Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space

Kang, Myung-Gyun; Kang, Nam Sook

doi:10.3390/molecules26247548

Cited by 13 publications

(11 citation statements)

References 52 publications

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“…Kang et al developed a new tool for identifying DILI risk and evaluating drug safety using extended connectivity fingerprints of diameter 4 (ECFP4)-based DNN model in a study with an almost equal number of DILI-positive and DILI-negative drugs from the DILIrank and LiverTox databases. 235 Integer ECFP4 fingerprint bits were used to define the applicability domain of the developed model, with each bit corresponding to a specific substructure. 235 The applicability domain refers to the range of compounds for which the model can accurately predict DILI risk.…”

Section: Dnnmentioning

confidence: 99%

“…235 Integer ECFP4 fingerprint bits were used to define the applicability domain of the developed model, with each bit corresponding to a specific substructure. 235 The applicability domain refers to the range of compounds for which the model can accurately predict DILI risk. The researchers used integer ECFP4 fingerprint bits to define this domain and calculated the ratio of bits outside the domain for each compound, which they called the "endurance level".…”

Section: Dnnmentioning

confidence: 99%

“…The researchers used integer ECFP4 fingerprint bits to define this domain and calculated the ratio of bits outside the domain for each compound, which they called the "endurance level". 235 This helped them develop a more reliable model by ensuring that predictions were based only on compounds within the applicability domain. The model was constructed using curated data sets and achieved an accuracy of 0.731, sensitivity of 0.714, and specificity of 0.750 on the validation data set.…”

Section: Dnnmentioning

confidence: 99%

“…This model provides a promising tool for the early detection of DILI potential in preclinical settings, especially for oncology drugs. Kang et al developed a new tool for identifying DILI risk and evaluating drug safety using extended connectivity fingerprints of diameter 4 (ECFP4)-based DNN model in a study with an almost equal number of DILI-positive and DILI-negative drugs from the DILIrank and LiverTox databases . Integer ECFP4 fingerprint bits were used to define the applicability domain of the developed model, with each bit corresponding to a specific substructure .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: Dnnmentioning

confidence: 99%

Section: Dnnmentioning

confidence: 99%

Section: Dnnmentioning

confidence: 99%

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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“…DILI is a major concern for drug developers, regulatory authorities, and clinicians. However, we currently lack an adequate model system for assessing drug-associated DILI risk in humans [ 14 ]. The observable morphological patterns of acute hepatocellular injury include acute hepatitis, necrosis, and resolving hepatitis.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats

et al. 2022

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Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.

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New Alternative Methods in Drug Safety Assessment

Yang,

Shi,

Chen

et al. 2023

The Quintessence of Basic and Clinical Research and Scientific Publishing

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Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space

Cited by 13 publications

References 52 publications

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats

New Alternative Methods in Drug Safety Assessment

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