AIMC Topic: Antineoplastic Agents

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Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology ...

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy.

IEEE/ACM transactions on computational biology and bioinformatics
Existing drug discovery processes follow a reductionist model of "one-drug-one-gene-one-disease," which is inadequate to tackle complex diseases involving multiple malfunctioned genes. The availability of big omics data offers opportunities to transf...

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

Clinical and translational science
Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized...

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Cell chemical biology
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, who...

Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity.

European journal of medicinal chemistry
Primaquine (PQ) is a commonly used drug that can prevent the transmission of Plasmodium falciparum malaria, however toxicity limits its use. We prepared five groups of PQ derivatives: amides 1a-k, ureas 2a-k, semicarbazides 3a,b, acylsemicarbazides 4...

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Cell cycle (Georgetown, Tex.)
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficien...

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Nature communications
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatm...

Cytotoxicity of the Defensive Secretion from the Medicinal Insect Blaps rynchopetera.

Molecules (Basel, Switzerland)
Fairmaire has long been used as a folk medicine by the Yi and Bai ethnic groups in China to treat fever, cough, gastritis, boils, and tumors. In the present study, the cytotoxicity of the defensive secretion (TDS) of against AGS Caco-2, HepG2 U251 ...