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

Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design

INTRODUCTION

Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs often leads to adverse drug reactions (ADRs) and therapeutic failure. As such, early prediction of such inhibitors is vital in drug development. In this context, the limitations of the traditional in vitro assays and QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques.

AREAS COVERED

This narrative review presents the insights gained from the application of AI for predicting DME and DT inhibitors over the past decade. Several case studies demonstrate successful AI applications in enzyme-transporter interaction prediction, and the authors discuss workflows for integrating these predictions into drug design and regulatory frameworks.

EXPERT OPINION

The application of AI in predicting DME and DT inhibitors has demonstrated significant potential toward enhancing drug safety and effectiveness. However, critical challenges involve the data quality, biases, and model transparency. The availability of diverse, high-quality datasets alongside the integration of pharmacokinetic and genomic data are essential. Lastly, the collaboration among computational scientists, pharmacologists, and regulatory bodies is pyramidal in tailoring AI tools for personalized medicine and safer drug development.

Interaction between post-tumor inflammation and vascular smooth muscle cell dysfunction in sepsis-induced cardiomyopathy

Background

Sepsis-induced cardiomyopathy (SIC) presents a critical complication in cancer patients, contributing notably to heart failure and elevated mortality rates. While its clinical relevance is well-documented, the intricate molecular mechanisms that link sepsis, tumor-driven inflammation, and cardiac dysfunction remain inadequately explored. This study aims to elucidate the interaction between post-tumor inflammation, intratumor heterogeneity, and the dysfunction of VSMC in SIC, as well as to evaluate the therapeutic potential of exercise training and specific pharmacological interventions.

Methods

Transcriptomic data from NCBI and GEO databases were analyzed to identify differentially expressed genes (DEGs) associated with SIC. Weighted gene co-expression network analysis (WGCNA), gene ontology (GO), and KEGG pathway enrichment analyses were utilized to elucidate the biological significance of these genes. Molecular docking and dynamics simulations were used to investigate drug-target interactions, and immune infiltration and gene mutation analyses were carried out by means of platforms like TIMER 2.0 and DepMap to comprehend the influence of DVL1 on immune responsiveness.

Results

Through the utilization of the datasets, we discovered the core gene DVL1 that exhibited remarkable up-regulated expression both in SIC and in diverse kinds of cancers, which were associated with poor prognosis and inflammatory responses. Molecular docking revealed that Digoxin could bind to DVL1 and reduce oxidative stress in SIC. The DVL1 gene module related to SIC was identified by means of WGCNA, and the immune infiltration analysis demonstrated the distinctive immune cell patterns associated with DVL1 expression and the impact of DVL1 on immunotherapeutic resistance.

Conclusions

DVL1 is a core regulator of SIC and other cancers and, therefore, can serve as a therapeutic target. The present study suggests that targeted pharmacological therapies to enhance response to exercise regimens may be a novel therapeutic tool to reduce the inflammatory response during sepsis, particularly in cancer patients. The identified drugs, Digoxin, require further in vivo and clinical studies to confirm their effects on SIC and their potential efforts to improve outcomes in immunotherapy-resistant cancer patients.

Analysis of 4616 clinical trial initial submissions received by the Medicines and Healthcare products Regulatory Agency between February 2019 and October 2023

AIMS

This study aimed to analyse clinical trial initial submissions received by the MHRA between February 2019 and October 2023.

METHODS

Data on submissions were extracted from the clinical trials unit data bank. The primary end-point was the type of clinical trial initial submissions. Secondary end-points were sponsor types, participant demographics, healthy volunteers, health categories and studies involving first in human and advanced therapy medicinal products. The analysis used descriptive statistics for all categorical variables.

RESULTS

MHRA received 4616 submissions. The highest percentage was in 2020 (22.8%) and the lowest in 2023 (17.2%). Phase 3 submissions were the highest (32.6%) and and phase 4 the lowest (5.2%). Commercial sponsors represented 85.1% of the total submissions. Both sexes were included in most trials (90%), while the number of submissions involving females only (3.7%) was lower than male only trials (6.1%). The elderly population was represented in 67.7% of trials with pregnant and breastfeeding women represented in 1.1% and 0.6% of trials, respectively. Breastfeeding women were not included in phase 1. Paediatric trials mostly involved adolescents. Healthy volunteers were included in 16.5% of the total submissions. The most common health category was cancer (29.4%), with the lowest being pain. First in human submissions represented 12.7% and advanced therapy medicinal products 3.4% of submissions.

CONCLUSIONS

These results highlight the clinical trial landscape in the United Kingdom and represent an important baseline for policymakers, healthcare providers, sponsors and patients and will enable an assessment of how policy changes can improve the variety and number of clinical trials.

Regulation of immune-mediated chemoresistance in cancer by lncRNAs: an in-depth review of signaling pathways

Resistance to cancer therapies is increasingly recognized as being influenced by long non-coding RNAs (lncRNAs), which are pivotal in regulating cellular functions and gene expression. Elucidating the intricate relationship between lncRNAs and the mechanisms underlying drug resistance is critical for advancing effective therapeutic strategies. This study offers an in-depth review of the regulatory roles lncRNAs play in various signaling and immunological pathways implicated in cancer chemoresistance. lncRNA-mediated influence on drug resistance-related signaling pathways will be presented, including immune evasion mechanisms and other essential signaling cascades. Furthermore, the interplay between lncRNAs and the immune landscape will be dissected, illustrating their substantial impact on the development of chemoresistance. Overall, the potential of lncRNA-mediated signaling networks as a therapeutic strategy to combat cancer resistance has been highlighted. This review reiterates the fundamental role of lncRNAs in chemoresistance and proposes promising avenues for future research and the development of targeted therapeutic interventions.

