The TEOGIC study project: a comprehensive characterization of early onset gastrointestinal cancer in the Northern area of Spain.

BACKGROUND: Gastrointestinal cancers represent one of the most prevalent diseases worldwide. Strikingly, the incidence of Early Onset Gastrointestinal Cancer (EOGIC) has been rising during the last decades and changes in lifestyle and environmental exposure seem to play a role. EOGIC has been defined as a different entity compared to on-average gastrointestinal cancer, with distinct clinical and molecular characteristics. Inherent to the particularities of younger age, there is an unmet need for a tailored approach for the management of these patients. The TEOGIC proposes a comprehensive study to characterize EOGIC patients in the northern of Spain. METHODS: Patients with histologically confirmed new diagnosis of colorectal, gastroesophageal and pancreatic adenocarcinoma will be considered for two cohorts: EOGIC (≤ 50 years old) and non-EOGIC (60-75 years old), with a ratio of 1:2. Two hundred and forty patients will be recruited in 4 Public Hospitals from northern Spain. After receiving unified informed consent, demographic and clinical data of the patients will be collected in a REDCap database. Lifestyle related data will be obtained in questionnaires assessing diet, physical activity and the general quality of life of the patients before diagnosis. Biological samples prior to any onco-specific treatment will be obtained for the analyses of circulating inflammatory proteins, gut microbiota, and the proteome of the tumor microenvironment. Histologic characteristics and routine biomarkers will be also collected. Thereafter, data will be integrated and analyzed to assess tumor specific, pan-tumor and sex-associated differential characteristics of EOGIC. DISCUSSION: The underlying risk factors and differential characteristics of EOGIC remain poorly studied, particularly in our geographical area. Although limited by the exploratory nature and the small sample size estimated to be recruited, TEOGIC represents the first attempt to comprehensively characterize these young patients, and thus attend to their special needs. Findings derived from this study could contribute to raise awareness and preventive behaviors in the population. In parallel, molecular studies could lead to the identification of potential novel non-invasive biomarkers and therapeutic targets that would help in the development of the tailored clinical management of these patients, focusing on screening programs for early diagnosis and precision medicine.

Biomarkers for personalised prevention of chronic diseases: a common protocol for three rapid scoping reviews.

INTRODUCTION: Personalised prevention aims to delay or avoid disease occurrence, progression, and recurrence of disease through the adoption of targeted interventions that consider the individual biological, including genetic data, environmental and behavioural characteristics, as well as the socio-cultural context. This protocol summarises the main features of a rapid scoping review to show the research landscape on biomarkers or a combination of biomarkers that may help to better identify subgroups of individuals with different risks of developing specific diseases in which specific preventive strategies could have an impact on clinical outcomes. This review is part of the "Personalised Prevention Roadmap for the future HEalThcare" (PROPHET) project, which seeks to highlight the gaps in current personalised preventive approaches, in order to develop a Strategic Research and Innovation Agenda for the European Union. OBJECTIVE: To systematically map and review the evidence of biomarkers that are available or under development in cancer, cardiovascular and neurodegenerative diseases that are or can be used for personalised prevention in the general population, in clinical or public health settings. METHODS: Three rapid scoping reviews are being conducted in parallel (February-June 2023), based on a common framework with some adjustments to suit each specific condition (cancer, cardiovascular or neurodegenerative diseases). Medline and Embase will be searched to identify publications between 2020 and 2023. To shorten the time frames, 10% of the papers will undergo screening by two reviewers and only English-language papers will be considered. The following information will be extracted by two reviewers from all the publications selected for inclusion: source type, citation details, country, inclusion/exclusion criteria (population, concept, context, type of evidence source), study methods, and key findings relevant to the review question/s. The selection criteria and the extraction sheet will be pre-tested. Relevant biomarkers for risk prediction and stratification will be recorded. Results will be presented graphically using an evidence map. INCLUSION CRITERIA: Population: general adult populations or adults from specific pre-defined high-risk subgroups; concept: all studies focusing on molecular, cellular, physiological, or imaging biomarkers used for individualised primary or secondary prevention of the diseases of interest; context: clinical or public health settings. SYSTEMATIC REVIEW REGISTRATION: https://doi.org/10.17605/OSF.IO/7JRWD (OSF registration DOI).

Biomarker-driven basket trial designs: origins and new methodological developments.

Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.

Updates on Rare Genetic Variants, Genetic Testing, and Gene Therapy in Individuals With Obesity.

