AIMC Topic: Antineoplastic Agents

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SHIFT-DRP: Dynamic Multi-Scale Active Learning for Drug Response Prediction.

Journal of chemical information and modeling
Deep learning models show promise for drug response prediction in personalized cancer treatment, but exhibit limited prediction capability for novel drug-cell line combinations due to insufficient coverage of the chemical spaces in training data. The...

Machine learning-guided identification and simulation-based validation of potent JAK3 inhibitors for cancer therapy.

PloS one
Janus kinase 3 (JAK3) is a hematopoietic-specific kinase implicated in cytokine signaling and immune dysregulation and has recently been associated with cancer progression. However, selective and potent JAK3 inhibitors remain underdeveloped. In this ...

Pluronic nanoparticle-modified modular bacterial robots for therapy of tumors and inflammatory bowel disease.

Journal of materials chemistry. B
Bacterial-mediated drug delivery has emerged as a promising strategy for disease treatment, leveraging bacteria's innate ability to penetrate biological barriers and target diseased tissues. However, existing bacteria-nanoparticle hybrid systems ofte...

Comparison of in vitro migration assays evaluating nintedanib's migration inhibitory effects on melanoma cells.

Scientific reports
Cell migration plays a central role in tumor progression and metastasis, making it a critical parameter in both cancer biology and therapeutic evaluation. A range of in vitro migration assays are commonly used to assess treatment-induced effects on m...

Computational screening and in vitro evaluation of sphingosine-1-phosphate analogues as therapeutics for Non-Hodgkin's lymphoma.

Scientific reports
Non-Hodgkin's lymphoma (NHL) is a prevalent hematological malignancy that includes a variety of B-cell and T-cell proliferations. The S1P (sphingosine-1-phosphate) pathway, involved in cell survival, proliferation, and migration, plays a critical rol...

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Scientific reports
Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integr...

Integrating machine-learning and nanotechnology to quantify pH-modulated oxaliplatin release.

Scientific reports
The purpose of this work was to formulate and characterize pH-sensitive, surfactant-based nanomicelles for the targeted delivery of Oxaliplatin to breast cancer cells. A secondary aim was to utilize machine learning (ML) models to interpolate and dis...

Machine learning-based fabrication of phytogenic NiO nanoparticles for anticancer activity in HepG2 Cell Culture.

Journal of materials science. Materials in medicine
Metal oxide nanomaterials play a central role in biomedical applications due to their unique physicochemical properties. In particular, various treatment methods such as drug delivery, hyperthermia therapy, radiation, and chemotherapy are used for th...

Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.

Molecular biomedicine
Therapeutic resistance remains a defining challenge in oncology, limiting the durability of current therapies and contributing to disease relapse and poor patient outcomes. This review systematically integrates recent progress in understanding the mo...

Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer.

BMC medical genomics
Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analy...