Hepatotoxicity evaluation of traditional Chinese medicines using a computational molecular model

Therapeutic Potential of the Genus Zanthoxylum Phytochemicals: A Theoretical ADME/Tox Analysis

Natural products (NPs) are essential in the search for new drugs to treat a wide range of diseases, including infectious and malignant disorders. However, despite the discovery of many bioactive NPs, they often do not make it to market as drugs due to toxicity and other challenges. The development of NPs into drugs is a long and expensive process, and many promising compounds are abandoned along the way. These molecules require in silico ADMET profiling in order to speed up their development into drugs lower costs, and the high attrition rate. The objective of this work was to produce thorough ADMET profiles of secondary metabolites from several classes that were isolated from Zanthoxylum species. The genus has a long history of therapeutic use, including treating tumours, hypertension, gonorrhoea, coughs, bilharzia, chest pains, and toothaches. The study used a dataset of 406 compounds from the genus for theoretical ADMET analysis. The findings revealed that 81% of the compounds met Lipinski's rule of five, indicating good oral bioavailability. The drug-likeness criteria were taken into account, with percentages ranging from 66.2 to 88.1 percent. Additionally, 9.2% of the compounds were predicted to be lead-like, demonstrating their potential as promising drug development candidates. Interestingly, none of the compounds inhibited hERG I, while 33% inhibited hERG II, potentially having cardiac implications. Additionally, 30% of the compounds exhibited AMES toxicity inhibition, while 23.6% were identified as hepatotoxic and 22.2% would cause skin sensitivity. Moreover, 81.8% of the compounds demonstrated high intestinal absorption, making them desirable for oral drugs. In conclusion, these findings highlight the diverse properties of the investigated compounds and their potential for drug development.

In silico toxicity studies of traditional Chinese herbal medicine: A mini review

With the availability of public databases that store compound-target/compound-toxicity information, and Traditional Chinese medicine (TCM) databases, in silico approaches are used in toxicity studies of TCM herbal medicine. Here, three in silico approaches for toxicity studies were reviewed, which include machine learning, network toxicology and molecular docking. For each method, its application and implementation e.g., single classifier vs. multiple classifier, single compound vs. multiple compounds, validation vs. screening, were explored. While these methods provide data-driven toxicity prediction that is validated in vitro and/or in vivo, it is still limited to single compound analysis. In addition, these methods are limited to several types of toxicity, with hepatotoxicity being the most dominant. Future studies involving the testing of combination of compounds on the front end i.e., to generate data for in silico modeling, and back end i.e., validate findings from prediction models will advance the in silico toxicity modeling of TCM compounds.

Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models

This study aimed to discover concurrences of adverse drug reactions (ADRs) and derive models of the most frequent items of ADRs based on the SIDER database, which included 1430 marketed drugs and 5868 ADRs. First, common ADRs of organic drugs were manually reclassified according to side effects in the human system and followed by an association rule analysis, which found ADRs of digestive and nervous systems often occurred at the same time with a good association rule. Then, three algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and deep learning, were used to derive models of ADRs of digestive and nervous systems based on 497 organic monomer drugs and to identify key structural features in defining these ADRs. The statistical results indicated that these kinds of QSAR models were good tools for screening ADRs of digestive and nervous systems, which gave the ROC AUC values of 81.5%, 98.9%, 91.5%, 69.5%, 78.4% and 78.8%, respectively. Then, these models were applied to investigate ADRs of 1536 organic compounds with four phase and zero rule-of-five (RO5) violations from the ChEMBL database. Based on the consensus ADRs’ predictions of models, 58.1% and 42.6% of compounds were predicted to cause these two ADRs, respectively, indicating the significance of initial assessment of ADRs in early drug discovery.

Similar hepatotoxicity response induced by Rhizoma Paridis in zebrafish larvae, cell and rat

Rhizoma Paridis, as a Traditional Chinese Medicine (TCM), has been used in clinic for thousands of years. Recently, the hepatic toxicity was reported in some published articles while its hepatotoxicity mechanisms have not been well established. Therefore, the present study was performed to determine the effect of Rhizoma Paridis treatment on the lipid deposition and metabolism, oxidative stress and mitochondrial dysfunction, and explore the underlying molecular mechanism through L02 cell, rat and zebrafish larvae. Rhizoma Paridis could diminish cell activity and cell proliferation, brought on cell apoptosis and elevated the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) compared with the control group, as evaluated in cell cultures. Rhizoma Paridis could result in the change of the liver structure and the liver function in the rat model and zebrafish larvae. Our results showed that Rhizoma Paridis could increase hepatic lipid accumulation, which was similar to the previous study and probably exerted toxic effect through intensive fatty acid lipogenesis, inhibition of fat degradation. Meanwhile, this experiment highlighted the importance of the oxidative stress, mitochondrial dysfunction, ER function, and the inflammation response in Rhizoma Paridis-induced disorder of hepatic lipid metabolism, which proposed a novel mechanism for interpretation of Rhizoma Paridis exposure inducing the disorder of lipid metabolism in vertebrates. Furthermore, the result of this experiment suggested that the toxicity response of zebrafish larvae was similar to the conventional model with a significant advantage.

