AI Medical Compendium Journal:
Regulatory toxicology and pharmacology : RTP

Showing 1 to 10 of 14 articles

Interpretable machine learning unveils key predictors and default values in an expanded database of human in vitro dermal absorption studies with pesticides.

Regulatory toxicology and pharmacology : RTP
The skin is the main route of exposure to plant protection products for operators, workers, residents, and bystanders. Assessing dermal absorption is key for evaluating pesticide exposure. The initial approach to risk assessment involves using defaul...

The role of trust in the use of artificial intelligence for chemical risk assessment.

Regulatory toxicology and pharmacology : RTP
Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Mach...

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

Regulatory toxicology and pharmacology : RTP
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the In...

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

Regulatory toxicology and pharmacology : RTP
In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new ...

Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review.

Regulatory toxicology and pharmacology : RTP
In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output...

RespiraTox - Development of a QSAR model to predict human respiratory irritants.

Regulatory toxicology and pharmacology : RTP
Respiratory irritation is an important human health endpoint in chemical risk assessment. There are two established modes of action of respiratory irritation, 1) sensory irritation mediated by the interaction with sensory neurons, potentially stimula...

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost.

Regulatory toxicology and pharmacology : RTP
The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, anima...

Support vector machine-based model for toxicity of organic compounds against fish.

Regulatory toxicology and pharmacology : RTP
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity r...

Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction.

Regulatory toxicology and pharmacology : RTP
In vitro genotoxicity bioassays are cost-efficient methods of assessing potential carcinogens. However, many genotoxicity bioassays are inappropriate for detecting chemicals eliciting non-genotoxic mechanisms, such as tumour promotion, this necessita...