scholarly journals Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253855
Author(s):  
Yuki Shimizu ◽  
Takamitsu Sasaki ◽  
Jun-ichi Takeshita ◽  
Michiko Watanabe ◽  
Ryota Shizu ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Liver Injury ◽  
Drug Development ◽  
Liver Toxicity ◽  
Early Stage ◽  
Average Molecular Weight ◽  
Chemical Properties ◽  
Importance Value ◽  
Drug Induced ◽  
Chemical Descriptor

Drug-induced liver injury (DILI) is one of major causes of discontinuing drug development and withdrawing drugs from the market. In this study, we investigated chemical properties associated with DILI using in silico methods, to identify a physicochemical property useful for DILI screening at the early stages of drug development. Total of 652 drugs, including 432 DILI-positive drugs (DILI drugs) and 220 DILI-negative drugs (no-DILI drugs) were selected from Liver Toxicity Knowledge Base of US Food and Drug Administration. Decision tree models were constructed using 2,473 descriptors as explanatory variables. In the final model, the descriptor AMW, representing average molecular weight, was found to be at the first node and showed the highest importance value. With AMW alone, 276 DILI drugs (64%) and 156 no-DILI drugs (71%) were correctly classified. Discrimination with AMW was then performed using therapeutic category information. The performance of discrimination depended on the category and significantly high performance (>0.8 balanced accuracy) was obtained in some categories. Taken together, the present results suggest AMW as a novel descriptor useful for detecting drugs with DILI risk. The information presented may be valuable for the safety assessment of drug candidates at the early stage of drug development.

scholarly journals Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yukun Wang ◽  
Xuebo Chen
Keyword(s):  
Decision Making ◽  
Liver Injury ◽  
Drug Development ◽  
Early Stage ◽  
Drug Induced ◽  
Joint Decision ◽  
Drug Induced Liver Injury ◽  
Majority Principle ◽  
Consensus Models ◽  
Decision Making Model

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

scholarly journals Chemotherapy induced liver toxicity in children with malignant diseases

2018 ◽  
Vol 91 (1) ◽  
pp. 37-41
Author(s):  
Horváth Adrienne ◽  
Papp Zsuzsanna Erzsébet
Keyword(s):  
Liver Injury ◽  
Maintenance Therapy ◽  
Esophageal Varices ◽  
Liver Toxicity ◽  
Lymphoblastic Leukemia ◽  
Treatment Options ◽  
Clinical Signs ◽  
Treatment Strategies ◽  
Malignant Diseases ◽  
Drug Induced

Abstract A broad spectrum of chemotherapy is being used in the therapy of childhood cancers, which may induce liver injury, impairing quality of life and efficacy of the treatment. History of, especially viral, liver diseases may increase toxicity. The aim of the paper is to assess the incidence, type and grade, predisposing factors and treatment options of drug-induced liver injury in children with malignant diseases under cytostatic therapy at the Hemato-Oncology Department of the Pediatric Clinic 2 from Targu-Mures, over a time period spanning from 2012 to 2017. The results of the study may serve as a foundation for such treatment strategies which would enable optimal outcomes with fewer cases of liver toxicity. During this period, we treated 26 patients with acute lymphoblastic leukemia (ALL), two patients with acute myeloblastic leukemia (AML), one patient with lymphoma and seven with solid tumors. We found liver toxicity in 77% of the patients treated for ALL, mainly during the maintenance therapy (65%) with oral 6-mercaptopurine and methotrexate. The most common clinical signs were anorexia, nausea, vomiting, abdominal pain and faltering weight gain. Cholestasis developed in two patients, while hepatocytolysis was the most common observed event (n = 24). Liver fibrosis, hypersplenism, portal hypertension and esophageal varices were found in two patients. One patient required endoscopic ligation of esophageal varices. Elevation of serum bilirubin appeared in two patients, while hypoproteinemia was observed in nine patients. None of the patients developed acute liver failure. We treated liver toxicity with hydration, alkalinization, i.v. Aspatofort, Aminosteril-N Hepa 8%, oral acetylcysteine, silymarin, ursodeoxycholic acid, Liv-52, Sargenor, and Essentiale forte. We found hepatotoxicity in 77% of the ALL patients undergoing chemotherapy, similar results have been published by other authors. Hepatotoxicity may develop through direct hepatic effects of cytostatics, or a preexisting liver disease impairs the metabolism and excretion of the drug, increasing its toxic effects. In our patients hepatotoxicity can be explained mainly by direct liver-injury, previous infections with hepatotropic viruses, such as cytomegalovirus, were detected only in three patients. Liver injury appeared in 77% of our ALL patients; 65% occurred during maintenance therapy with oral 6-mercaptopurine and methotrexate. Close followup of liver function during chemotherapy is mandatory for optimal results.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Rosa Chan ◽  
Leslie Z. Benet
Keyword(s):  
Liver Injury ◽  
Drug Development ◽  
Safety Concern ◽  
Drug Induced ◽  
Development Review ◽  
Drug Induced Liver Injury ◽  
Underlying Mechanisms ◽  
Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

scholarly journals Drug-induced liver injury after switching from tamoxifen to anastrozole in a patient with a history of breast cancer being treated for hypertension and diabetes

