Precision Medicine—Are We There Yet? A Narrative Review of Precision Medicine’s Applicability in Primary Care DOI Open Access
William Evans, Eric M. Meslin, Joe Kai

et al.

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(4), P. 418 - 418

Published: April 15, 2024

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many “-omics” arising from increased capacity understand human genome “big data” data analytics, including artificial intelligence (AI). PM has potential transform an individual’s health, moving population-based disease prevention more management. There is however tension between two, with real risk that this will exacerbate inequalities divert funds attention basic healthcare requirements leading worse outcomes for many. All areas should consider how affect their practice, now strongly encouraged supported by government initiatives research funding. In review, we discuss examples in current practice its applications primary care, such as clinical prediction tools incorporate genomic markers pharmacogenomic testing. We look towards future some key questions PM, evidence real-world impact, affordability, exacerbating inequalities, computational storage challenges applying at scale.

Language: Английский

MACHINE LEARNING IN DRUG DISCOVERY: A CRITICAL REVIEW OF APPLICATIONS AND CHALLENGES DOI Creative Commons
Francisca Chibugo Udegbe,

Ogochukwu Roseline Ebulue,

Charles Chukwudalu Ebulue

et al.

Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(4), P. 892 - 902

Published: April 17, 2024

This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit lead optimization, and predictive toxicology. Despite ML's potential to revolutionize discovery through enhanced efficiency, accuracy, novel insights, significant challenges persist. These include issues related data quality, model interpretability, into existing workflows, regulatory ethical considerations. The advocates for advancements algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, evolving frameworks as solutions these challenges. By addressing hurdles leveraging capabilities ML, process can be significantly accelerated, paving way development new therapeutics. calls continued research, dialogue among stakeholders realize transformative ML fully. Keywords: Learning, Drug Discovery, Predictive Toxicology, Data Quality, Interdisciplinary Collaboration.

Language: Английский

Citations

36
PRECISION MEDICINE AND GENOMICS: A COMPREHENSIVE REVIEW OF IT-ENABLED APPROACHES DOI Creative Commons
Francisca Chibugo Udegbe,

Ogochukwu Roseline Ebulue,

Charles Chukwudalu Ebulue

et al.

International Medical Science Research Journal, Journal Year: 2024, Volume and Issue: 4(4), P. 509 - 520

Published: April 20, 2024

This review delves into Information Technology's (IT) transformative impact on precision medicine and genomics, spotlighting the pivotal role of bioinformatics, data mining, machine learning, blockchain technologies in advancing personalized healthcare. A comprehensive analysis outlines how these IT-enabled approaches facilitate analysis, interpretation, application vast genomic sets, thereby enhancing disease prediction, diagnosis, treatment an individual level. Despite promising advancements, also addresses significant challenges, including complexity, interoperability, ethical considerations, digital divide, underscoring necessity for multidisciplinary collaboration innovation to overcome hurdles. The paper concludes by emphasizing potential emerging patient-centred care realizing vision medicine, which promises improved healthcare outcomes through strategies. Keywords: Precision Medicine, Genomics, Bioinformatics, Machine Learning, Data Security.

Language: Английский

Citations

26
The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review DOI Open Access
Ben Allen

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(3), P. 277 - 277

Published: March 1, 2024

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, integration of artificial intelligence with becomes crucial. Leveraging a topic-modeling approach, this paper distills key themes 27 journal articles. We included peer-reviewed articles written English, no time constraints search. A Google Scholar search, conducted up 19 September 2023, yielded Through identified topics encompassed optimizing patient through data-driven medicine, predictive modeling and algorithms, predicting diseases deep learning biomedical data, machine delves into specific applications explainable intelligence, emphasizing its role fostering transparency, accountability, trust within domain. Our highlights necessity further development validation explanation methods advance delivery.

Language: Английский

Citations

20
Recent advancements in nanoconstructs for the theranostics applications for triple negative breast cancer DOI Creative Commons
Ashutosh Gupta, Kumar Nishchaya, Moumita Saha

et al.

Journal of Drug Delivery Science and Technology, Journal Year: 2024, Volume and Issue: 93, P. 105401 - 105401

Published: Jan. 25, 2024

Cancer is a major public health concern worldwide; it the second-highest cause of death in United States. According to projections cancer incidence and mortality rates throughout world for year 2023, triple-negative breast (TNBC) expected be leading related among women worldwide. Traditional strategies treatment TNBC have many drawbacks, such as drug resistance, toxicity etc. Discovering novel delivery techniques researching innovative, efficient methods important. This review discusses types subtypes TNBC. The problems associated with standard therapies, mechanism resistance highlights need develop therapeutic strategies. It provides information on relative prevalence severity cancer. Several approaches viz. targeted therapy, gene bacterial-mediated nanomedicine, immune checkpoint inhibitors, theranostic, radiotherapy, chemotherapy, immunotherapy, herbal AI-based TNBC, are discussed detail. Additionally, diagnostic techniques, including imaging biopsy, expression profiling, mammography, magnetic resonance imaging, ultrasound, computed tomography scan, positron emission immunohistochemistry, been effective treatment. in-depth analysis innovative individualized care serve patients better.

