Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

International Journal of Molecular Sciences, Journal Year: 2022, Volume and Issue: 23(9), P. 4645 - 4645

Published: April 22, 2022

Big data in health care is a fast-growing field and new paradigm that transforming case-based studies to large-scale, data-driven research. As big dependent on the advancement of standards, technology, relevant research, future development applications holds foreseeable promise modern day revolution. Enormously large, rapidly growing collections biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) clinical create major challenges opportunities for their analysis interpretation open computational gateways address these issues. The design robust algorithms are most suitable properly analyze this by taking into account individual variability genes has enabled creation precision (personalized) medicine. We reviewed highlighted significance analytics personalized medicine focusing mostly machine learning perspectives medicine, genomic models with respect application mining as well we facing right now analytics.

Language: Английский

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Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(3), P. 332 - 332

Published: Feb. 27, 2024

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in revolutionary era healthcare by individualizing diagnostics and according to each patient’s uniquely evolving health status. This groundbreaking method tailoring disease prevention treatment considers individual variations genes, environments, lifestyles. goal precision target the “five rights”: right patient, drug, time, dose, route. In this pursuit, silico techniques have emerged as an anchor, driving forward making realistic promising avenue for personalized therapies. With advancements high-throughput DNA sequencing technologies, genomic data, including genetic variants their interactions with other environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) pharmacodynamic (PD) mathematical models further contribute drug optimization, behavior prediction, drug–drug interaction identification. Digital health, wearables, computational tools offer continuous monitoring real-time data collection, enabling adjustments. Furthermore, incorporation extensive datasets tools, such electronic records (EHRs) omics also another pathway acquire meaningful information field. Although they are fairly new, machine learning (ML) algorithms artificial intelligence (AI) resources researchers use analyze big develop predictive models. review explores interplay these multiple approaches advancing fostering healthcare. Despite intrinsic challenges, ethical considerations, protection, need more comprehensive research, marks new patient-centered Innovative hold potential reshape future generations come.

Language: Английский

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Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges

Journal of Molecular Liquids, Journal Year: 2023, Volume and Issue: 395, P. 123888 - 123888

Published: Dec. 27, 2023

Efficient drug delivery systems (DDSs) play a pivotal role in ensuring pharmaceuticals' targeted and effective administration. However, the intricate interplay between formulations poses challenges their design optimization. Simulations have emerged as indispensable tools for comprehending these interactions enhancing DDS performance to address this complexity. This comprehensive review explores latest advancements simulation techniques provides detailed analysis. The encompasses various methodologies, including molecular dynamics (MD), Monte Carlo (MC), finite element analysis (FEA), computational fluid (CFD), density functional theory (DFT), machine learning (ML), dissipative particle (DPD). These are critically examined context of research. article presents illustrative case studies involving liposomal, polymer-based, nano-particulate, implantable DDSs, demonstrating influential simulations optimizing systems. Furthermore, addresses advantages limitations It also identifies future directions research development, such integrating multiple techniques, refining validating models greater accuracy, overcoming limitations, exploring applications personalized medicine innovative DDSs. employing like MD, MC, FEA, CFD, DFT, ML, DPD offer crucial insights into behaviour, aiding Despite advantages, rapid cost-effective screening, require validation addressing limitations. Future should focus on models, enhance outcomes. paper underscores contribution emphasizing providing valuable facilitating development optimization ultimately patient As we continue explore impact advancing discovery improving DDSs is expected be profound.

Language: Английский

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Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies

Theranostics, Journal Year: 2024, Volume and Issue: 14(9), P. 3404 - 3422

Published: Jan. 1, 2024

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment different types cancers.However, current approaches to often follow somewhat inflexible "one size fits all" paradigm, where patients are administered same amount radioactivity per cycle regardless their individual characteristics and features.This approach fails consider inter-patient variations radiopharmacokinetics, radiation biology, immunological factors, which can significantly impact outcomes.To address this limitation, we propose development theranostic digital twins (TDTs) personalize based on actual patient data.Our proposed roadmap outlines steps needed create refine TDTs that optimize dose tumors while minimizing toxicity organs at risk.The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, additionally linked radiobiological optimizer an modulator, taking into account factors influence RPT response.By using envisage ability perform virtual clinical trials, selecting therapies towards improved outcomes risks associated secondary effects.This framework could empower practitioners ultimately develop tailored solutions for subgroups patients, thus improving precision, accuracy, efficacy treatments patients.By incorporating RPTs, pave way new era precision medicine cancer treatment.

Language: Английский

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Advancing the quantitative understanding of Adverse Outcome Pathways: current status, methodologies, and future directions

Environmental Toxicology and Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Abstract An adverse outcome pathway (AOP) framework maps the sequence of events leading to outcomes from chemical exposures, providing a mechanistic understanding often absent in traditional methods. The quantitative AOP (qAOP) advances by integrating data and mathematical modeling, thereby more precise comprehension relationships between molecular initiating events, key outcomes. This review critically examines three primary methodologies: systems toxicology, regression Bayesian network highlighting their strengths, limitations, specific requirements within toxicology. Through an analysis current methodologies challenges, this emphasizes integration experimental computational approaches elucidate event proposes strategies for overcoming limitations through standardized protocols advanced tools. By outlining future research directions potential qAOPs transform risk assessment, aims contribute advancement regulatory science protection public health environment.

