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Evaluation of drug carrier hepatotoxicity using primary cell culture models.

ABSTRACT: This study aims to establish a primary rat hepatocyte culture model to evaluate dose-dependent hepatotoxic effects of drug carriers (lipopolymer nanoparticles; LPNs) temporal. Primary rat hepatocyte cell cultures were used to determine half-maximal Inhibition Concentrations (IC₅₀) of the drug-carrier library. Drug-carrier library, at concentrations <50 μg/mL, is benign to primary rat hepatocytes as determined using albumin and urea secretions. Albumin, as a hepatic biomarker, exhibited a more sensitive and faster outcome, compared to urea, for the determination of the IC₅₀ value of LPNs. Temporal measurements of hepatic biomarkers including urea and albumin, and rigorous physicochemical (hydrodynamic diameter, surface charge, etc.) characterization, should be combined to evaluate the hepatotoxicity of drug carrier libraries in screens.

SUBMITTER: Kibar G

PROVIDER: S-EPMC10492629 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Evaluation of drug carrier hepatotoxicity using primary cell culture models.

Kibar Güneş G Dutta Subhadeep S Rege Kaushal K Usta O Berk OB

Nanomedicine : nanotechnology, biology, and medicine 20230107

This study aims to establish a primary rat hepatocyte culture model to evaluate dose-dependent hepatotoxic effects of drug carriers (lipopolymer nanoparticles; LPNs) temporal. Primary rat hepatocyte cell cultures were used to determine half-maximal Inhibition Concentrations (IC<sub>50</sub>) of the drug-carrier library. Drug-carrier library, at concentrations <50 μg/mL, is benign to primary rat hepatocytes as determined using albumin and urea secretions. Albumin, as a hepatic biomarker, exhibite ...[more]

PMID: 36623713

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Project description:BackgroundDrug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.ResultsIn this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model.ConclusionsThe prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important.

Project description:Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Dataset Information

Evaluation of drug carrier hepatotoxicity using primary cell culture models.

Publications

Evaluation of drug carrier hepatotoxicity using primary cell culture models.

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