In this work, we describe how stereochemically complex polycyclic compounds can be generated by applying a synthetic sequence comprising an intramolecular Ugi reaction followed by a Pictet‐Spengler cyclization on steroid‐derived scaffolds. The resulting compounds, which combine a fragment derived from a natural product and a scaffold not found in nature. are both structurally distinct and globally similar to natural products at the same time, and interrogate an alternative region of the chemical space. One of the new compounds showed significant antiproliferative activity on HepG2 cells through a caspase‐independent cell‐death mechanism, an appealing feature when new antitumor compounds are searched.

show abstract

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

Cited by 47 publications

References 32 publications

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

Beyond Pseudo‐natural Products: Sequential Ugi/Pictet‐Spengler Reactions Leading to Steroidal Pyrazinoisoquinolines That Trigger Caspase‐Independent Death in HepG2 Cells

Contact Info

Product

Resources

About