Language: Английский

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Challenges and opportunities in uncertainty quantification for healthcare and biological systems

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2025, Volume and Issue: 383(2292)

Published: March 13, 2025

Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science aerospace engineering, incorporate UQ in the development translation new technologies. In contrast, application to biological healthcare models understudied suffers from several critical knowledge gaps. era personalized medicine, patient-specific modelling, digital twins , a lack understanding appropriate implementation methodology limits success simulation clinical setting. The main contribution our review article emphasize importance current deficiencies frameworks for systems. As introduction special issue on this topic, we provide overview methodologies, their applications non-biological systems gaps opportunities development, as later highlighted by authors publishing issue. This part theme ‘Uncertainty (Part 1)’.

Language: Английский

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A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control

Journal of Fish Diseases, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Viral diseases pose a significant threat to the sustainability of global aquaculture, causing economic losses and compromising food security. Traditional control methods often demonstrate limited effectiveness, highlighting need for alternative approaches. The integration computational discovery natural compounds shows promise in developing antiviral treatments. This review critically explores how both traditional advanced silico techniques can efficiently identify with potential inhibitory effects on key pathogenic proteins major aquaculture pathogens. It highlights fundamental approaches, including structure-based ligand-based drug design, high-throughput virtual screening, molecular docking, absorption, distribution, metabolism, excretion toxicity (ADMET) profiling. Molecular dynamics simulations serve as comprehensive framework understanding interactions stability candidate drugs an approach, reducing extensive wet-lab experiments providing valuable insights targeted therapeutic development. covers entire process, from initial screening promising candidates their subsequent experimental validation. also proposes integrating tools enhance efficiency aquaculture. Finally, we explore future perspectives, particularly artificial intelligence multi-omics These innovative technologies significantly accelerate identification optimisation antivirals, contributing sustainable disease management

Language: Английский

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Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e46089 - e46089

Published: Nov. 22, 2023

Background The application of artificial intelligence (AI) in the delivery health care is a promising area, and guidelines, consensus statements, standards on AI regarding various topics have been developed. Objective We performed this study to assess quality field for medicine provide foundation recommendations about future development guidelines. Methods searched 7 electronic databases from database establishment April 6, 2022, screened articles involving eligibility. AGREE II (Appraisal Guidelines Research & Evaluation II) RIGHT (Reporting Items Practice Healthcare) tools were used methodological reporting included articles. Results This systematic review 19 guideline articles, 14 statement 3 standard published between 2019 2022. Their content involved disease screening, diagnosis, treatment; intervention trial reporting; imaging collaboration; data application; ethics governance applications. Our assessment revealed that average overall score was 4.0 (range 2.2-5.5; 7-point Likert scale) mean rate tool 49.4% 25.7%-77.1%). Conclusions results indicated important differences different standards. made improving their quality. Trial Registration PROSPERO International Prospective Register Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360

Language: Английский

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Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications

Micromachines, Journal Year: 2024, Volume and Issue: 15(3), P. 313 - 313

Published: Feb. 24, 2024

This article explores the challenges of continuum and magnetic soft robotics for medical applications, extending from model development to an interdisciplinary perspective. First, we established a unified framework based on algebra geometry. The research progress in principle models, data-driven, hybrid modeling were then analyzed depth. Simultaneously, numerical analysis was constructed. Furthermore, expanded encompass conducted comprehensive analysis, including in-depth case study. Current need address meta-problems identified through discussion. Overall, this review provides novel perspective understanding complexities paving way researchers assimilate knowledge domain rapidly.

Language: Английский

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Network pharmacology and molecular docking: combined computational approaches to explore the antihypertensive potential of Fabaceae species

Bioresources and Bioprocessing, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 20, 2024

Abstract Hypertension is a major global public health issue, affecting quarter of adults worldwide. Numerous synthetic drugs are available for treating hypertension; however, they often come with higher risk side effects and long-term therapy. Modern formulations active phytoconstituents gaining popularity, addressing some these issues. This study aims to discover novel antihypertensive compounds in Cassia fistula , Senna alexandrina occidentalis from family Fabaceae understand their interaction mechanism hypertension targeted genes, using network pharmacology molecular docking. Total 414 were identified; initial screening was conducted based on pharmacokinetic ADMET properties, particular emphasis adherence Lipinski's rules. 6 compounds, namely Germichrysone, Benzeneacetic acid, Flavan-3-ol, 5,7,3',4'-Tetrahydroxy-6, 8-dimethoxyflavon, Dihydrokaempferol, Epiafzelechin, identified as effective agents. Most the found non-toxic against various indicators greater bioactivity score. 161 common targets obtained followed by compound-target construction protein–protein interaction, which showed role diverse biological system. Top hub genes TLR4, MMP9, MAPK14, AKT1, VEGFA HSP90AA1 respective associates. Higher binding affinities three Flavan-3-ol −7.1, −9.0 −8.0 kcal/mol, respectively. The MD simulation results validate structural flexibility two complexes Flavan-MMP9 Germich-TLR4 no. hydrogen bonds, root mean square deviations energies. concluded that C. (Dihydrokaempferol, Flavan-3-ol) (Germichrysone) have potential therapeutic constituents treat future drug formulation. Graphical

Language: Английский

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