Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)
Published: May 25, 2025
Language: Английский
Metabolites, Journal Year: 2025, Volume and Issue: 15(3), P. 201 - 201
Published: March 13, 2025
Background: Tumor cells engage in continuous self-replication by utilizing a large number of resources and capabilities, typically within an aberrant metabolic regulatory network to meet their own demands. This dysregulation leads the formation tumor microenvironment (TME) most solid tumors. Nanomedicines, due unique physicochemical properties, can achieve passive targeting certain tumors through enhanced permeability retention (EPR) effect, or active deliberate design optimization, resulting accumulation TME. The use nanomedicines target critical pathways holds significant promise. However, requires careful selection relevant drugs materials, taking into account multiple factors. traditional trial-and-error process is relatively inefficient. Artificial intelligence (AI) integrate big data evaluate delivery efficiency nanomedicines, thereby assisting nanodrugs. Methods: We have conducted detailed review key papers from databases, such as ScienceDirect, Scopus, Wiley, Web Science, PubMed, focusing on reprogramming, mechanisms action development metabolism, application AI empowering nanomedicines. integrated content present current status research metabolism potential future directions this field. Results: Nanomedicines possess excellent TME which be utilized disrupt cells, including glycolysis, lipid amino acid nucleotide metabolism. disruption selective killing disturbance Extensive has demonstrated that AI-driven methodologies revolutionized nanomedicine development, while concurrently enabling precise identification molecular regulators involved oncogenic reprogramming pathways, catalyzing transformative innovations targeted cancer therapeutics. Conclusions: great Additionally, will accelerate discovery metabolism-related targets, empower optimization help minimize toxicity, providing new paradigm for development.
Language: Английский
Citations
2
Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104961 - 104961
Published: March 1, 2025
Language: Английский
Citations
1
MedComm – Future Medicine, Journal Year: 2025, Volume and Issue: 4(1)
Published: March 1, 2025
ABSTRACT Triple‐negative breast cancer (TNBC) is a highly aggressive subtype of cancer, characterized by poor prognosis and limited therapeutic options. Although neoadjuvant chemotherapy (NACT) remains the established treatment approach, its suboptimal efficacy associated with TNBC highlight urgent need for optimized strategies to improve pathological complete response (pCR) rates. This review provides comprehensive overview recent advancements in TNBC, emphasizing pivotal breakthroughs ongoing pursuit innovative approaches enhance precision medicine. It emphasizes clinical value platinum‐based agents, such as carboplatin cisplatin, which have shown significant improvements pCR rates, particularly patients BRCA mutations. Additionally, explores progress targeted therapies, including PARP inhibitors, AKT Antiangiogenic showcasing their potential personalized approaches. The integration immunotherapy, immune checkpoint inhibitor like pembrolizumab atezolizumab, has demonstrated substantial high‐risk cases. Future research priorities include refining biomarker‐driven strategies, optimizing combinations, developing antibody‐drug conjugates (ADCs) targeting TROP2 other biomarkers, reducing treatment‐related toxicity develop safer therapies. Furthermore, artificial intelligence also emerged transformative tool predicting decision‐making TNBC. These aim long‐term outcomes quality life
Language: Английский
Citations
1
Clinical Reviews in Allergy & Immunology, Journal Year: 2025, Volume and Issue: 68(1)
Published: March 13, 2025
Language: Английский
Citations
1
Therapeutic Advances in Gastroenterology, Journal Year: 2025, Volume and Issue: 18
Published: Jan. 1, 2025
Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have enhance important aspects of IBD trials—from patient recruitment trial design data analysis personalized treatment strategies. As AI advances, it improve long-standing challenges in efficiency, accuracy, personalization with goal accelerating discovery novel therapies outcomes people living IBD. can streamline multiple phases, from target identification monitoring. By integrating multi-omics data, electronic health records, imaging repositories, uncover molecular targets personalize strategies, ultimately expediting drug development. However, adoption encounters significant challenges. These include technical barriers integration, ethical concerns regarding privacy, regulatory issues related validation standards. Additionally, models risk producing biased if training datasets lack diversity, potentially impacting underrepresented populations trials. Addressing these limitations requires standardized formats, interdisciplinary collaboration, robust frameworks ensure inclusivity accuracy. Continued partnerships among clinicians, researchers, scientists, regulators will essential establish transparent, patient-centered frameworks. overcoming obstacles, equity, efficacy trials, benefiting care.
