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Dose-Response Relationship, Drug

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Toxicity, genotoxicity, and carcinogenicity of 2-methylfuran in a 90-day comprehensive toxicity study in gpt delta rats.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
2-Methylfuran (2-MF) exists naturally in foods and is used as a flavoring agent. Furan, the core structure of 2-MF, possesses hepatocarcinogenicity in rodents. Accumulation of toxicological information on furan derivatives is needed to elucidate thei...

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

Archives of toxicology
The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it re...

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Molecular diversity
Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can le...

CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes.

Bioorganic & medicinal chemistry
The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences....

Application of Deep Neural Network Models in Drug Discovery Programs.

ChemMedChem
In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a ...

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Bioorganic & medicinal chemistry
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...

Raster plots machine learning to predict the seizure liability of drugs and to identify drugs.

Scientific reports
In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to pre...

Automated warfarin dose prediction for Asian, American, and Caucasian populations using a deep neural network.

Computers in biology and medicine
Existing warfarin dose prediction algorithms based on pharmacogenetics and clinical parameters have not been used clinically due to the absence of external validation, lack of assessment for clinical utility, and high risk of bias. Moreover, given th...

Machine Learning: A New Approach for Dose Individualization.

Clinical pharmacology and therapeutics
The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its earl...

Dose reduction and toxicity of lenalidomide-dexamethasone in multiple myeloma: A machine-learning prediction model.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
PURPOSE: Lenalidomide remains an effective drug for multiple myeloma, but it is often associated with adverse events and requires dose adjustments. The objective of this study was to propose a model for predicting whether a patient would require dose...