Virtual screening and molecular dynamics of anti-Alzheimer compounds from Cardiospermum halicacabum via GC-MS

Background

Ayurveda is an ancient Indian medicinal system that uses medicinal plants for their neuroprotective effects. Ayurveda claims that the (C. halicacabum) leaves possess significant neuroprotective properties. Alzheimer's is characterized by the accumulation of amyloid-β, acetylcholinesterase, and tau tangles that interfere with neural transmission and impair cognitive abilities.

Objectives

This study aimed to identify novel potential anti-Alzheimer phytoconstituents of C. halicacabum leaves using in silico methods.

Methods

This study utilized the Box-Behnken design within the response surface methodology (RSM) to optimize and combine the effects of process variables, namely powder weight, solvent volume, and extraction time, on the microwave-assisted extraction (MAE) of C. halicacabum leaves. The optimization process revealed that these variables, along with microwave usage, significantly influenced the extraction yield. The ethanolic extract was examined using gas chromatography-mass spectrometry (GC-MS) analysis, and the identified phytoconstituents were further analyzed through computer-based simulations, including docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, assessment of drug-likeness, molecular dynamics, LigPlot analysis, and density functional theory (DFT) analysis.

Results

Gas chromatography-mass spectrometry (GC-MS) analysis identified 40 phytoconstituents and 37 were successfully characterized. Molecular docking and dynamics simulations revealed two lead compounds, acetic acid (dodecahydro-7-hydroxy-1,4b,8,8-tetramethyl-10-oxo-2(1H)-phenanthrenylidene)-,2-(dimethylamino)ethyl ester, [1R-(1. alpha)], and 1-(2-hydroxyethoxy)-2-methyldodecane, which exhibited superior stability in the docked complex compared to galantamine.

Conclusion

Based on computational predictions and observed pharmacological properties, these findings suggest that phytoconstituents may have therapeutic effects against selected AD targets.

Insights into the Technological Evolution and Research Trends of Mobile Health: Bibliometric Analysis

Background/Objectives: Smartphones, with their widespread popularity and diverse apps, have become essential in our daily lives, and ongoing advancements in information technology have unlocked their significant potential in healthcare. Our goal is to identify the future research directions of mobile health (mHealth) by examining its research trends and emerging hotspots. Methods: This study collected mHealth-related literature published between 2005 and 2024 from the Web of Science database. We conducted a descriptive statistic of the annual publication count and categorized the data by authors and institutions. In addition, we developed visualization maps to display the frequency of keyword co-occurrences. Furthermore, overlay visualizations were created to showcase the average publication year of specific keywords, helping to track the changing trends in mHealth research over time. Results: Between 2005 and 2024, a total of 6093 research papers related to mHealth were published. The data have revealed a rapid increase in the number of publications since 2011. However, it was found that research on mHealth has reached a saturation point since 2021. The University of California was the dominant force in mHealth research, with 248 articles. The University of California, the University of London, Harvard University, and Duke University are actively collaborating, which shows a geographical pattern of collaboration. From the analysis of keyword co-occurrence and timeline, the research focus has gradually shifted from solely mHealth technologies to exploring how new technologies, such as artificial intelligence (AI) in mobile apps, can actively intervene in patient conditions, including breast cancer, diabetes, and other chronic diseases. Privacy protection policies and transparency mechanisms have emerged as an active research focus in current mHealth development. Notably, cutting-edge technologies such as the Internet of Things (IoT), blockchain, and virtual reality (VR) are being increasingly integrated into mHealth systems. These technological convergences are likely to constitute key research priorities in the field, particularly in addressing security vulnerabilities while enhancing service scalability. Conclusions: Although the volume of core research in mobile health (mHealth) is gradually declining, its practical applications continue to expand across diverse domains, increasingly integrating with multiple emerging technologies. It is believed that mobile health still holds enormous potential.

Immunotherapy in Prostate Cancer: From a "Cold" Tumor to a "Hot" Prospect

Immunotherapy has shown limited efficacy in prostate cancer, largely due to low tumor immunogenicity, sparse tumor-infiltrating lymphocytes, and a suppressive microenvironment. Recent therapeutic strategies aim to boost immune responses and counteract immunosuppressive factors through interventions such as immune checkpoint inhibitors, immunogenic cell death-inducing therapies, and the targeted blockade of pathways like that of transforming growth factor-β. Vaccine-based approaches, potent immune adjuvants, and engineered chimeric antigen receptor (CAR) T cells are also being investigated to overcome local immune inhibitory signals. Advancements in imaging, multi-omic profiling, and liquid biopsies offer promising avenues for real-time monitoring, better patient selection, and precision treatment. This review provides an overview of the key immunosuppressive features of prostate cancer, current immunotherapeutic modalities, and emerging strategies to transform "cold" tumors into more responsive "hot" targets. By integrating these approaches, we may achieve more durable clinical benefits for patients with advanced or metastatic prostate cancer.

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Cardiovascular diseases have long been a major challenge to human health, and the treatment differences caused by individual variability remain unresolved. In recent years, personalized cardiovascular drug therapy has attracted widespread attention. This paper reviews the strategies for achieving personalized cardiovascular drug therapy through traditional dynamic monitoring and multidimensional data integration and analysis. It focuses on key technologies for dynamic monitoring, dynamic monitoring based on individual differences, and multidimensional data integration and analysis. By systematically reviewing the relevant literature, the main challenges in current research and the proposed potential directions for future studies were summarized.

