A multiparametric organ toxicity predictor for drug discovery

Anxiolytic Efficacy of Indirubin: In Vivo Approach Along with Receptor Binding Profiling and Molecular Interaction with GABAergic Pathways

Anxiety is a natural response to stress, characterized by feelings of worry, fear, or unease. The current research was conducted to investigate the anxiolytic effect of indirubin (IND) in different behavioral paradigms in Swiss albino mice. To observe the animal's behavioural response to assess anxiolytic activity, different tests were performed, such as the open-field (square cross, grooming, and rearing), swing, dark-light, and hole cross tests. The experimental mice were administered IND (5 and 10 mg/kg, p.o.), where diazepam (DZP) and vehicle were used as positive and negative controls, respectively. In addition, a combination treatment (DZP+IND-10) was provided to the animals to determine the modulatory effect of IND on DZP. Molecular docking approach was also conducted to determine the binding energy of IND with the GABAA receptor (α2 and α3 subunits) and pharmacokinetics were also estimated. The findings revealed that IND dose-dependently significantly (p<0.05) reduced the animal's movement exerting calming behavior like DZP. IND also demonstrated the highest docking score (-7.7 kcal/mol) against the α3 subunit, while DZP showed a lower docking value (-6.4 kcal/mol) than IND. The ADMET analysis revealed that IND has proper drug-likeness and pharmacokinetic characteristics. In conclusion, IND exerted anxiolytic effects through GABAergic Pathways.

Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides

It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.

Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.

Predicting phase-I metabolism of piceatannol: an in silico study

Piceatannol is a natural compound found in plants and can be derived from resveratrol. While resveratrol has been extensively researched for its effects and how the body processes it, there are concerns about its use. These concerns include its limited absorption in the body, the need for specific dosages, potential interactions with other drugs, lack of standardization, and limited clinical evidence to support its benefits. Interestingly, Piceatannol, another compound derived from resveratrol, has received less attention from researchers but appears to offer advantages. It has better bioavailability and seems to have a more favorable therapeutic profile compared to resveratrol. Surprisingly, no previous attempts have been made to explore or predict the metabolites of piceatannol when it interacts with the enzyme cytochrome P450. This study aims to fill that gap by predicting how piceatannol is metabolized by cytochrome P450 and assessing any potential toxicity associated with its metabolites. This research is interesting because it's the first of its kind to investigate the metabolic fate of piceatannol, especially in the context of cytochrome P450. The findings have the potential to significantly contribute to the field of piceatannol research, particularly in the food industry where this compound has applications and implications.

Graphical abstract

Azithromycin exposure during pregnancy disturbs the fetal development and its characteristic of multi-organ toxicity

AIMS

Azithromycin is widely used in clinical practice for treating maternal infections during pregnancy. Meanwhile, azithromycin, as an "emerging pollutant", is increasingly polluting the environment due to the rapidly increasing usage (especially after the COVID-19). Previous studies have suggested a possible teratogenic risk of prenatal azithromycin exposure (PAzE), but its effects on fetal multi-organ development are still unclear. This study aimed to explore the potential impacts of PAzE.

MATERIALS AND METHODS

We focused on pregnancy outcomes, maternal/fetal serum phenotypes, and fetal multiple organ development in mice at different doses (50/200 mg/kg·d) during late pregnancy or at 200 mg/kg·d during different stages (mid-/late-pregnancy) and courses (single-/multi-course).

KEY FINDINGS

The results showed PAzE increased the rate of the absorbed fetus during mid-pregnancy and increased the intrauterine growth retardation rate (IUGR) during late pregnancy. PAzE caused multiple blood phenotypic changes in maternal and fetal mice, among which the number and degree of changes in fetal blood indicators were more significant. Moreover, PAzE inhibited long bone/cartilage development and adrenal steroid synthesis, promoting hepatic lipid production and ovarian steroid synthesis in varying degrees. The order of severity might be bone/cartilage > liver > gonads > other organs. PAzE-induced multi-organ alterations differed in stages, courses doses and fetal sex. The most apparent changes might be in high-dose, mid-pregnancy, multi-course, and female, while there was no typical rule for a dose-response relationship.

SIGNIFICANCE

This study confirmed PAzE could cause fetal developmental abnormalities and multi-organ functional alterations, which deepens the comprehensive understanding of azithromycin's fetal developmental toxicity.

Prenatal exposure to corn oil, CMC-Na or DMSO affects physical development and multi-organ functions in fetal mice

Corn oil, sodium carboxymethyl cellulose (CMC-Na), and dimethyl sulfoxide (DMSO) are widely used as solvents or suspensions in animal experiments, but the effects of prenatal exposure to them on fetal development have not been reported. In this study, Kunming mice were given a conventional dose of corn oil (9.2g/kg·d), CMC-Na (0.05g/kg·d) or DMSO (0.088g/kg·d) during gestation days 10-18, and the pregnancy outcome, fetal physical development, serum phenotype, and multi-organ function changes were observed. The results showed that corn oil decreased serum triglyceride level in males but increased their serum testosterone and CORT levels, and affected female placenta and female/male multi-organ functions (mainly bone, liver, kidney). CMC-Na increased female/male body lengths and tail lengths, decreased serum glucose and total cholesterol levels in males as well as increased their serum LDL-C/HDL-C ratio and testosterone level, decreased female serum bile acid level, and affected male/female placenta and multi-organ functions (mainly bone, liver, hippocampus). DMSO decreased male body weight and serum glucose level, decreased male/female serum bile acid levels, and affected male/female placenta and multi-organs functions (mainly bone, hippocampus, adrenal gland). In conclusion, prenatal exposure to a conventional dose of corn oil, CMC-Na or DMSO could affect fetal physical development and multi-organ functions, and has the characteristics of "multi-pathway, multi-organ and multi-target". This study provides the experimental basis for the rational selection of solvents or suspensions in pharmacology and toxicology studies. DATA AVAILABILITY: Data will be made available on request.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Transparency in Non-Technical Project Summaries to Promote the Three Rs in Respiratory Disease Research

Non-Technical Project Summaries (NTS) are legal documents that were first introduced by the Directive 2010/63/EU to enhance transparency within scientific animal experimentation. Researchers intending to conduct biological research on animal models must fulfil the NTS requirements by outlining their proposed use of animals and how they plan to implement the Three Rs (replacement, reduction and refinement of animal use) in their experiments. This study outlines a novel systematic analysis approach that enables the assessment of NTS transparency based on the accuracy of reporting of certain Three Rs-specific information. This potentially customisable strategy could help toward the development of practical guidelines for use by Animal Welfare and Ethical Review Bodies (AWERBs) in establishments conducting animal research, in the process of scrutinising NTS during their pre-submission review of proposed licence applications. This could help to identify gaps in reporting of Three Rs-specific information relating to the planned animal experiments, which represents a remarkable step toward achieving greater openness in scientific communication. This study supports the concept that NTS transparency can promote the implementation of non-animal alternatives in fields where this is currently lacking, such as respiratory disease research. Although NTS were originally conceived as informative documents for a lay audience, we can conclude that data in NTS can be successfully used as a basis for systematic analysis. By reviewing the NTS, the experimental limitations of the currently available replacement strategies can also be highlighted, potentially pinpointing where there is a need for future method development.

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

Background:

Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules.

Aims:

The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs.

Methods:

A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF).

Results:

The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity.

Conclusion:

The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.