PURPOSE OF REVIEW: The goal of this paper is to aggregate information on monogenic contributions to obesity in the past five years and to provide guidance for genetic testing in clinical care. RECENT FINDINGS: Advances in sequencing technologies, increasing awareness, access to testing, and new treatments have increased the utilization of genetics in clinical care. There is increasing recognition of the prevalence of rare genetic obesity from variants with mean allele frequency < 5% -new variants in known genes as well as identification of novel genes- causing monogenic obesity. While most of these genes are in the leptin melanocortin pathway, those in adipocytes may also contribute. Common variants may contribute either to higher lifetime tendency for weight gain or provide protection from monogenic obesity. While specific genetic mutations are rare, these segregate in individuals with early-onset severe obesity; thus, collectively genetic etiologies are not as rare. Some genetic conditions are amenable to targeted treatment. Research into the discovery of novel genetic causes as well as targeted treatment is growing over time. The utility of therapeutic strategies based on the genetic risk of obesity is an advancing frontier.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.

PURPOSE OF REVIEW: This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS: In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Recommendations for the use of next-generation sequencing (NGS) for patients with advanced cancer in 2024: a report from the ESMO Precision Medicine Working Group.

BACKGROUND: Advancements in the field of precision medicine have prompted the European Society for Medical Oncology (ESMO) Precision Medicine Working Group to update the recommendations for the use of tumour next-generation sequencing (NGS) for patients with advanced cancers in routine practice. METHODS: The group discussed the clinical impact of tumour NGS in guiding treatment decision using the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) considering cost-effectiveness and accessibility. RESULTS: As for 2020 recommendations, ESMO recommends running tumour NGS in advanced non-squamous non-small-cell lung cancer, prostate cancer, colorectal cancer, cholangiocarcinoma, and ovarian cancer. Moreover, it is recommended to carry out tumour NGS in clinical research centres and under specific circumstances discussed with patients. In this updated report, the consensus within the group has led to an expansion of the recommendations to encompass patients with advanced breast cancer and rare tumours such as gastrointestinal stromal tumours, sarcoma, thyroid cancer, and cancer of unknown primary. Finally, ESMO recommends carrying out tumour NGS to detect tumour-agnostic alterations in patients with metastatic cancers where access to matched therapies is available. CONCLUSION: Tumour NGS is increasingly expanding its scope and application within oncology with the aim of enhancing the efficacy of precision medicine for patients with cancer.

An open-label study of pemigatinib in cholangiocarcinoma: final results from FIGHT-202.

BACKGROUND: Fibroblast growth factor receptor 2 (FGFR2) fusions and rearrangements are clinically actionable genomic alterations in cholangiocarcinoma (CCA). Pemigatinib is a selective, potent, oral inhibitor of FGFR1-3 and demonstrated efficacy in patients with previously treated, advanced/metastatic CCA with FGFR2 alterations in FIGHT-202 (NCT02924376). We report final outcomes from the extended follow-up period. PATIENTS AND METHODS: The multicenter, open-label, single-arm, phase II FIGHT-202 study enrolled patients ≥18 years old with previously treated advanced/metastatic CCA with FGFR2 fusions or rearrangements (cohort A), other FGF/FGFR alterations (cohort B), or no FGF/FGFR alterations (cohort C). Patients received once-daily oral pemigatinib 13.5 mg in 21-day cycles (2 weeks on, 1 week off) until disease progression or unacceptable toxicity. The primary endpoint was objective response rate (ORR) in cohort A assessed as per RECIST v1.1 by an independent review committee; secondary endpoints included duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety. RESULTS: FIGHT-202 enrolled 147 patients (cohort A, 108; cohort B, 20; cohort C, 17; unconfirmed FGF/FGFR alterations, 2). By final analysis, 145 (98.6%) had discontinued treatment due to progressive disease (71.4%), withdrawal by patient (8.2%), or adverse events (AEs; 6.8%). Median follow-up was 45.4 months. The ORR in cohort A was 37.0% (95% confidence interval 27.9% to 46.9%); complete and partial responses were observed in 3 and 37 patients, respectively. Median DOR was 9.1 (6.0-14.5) months; median PFS and OS were 7.0 (6.1-10.5) months and 17.5 (14.4-22.9) months, respectively. The most common treatment-emergent AEs (TEAEs) were hyperphosphatemia (58.5%), alopecia (49.7%), and diarrhea (47.6%). Overall, 15 (10.2%) patients experienced TEAEs leading to pemigatinib discontinuation; intestinal obstruction and acute kidney injury (n = 2 each) occurred most frequently. CONCLUSIONS: Pemigatinib demonstrated durable response and prolonged OS with manageable AEs in patients with previously treated, advanced/metastatic CCA with FGFR2 alterations in the extended follow-up period of FIGHT-202.

