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Drug Discovery · 28 stories · page 1 of 2

[Design and implementation of a clinical trial image medical record desensitization system based on artificial intelligence technology]
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[Design and implementation of a clinical trial image medical record desensitization system based on artificial intelligence technology]

Objective: To designe a clinical trial image medical record desensitization system based on artificial intelligence technology and to conduct preliminary applications, achieving the protection of patient privacy and promoting the safe use of clinical image data in clinical trial projects. Methods: The clinical trial image record desensitization system based on artificial intelligence technology was first performed to preprocess operations, such as cropping and compression on medical images to initialize the imag…

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
Artificial Intelligence Across the Pharmaceutical Life Cycle: Implications for Industry-Based Clinical Pharmacists
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Artificial Intelligence Across the Pharmaceutical Life Cycle: Implications for Industry-Based Clinical Pharmacists

Artificial intelligence (AI) is increasingly shaping the pharmaceutical industry. This ACCP commentary examines the implications of AI for industry-based clinical pharmacists across the pharmaceutical life cycle, including drug development, regulatory affairs, medical affairs, health economics and outcomes research, and pharmacovigilance. Artificial intelligence-enabled tools may support target identification, clinical trial design, regulatory intelligence, evidence synthesis, medical content generation, real-wo…

Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis
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Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis

Coronary atherosclerosis (CA) is characterized by profound macrophage heterogeneity that drives plaque progression and vulnerability, yet the precise subpopulations and their niche-specific functions remain incompletely defined. Here, we constructed a single-cell transcriptomic atlas of human CA using dataset GSE131778 and identified six distinct macrophage subpopulations. Among these, the THBS1+ macrophage subset emerged as a terminally differentiated, hypoxia-adaptive population with elevated TGF-β signaling a…

Incremental Propensity Score Interventions: A Primer for Pharmacoepidemiologists
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Incremental Propensity Score Interventions: A Primer for Pharmacoepidemiologists

Background In observational pharmacoepidemiology, estimating average treatment effects (ATEs) is often challenging due to a lack of practical positivity. In highly selective clinical settings, certain patients almost always or never receive treatment, causing ATE estimators to rely on unstable extrapolation. Incremental propensity score interventions (IPSIs) offer a stochastic alternative by shifting each patient's probability of treatment, providing a more clinically realistic framework that circumvents positiv…

Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data
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Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data

The Cox proportional hazards model is often used to analyze data from randomized controlled trials (RCTs) with time-to-event outcomes. Random survival forest (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the performance of Cox regression and RSF in various simulation scenarios based on two reference datasets from RCTs. The motivation is to identify settings in which one method is preferable over the other when compa…

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Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder

The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD…

Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder
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Deep Learning-Powered Plasmonic Platform for Decoding Dynamic Protein Aggregation Landscapes in Parkinson's Disease Progression

In-depth characterization of pathological protein aggregate with its subcomponents is pivotal for early diagnosis and staging of neurodegenerative disorders. Here, we introduce the Protein Aggregate NanoDynamics Analyzer (PANDA), a plasmonic nanotechnology-powered system that translates the supramolecular size distribution of protein aggregates into distinct scattering signatures via sterically constrained immunogold clustering. In this work, PANDA enabled the profiling of α-synuclein (α-syn) aggregates with div…

Deep Learning-Powered Plasmonic Platform for Decoding Dynamic Protein Aggregation Landscapes in Parkinson's Disease Progression
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FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized therapeutic guidance. This research work introduces FedMediFormer-XAI, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks (GNNs) for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence. The system utilizes diverse healthcar…

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes
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Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation

Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporation of computational technologies within both academic and pharmaceutical environments. This evolution is marked by a significant accumulation of data pertaining to detailed three-dimensional structural information, ligand properties, and their interactions with therapeutic targets. The augmentation of computational capabilities and the accessibility of extensive chemical libraries containing billions of d…

Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation
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Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis

Background Artificial intelligence (AI) methods are increasingly used to strengthen policy evaluation in managed care pharmacy. Among Medicare beneficiaries with cancer, which is one of the most clinically complex and costly populations, prescription drug coverage is obtained through either integrated Medicare Advantage Prescription Drug plans (MA-PDs) or stand-alone Prescription Drug Plans (PDPs). However, causal evidence of plans' impact remains limited because of nonrandom enrollment. Objective To apply an AI…

Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis
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A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1)

Prior authorization (PA) imposes substantial administrative burdens on clinicians, contributing to burnout, delayed care, and excess health care spending. Artificial intelligence (AI) is emerging as a tool to automate PA tasks in managed care pharmacy, yet concerns persist regarding transparency, bias, and overreliance on autonomous systems. This is particularly true in payer-deployed AI systems that may deny claims without adequate clinical review. This viewpoint proposes a pharmacist-overseen, AI-enabled syste…

A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1)