Absorption of Toxicants from the Ocular Surface: Potential Applications in Toxicology

In relation to the eye, the body can absorb substances from the ocular surface fluid (OSF) in a few ways: directly through the conjunctival sac, through the nasal mucosa as the fluid drains into the nose, or through ingestion. Regardless of the absorption method, fluid from the conjunctival sac should be used as a toxicological matrix, even though only small quantities are needed. Contemporary analytical techniques make it a suitable matrix for toxicological research. Analyzing small quantities of the matrix and nano-quantities of the analyte requires high-cost, sophisticated tools, which is particularly relevant in the high-throughput environment of new drug or cosmetics testing. Environmental toxicology also presents a challenge, as many pollutants can enter the system using the same ocular surface route. A review of the existing literature was conducted to assess potential applications in clinical and forensic toxicology related to the absorption of toxicants from the ocular surface. The selection of the studies used in this review aimed to identify new, more efficient, and cost-effective analytical technology and diagnostic methods.

Enhancing antibiotic therapy through comprehensive pharmacokinetic/pharmacodynamic principles

Antibiotic therapy relies on understanding both pharmacokinetics (PK) and pharmacodynamics (PD), which respectively address drug absorption, distribution, and elimination, and the relationship between drug concentration and antimicrobial efficacy. This review synthesizes decades of research, drawing from in-vitro studies, in-vivo models, and clinical observations, to elucidate the temporal dynamics of antibiotic activity. We explore how these dynamics, including concentration-effect relationships and post antibiotic effects, inform the classification of antibiotics based on their PD profiles. Additionally, we discuss the pivotal role of PK/PD principles in determining optimal dosage regimens. By providing a comprehensive overview of PK/PD principles in antibiotic therapy, this review aims to enhance understanding and improve treatment outcomes in clinical practice.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Future prospective of AI in drug discovery

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and lower productivity in pharmaceutical industries, to discover effective drugs and their development. AI can revolutionize the history of drug discovery and development because it will solve all these problems. AI can identify some promising drug candidates, reduce costs, and increase precision. AI algorithms analyze large datasets, predict molecular interactions, and help optimize the design of clinical trials, making the process of drug discovery and biomedical research much more efficient. By combining cutting-edge computation with more conventional pharmaceutical strategy, AI aids in expediting the process of therapeutics development. This chapter is an investigation of the core reasons behind lower approval rates of new drugs, the potential scope of AI to improve the drug discovery and development scenario, and the practical applications in the field. This article will further explore future opportunities, key methodologies, and challenges in the implementation of AI in pharmaceutical research.

Recent Issues in the Development and Application of Targeted Therapies with Respect to Individual Animal Variability

This literature review explores the impact of molecular, genetic, and environmental factors on the efficacy of targeted therapies in veterinary medicine. Relevant studies were identified through systematic searches of PubMed, Web of Science, Scopus, and ScienceDirect using keywords such as "species-specific treatment strategies", "signalling pathways", "epigenetic and paragenetic influences", "targeted therapies", "veterinary medicine", "genetic variation", and "free radicals and oxidative stress". Inclusion criteria included studies focusing on species-specific therapeutic responses, genetic influences, and oxidative stress. To ensure that only the most recent and relevant evidence was included, only peer-reviewed publications from the last two decades were considered. Each study selected for analysis was critically appraised, with a particular emphasis on methodological quality, experimental design, and scientific contribution to the understanding of how environmental and biological factors influence therapeutic outcomes. A special emphasis was placed on studies that used a comparative, cross-species approach to assess variability in therapeutic responses and potential adverse effects. The review synthesises evidence on the role of epigenetic and paragenetic factors and highlights the importance of cross-species studies to understand how environmental and biological factors influence treatment outcomes. By highlighting genetic variation, oxidative stress, and individual species differences, the review argues for personalised and species-specific therapeutic approaches. The review emphasises that such an approach would improve veterinary care and inform future research aimed at optimising targeted therapies, ultimately leading to better animal health and treatment efficacy. A key contribution of the review is its emphasis on the need for more personalised treatment protocols that take into account individual genetic profiles and environmental factors; it also calls for a greater integration of cross-species studies.

Medical oncologists' dance with international guidelines and national reimbursement: insights from a survey in Türkiye

BACKGROUND

This study explores Turkish medical oncologists' perceptions of integrating international treatment guidelines with national reimbursement policies, considering local legal, economic, and healthcare constraints.

RESEARCH DESIGN AND METHODS

A cross-sectional online survey was conducted from March 24-31, 2024, targeting all 1,096 active oncologists registered with the Turkish Medical Oncology Association, as these specialists are exclusively authorized to prescribe anticancer drugs under national regulations. The survey included 25 questions on demographics, perceptions of guidelines, and integration preferences. Statistical analyses, including chi-square tests and logistic regression, identified factors influencing guideline preferences.

RESULTS

Among 337 respondents (31%), 94% found international guidelines essential, but 62% noted a lack of clear real-world algorithms. Significant predictors for preferring national guidelines included working in public institutions (OR: 3.90, p < 0.001), concerns about pharmaceutical industry influence (OR: 4.38, p = 0.017), legal challenges (OR: 5.89, p < 0.001), and variability among clinical research centers (OR: 2.95, p = 0.019). Despite these challenges, 57% favored national guidelines for their compatibility with local healthcare policies.

CONCLUSIONS

The findings highlight the need for hybrid models that merge the evidence-based rigor of international frameworks with local healthcare priorities. Such models can enhance equitable and effective cancer care in Türkiye by addressing both global standards and national realities.

