A Robust, Mechanistically Based <i>In Silico</i> Structural Profiler for Hepatic Cholestasis

Firman, James W.; Pestana, Cynthia B; Rathman, James F.; Vinken, Mathieu; Yang, Chihae; Cronin, Mark T.D.

doi:10.1021/acs.chemrestox.0c00465

Cited by 6 publications

(7 citation statements)

References 89 publications

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“…Several in silico models for potential use in predicting human hepatotoxicity from molecular structure have been described in the literature [145,[166][167][168][169][170]133,[171][172][173][174][175][176] including advanced modelling based on deep learning algorithms [177][178][179] and prediction models that combine structural descriptors and in vitro ToxCast assay data for the prediction of in vivo organ toxicity [180][181][182][183][184]. Different reviews have thoroughly summarized and discussed available models for this endpoint [126,133,[185][186][187][188][189][190].…”

Section: In Silico Methodsmentioning

confidence: 99%

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

Bassan

Alves

Amberg

et al. 2021

Computational Toxicology

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Section: In Silico Methodsmentioning

confidence: 99%

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

Bassan

Alves

Amberg

et al. 2021

Computational Toxicology

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“…Information regarding rules development will be published separately as part of the larger liver ontology project. 54 2.3. Battery of QSAR Models.…”

Section: Methodsmentioning

confidence: 99%

“…In most cases, ToxPrint chemotypes gave sufficient differentiating power without additional modifications. Information regarding rules development will be published separately as part of the larger liver ontology project …”

Section: Methodsmentioning

confidence: 99%

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment

Rathman

Yang

Ribeiro

et al. 2020

Chem. Res. Toxicol.

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Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure–activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster–Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

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“…(i) Expert knowledge-based alerts: These alerts are constructed by human experts using their own experience and knowledge, scientific publications, study of structure−activity relationships, toxicological pathways, metabolism, etc. 2,3 The mechanistic basis of the toxic effects of these chemical functionalities is usually well established and easier to interpret because they are annotated with mechanistic reasoning. 4 (ii) Statistically mined alerts: Alternatively, structural alerts can be automatically mined from data using various statistical pattern recognition techniques, also known as "machine learning".…”

Section: ■ Introductionmentioning

confidence: 99%

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Chakravarti

2023

Chem. Res. Toxicol.

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Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

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A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis

Cited by 6 publications

References 89 publications

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

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