In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

Cui, Xueyan; Liu, Juan; Zhang, Jinfeng; Wu, Qiuyun; Li, Xiao

doi:10.1002/jat.3808

Cited by 23 publications

(17 citation statements)

References 51 publications

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“…However, when these processes take place in kidney cells, kidney damage can occur. Toluene-contained structures (Nos 1 and 7) were also selected as structural alerts for drug induced rhabdomyolysis ( Cui et al, 2019 ), which always associated to oliguric renal failure. In the setting of toluene intoxication, electrolyte disturbances may play important roles on causing rhabdomyolysis ( Camara-Lemarroy et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

“…The model building was performed on the online chemical database and modeling environment (OCHEM), which is a user friendly web-based platform for automatic and simple QSAR modeling ( Sushko et al, 2011 ). OCHEM supports the typical steps of QSAR modeling, and the models can be published and publicly used on the web ( Oprisiu et al, 2013 ; Cui et al, 2019 ; Pawar et al, 2019 ; Cui et al, 2021 ; Hua et al, 2021 ; Huang et al, 2021 ; Ta et al, 2021 ). Among the many state-of-the-art modeling methods available on OCHEM, we applied five widely used traditional machine learning (ML) approaches and five different deep learning (DL) algorithms.…”

Section: Methodsmentioning

confidence: 99%

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In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

et al. 2022

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Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

et al. 2022

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“…If compounds’ Euclidean distance values are larger, it is considered that these compounds are outside the AD of the model and have lower prediction accuracy than compounds with Euclidean distance values smaller. This work was conducted by the AMBIT Discovery software (version 0.04) ( (accessed on 25 August 2021)), and the threshold was set to 95% in the training set to determine the domain of the model [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

et al. 2022

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Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.

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“…Although the results are interesting in their context (7 approved HIV‐1 drugs, 4 approved HCV drugs, and 9 other compounds were predicted to be HIV/HCV coinfection multitarget inhibitors), the limited database used (46 compounds, including 27 approved HIV‐1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV‐1 or HCV) makes the potential extrapolation of the results to other compounds outside of those studied unlikely. Also, Cui et al 109 developed a series of ML models to predict drug‐induced rhabdomyolysis (DIR), a serious adverse reaction that can be fatal, identifying structural alerts responsible for DIR and providing the best model available at https://ochem.eu/model/32214665. Lee et al 110 predicted in vitro pulmonary toxicity using high throughput imaging and AI, accurately classifying a number of chemicals with 89% balance accuracy, 85% sensitivity and 93% specificity.…”

Section: Ai‐based Toxicity Predictionmentioning

confidence: 99%

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Pérez‐Santín

Rodríguez‐Solana

García

et al. 2021

WIREs Comput Mol Sci

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The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)‐based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

Cited by 23 publications

References 51 publications

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

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