An Optimised Mobilenet V2 Attention Parallel Network for Predicting Drug-Drug Interactions Through Combining Local and Global Features

Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and reduced risk factors. This work presents a new technique, namely MV2SAPCNNO: MobileNetV2 with simplicial attention network-based parallel convolutional neural network and narwhal optimiser, for improving the precision of DDI prediction. The proposed method starts with data preprocessing, including normalisation and noise reduction, to enhance the quality of the data. Then, MobileNetV2 with simplicial attention network (MV2SAN) is used to extract both local and global features from the dataset. These features are processed using a parallel convolutional neural network (PCNN), optimised by the narwhal optimiser (NO) to improve parameter tuning, minimise error and reduce computational complexity. The performance of the model is evaluated using accuracy, precision, recall and F-score. Experimental results prove that MV2SAPCN-NO achieves better performance over the current models of DDI prediction in accuracy and enhanced classification metrics. The narwhal optimiser enhances the model's convergence efficiency and decreases computational time with an excellent predictive performance. An efficient and accurate DDI prediction model was proposed called MV2SAPCNNO. This model actually outperformed traditional models, and such findings were exhibited to contribute towards secure medication administration, drug development processes and protection of patients in clinical practice.

siRNA-mediated inhibition of hTERT enhances the effects of curcumin in promoting cell death in precursor-B acute lymphoblastic leukemia cells: an in silico and in vitro study

This study investigates the interrelationship between human telomerase reverse transcriptase (hTERT) and ferroptosis in precursor-B (pre-B) acute lymphoblastic leukemia (ALL), specifically examining how hTERT modulation affects ferroptotic cell death pathways. Given that hTERT overexpression characterizes various cancer phenotypes and elevated telomerase activity is observed in early-stage and relapsed ALL, we investigated the molecular mechanisms linking hTERT regulation and ferroptosis in leukemia cells. The experimental design employed Nalm-6 and REH cell lines under three distinct conditions: curcumin treatment, hTERT siRNA knockdown, and their combination. Cell viability and proliferation were assessed via MTT and BrdU assays at 24- and 48-hour intervals post-treatment. Ferroptotic and oxidative markers were quantified using commercial assays, while cell death parameters and gene expression were evaluated through flow cytometry and qRT-PCR analyses. Molecular docking studies were performed to evaluate protein-ligand interactions. Results demonstrated that combined curcumin treatment and hTERT knockdown significantly enhanced cytotoxicity in Nalm-6 cells compared to individual interventions. This was characterized by the upregulation of ferroptosis promoters (lipid-ROS, Fe²⁺, ACSL4) and suppression of inhibitors (GSH, GPx, SLC7A11, GPx4). The response showed cell-line specificity, with Nalm-6 cells exhibiting enhanced ferroptotic sensitivity while REH cells underwent apoptotic cell death. Molecular docking revealed strong curcumin-protein interactions (∆G = -34.24 kcal/mol for hTERT). This study establishes hTERT as a critical regulator of ferroptotic cell death in pre-B ALL, operating through redox homeostasis, iron metabolism, and lipid peroxidation pathways. The cell-type-specific responses suggest promising therapeutic strategies through combined hTERT suppression and ferroptosis induction.

Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates

Introduction

With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations.

Methods

Compiling literature concerning the utilization of AI in fetal medicine does not appear to modify the subject or provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI's integration into fetal medicine practice.

Results

The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted.

Conclusion

The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI's potential in driving forward the practice of fetal medicine.

Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

Translational research serves as the bridge between basic research and practical applications in clinical settings. The journey from "bench to bedside" is fraught with challenges and complexities such as the often-observed disparity between how compounds behave in a laboratory setting versus in the complex systems of living organisms. The challenge is further compounded by the limited ability of in vitro models to mimic the specific biochemical environment of human tissues. This article explores and details the recent advancements and innovative approaches that are increasingly successful in bridging the gap between laboratory research and patient care. These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. The article aims to showcase how these technologies improve the predictability of drug performance in human bodies and significantly speed up the drug development process. Furthermore, it discusses the role of biomarker discovery in preparation of more targeted and personalized therapy approaches and covers the impact of regulatory changes designed to facilitate drug approvals. Additionally, by providing detailed case studies of successful applications, we illustrate the practical impacts of these innovations on drug discovery and patient care.

Probiotics and Food Bioactives: Unraveling Their Impact on Gut Microbiome, Inflammation, and Metabolic Health

This review paper delves into the role of probiotics and food bioactives in influencing gut health and overall well-being, within the context of probiotics and food bioactives, emphasizing their roles in modulating inflammation, gut microbiota, and metabolic health. Probiotics are defined as live microorganisms that confer health benefits to the host, primarily through their impact on the gut microbiome; a complex community of microorganisms crucial for maintaining health. The review aims to elucidate how probiotics, incorporated into both traditional and modern food systems, can enhance gut health and address metabolic disorders. It examines the types of probiotics present in various foods and their mechanisms of action, including their effects on immune function and metabolic health. By exploring the links between probiotics and health outcomes such as digestive health, immune support, and mental health, the review identifies specific conditions where probiotics show significant promise. Hurldes such as inconsistencies in research findings, variability in probiotic strains, and dosages are addressed. The paper also suggests future research directions, including the potential for personalized probiotic interventions. The review concludes by summarizing key findings and emphasizing the critical role of probiotics in food systems for promoting overall health and mitigating metabolic diseases.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

Harnessing Artificial Intelligence to Overcome Key Challenges in Psychedelic Research and Therapy

Artificial intelligence (AI) offers transformative potential in psychedelic research by addressing limitations in personalized treatment, predicting therapeutic outcomes, and understanding complex biological and environmental factors. AI-driven models provide new insights into long-term efficacy, set and setting optimization, and alternative treatment methods, advancing psychedelic therapy into personalized medicine.