A Small Molecule Allele-selective Transcriptional Inhibitor of the MIF Immune Susceptibility Locus.

Functional variants of the gene for the cytokine macrophage migration inhibitory factor (MIF) are defined by a 4-nucleotide promoter microsatellite (-794 CATT5-8, rs5844572) and confer risk for autoimmune, infectious, and oncologic diseases. We describe herein the discovery of a prototypic, small molecule inhibitor of MIF transcription with selectivity for high microsatellite repeat number and correspondingly high gene expression. Utilizing a high-throughput luminescent proximity screen, we identify 1-carbomethoxy-5-formyl-4,6,8-trihydroxyphenazine (CMFT) to inhibit the functional interaction between the transcription factor ICBP90 (a.k.a. UHRF1) and the MIF -794 CATT5-8 promoter microsatellite. CMFT inhibits MIF mRNA expression in a -794 CATT5-8 length-dependent manner with an IC50 of 470 nM, and preferentially reduces ICBP90-dependent MIF mRNA and protein expression in high-genotypic versus low-genotypic MIF - expressing macrophages. RNA expression analysis also showed CMFT to downregulate MIF-dependent, inflammatory gene expression with little evidence of off-target metabolic toxicity. These findings provide proof-of-concept for advancing the pharmacogenomic development of precision-based MIF inhibitors for diverse autoimmune and inflammatory conditions.

Organoids as a new approach for improving pediatric cancer research.

A key challenge in cancer research is the meticulous development of models that faithfully emulates the intricacies of the patient scenario, with emphasis on preserving intra-tumoral heterogeneity and the dynamic milieu of the tumor microenvironment (TME). Organoids emerge as promising tool in new drug development, drug screening and precision medicine. Despite advances in the diagnoses and treatment of pediatric cancers, certain tumor subtypes persist in yielding unfavorable prognoses. Moreover, the prognosis for a significant portion of children experiencing disease relapse is dismal. To improve pediatric outcome many groups are focusing on the development of precision medicine approach. In this review, we summarize the current knowledge about using organoid system as model in preclinical and clinical solid-pediatric cancer. Since organoids retain the pivotal characteristics of primary parent tumors, they exert great potential in discovering novel tumor biomarkers, exploring drug-resistance mechanism and predicting tumor responses to chemotherapy, targeted therapy and immunotherapies. We also examine both the potential opportunities and existing challenges inherent organoids, hoping to point out the direction for future organoid development.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Personality and quality-of-life improvement after apomorphine infusion in Parkinson's disease.

People with Parkinson's disease with motor fluctuations can be treated by continuous subcutaneous apomorphine infusion (CSAI) to reduce their symptoms. Nonetheless, factors are lacking to predict patients' quality-of-life amelioration after CSAI. This pilot study aimed to evaluate associations between personality dimensions and quality-of-life improvement after 6 months of CSAI. Thirty-nine people with Parkinson's disease awaiting CSAI were included. Linear regression models between 'Temperament and Character Inventory' personality dimensions at baseline and percentage of change in Parkinson's Disease Questionnaire-39 scores after 6 months of CSAI were realized (n = 35). The Temperament and Character Inventory was also compared between patients awaiting CSAI and patients awaiting deep brain stimulation of the sub-thalamic nucleus (n = 39 from the PREDI-STIM study). Higher reward dependence scores were associated with a better quality-of-life outcome after 6 months of CSAI, while self-directedness scores were associated with a better quality of life before CSAI (as opposed to harm avoidance, reward dependence and self-transcendence scores associated with a worse quality of life). Moreover, people with Parkinson's disease awaiting deep brain stimulation of the sub-thalamic nucleus had similar Temperament and Character Inventory dimensions compared to patients awaiting CSAI. People with Parkinson's disease with higher reward dependence scores at baseline had the best quality-of-life improvement after 6 months of CSAI. This finding could be used to better prepare and accompany people with Parkinson's disease during CSAI establishment. Moreover, this result could serve as an orientation factor to second-line treatments.

Case report: Early use of whole exome sequencing unveils HNRNPU-related neurodevelopmental disorder and answers additional clinical questions through reanalysis.

