An inadequate biomarker validation can affect many patients\' diagnosis, treatment, and follow-up. Therefore, special interest should be placed on performing these analyses correctly so that biomarkers can be applicable to patients and evidence of their clinical usefulness can be generated. A methodological work on the concept of biomarkers is presented, as well as the difficulties associated with the methodological approach to their development, validation, and implementation in clinical practice.

BACKGROUND: 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.
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.
CONCLUSIONS: 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.

This article examines the use of artificial intelligence (AI) in the field of paediatric care within the framework of the 7P medicine model (Predictive, Preventive, Personalized, Precise, Participatory, Peripheral and Polyprofessional). It highlights various applications of AI in the diagnosis, treatment and management of paediatric diseases as well as the role of AI in prevention and in the efficient management of health care resources and the resulting impact on the sustainability of public health systems. Successful cases of the application of AI in the paediatric care setting are presented, placing emphasis on the need to move towards a 7P health care model. Artificial intelligence is revolutionizing society at large and has a great potential for significantly improving paediatric care.

BACKGROUND: 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.
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\", \"individualizing 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 2 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-TNFα 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.
CONCLUSIONS: 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.

The treatment of inflammatory bowel disease has undergone a significant transformation following the introduction of biologic drugs. Thanks to these drugs, treatment goals have evolved from clinical response and remission to more ambitious objectives, such as endoscopic or radiologic remission. However, even though biologics are highly effective, a significant percentage of patients will not achieve an initial response or may lose it over time. We know that there is a direct relationship between the trough concentrations of the biologic and its therapeutic efficacy, with more demanding therapeutic goals requiring higher drug levels, and inadequate exposure being common. Therapeutic drug monitoring of biologic medications, along with pharmacokinetic models, provides us with the possibility of offering a personalized approach to treatment for patients with IBD. Over the past few years, relevant information has accumulated regarding its utility during or after induction, as well as in the maintenance of biologic treatment, in reactive or proactive strategies, and prior to withdrawal or treatment de-escalation. The aim of this document is to establish recommendations regarding the utility of therapeutic drug monitoring of biologics in patients with inflammatory bowel disease, in different clinical practice scenarios, and to identify areas where its utility is evident, promising, or controversial.

Urothelial carcinoma (UC) has histological subtypes whose phenotype reflects their molecular diversity, behavior and response to conventional therapy. Immune checkpoint inhibitors (ICIs) have improved the management of UC by evaluation of PD-L1. In the case of PD-L1 22C3, the initiation of ICI is considered from a combined positive score (CPS) greater than 10. However, UC subtypes with absent PD-L1 22C3 expression in cases with CPS>10 may not respond to these treatments. This study aims to establish a correlation between the PD-L1 immunoexpression and molecular alterations in divergent differentiation and histological subtypes of UC (UC-s).
Twenty-six samples of UC were detected from a total of 24 patients. Two pathologists performed separately an assessment of UC-s on hematoxylin-eosin as well as PD-L1 expression. Molecular study of each case was performed by next generation sequencing (NGS). A descriptive analysis of the variables included was conducted.
Nine cases (34.61%) showed a CPS>10, some with negative PD-L1 immunoexpression in aggressive UC-s. The molecular study revealed alterations in genes belonging to the p53/cell cycle control, RAS, and DNA repair pathways, among others. None of the alterations were exclusive to any histological subtype.
Special attention should be paid to CPS>10 cases that include histological subtypes of UC with divergent expression for PD-L1 as they may not respond to treatment with ICI. We recommend examining the proportion and PD-L1 status of each subtype, especially if it has aggressive behavior.