Language: Английский

Citations

19
Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology DOI Creative Commons
Kamelia Hristova‐Panusheva, Charilaos Xenodochidis, Milena Georgieva

et al.

Pharmaceuticals, Journal Year: 2024, Volume and Issue: 17(6), P. 677 - 677

Published: May 24, 2024

Nanotechnology has emerged as a transformative force in oncology, facilitating advancements site-specific cancer therapy and personalized oncomedicine. The development of nanomedicines explicitly targeted to cells represents pivotal breakthrough, allowing the precise interventions. These cancer-cell-targeted operate within intricate milieu tumour microenvironment, further enhancing their therapeutic efficacy. This comprehensive review provides contemporary perspective on precision medicine underscores critical role nanotechnology advancing It explores categorization nanoparticle types, distinguishing between organic inorganic variants, examines significance delivery anticancer drugs. Current insights into strategies for developing actively across various types are also provided, thus addressing relevant challenges associated with drug barriers. Promising future directions nanomedicine approaches delivered, emphasising imperative continued optimization nanocarriers medicine. discussion translational research’s need enhance patients’ outcomes by refining nanocarrier technologies nanotechnology-driven, therapy.

Language: Английский

Citations

19
A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe DOI Creative Commons
Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

et al.

Systems and Soft Computing, Journal Year: 2024, Volume and Issue: 6, P. 200110 - 200110

Published: June 25, 2024

Language: Английский

Citations

18
AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling DOI
Mayur B. Kale, Nitu L. Wankhede,

Rupali S. Pawar

et al.

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 102497 - 102497

Published: Sept. 1, 2024

Language: Английский

Citations

18
COX 2-inhibitors; a thorough and updated survey into combinational therapies in cancers DOI
Paul Rodrigues,

Harun Bangali,

Ahmad Hammoud

et al.

Medical Oncology, Journal Year: 2024, Volume and Issue: 41(1)

Published: Jan. 2, 2024

Language: Английский

Citations

16
Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease DOI Open Access
Nasser Alotaiq, Doni Dermawan

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(2), P. 462 - 462

Published: Jan. 8, 2025

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure tools, including AlphaFold 3, I-TASSER 5.1, PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) template-based (I-TASSER 5.1) fragment-based (PEP-FOLD), selected their proven capabilities in predicting reliable structures. Molecular was conducted using four (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro HawDock 2.0) assess binding affinities interactions. A 100 ns dynamics (MD) simulation performed evaluate stability peptide–receptor complexes, along with Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) calculations determine free energies. The results demonstrated that Apelin, a peptide, exhibited superior across all platforms, making it promising candidate CAD therapy. Apelin’s interactions key receptors involved cardiovascular health notably stronger more stable compared other tested. findings underscore importance integrating advanced computational design evaluation, offering valuable insights future applications CAD. Future work should focus on vivo validation combination therapies fully explore clinical potential these peptides.

Language: Английский

Citations

3
Integrating pharmacogenomic testing into personalized medicine practices in the USA: Implications for medication quality control and therapeutic efficacy DOI Creative Commons

James Tabat Bature,

Michael Alurame Eruaga,

Esther Oleiye Itua

et al.

GSC Biological and Pharmaceutical Sciences, Journal Year: 2024, Volume and Issue: 26(3), P. 019 - 026

Published: March 9, 2024

This concept paper explores the integration of pharmacogenomic testing into personalized medicine practices in USA and its implications for medication quality control therapeutic efficacy. By leveraging genetic information to optimize selection dosing, this aims improve patient outcomes minimize adverse drug reactions, thereby enhancing safety efficacy clinical practice. Integrating has potential revolutionize healthcare by improving USA. The begins discussing current landscape role optimizing dosing. It then examines benefits integrating practice, including improved safety, efficacy, cost-effectiveness. Key considerations implementing are discussed, regulatory considerations, reimbursement challenges, ethical considerations. also highlights importance provider education engagement successful implementation testing. Through a comprehensive analysis, provide insights testing, providers can personalize leading patients.

Language: Английский

Citations

15