Effects of mulberry leaf extracts on activity and mRNA expression of five cytochrome P450 enzymes in rat

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450)inhibition is an important consideration in drug discovery. Mulberry leaves are of broad popular use for food or remedy purposes, which is believed to contain substances that are beneficial for preventing and alleviating diabetes. However, there is a paucity of information about the effect of mulberry leaves on rat CYP450 enzymes activities and the mRNA expression levels in vivo. The present study aimed to investigate the effect of mulberry leaves on activities of rat CYP450 enzymes (CYP3A4, CYP2C8, CYP2C19, CYP2D6, and CYP1A2) through both probe-drug cocktail approach and real-time polymerase chain reaction (RT-qPCR). The pharmacokinetic results indicated that the aqueous extract of mulberry leaves (AML) exhibited induction effects on CYP3A4 activities, and AML exhibited inhibitory effects on CYP1A2, CYP2D6, and CYP2C8, while no obvious effect was observed on CYP2C19 activity. Additionally, the ethanol extract of mulberry leaves (EML) could induce the activities of CYP3A4. In addition, EML exhibited inhibitory effects on CYP1A2, CYP2D6, and CYP2C19, while no significant change in CYP2C8 activity was observed. Accordingly, the level of mRNA expression of five CYP enzymes were consistent with the result of pharmacokinetic. The results of our study may form a practical strategy for assessing CYP-mediated HDI

Definición y caracterización del fenómeno “Diagnóstico Lastre Generado por Medicamentos”

Proponemos el presente estudio para la identificación del fenómeno "Diagnóstico Lastre Generado por Medicamentos" (DLGM), que es la traducción farmacéutica de la interpretación médica de un problema de salud generado por medicamentos y atribuido a causas clínicas con la consiguiente pérdida de identidad que limita su identificación y manejo. No habrá mejoría de la enfermedad si no se corrige la causa del problema, por lo que cabe esperar un empeoramiento y persistencia de la enfermedad marcados por el fracaso farmacoterapéutico, convirtiendo el problema de salud en un verdadero lastre para los pacientes a la espera de ser identificado. La propuesta de un algoritmo de caracterización del problema como herramienta de cribado se ha aplicado a 10 pacientes en el servicio de seguimiento farmacoterapéutico, confirmando la sospecha de DLGM, y demostrando que las reacciones adversas a medicamentos habían adquirido la identidad de una enfermedad. Un DLGM podría definirse como la entidad que surge al diagnosticar una enfermedad sobre un resultado negativo asociado al uso del medicamento y que, por tanto, no recibe el tratamiento adecuado. La identificación del fenómeno DLGM permite detectar muchos resultados negativos asociados a la medicación (RNM) y contribuye a su adecuado tratamiento. No identificar un DLGM complica el estado clínico del paciente y limita su recuperació

Definición y caracterización del fenómeno “Diagnóstico Lastre Generado por Medicamentos”

Proponemos el presente estudio para la identificación del fenómeno "Diagnóstico Lastre Generado por Medicamentos" (DLGM), que es la traducción farmacéutica de la interpretación médica de un problema de salud generado por medicamentos y atribuido a causas clínicas con la consiguiente pérdida de identidad que limita su identificación y manejo. No habrá mejoría de la enfermedad si no se corrige la causa del problema, por lo que cabe esperar un empeoramiento y persistencia de la enfermedad marcados por el fracaso farmacoterapéutico, convirtiendo el problema de salud en un verdadero lastre para los pacientes a la espera de ser identificado. La propuesta de un algoritmo de caracterización del problema como herramienta de cribado se ha aplicado a 10 pacientes en el servicio de seguimiento farmacoterapéutico, confirmando la sospecha de DLGM, y demostrando que las reacciones adversas a medicamentos habían adquirido la identidad de una enfermedad. Un DLGM podría definirse como la entidad que surge al diagnosticar una enfermedad sobre un resultado negativo asociado al uso del medicamento y que, por tanto, no recibe el tratamiento adecuado. La identificación del fenómeno DLGM permite detectar muchos resultados negativos asociados a la medicación (RNM) y contribuye a su adecuado tratamiento. No identificar un DLGM complica el estado clínico del paciente y limita su recuperació

Risk Factors Affecting Death from Hospital-Acquired Infections in Trauma Patients: Association Rule Mining

Introduction: Trauma patients are potentially at high risk of acquiring infections in hospitals,which is the main cause of in-hospital mortality. The aim of this study was to identify the riskfactors contributing to death from hospital-acquired infections in trauma patients by datamining techniques.Methods: This is a cohort study. A total of 549 trauma patients with nosocomial infectionwho were admitted to Shiraz trauma hospital between 2017 and 2018 were studied. Sex,age, mechanism of injury, body region injured, injury severity score, length of stay, typeof intervention, infection day after admission, microorganism cause of infections, andthe outcomes were collected. Association rule mining techniques were applied to extractknowledge from the data set. The IBM SPSS Modeler data mining software version 18.0 wasused as a tool for data mining of the trauma patients with hospital queried infections database.Results: The age older than 65, surgical site infection skin, bloodstream infection, mechanisminjury of car accident, invasive intervention of tracheal intubation, injury severity score higherthan 16, and multiple injuries with higher than 71 percent confidence level were associatedwith in-hospital mortality. The relationship between those predicators and death amonghospital-acquired infection was strong (Lift value >1).Conclusion: Factors such as increasing age, tracheal intubation, mechanical ventilator,surgical site infection skin, upper respiratory infection are associated with death fromhospital-acquired infections in trauma patients by data mining

Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports

This work is licensed under a Creative Commons Attribution Non-Commercial-No Derivatives 4.0 International License.Objective Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration’s adverse event reporting system. Methods Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification. Results Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR. Conclusion Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

Using Big Data Analytics and Statistical Methods for Improving Drug Safety

This dissertation includes three studies, all focusing on utilizing Big Data and statistical methods for improving one of the most important aspects of health care, namely drug safety. In these studies we develop data analytics methodologies to inspect, clean, and model data with the aim of fulfilling the three main goals of drug safety; detection, understanding, and prediction of adverse drug effects.In the first study, we develop a methodology by combining both analytics and statistical methods with the aim of detecting associations between drugs and adverse events through historical patients' records. Particularly we show applicability of the developed methodology by focusing on investigating potential confounding role of common diabetes drugs on developing acute renal failure in diabetic patients. While traditional methods of signal detection mostly consider one drug and one adverse event at a time for investigation, our proposed methodology takes into account the effect of drug-drug interactions by identifying groups of drugs frequently prescribed together.In the second study, two independent methodologies are developed to investigate the role of prescription sequence factor on the likelihood of developing adverse events. In fact, this study focuses on using data analytics for understanding drug-event associations. Our analyses on the historical medication records of a group of diabetic patients using the proposed approaches revealed that the sequence in which the drugs are prescribed, and administered, significantly do matter in the development of adverse events associated with those drugs.The third study uses a chronological approach to develop a network of approved drugs and their known adverse events. It then utilizes a set of network metrics, both similarity- and centrality-based, to build and train machine learning predictive models and predict the likely adverse events for the newly discovered drugs before their approval and introduction to the market. For this purpose, data of known drug-event associations from a large biomedical publication database (i.e., PubMed) is employed to construct the network. The results indicate significant improvements in terms of accuracy of prediction of drug-evet associations compared with similar approaches

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service