AIMC Topic: Antineoplastic Agents
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Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.
Biochimica et biophysica acta. Reviews on cancer
Apr 26, 2019
The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and diffi...
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.
International journal of molecular sciences
Apr 22, 2019
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning ...
Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy.
Computers in biology and medicine
Apr 19, 2019
Numerous models have investigated cancer behavior by considering different factors in chemotherapy. The subject of a controller design approach for these models in order to find the best rate of drug injection during the course of treatment has recen...
Qualification of a chemotherapy-compounding robot.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Apr 18, 2019
KIRO® Oncology (Kiro Grifols, Spain) is a robotic system for automated compounding of sterile injectable drugs including intravenous cytotoxic treatments. The present article describes the qualification procedure applied prior to production phases. P...
SLMF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 9, 2019
Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. The...
Predicting drug-target interaction network using deep learning model.
Computational biology and chemistry
Mar 25, 2019
BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target...
Relative Factors Analysis of Imatinib Trough Concentration in Chinese Patients with Gastrointestinal Stromal Tumor.
Chemotherapy
Mar 5, 2019
AIMS: Imatinib plasma trough levels (IM Cmin) have been reported to have a considerable clinical impact in patients with gastrointestinal stromal tumors (GISTs). We therefore have investigated the factors affecting IM plasma concentration in Chinese ...
Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.
Computational biology and chemistry
Feb 16, 2019
BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline m...
Time Factors
Cell Line, Tumor
Cell Proliferation
Drug Screening Assays, Antitumor
Structure-Activity Relationship
Support Vector Machine
Dose-Response Relationship, Drug
Machine Learning
Algorithms
Antineoplastic Agents
Humans
PC-3 Cells
Models, Molecular
Artificial Intelligence
Molecular Structure
Gallic Acid
Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.
Journal of chemical information and modeling
Feb 15, 2019
Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drug...
Predicting drug response of tumors from integrated genomic profiles by deep neural networks.
BMC medical genomics
Jan 31, 2019
BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer ...
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