AIMC Topic: Antineoplastic Agents

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A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

Cancer medicine
OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on pr...

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

PLoS computational biology
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, ...

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Molecular diversity
Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differen...

Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3.

Journal of biomolecular structure & dynamics
MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role in the degradation of the extracellular matrix (ECM). The normal function of MMP-9 includes the breakdown of ECM, a process that aids in normal physio...

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Computers in biology and medicine
Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tool...

Game-based learning as training to use a chemotherapy preparation robot.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
INTRODUCTION: In 2015, our university hospital pharmacy acquired the PharmaHelp robot system to automate part of its chemotherapy production. Complex technical use, downtime periods, and insufficient training caused a drop in motivation and dispariti...

Evaluation of cancer drug infusion devices prior to the implementation of a compounding robot.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
INTRODUCTION: Compounding robots are increasingly being implemented in hospital pharmacies. In our hospital, the recent acquisition of a robot (RIVA, ARxIUM) for intravenous cancer drug compounding obliged us to replace the previously used infusion d...

[Robotic production of injectable anticancer drugs in hospital pharmacies].

Bulletin du cancer
INTRODUCTION: Following the 2005 decree on securing the medicine supply chain, the production of "chemotherapies", anticancer drugs (cytotoxic, cytostatic, immunotherapy), was centralised within hospital pharmacies. To cope with increasingly growing ...

DeepCancerMap: A versatile deep learning platform for target- and cell-based anticancer drug discovery.

European journal of medicinal chemistry
Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensiv...

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Scientific reports
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examinati...