Leveraging Single Nucleotide Polymorphism Profiling for Precision Skin Care: How SNPs Shape Individual Responses in Cosmetic Dermatology

INTRODUCTION

Single-nucleotide polymorphisms (SNPs) represent a significant genetic variation influencing individual responses to cosmetic dermatology treatments. SNP profiling offers a pathway to personalized skincare by enabling practitioners to predict patient outcomes, customize interventions, and mitigate risks.

BACKGROUND

The integration of genetic insights into dermatology has gained traction, with SNP analysis revealing predispositions in skin characteristics, such as collagen degradation, pigmentation, and inflammatory responses. Key SNPs, including MMP1, SOD2, TYR, and IL-6, are pivotal in determining skin health and treatment outcomes. Despite its promise, the adoption of SNP profiling in cosmetic dermatology is in its infancy, requiring further exploration of its practical applications.

RESULTS

SNPs significantly influence skin responses to aesthetic treatments, offering insights for personalized care. Variations in MMP1 correlate with collagen degradation, suggesting collagen-stimulating therapies, while SOD2 SNPs highlight the need for antioxidant support. TYR variations affect pigmentation risks in light-based treatments, and IL-6 SNPs reveal inflammatory predispositions, guiding anti-inflammatory protocols. AI integration enhances SNP profiling by improving prediction accuracy and treatment customization. Challenges remain, including standardization, ethical considerations, and cost-effectiveness. Combining genetic insights with epigenetics and leveraging AI technologies can amplify precision and safety in dermatologic care.

CONCLUSION

SNP profiling marks a transformative step toward precision medicine in cosmetic dermatology, enabling tailored treatments that enhance efficacy and minimize adverse effects. Integrating AI-driven SNP analysis with epigenetic insights provides a comprehensive approach to patient care, fostering a new era of personalized skincare that respects genetic and environmental interactions. This paradigm shift holds the potential to redefine dermatologic practices, improving outcomes and patient satisfaction.

Familial Hypercholesterolemia: A Comprehensive Review of Advances in Treatment Strategies and the Role of Patient Beliefs

Familial hypercholesterolemia (FH) constitutes the most common inherited lipid disorder caused by mutations in any of the genes involved in the metabolism of low-density lipoprotein (LDL), including the LDL receptor (LDLR), Apolipoprotein B (APOB), or Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9). FH causes increased LDL-cholesterol levels, leading to an increased risk for premature atherosclerotic cardiovascular disease. This comprehensive review aims to discuss the progress of FH management, from classic statin therapy to relatively new therapies such as PCSK9 inhibitors and emerging gene-editing technologies like CRISPR. Furthermore, the article focuses on psychosocial aspects of adherence, such as patient beliefs, cultural influences, and healthcare access, and their impact on treatment outcomes. By examining these emerging treatment approaches, this review aims to create a broader understanding of FH management, focusing on better patient care and reducing the global burden of this condition.

Challenges in Implementing Comprehensive Precision Medicine Screening for Ovarian Cancer

Precision medicine has revolutionised targeted cancer treatments; however, its implementation in ovarian cancer remains challenging. Diverse tumour biology and extensive heterogeneity in ovarian cancer can limit the translatability of genetic profiling and contribute to a lack of biomarkers of treatment response. This review addresses the barriers in precision medicine for ovarian cancer, including obtaining adequate and representative tissue samples for analysis, developing functional and standardised screening methods, and navigating data infrastructure and management. Ethical concerns related to patient consent, data privacy and health equity are also explored. We highlight the socio-economic complexities for precision medicine and propose strategies to overcome these challenges with an emphasis on accessibility and education amongst patients and health professionals and the development of regulatory frameworks to support clinical integration. Interdisciplinary collaboration is essential to drive progress in precision medicine to improve disease management and ovarian cancer patient outcomes.

Retinal Inflammation and Reactive Müller Cells: Neurotrophins' Release and Neuroprotective Strategies

Millions of people worldwide suffer from retinal disorders. Retinal diseases require prompt attention to restore function or reduce progressive impairments. Genetics, epigenetics, life-styling/quality and external environmental factors may contribute to developing retinal diseases. In the physiological retina, some glial cell types sustain neuron activities by guaranteeing ion homeostasis and allowing effective interaction in synaptic transmission. Upon insults, glial cells interact with neuronal and the other non-neuronal retinal cells, at least in part counteracting the biomolecular changes that may trigger retinal complications and vision loss. Several epigenetic and oxidative stress mechanisms are quickly activated to release factors that in concert with growth, fibrogenic and angiogenic factors can influence the overall microenvironment and cell-to-cell response. Reactive Müller cells participate by secreting neurotrophic/growth/angiogenic factors, cytokines/chemokines, cytotoxic/stress molecules and neurogenic inflammation peptides. Any attempt to maintain/restore the physiological condition can be interrupted by perpetuating insults, vascular dysfunction and neurodegeneration. Herein, we critically revise the current knowledge on the cell-to-cell and cell-to-mediator interplay between Müller cells, astrocytes and microglia, with respect to pro-con modulators and neuroprotective/detrimental activities, as observed by using experimental models or analyzing ocular fluids, altogether contributing a new point of view to the field of research on precision medicine.

