Revisión de medicamentos sin receta con aspartamo: Indicación Farmacéutica segura en fenilcetonuria / Review of non-prescription medicines with aspartame: safe pharmaceutical indication in phenylketonuria

Introducción: La fenilcetonuria es el trastorno hereditario más frecuente del metabolismo de los aminoácidos y su abordaje suele centrarse en die-tas restringidas en fenilalanina, un aminoácido presente en el edulcorante aspartamo habitualmente usado como excipiente en tecnología farmacéutica. Objetivo: El objetivo principal es la revisión de los medicamentos sin receta comercializados en España hasta marzo de 2023 y que contienen aspartamo en su composición. Método: Se realizó una revisión en la base de datos BOT plus de todos los medicamentos comercializados en España que contienen aspartamo. Se seleccionaron solo los MSR. Se consultaron las fichas técnicas en el Centro de información online de medicamentos de la AEMPS (CIMA), y los datos obtenidos se registraron en una tabla. Resultados: Se obtuvieron 570 medicamentos; 58 eran MSR. Cuando exista petición de MSR con aspartamo en pacientes con fenilcetonuria, en el SIF, tras su evaluación, en el 100% de los casos, el farmacéutico aplicando el Servicio de Indicación Farmacéutica podría indicar un MSR alternativo, con el mismo principio activo pero sin aspartamo como excipiente. Conclusiones: La actuación del farmacéutico comunitario para aplicar el SIF es muy importante en pacientes con fenilcetonuria. Existen medicamentos que no requieren prescripción y se pueden indicar en estos pacientes. El farmacéutico debe tener a su disposición las herramientas necesarias que le faciliten el SIF con este tipo de enfermos. (AU)

Introduction: Phenylketonuria is the most common inherited disorder of amino acid metabolism and its management usually focuses on diets restricted in phenylalanine, an amino acid present in the sweet-ener aspartame commonly used as an excipient in pharmaceutical technology. Objective: The main objective is the review of non-prescription medicines marketed in Spain until March 2023 and that contain aspartame in their composition.Methods: A review of all medicines marketed in Spain containing aspartame was carried out in the BOT plus database. Only MSRs were selected. The data sheets were consulted at the AEMPS online medicines information centre (CIMA), and the data obtained were recorded in a table.Results: 570 medicines were obtained; 58 were MSRs. When there is a request for MSRs with aspartame in patients with phenylketonuria, in the SIF, after evaluation, in 100% of the cases, the pharmacist applying the Pharmaceutical Indication Service could indicate an alternative MSR, with the same active ingredient but without aspartame as an excipient.Conclusions: The action of the community phar-macist to apply the SIF is very important in patients with phenylketonuria. There are medicines that do not require a prescription and can be prescribed for these patients. Pharmacists should have the necessary tools at their disposal to facilitate the SIF with this type of patient. (AU)

Drug databases and their contributions to drug repurposing.

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

The prevalence of major potential drug-drug interactions at a University health centre pharmacy in Jamaica / Prevalencia de interacciones potenciales medicamento-medicamento en una farmacia de centro de salud universitario en Jamaica

Objective: To identify major potential drug-drug interactions (DDIs) on prescriptions filled at the University Health Centre Pharmacy, Mona Campus, Jamaica. Methods: This investigation utilised a cross-sectional analysis on all prescriptions with more than one drug that were filled at the Health Centre Pharmacy between November 2012 and February 2013. Potential DDIs were identified using the online Drug Interactions Checker database of Drugs.com. Results: During the period of the study, a total of 2814 prescriptions were analysed for potential DDIs. The prevalence of potential DDIs found during the study period was 49.82%. Major potential DDIs accounted for 4.7 % of the total number of interactions detected, while moderate potential DDIs and minor potential DDIs were 80.8 % and 14.5 % respectively. The three most frequently occurring major potential DDIs were amlodipine and simvastatin (n=46), amiloride and losartan (n=27) and amiloride and lisinopril (n=16). Conclusion: This study has highlighted the need for educational initiatives to ensure that physicians and pharmacists collaborate in an effort to minimise the risks to the patients. These interactions are avoidable for the most part, as the use of online tools can facilitate the selection of therapeutic alternatives or guide decisions for closer patient monitoring and thus reduce the risks of adverse events (AU)

Objetivo: Identificar interacciones potenciales medicamento-medicamento (DDI) en las prescripciones atendidas en la farmacia del centro de salud de Universitario del campus de Mona, Jamaica. Métodos: Esta investigación utilizó un análisis transversal de todas las prescripciones con más de un medicamento que fueron atendidas en el centro de salud universitario entre noviembre de 2012 y febrero de 2013. Las DDI potenciales se identificaban en el Drug Interactions Checker de la base de datos Drugs.com. Resultados: Durante el periodo de estudio, se analizaron a la busca de DDI un total de 2.814 prescripciones. La prevalencia de DDI potenciales encontrada durante el estudio fue del 49,82%. Las DDI major potenciales totalizaron el 4,7% del total de interaciones detectadas, mientras que las moderadas y minor fueron el 80,8% y el 14,5%, respectivamente. Las tres DDI potenciales major que aparecieron más frecuentemente fueron amlodipina y simvastatina (n=46), amilorida y losartan (n=27), y amilorida y lisinopril (n=16). Conclusión: Este estudio ha remarcado la necesidad de iniciativas educativas para asegurar que los médicos y los farmacéuticos colaboren en el esfuerzo de minimizar los riesgos de los pacientes. Estas interacciones eran evitables en su mayor parte, ya que el uso de herramientas online puede facilitar la selección de tratamientos alternativos o guiar decisiones para monitorizar más de cerca a los pacientes, y así reducir el riesgo de eventos adversos (AU)

Text Mining and Data Modeling of Karyotypes to aid in Drug Repurposing Efforts.

Karyotyping, or visually examining and recording chromosomal abnormalities, is commonly used to diagnose and treat disease. Karyotypes are written in the International System for Human Cytogenetic Nomenclature (ISCN), a computationally non-readable language that precludes full analysis of these genomic data. In response, we developed a cytogenetic platform that transfers the ISCN karyotypes to a machine-readable model available for computational analysis. Here we use cytogenetic data from the National Cancer Institute (NCI)-curated Mitelman database1 to create a structured karyotype language. Then, drug-gene-disease triplets are generated via a computational pipeline connecting public drug-gene interaction data sources to identify potential drug repurposing opportunities.

Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

There remain significant difficulties selecting probable candidate drugs from existing databases. We describe an ontology-oriented approach to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources. We also report a case study in which we attempted to explore candidate drugs effective for bipolar disorder and epilepsy. We constructed an ontology incorporating knowledge between the two diseases and performed semantic reasoning tasks with the ontology. The results suggested 48 candidate drugs that hold promise for further breakthrough. The evaluation demonstrated the validity our approach. Our approach prioritizes the candidate drugs that have potential associations among genes, phenotypes and symptoms, and thus facilitates the data integration and drug repurposing in psychiatric disorders.

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.

Medication extraction and guessing in Swedish, French and English.

Extraction of information related to the medication is an important task within the biomedical area. Our method is applied to different types of documents in three languages. The results indicate that our approach can efficiently update and enrich the existing drug vocabularies.