OBJECTIVE: The clinical value of electrocardiogram (ECG) repolarization parameters associated with ventricular arrhythmias (VAs) in tako-tsubo syndrome is still under debate. We aimed to evaluate ECG predictors of subacute VAs, defined as those occurring after the first 48hours from admission.
METHODS: This single-center observational study enrolled patients admitted to the cardiology department between 2012 and 2018 with a confirmed diagnosis of tako-tsubo syndrome. Data collection included a 12-lead ECG on admission and at 48hours, continuous telemetry monitoring, blood testing, transthoracic echocardiography, and coronary angiography during hospitalization. VAs events were defined as: premature ventricular contractions ≥ 2000 within a 24-hour window of telemetry monitoring, ventricular fibrillation, sustained ventricular tachycardia (VT), polymorphic VT, and non-sustained VT.
RESULTS: A total of 87 patients (age 72±12 years) were enrolled. During a median of 8 days of hospitalization, subacute VAs were documented in 22 patients (25%) after a median of 91hours from admission. Subacute VAs were associated with an increase in mortality during hospitalization (P=.030). The corrected global (mean of the 12-lead ECG values) Tpeak-Tend interval at 48hours from admission was an independent predictor of subacute VAs and was statistically superior to the standard corrected QT interval (Z test, P=.040). A cut-off of 108 msec for the corrected global Tpeak-Tend yielded a 71% sensitivity and 72% specificity for subacute VAs.
CONCLUSIONS: In patients with tako-tsubo syndrome, subacute VAs are associated with repolarization alterations that can be identified on conventional ECG using the Tpeak-Tend interval.

OBJECTIVE: In a clinical phenotype-based management strategy for COPD, it would be preferable to at least assign all patients to a phenotype, but to a single phenotype only. The aim of this study was to evaluate whether all patients are assigned to one and only one phenotype using the Spanish COPD guidelines (GesEPOC) 2017 and to evaluate the criteria that define these categories.
METHODS: The Time-based Register and Analysis of COPD Endpoints study (TRACE; clinicaltrials.gov NCT03485690) is a prospective cohort of COPD patients attending annual visits since 2012, which collects GesEPOC phenotypes. Although the GesEPOC recommends that patients considered to be at low risk are not phenotyped, an analysis of the criteria for identifying high- and low-risk phenotypes was performed, comparing the distribution of phenotypes and the criteria applied between these 2 groups.
RESULTS: The cohort included 970 patients with a confirmed diagnosis of COPD, divided into 427 (44.02%) low-risk and 543 (55.9%) high-risk patients. The most frequent phenotype was the non-exacerbator (44.9% of high-risk patients). Overall, 20.6% of low-risk patients met criteria for asthma-COPD overlap syndrome, while 9.2% of the cohort did not meet the diagnostic criteria for any phenotype, and 19.1% met the criteria for 2 phenotypes, with no differences between risk groups.
CONCLUSIONS: Our data highlight some of the weaknesses of the current clinical phenotype strategy, revealing overlapping categories in some cases, and patients to whom no phenotype was assigned.

BACKGROUND: Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment.
METHODS: Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results.
RESULTS: Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA.
CONCLUSIONS: The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

OBJECTIVE: In a clinical phenotype-based management strategy for COPD, it would be preferable to at least assign all patients to a phenotype, but to a single phenotype only. The aim of this study was to evaluate whether all patients are assigned to one and only one phenotype using the Spanish COPD guidelines (GesEPOC) and to evaluate the criteria that define these categories.
METHODS: The Time-based Register and Analysis of COPD Endpoints study (TRACE; clinicaltrials.gov NCT03485690) is a prospective cohort of COPD patients attending annual visits since 2012, which collects GesEPOC phenotypes. Although the GesEPOC recommends that patients considered to be at low risk are not phenotyped, an analysis of the criteria for identifying high- and low-risk phenotypes was performed, comparing the distribution of phenotypes and the criteria applied between these 2 groups.
RESULTS: The cohort included 970 patients with a confirmed diagnosis of COPD, divided into 427 (44.02%) low-risk and 543 (55.9%) high-risk patients. The most frequent phenotype was the non-exacerbator (44.9% of high-risk patients). Overall, 20.6% of low-risk patients met criteria for asthma-COPD overlap syndrome, while 9.2% of the cohort did not meet the diagnostic criteria for any phenotype, and 19.1% met the criteria for 2 phenotypes, with no differences between risk groups.
CONCLUSIONS: Our data highlight some of the weaknesses of the current clinical phenotype strategy, revealing overlapping categories in some cases, and patients to whom no phenotype was assigned.