Literature Review of Traditional Chinese Medicine Herbs-Induced Liver Injury From an Oncological Perspective With RUCAM

Traditional Chinese medicine (TCM) herbs are commonly regarded to be safe with minimal toxicities in Chinese communities. Cancer patients who are receiving Western oncology therapy often concurrently take TCM herbs for anticancer and symptom relief purposes. We performed a literature review for current evidence on TCM herb–induced liver injury from an oncological perspective. A literature search on PubMed was performed to identify publications regarding TCM herbs and concoctions with hepatoprotective or hepatotoxic properties. Lists of commonly used herbs and their causality levels were compiled. In view of the wide range of evidence available, cases assessed by the well-established RUCAM (Roussel Uclaf Causality Assessment Method) algorithm were categorized as the highest level of evidence. More than one case of TCM herb–induced liver injury was confirmed by RUCAM in the following herbs and concoctions: Lu Cha (Camellia sinensis), Bai Xian Pi (Dictamnus dasycarpus), Tu San Qi (Gynura segetum), Jin Bu Huan (Lycopodium serratum), He Shou Wu (Polygoni multiflora), Ge Gen (Pueraria lobata), Dan Lu Tong Du tablet, Shou Wu Pian, Xiao Chai Hu Tang, Xiao Yin pill, and Yang Xue Sheng Fa capsule. Finally, TCM with anticancer or symptom relief uses were discussed in detail with regard to their hepatotoxic or hepatoprotective properties.

A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

Intracellular Accumulation as an Indicator of Cytotoxicity to Screen Hepatotoxic Components of Chelidonium majus L. by LC-MS/MS

A novel strategy was developed to identify hepatotoxic compounds in traditional Chinese medicines (TCMs). It is based on the exposure of HL-7702 cells to a TCM extract, followed by the identification and further determination of potential hepatotoxic compounds accumulated in the cells by liquid chromatography–tandem mass spectrometry (LC–MS/MS). As a case study, potential hepatotoxic components in Chelidonium majus L. were screened out. Five alkaloids (sanguinarine, coptisine, chelerythrine, protopine, and chelidonine) were identified by LC–MS/MS within 10 min, and their intracellular concentrations were first simultaneously measured by LC–MS/MS with a run time of 4 min. A cell viability assay was performed to assess the cytotoxicity of each alkaloid. With their higher intracellular concentrations, sanguinarine, coptisine, and chelerythrine were identified as the main hepatotoxic constituents in Ch. majus. The study provides a powerful tool for the fast prediction of cytotoxic components in complex natural mixtures on a high-throughput basis.

An In Silico Model for Predicting Drug-Induced Hepatotoxicity

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

Development of quantitative structure-activity relationship models to predict potential nephrotoxic ingredients in traditional Chinese medicines

The broad use of traditional Chinese medicines (TCMs) and the accompanied incidences of kidney injury have attracted considerable interest in investigating the responsible toxic ingredients. It is challenging to evaluate toxicity of TCMs since they contain complex mixtures of phytochemicals. Quantitative structure-activity relationship (QSAR) is an efficient tool to predict toxicity and QSAR study on TCMs-induced nephrotoxicity remains lacked. We developed QSAR models using three datasets of 609 compounds: natural products, drugs, and mixed (contained both kinds of data) datasets. Each dataset was used for modelling by utilizing artificial neural networks (ANN) and support vector machines (SVM) algorithms separately. Both internal and external validations were performed on each model. Six QSAR models were developed and yielded reliable performance in the internal validation. For external validation, 30 ingredients in the TCMs were predicted well by the natural product models (accuracy: ANN 96.7%, SVM 93.3%). The mixed models (accuracy: ANN 76.7%, SVM 66.7%) showed a better performance than the drug models (accuracy: ANN 50%, SVM 53.3%). Particularly, natural product models produced the most reliable results. It has the application not only on screening the nephrotoxic ingredients in TCMs, but it is also helpful at prioritizing the subsequent toxicity testing of natural products.

Hydrogen Sulfide as a Novel Regulatory Factor in Liver Health and Disease

Hydrogen sulfide (H₂S), a colorless gas smelling of rotten egg, has long been recognized as a toxic gas and environment pollutant. However, increasing evidence suggests that H₂S acts as a novel gasotransmitter and plays important roles in a variety of physiological and pathological processes in mammals. H₂S is involved in many hepatic functions, including the regulation of oxidative stress, glucose and lipid metabolism, vasculature, mitochondrial function, differentiation, and circadian rhythm. In addition, H₂S contributes to the pathogenesis and treatment of a number of liver diseases, such as hepatic fibrosis, liver cirrhosis, liver cancer, hepatic ischemia/reperfusion injury, nonalcoholic fatty liver disease/nonalcoholic steatohepatitis, hepatotoxicity, and acute liver failure. In this review, the biosynthesis and metabolism of H₂S in the liver are summarized and the role and mechanism of H₂S in liver health and disease are further discussed.