AIMC Topic: Drug Combinations
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Accurate prediction of synergistic drug combination using a multi-source information fusion framework.
BMC biology
Jul 3, 2025
BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alon...
MSFCL: Drug Combination Risk Level Prediction Based on Multi-Source Feature Fusion and Contrastive Learning.
Journal of chemical information and modeling
Jun 20, 2025
Accurate assessment of drug combination risk levels is crucial for guiding rational clinical medication and avoiding adverse reactions. However, most existing methods are limited to binary classification, which fails to quantify distinctions between ...
Multi-omics profiling of cross-resistance between ceftazidime-avibactam and meropenem identifies common and strain-specific mechanisms in clinical isolates.
mBio
Jun 4, 2025
is a highly versatile and resilient pathogen that can infect different tissues and rapidly develop resistance to multiple drugs. Ceftazidime-avibactam (CZA) is an antibiotic often used to treat multidrug-resistant infections; however, the knowledge ...
Impact of dental pulp cells-derived small extracellular vesicles on the properties and behavior of dental pulp cells: an in-vitro study.
BMC oral health
May 10, 2025
BACKGROUND: Dental pulp cells-derived small extracellular vesicles (DPCs-sEVs) had shown immunomodulatory, anti-inflammatory, and tissue function restorative abilities. Therefore, DPCs-sEVs should be considered as a promising regenerative tool for de...
Step up to triple therapy versus switch to dual bronchodilator therapy in patients with COPD on an inhaled corticosteroid/long-acting β-agonist: post-hoc analyses of KRONOS.
Respiratory research
May 8, 2025
BACKGROUND: In people with chronic obstructive pulmonary disease (COPD) on inhaled corticosteroid/long-acting β-agonist (ICS/LABA) therapy, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends stepping up to ICS/long-acting mu...
Aged
Pulmonary Disease, Chronic Obstructive
Adrenergic beta-2 Receptor Agonists
Male
Drug Combinations
Middle Aged
Drug Therapy, Combination
Double-Blind Method
Adrenal Cortex Hormones
Humans
Treatment Outcome
Administration, Inhalation
Female
Bronchodilator Agents
Drug Substitution
Muscarinic Antagonists
Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.
BMC bioinformatics
Jan 15, 2025
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably...
DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.
Scientific reports
Jan 15, 2025
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method,...
Hepatoid adenocarcinoma of the stomach with ideal response to neoadjuvant chemo-immunotherapy: a case report.
Frontiers in immunology
Jan 8, 2025
Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer characterized by histological features resembling hepatocellular carcinoma. Surgical intervention remains the preferred treatment modality for eligible patients. However...
ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects.
Journal of chemical information and modeling
Jan 3, 2025
As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single vie...
Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.
Bipolar disorders
Oct 3, 2024
OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score ...
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