Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging "phenotype" over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1053/j.semnuclmed.2025.01.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
iBCS: 4. Application of the Inhalation Biopharmaceutics Classification System to the Development of Orally Inhaled Drug Products.
Mol Pharm
March 2025
Merck & Co., Inc., Rahway, New Jersey 07065, United States.
This is the fourth paper in a series describing an inhalation biopharmaceutics classification system (iBCS), an initiative supported by the Product Quality Research Institute. The paper examines the application of the inhalation Biopharmaceutics Classification System (iBCS) through the drug discovery, development, and postapproval phases for orally inhaled drug products (OIDP) and for the development of generic OIDPs. We consider the implication of the iBCS class in terms of product performance and identify the practical gaps that must be filled to enable the classification system to be adopted into day-to-day practice.
View Article and Find Full Text PDFDesign, synthesis and biological evaluation of pyrazolo[3,4-]pyridine derivatives as dual CDK2/PIM1 inhibitors with potent anti-cancer activity and selectivity.
J Biomol Struct Dyn
March 2025
Applied Organic Chemistry Department, National Research Center, Dokki, Egypt.
The discovery of novel, selective inhibitors targeting CDK2 and PIM1 kinases, which regulate cell survival, proliferation, and treatment resistance, is crucial for advancing cancer therapy. This study reports the design, synthesis, and biological evaluation of three novel pyrazolo[3,4-]pyridine derivatives (), confirmed spectral analyses. These compounds were assessed for anti-cancer activity against breast, colon, liver, and cervical cancers using the MTT assay.
View Article and Find Full Text PDFChronic unpredictable mild stress induces anxiety-like behavior in female C57BL/6N mice, accompanied by alterations in inflammation and the kynurenine pathway of tryptophan metabolism.
Front Neurosci
February 2025
Engineering Research Center of Storage and Processing of Xinjiang Characteristic Fruits and Vegetables, Ministry of Education, School of Food Science, Shihezi University, Shihezi, Xinjiang, China.
Chronic stress can impact brain function through various mechanisms, contributing to the development of anxiety disorders. Chronic unpredictable mild stress (CUMS) is a well-established model for studying the effects of chronic stress. This study assessed the impacts of different durations of CUMS on anxiety-like behavior, inflammation, and tryptophan metabolism in female C57BL/6N mice.
View Article and Find Full Text PDFFoundation models in bioinformatics.
Natl Sci Rev
April 2025
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities.
View Article and Find Full Text PDFHigh-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach.
Front Pharmacol
February 2025
Department of Drug Discovery and Development, GenFleet Therapeutics (Shanghai) Inc., Shanghai, China.
Introduction: The cytochrome P450 enzyme 3A4 (CYP3A4) mediates numerous drug-drug interactions (DDIs) by inducing the metabolism of co-administered drugs, which can result in reduced therapeutic efficacy or increased toxicity. This study developed and validated a Physiologically Based Pharmacokinetic (PBPK) model to predict CYP3A4 induction-mediated DDIs, focusing on the early stages of clinical drug development.
Methods: The PBPK model for rifampicin, a potent CYP3A4 inducer, was developed and validated using human pharmacokinetic data.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!