This case report chronicles the diagnostic odyssey and resolution of a 27-year-old female with a complex neurodevelopmental disorder (NDD) using Whole Exome Sequencing (WES). The patient presented to a precision medicine clinic with multiple diagnoses including intellectual disability, autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), tics, seizures, and pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS). Although this patient previously had chromosomal microarray and several single-gene tests, the underlying cause of this patient's symptoms remained elusive. WES revealed a pathogenic missense mutation in the HNRNPU gene, associated with HNRNPU-related neurodevelopmental disorder (HNRNPU-NDD) and developmental and epileptic encephalopathy-54 (DEE54, OMIM: # 617391). Following this diagnoses, other treating clinicians identified additional indications for genetic testing, however, as the WES data was readily available, the clinical team was able to re-analyze the WES data to address their inquiries without requiring additional tests. This emphasizes the pivotal role of WES in expediting diagnoses, reducing costs, and providing ongoing clinical utility throughout a patient's life. Accessible WES data in primary care settings can enhance patient care by informing future genetic inquiries, enhancing coordination of care, and facilitating precision medicine interventions, thereby mitigating the burden on families and the healthcare system.

Unraveling the Tau Puzzle: A Brief Discussion on Biomarkers in Alzheimer's Disease.

This commentary provides an in-depth analysis of a recently published systematic review on 'Biomarkers of Tau Pathology in Alzheimer's Disease', elucidating insights into its implications for the field. This meta-analysis highlights the potential of plasma and CSF p-tau 181/231 as promising, non-invasive, and cost-effective diagnostic tools for patients suffering from AD continuum. The study comprehensively reviews the diagnostic potential of these p-tau isoforms, shedding light on their role in the precision diagnosis of Alzheimer's disease. Here we discuss the significance of these findings and the methodological nuances, emphasizing broader implications for advancing personalized medicine in neurodegenerative disorders.

Advances in nanobubbles for cancer theranostics: Delivery, imaging and therapy.

Maximizing treatment efficacy and forecasting patient prognosis in cancer necessitates the strategic use of targeted therapy, coupled with the prompt precise detection of malignant tumors. Theutilizationof gaseous systems as an adaptable platform for creating nanobubbles (NBs) has garnered significant attention as theranostics, which involve combining contrast chemicals typically used for imaging with pharmaceuticals to diagnose and treattumorssynergistically in apersonalizedmanner for each patient. This review specifically examines the utilization of oxygen NBsplatforms as a theranostic weapon in the field of oncology. We thoroughly examine the key factors that impact the effectiveness of NBs preparations and the consequences of these treatment methods. This review extensively examines recent advancements in composition schemes, advanced developments in pre-clinical phases, and other groundbreaking inventions in the area of NBs. Moreover, this review offers a thorough examination of the optimistic future possibilities, addressing prospective methods for improvement and incorporation into widely accepted therapeutic practices. As we explore the ever-changing field of cancer theranostics, the incorporation of oxygen NBs appears as a promising development, providing new opportunities for precision medicine and marking a revolutionary age in cancer research and therapy.

Exploring gender differences in pharmacokinetics of central nervous system related medicines based on a systematic review approach.

Due to the inevitable differences in physiological and/or genetic factors between genders, the possibility that differences in pharmacokinetics between genders may occur when exposed to the same dose of the same drug is subject to reasonable inference and suspicion. Nevertheless, a significant number of medicines still rely on empirical usage and uniform clinical application without consideration of inter-individual diversity factors. In particular, in the pharmacokinetic diversity of medicines related to central nervous system (CNS) activity, consideration of gender factors and access to comparative analysis are very limited. The purpose of this study was to conduct an integrated analysis and review of differences in pharmacokinetics between genders that have not been specifically reported to date for medicines related to CNS effects, which are a group of drugs with relatively significant concerns about systemic side effects. This study was accessible through extensive data collection and analyzes using a web-based scientific literature search engine of pharmacokinetic results of CNS-related drugs performed on humans, taking gender into account. As a result, significant differences in pharmacokinetics between genders were identified for many drugs related to CNS. And most of the pharmacokinetic differences between genders suggested a higher in vivo exposure in females. This study suggests that consideration of gender factors cannot be ignored and will be an important point of interest in the precision medicine application of CNS-related medicines.

Treatment effect heterogeneity of cognitive behavioral therapy for insomnia - A meta-analysis.