Empowering precision medicine: Insights from a national survey on pharmacogenomics knowledge, attitudes, and perceptions among community pharmacists in the UAE

Background

Community pharmacists are essential to pharmacogenomics implementation because they can help trainers, clinical advisors, and other medical professionals understand the importance of pharmacogenomics and encourage them to use it in their practice. This study is to evaluate the community pharmacists' understanding, attitudes, and perceptions of pharmacogenomics in the United Arab Emirates (UAE).

Methods

Professionals employed at community pharmacies in Abu Dhabi, Dubai, and the Northern Emirates participated in a cross-sectional study design. From July 2023 to February 2024, five pharmacy students in their last year conducted the survey. The study team employed a structured questionnaire to collect data in addition to conducting in-person interviews. The study questionnaire comprised three distinct sections namely, demographic information, knowledge of pharmacogenomics concepts, and perceptions regarding pharmacogenomics.

Results

A total of 586 pharmacists enrolled in the study. The average knowledge score regarding pharmacogenomics was 75.1 % with a 95 % confidence interval (CI) of [72.4 %, 77.7 %]. The average attitude score toward pharmacogenomics was 67.5 % with a 95 % CI of [66.3 %, 68.7 %]. Better pharmacogenomics knowledge among several groups: independent pharmacies (OR 1.7; 95 % CI 1.2-2.4), Pharmacists in Charge (OR 1.4; 95 % CI 1.3-2.02), pharmacists with 11-15 years of experience (OR 2.1; 95 % CI 1.4-4.2), graduates from international universities (OR 4.6; 95 % CI 1.6-12.9), and those who received training on pharmacogenomics (OR 11.9; 95 % CI 3.3-14.5). Similarly, better attitude scores were observed among independent pharmacies (OR 1.5; 95 % CI 1.1-2.1), Pharmacists in Charge (OR 1.5; 95 % CI 1.07-2.1), pharmacists with 16-20 years of experience (OR 2.1; 95 % CI 1.16-3.7), graduates regional universities (OR 1.47; 95 % CI 1.05-2.1), and those who received training on pharmacogenomics (OR 4.8; 95 % CI 3.2-7.3).

Conclusion

The positive attitudes toward pharmacogenomics that we found in our research indicate that community pharmacists in the United Arab Emirates are beginning to realize the potential advantages of pharmacogenomics in terms of improving patient care. Policies ensuring the privacy and confidentiality of genetic information are also necessary in considering concerns about the availability of genetic test results to insurance companies and potential employers.

Epigenetic biomarkers in Alzheimer's disease: Diagnostic and prognostic relevance

Alzheimer's disease (AD) is a leading cause of cognitive decline in the aging population, presenting a critical need for early diagnosis and effective prognostic tools. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNAs, have emerged as promising biomarkers for AD due to their roles in regulating gene expression and potential for reversibility. This review examines the current landscape of epigenetic biomarkers in AD, emphasizing their diagnostic and prognostic relevance. DNA methylation patterns in genes such as APP, PSEN1, and PSEN2 are highlighted for their strong associations with AD pathology. Alterations in DNA methylation at specific CpG sites have been consistently observed in AD patients, suggesting their utility in early detection. Histone modifications, such as acetylation and methylation, also play a crucial role in chromatin remodelling and gene expression regulation in AD. Dysregulated histone acetylation and methylation have been linked to AD progression, making these modifications valuable biomarkers. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), further contribute to the epigenetic regulation in AD. miRNAs can modulate gene expression post-transcriptionally and have been found in altered levels in AD, while lncRNAs can influence chromatin structure and gene expression. The presence of these non-coding RNAs in biofluids like blood and cerebrospinal fluid positions them as accessible and minimally invasive biomarkers. Technological advancements in detecting and quantifying epigenetic modifications have propelled the field forward. Techniques such as next-generation sequencing, bisulfite sequencing, and chromatin immunoprecipitation assays offer high sensitivity and specificity, enabling the detailed analysis of epigenetic changes in clinical samples. These tools are instrumental in translating epigenetic research into clinical practice. This review underscores the potential of epigenetic biomarkers to enhance the early diagnosis and prognosis of AD, paving the way for personalized therapeutic strategies and improved patient outcomes. The integration of these biomarkers into clinical workflows promises to revolutionize AD management, offering hope for better disease monitoring and intervention.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time-series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.

Advances in understanding biomarkers and treating neurological diseases - Role of the cerebellar dysfunction and emerging therapies

Cerebellar dysfunction is increasingly recognized as a critical factor in various neurological diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Research has revealed distinct cerebellar atrophy patterns in conditions such as AD and multiple system atrophy, and studies in mice have highlighted its impact on motor control and cognitive functions. Emerging research into autism spectrum disorder (ASD) has identified key targets, such as elevated levels of chemokine receptors and ZIC family genes. Biomarkers, including cerebrospinal fluid (CSF), genetic markers, and advances in AI and bioinformatics, are enhancing early diagnosis and personalized treatment across neurodegenerative disorders. Notable advancements include improved diagnostic tools, gene therapy, and novel clinical trials. Despite progress, challenges such as the bloodbrain barrier and neuroinflammation persist. Current therapies for AD, PD, HD, and ALS, including antisense oligonucleotides and stem cell treatments, show promise but require further investigation. A comprehensive approach that integrates diagnostic methods and innovative therapies is essential for effective management and improved patient outcomes.

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Perspectives, Challenges, and the Future of Biomedical Technology and Artificial Intelligence

Biomedical technologies are the compound of engineering principles and technologies used to diagnose, treat, monitor, and prevent illness [...]