Language: Английский
Citations
0
Molecules, Journal Year: 2025, Volume and Issue: 30(5), P. 1047 - 1047
Published: Feb. 25, 2025
G-protein coupled receptors (GPCRs) are the largest family of membrane proteins engaged in transducing signals from extracellular environment into cell. GPCR-biased signaling occurs when two different ligands, sharing same binding site, induce distinct pathways. This selective offers significant potential for design safer and more effective drugs. Although its molecular mechanism remains elusive, big efforts made to try explain this using a wide range methods. Recent advances computational techniques AI technology have introduced variety simulations machine learning tools that facilitate modeling GPCR signal transmission analysis ligand-induced biased signaling. In review, we present current state silico approaches elucidate structural includes dynamics capture main interactions causing bias. We also highlight major contributions impacts transmembrane domains, loops, mutations mediating Moreover, discuss impact models on bias prediction diffusion-based generative ligands. Ultimately, review addresses future directions studying problem through approaches.
Language: Английский
Citations
0
Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 26, 2025
Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in decline patients' life quality. In response to the increased incidence osteoporosis, related drug discovery has attracted more attention, but it faced with challenges due long development cycle high cost. Deep learning powerful data processing capabilities shown significant advantages field discovery. With technology, applied all stages particular, large models, which have been developed rapidly recently, provide new methods for understanding disease mechanisms promoting because their parameters ability deal complex tasks. This review introduces traditional models deep domain, systematically summarizes applications each stage discovery, analyzes application prospect osteoporosis Finally, limitations are discussed depth, order help future
Language: Английский
Citations
0
Microbial Biotechnology, Journal Year: 2025, Volume and Issue: 18(3)
Published: March 1, 2025
ABSTRACT The combination of artificial intelligence (AI) with microbial technology marks the start a major transformation, improving applications throughout biotechnology, especially in healthcare. With capability AI to process vast amounts biological big data, advanced allows for comprehensive understanding complex systems, advancing disease diagnosis, treatment and development therapeutics. This mini review explores impact AI‐integrated technologies healthcare, highlighting advancements biomarker‐based therapeutics production therapeutic compounds. exploration promises significant improvements design implementation health‐related solutions, steering new era biotechnological applications.
Language: Английский
Citations
0
Cancer Cell, Journal Year: 2025, Volume and Issue: unknown
Published: March 1, 2025
Language: Английский
Citations
0
Bioengineering, Journal Year: 2025, Volume and Issue: 12(4), P. 363 - 363
Published: March 31, 2025
One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with that can mimic human focal processes in order produce results. This technique includes data collection, effective usage system development, conclusion illustration, arrangements. Analysis algorithms are cognitive activities the most widespread application AI. Artificial studies have proliferated, field is quickly beginning understand its potential impact on medical services investigation. review delves deeper into pros cons AI across healthcare pharmaceutical research industries. Research articles published throughout last few years were selected from PubMed, Google Scholar, Science Direct, using search terms like ‘artificial intelligence’, ‘drug discovery’, ‘pharmacy research’, ‘clinical trial’, etc. article provides a comprehensive overview how being used diagnose diseases, treat patients digitally, find new drugs, predict when outbreaks or pandemics may occur. In intelligence, neural networks deep some popular tools; clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones processing natural languages employed recognizing trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, outbreak predictions made computation intelligence. The academic world hopeful development will lead more efficient less expensive investigations public services.
Language: Английский
Citations
0