Predictive Modeling of Metabolic Drug Toxicity

Acute toxicity refers to the toxic effect or death of the body due to a single exposure or multiple exposures to exogenous chemicals within 24 hours, which is an important indicator of drug safety evaluation. Predicting the potential toxicity of new drug candidates is a critical step in the drug discovery and development process. Determining the safety profile of drugs is essential to ensure patient well-being and regulatory compliance. In recent years, predictive modeling approaches have emerged as valuable tools for assessing the toxicity of drugs. These models utilize computational algorithms and large datasets to predict various types of drug toxicity, including acute oral toxicity, mutagenicity, carcinogenicity, hERG toxicity, hepatotoxicity, and endocrine disruption.

Prediction of Acute Oral Toxicity

Acute toxicity of drugs can be categorized into oral, percutaneous, and inhalation acute toxicity. Since drugs are mainly taken orally, the prediction model constructed in this way is the key research object in the R&D process. Existing prediction models include the local model and the global model.

Prediction of Mutagenicity

Mutagenicity is the alteration of DNA in the cell nucleus caused by a compound. Since such changes are passed on as the cell divides, the mutagenicity of a compound is an important indicator of a drug's safety. Bruce Ames proposed a mutant testing method (Ames test) in 1983 and detected 175 known carcinogens. The Ames test is commonly used as a pre-test method and is widely used as a model system for the toxicity and mutagenicity safety assessment of chemicals.

Carcinogenicity Prediction

Carcinogenicity is one of the most important indicators for drug safety evaluation. Predictive models for the carcinogenicity of compounds can also be categorized into local and global models. Local models mainly focus on congeners such as N-nitroso compounds, aromatic amine compounds, and polycyclic aromatic compounds.

Prediction of hERG Toxicity

hERG (human Ether-à-go-go-Related Gene) toxicity refers to the potential of a drug to inhibit the hERG potassium ion channels in the heart, which can lead to life-threatening cardiac arrhythmias. Predictive modeling techniques integrate chemical descriptors, structural information, and hERG inhibition data to develop models capable of predicting hERG toxicity. These models aid in the identification of compounds with a high risk of hERG inhibition, enabling researchers to prioritize drug candidates with a lower risk of cardiac side effects.

Prediction of Drug Hepatotoxicity

Hepatotoxicity caused by drug-induced liver injury (DILI) is a common adverse drug reaction. DILI not only jeopardizes the health of drug users, but also is the main reason for drug development failure, restriction of use, and withdrawal of drugs from the market.

With the development of modern life sciences, computers, bioinformatics, and other technologies, computational models based on the molecular structure of drugs have been widely applied to the prediction of drug-induced hepatotoxicity. By using Bayesian methods, deep learning techniques, and substructure pattern recognition methods. These computational models have obtained good results in the practical application of predicting hepatotoxicity.

Prediction of Drug Endocrine Disruption

Fig. 1 Machine learning models for endocrine disruption prediction. (Zorn KM, et al., 2020)

Endocrine-disrupting chemicals (EDCs) are compounds in the environment that are capable of disrupting the human endocrine system, which in turn can have adverse effects on the reproductive, developmental, neurological, and immune systems. Earlier, predictive models for EDCs were mostly built based on statistical algorithms and molecular descriptors, and most of them required limited in vitro data support. Recently, machine learning methods have emerged as a way to predict molecular structures prospectively, providing a more comprehensive and reliable prediction of the endocrine-disrupting properties of compounds with lower computational costs.

Creative Bioarray Relevant Recommendations

Creative Bioarray specializes in identifying potential drug toxicity to different tissues and organs using a panel of in vitro cell-based assays and is an ideal partner for testing the potential toxicities of candidate drug compounds.

Reference

1. Zorn KM, et al. (2020). "Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction." Environ Sci Technol. 54 (19), 12202-12213.

For research use only. Not for any other purpose.