Molecular and modular intricacies of precision oncology

Precision medicine is revolutionizing the world in combating different disease modalities, including cancer. The concept of personalized treatments is not new, but modeling it into a reality has faced various limitations. The last decade has seen significant improvements in incorporating several novel tools, scientific innovations and governmental support in precision oncology. However, the socio-economic factors and risk-benefit analyses are important considerations. This mini review includes a summary of some commendable milestones, which are not just a series of successes, but also a cautious outlook to the challenges and practical implications of the advancing techno-medical era.

Efficacy, safety and tolerability of GSK3858279, an anti-CCL17 monoclonal antibody and analgesic, in healthy volunteers and patients with knee osteoarthritis pain: a phase I, randomised, double-blind, placebo-controlled, proof-of-mechanism and proof-of-concept study

OBJECTIVES

The objective of this study was to evaluate efficacy, safety and tolerability of the first-in-class, anti-CCL17 monoclonal antibody, GSK3858279, in treating knee osteoarthritis (OA) pain.

METHODS

This was a phase I, randomised, placebo-controlled, two-part, proof-of-mechanism and proof-of-concept study. In part A, healthy participants were randomised 3:1 to receive GSK3858279 as either single intravenous (0.1-10 mg/kg) doses, a subcutaneous (3 mg/kg up to 240 mg maximum) dose, or placebo, to evaluate safety and tolerability. In part B, participants with knee OA pain were randomised 1:1 to receive weekly subcutaneous 240 mg GSK3858279, or placebo, for 8 weeks, to assess safety and change from baseline (CFB) in average and worst knee pain intensity. Exploratory endpoints included CFB in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, function and stiffness scores.

RESULTS

GSK3858279 demonstrated greater median CFB (95% credible interval (CrI)) in average and worst knee pain intensity versus placebo (average, -1.18 (-2.15, -0.20); worst, -1.09 (-2.29, 0.12)) at week 8. Median CFB (95% CrI) for GSK3858279 versus placebo in WOMAC pain and function scores were -1.41 (-2.35, -0.46) and -1.29 (-2.28, -0.29), respectively, at week 8. Overall, 72% (26/36; part A) and 88% (21/24; part B) of participants receiving GSK3858279 experienced adverse events (AEs); with nasopharyngitis being the most common in part A and injection site reactions in part B. No serious AEs or deaths were observed.GSK3858279 improved pain intensity and WOMAC pain and function scores in adults with knee OA pain and demonstrated favourable safety and tolerability in both healthy participants and adults with knee OA pain.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology

Digital pathology has emerged as a revolutionary field, transforming traditional diagnostic practices by integrating advanced imaging technologies, computational tools, and artificial intelligence (AI). Adopting digital slides over conventional glass slides enables high-resolution imaging, facilitating remote consultations, second opinions, and telepathology. The digitalization of pathology laboratories enhances workflow efficiency and allows for large-scale data storage, retrieval, and analysis, paving the way for developing robust diagnostic algorithms. One of the most transformative aspects of digital pathology is its synergy with AI and machine learning (ML). These technologies have enabled the automation of repetitive processes, including diseased feature detection, biomarker quantification, and tissue segmentation. This has decreased inter-observer variability and increased diagnostic accuracy. AI-driven algorithms are particularly beneficial in complex cases, assisting pathologists in detecting subtle patterns that might be missed through manual examination. Furthermore, digital pathology plays a critical role in personalized medicine by enabling the precise characterization of tumors, which leads to targeted therapy decisions. Integrating digital pathology with genomics and other omics data holds promise for a more holistic understanding of diseases, driving innovation in diagnostics and treatment. However, the transition to digital pathology is challenging. Issues such as data standardization, regulatory compliance, and the need for robust IT infrastructure must be addressed to realize its full potential. This review provides a detailed examination of these advances, their clinical applications, and the challenges faced in the widespread adoption of digital pathology. As the field continues to evolve, it is poised to play a pivotal role in shaping the future of diagnostics, offering new possibilities for improving patient outcomes. This comprehensive review explores the significant advances in digital pathology, highlighting its impact on diagnostics, research, and patient care.

Knee Arthroscopy in the Era of Precision Medicine: A Comprehensive Review of Tailored Approaches and Emerging Technologies

Knee arthroscopy, a minimally invasive procedure, has transformed the treatment of knee pathologies by enabling direct visualization and management with minimal tissue disruption. Recent advances in precision medicine have introduced a new dimension to this field, allowing for highly individualized surgical approaches considering each patient's unique genetic, environmental, and biomechanical characteristics. This review explores the integration of precision medicine into knee arthroscopy, focusing on tailored approaches and emerging technologies. Key innovations such as robotic-assisted surgery, advanced imaging, and patient-specific instrumentation have enhanced surgical accuracy and patient outcomes, reduced recovery times, and minimized postoperative complications. The review also examines the role of biomarkers in guiding personalized treatment strategies, including ligament reconstructions, meniscal repairs, and cartilage restoration, which are now being refined to cater to the specific needs of individual patients. While the benefits of these innovations are clear, there are challenges to widespread adoption, including cost, resource allocation, and the need for further research to validate the efficacy of precision-driven approaches in knee arthroscopy. Moreover, the ethical considerations surrounding personalized medicine, such as patient privacy and genetic data usage, must also be addressed. Despite these barriers, the future of knee arthroscopy in the era of precision medicine holds great promise, with ongoing developments in artificial intelligence, genomics, and biomarker discovery poised to further refine patient-centered care. This comprehensive review provides valuable insights into how precision medicine reshapes knee arthroscopy, offering a glimpse into the future of more targeted and effective orthopedic interventions. By embracing these advancements, surgeons and healthcare providers can ensure optimal outcomes for patients undergoing knee arthroscopy.