Investigation of the heterogeneity of the treatment effect (HTE) might guide the optimization of cognitive behavioral therapy for insomnia (CBT-I). This study examined HTE in CBT-I thereby analyzing if treatment setting, control group, different CBT-I components, and patient characteristics drive HTE. Randomized controlled trials investigating CBT-I were included. Bayesian random effect meta-regressions were specified to examine variances between the intervention and control groups regarding post-treatment symptom severity. Subgroup analyses analyzing treatment setting and control groups and covariate analysis analyzing treatment components and patient characteristics were specified. No significant HTE in CBT-I was found for the overall data set, settings and control groups. The covariate analyses yielded significant results for baseline severity and the treatment component relaxation therapy. Thus, this study identified potential causes for HTE in CBT-I for the first time, showing that it might be worthwhile to further examine possibilities for precision medicine in CBT-I.

Current Management of Allergic Rhinitis.

Allergic rhinitis (AR) is the most common allergic disease worldwide and one of the most common chronic diseases in general. Allergic rhinitis is caused by inhalant allergens from outdoor and indoor environments with varying significance of different allergens in global regions. We provide options for the current management for AR including pharmacological treatments and nonpharmacological options and allergen immunotherapy (AIT). A literature review has been conducted in Medline, Pubmed, as well as the national and international study (ClinicalTrials.gov) and guideline registers and the Cochrane Library. Human studies published on the topic in the period up to and including November 2023 were taken into account. Allergen avoidance measures, pharmacotherapy, and AIT are the cornerstones of AR treatment. Nonpharmacological measures and behavioral recommendations should be adequately added. Tools of precision medicine are already playing a significant role and will be part of the diagnostic and therapeutic standard in the future. Patients benefit most in a network of different pharmacological and nonpharmacological treatment measures including AIT. Application of precision medicine tools for diagnosis and treatment will improve standards of care.

Exploring the impact of pharmacogenetics on personalized medicine: A systematic review. / [Artículo traducido] Exploración del impacto de la farmacogenética en la medicina personalizada: una revisión sistemática.

INTRODUCTION: Pharmacogenetics evaluates how genetic variations influence drug responses. Nowadays, genetic tests have advanced, becoming more affordable, and its integration is supported by stronger clinical evidence. Guidelines such as those from CPIC (Clinical Pharmacogenetics Implementation Consortium) and resources like PharmGKB facilitate genotype-based prescribing; and organizations like the FDA promote genetic testing before initiating certain medications. Preventive pharmacogenetic panels seem promising, but further research on biomarkers and diverse populations is needed. The aim of this review is to analyze recent evidence on the genotype-drug response relationship to examine how the genetic profile of patients influences the clinical response to treatments, and analyze the areas of research that need further study to advance towards a genetic-based precision medicine. MATERIALS AND METHODS: A systematic search was conducted on PubMed to identify articles investigating the genotype-drug response relationship. The search strategy included terms such as "pharmacogenetics", "personalized treatment", "precision medicine", "dose adjustment", "individualized dosing", "clinical routine" and "clinical practice." Clinical trials, observational studies, and meta-analyses published in English or Spanish between 2013 and 2023 were included. The initial search resulted in a total of 136 articles for analysis. RESULTS: 49 articles were included for the final analysis following review by two investigators. A relationship between genetic polymorphisms and drug response or toxicity was found for drugs such as opioids, GLP-1 agonists, tacrolimus, oral anticoagulants, antineoplastics, atypical antipsychotics, efavirenz, clopidogrel, lamotrigine, anti-TNF-α agents, voriconazole, antidepressants, or statins. However, for drugs like metformin, quetiapine, irinotecan, bisoprolol, and anti-VEGF agents, no statistically significant association between genotype and response was found. CONCLUSION: The studies analyzed in this review suggest a strong correlation between genetic variability and individual drug responses, supporting the use of pharmacogenetics for treatment optimization. However, for certain drugs like metformin or quetiapine, the influence of genotype on their response remains unclear. More studies with larger sample sizes, greater ethnic diversity, and consideration of non-genetic factors are needed. The lack of standardization in analysis methods and accessibility to genetic testing are significant challenges in this field. As a conclusion, pharmacogenetics shows immense potential in personalized medicine, but further research is required.

Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development.

Patient-derived organoids (PDOs) have emerged as a promising platform for clinical and translational studies. A strong correlation exists between clinical outcomes and the use of PDOs to predict the efficacy of chemotherapy and/or radiotherapy. To standardize interpretation and enhance scientific communication in the field of cancer precision medicine, we revisit the concept of PDO-based drug sensitivity testing (DST). We present an expert consensus-driven approach for medication selection aimed at predicting patient responses. To further standardize PDO-based DST, we propose guidelines for clarification and characterization. Additionally, we identify several major challenges in clinical prediction when utilizing PDOs.