2020 ◽  
Vol 11 ◽  
pp. 204062232096415
Author(s):  
Petr Potmešil ◽  
Radka Szotkowská
Keyword(s):  
Breast Cancer ◽  
Liver Injury ◽  
Early Stage ◽  
Case Reports ◽  
Cancer Resistance ◽  
Cytochrome P450 Enzymes ◽  
Drug Induced ◽  
Peripheral Tissues ◽  
History Of ◽  
Resistance Protein

Anastrozole is a selective non-steroidal aromatase inhibitor that blocks the conversion of androgens to estrogens in peripheral tissues. It is used as adjuvant therapy for early-stage hormone-sensitive breast cancer in postmenopausal women. Significant side effects of anastrozole include osteoporosis and increased levels of cholesterol. To date, seven case reports on anastrozole hepatotoxicity have been published. We report the case of an 81-year-old woman with a history of breast cancer, arterial hypertension, type 2 diabetes mellitus, hyperlipidemia, and chronic renal insufficiency. Four days after switching hormone therapy from tamoxifen to anastrozole, icterus developed along with a significant increase in liver enzymes (measured in the blood). The patient was admitted to hospital, where a differential diagnosis of jaundice was made and anastrozole was withdrawn. Subsequently, hepatic functions quickly normalized. The observed liver injury was attributed to anastrozole since other possible causes of jaundice were excluded. However, concomitant pharmacotherapy could have contributed to the development of jaundice and hepatotoxicity, after switching from tamoxifen to anastrozole since several the patient’s medications were capable of inhibiting hepatobiliary transport of bilirubin, bile acids, and metabolized drugs through inhibition of ATP-binding cassette proteins. Telmisartan, tamoxifen, and metformin all block bile salt efflux pumps. The efflux function of multidrug resistance protein 2 is known to be reduced by telmisartan and tamoxifen and breast cancer resistance protein is known to be inhibited by telmisartan and amlodipine. Moreover, the activity of P-glycoprotein transporters are known to be decreased by telmisartan, amlodipine, gliquidone, as well as the previously administered tamoxifen. Finally, the role of genetic polymorphisms of cytochrome P450 enzymes and/or drug transporters cannot be ruled out since the patient was not tested for polymorphisms.

scholarly journals Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

2021 ◽  
Vol 12 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

scholarly journals A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

2021 ◽  
Vol 9 ◽  
Author(s):  
Ana S. Serras ◽  
Joana S. Rodrigues ◽  
Madalena Cipriano ◽  
Armanda V. Rodrigues ◽  
Nuno G. Oliveira ◽  
...  
Keyword(s):  
Drug Development ◽  
Liver Toxicity ◽  
Three Dimensional ◽  
Cell Types ◽  
Cell Phenotype ◽  
Drug Induced ◽  
Characterization Methods ◽  
Clinical Phase ◽  
Post Marketing

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

scholarly journals Epigallocatechin-3-Gallate (EGCG), An Active Constituent of Green Tea: Implications in the Prevention of Liver Injury Induced by Diethylnitrosamine (DEN) in Rats

2019 ◽  
Vol 9 (22) ◽  
pp. 4821 ◽  
Author(s):  
Saleh A. Almatroodi ◽  
Mohammed A. Alsahli ◽  
Hanan Marzoq Alharbi ◽  
Amjad Ali Khan ◽  
Arshad Husain Rahmani
Keyword(s):  
Cell Cycle ◽  
Liver Injury ◽  
Green Tea ◽  
Liver Toxicity ◽  
Oxidative Capacity ◽  
Oxidative Enzymes ◽  
Control Group ◽  
Active Constituent ◽  
Drug Induced ◽  
Oxidative Potential

Liver diseases are one of the most detrimental conditions that may cause inflammation, leading to tissue damage and perturbations in functions. Several drugs are conventionally available for the treatment of such diseases, but the emergence of resistance and drug-induced liver injury remains pervasive. Hence, alternative therapeutic strategies have to be looked upon. Epigallocatechin-3-gallate (EGCG) is a naturally occurring polyphenol in green tea that has been known for its disease-curing properties. In this study, we aimed to evaluate its anti-oxidative potential and protective role against diethylnitrosamine (DEN)-induced liver injury. Four different groups of rats were used for this study. The first group received normal saline and served as the control group. The second group received DEN (50 mg/kg body wt) alone and third group received DEN plus EGCG (40 mg/kg body wt) only. The fourth group were treated with EGCG only. The liver protective effect of EGCG against DEN toxicity through monitoring the alterations in aspartate transaminase (AST), and alanine transaminase (ALT) and alkaline phosphatase (ALP) activities, serum level of pro-inflammatory mediators and anti-oxidant enzymes, histopathological alterations, measurement of cellular apoptosis, and cell cycle analysis was examined. The rats that were given DEN only had a highly significantly elevated levels of liver enzymes and pro-inflammatory cytokines, highly decreased anti-oxidative enzymes, and histological changes. In addition, a significant elevation in the percentage of apoptotic nuclei and cell cycle arrest in the sub- G1 phase was detected. EGCG acts as a hepatoprotectant on DENs by reducing the serum levels of liver functional enzymes, increasing total anti-oxidative capacity, reducing pathological changes and apoptosis, as well as causing the movement of cells from the sub G1 to S or G2/M phase of the cell cycle. In conclusion, EGCG displayed a powerful hepatoprotective additive as it considerably mitigates the liver toxicity and apoptosis induced by DEN.

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