Improving tumor microenvironment assessment in chip systems through next-generation technology integration

The tumor microenvironment (TME) comprises a diverse array of cells, both cancerous and non-cancerous, including stromal cells and immune cells. Complex interactions among these cells play a central role in driving cancer progression, impacting critical aspects such as tumor initiation, growth, invasion, response to therapy, and the development of drug resistance. While targeting the TME has emerged as a promising therapeutic strategy, there is a critical need for innovative approaches that accurately replicate its complex cellular and non-cellular interactions; the goal being to develop targeted, personalized therapies that can effectively elicit anti-cancer responses in patients. Microfluidic systems present notable advantages over conventional in vitro 2D co-culture models and in vivo animal models, as they more accurately mimic crucial features of the TME and enable precise, controlled examination of the dynamic interactions among multiple human cell types at any time point. Combining these models with next-generation technologies, such as bioprinting, single cell sequencing and real-time biosensing, is a crucial next step in the advancement of microfluidic models. This review aims to emphasize the importance of this integrated approach to further our understanding of the TME by showcasing current microfluidic model systems that integrate next-generation technologies to dissect cellular intra-tumoral interactions across different tumor types. Carefully unraveling the complexity of the TME by leveraging next generation technologies will be pivotal for developing targeted therapies that can effectively enhance robust anti-tumoral responses in patients and address the limitations of current treatment modalities.

Emerging Strategies in Drug Development and Clinical Care in the Era of Personalized and Precision Medicine

In the ever-changing landscape of modern medicine, we face an important moment where the interplay of disease, drugs, and patients defines a new paradigm [...].

Strategies for Cardiovascular Disease Prevention in Type 1 Diabetes: A Comprehensive Review

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality among individuals with type 1 diabetes (T1D), necessitating effective prevention strategies. This comprehensive review consolidates current knowledge and evidence on preventing CVD in T1D patients. It begins by exploring the pathophysiological mechanisms that link T1D to an increased risk of CVD, highlighting factors such as chronic hyperglycemia, hypertension, dyslipidemia, and inflammation. The review also examines the epidemiology and specific risk factors for CVD in this population, emphasizing the need for rigorous risk assessment and screening. Lifestyle modifications, including dietary interventions, regular physical activity, and smoking cessation, are evaluated for their effectiveness in reducing CVD risk. Additionally, the review discusses pharmacological interventions, such as insulin therapy for glycemic control, antihypertensive medications, lipid-lowering agents, and antiplatelet therapy, underscoring their critical role in CVD prevention. Emerging therapies and future research directions are explored, focusing on novel pharmacological agents, advances in insulin delivery systems, and personalized medicine approaches. The importance of integrated care models involving multidisciplinary teams and the use of technology is highlighted as essential for comprehensive management. Challenges and barriers to implementing these strategies, including healthcare system limitations, patient adherence, and socioeconomic factors, are also addressed. This review provides a detailed synthesis of current strategies and future directions for preventing CVD in individuals with T1D, serving as a valuable resource for clinicians, researchers, and policymakers dedicated to improving cardiovascular outcomes in this high-risk population.

Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes

BACKGROUND

As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive.

METHODS

We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay.

RESULTS

Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells.

CONCLUSIONS

We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC's molecular mechanisms and personalized treatment approaches.

Advances in Psychotropic Treatment for Pregnant Women: Efficacy, Adverse Outcomes, and Therapeutic Monitoring

Advancements in psychotropic therapy for pregnant women are pivotal for addressing maternal mental health during the perinatal period. Screening for mood and anxiety symptoms during pregnancy is recommended to enable early intervention. Psychotropic medications, including antidepressants, benzodiazepines, antipsychotics, and mood stabilizers, are commonly used, but challenges remain regarding their safety and efficacy during pregnancy. Pregnancy induces significant changes in pharmacokinetics, necessitating personalized dosing strategies and careful monitoring. Real-time monitoring technologies, such as smartphone-integrated platforms and home-based monitoring, enhance accessibility and accuracy. Prospective studies and collaboration among healthcare providers are essential for evidence-based guidelines and optimal treatment strategies. Reducing stigma around mental health during pregnancy is crucial to ensure women seek help and discuss treatment options, promoting understanding and acceptance within the community.

Safety and Efficacy of Antiviral Drugs and Vaccines in Pregnant Women: Insights from Physiologically Based Pharmacokinetic Modeling and Integration of Viral Infection Dynamics

Addressing the complexities of managing viral infections during pregnancy is essential for informed medical decision-making. This comprehensive review delves into the management of key viral infections impacting pregnant women, namely Human Immunodeficiency Virus (HIV), Hepatitis B Virus/Hepatitis C Virus (HBV/HCV), Influenza, Cytomegalovirus (CMV), and SARS-CoV-2 (COVID-19). We evaluate the safety and efficacy profiles of antiviral treatments for each infection, while also exploring innovative avenues such as gene vaccines and their potential in mitigating viral threats during pregnancy. Additionally, the review examines strategies to overcome challenges, encompassing prophylactic and therapeutic vaccine research, regulatory considerations, and safety protocols. Utilizing advanced methodologies, including PBPK modeling, machine learning, artificial intelligence, and causal inference, we can amplify our comprehension and decision-making capabilities in this intricate domain. This narrative review aims to shed light on diverse approaches and ongoing advancements, this review aims to foster progress in antiviral therapy for pregnant women, improving maternal and fetal health outcomes.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

BACKGROUND

Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features.

METHODS

This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023.

RESULTS

The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects.

CONCLUSIONS

This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.