Calidad estructural de las bases de datos de interacciones

Real clinical impact of drug-drug interactions of immunosuppressants in transplant patients

The main objective was to determine the prevalence of real drug-drug interactions (DDIs) of immunosuppressants in transplant patients. We conducted a prospective, observational 1-year study at a tertiary hospital, including all transplanted patients. We evaluated data from monitoring blood concentrations of immunosuppressive drugs and adverse drug events (ADEs) caused by DDIs. The DDIs were classified as C, D, or X according to their Lexi-Interact rating (C = monitor therapy, D = consider therapy modification, X = avoid combination). The clinical importance of real DDIs was expressed in terms of patient outcomes. The causality of DDIs was determined using Drug Interaction Probability Scale. The data were analyzed using Statistical Package for Social Sciences v. 25.0. A total of 309 transplant patients were included. Their mean age was 52.0 ± 14.7 years (18-79) and 69.9% were male. The prevalence of real DDIs was 21.7%. Immunosuppressive drugs administered with antifungal azoles and tacrolimus (TAC) with nifedipine have a great clinical impact. Real DDIs caused ADEs in 22 patients. The most common clinical outcome was nephrotoxicity (1.6%; n = 5), followed by hypertension (1.3%; n = 4). Suggestions for avoiding category D and X DDIs included: changing the immunosuppressant dosage, using paracetamol instead of non-steroidal anti-inflammatory drugs, and interrupting atorvastatin. The number of drugs prescribed and having been prescribed TAC was associated with an increased risk of real DDIs. There are many potential DDIs described in the literature but only a small percentage proved to be real DDIs, based on the patients´ outcomes.

Drug-drug Interactions between COVID-19 Treatments and Antidepressants, Mood Stabilizers/Anticonvulsants, and Benzodiazepines: Integrated Evidence from 3 Databases

INTRODUCTION

The SARS-CoV-2 pandemic with psychiatric comorbidities leads to a scenario in which the use of psychotropic drugs may be required. This requires the support of evidence-based medicine to take into account possible interactions between antidepressants, mood stabilizers, benzodiazepines, and coronavirus infection treatments.

METHODS

Three databases were consulted: (a) Lexicomp Drug Interactions, (b) Micromedex Solutions Drugs Interactions, (c)Liverpool Drug Interaction Group for COVID-19 therapies. The CredibleMeds QTDrugs List was also queried. Hydroxychloroquine, chloroquine, azithromycin, lopinavir-ritonavir, remdesivir, favipiravir, tocilizumab, baricitinib, anakinra, and dexamethasone - drugs used for SARS-CoV-2 - were analyzed, and consensus recommendations are made.

RESULTS

The potential interactions of agomelatine, desvenlafaxine, duloxetine, milnacipran, and vortioxetine with COVID-19 treatments shall be considered less risky. Antidepressant interactions with hydroxychloroquine, chloroquine, and azithromycin enhance the risk of QT prolongation, and ECG monitoring is advised for most antidepressants. Antidepressants with lopinavir/ritonavir involve multiple CYP enzyme interactions (except with milnacipran). Gabapentin, oxcarbazepine, pregabalin, topiramate, and zonisamide are safe treatment options that have no significant interactions with COVID-19 treatments. Lithium is contraindicated with hydroxychloroquine, chloroquine, and azithromycin. Precaution should be taken in using valproic acid with lopinavir-ritonavir. The use of benzodiazepines does not present a risk of drug interaction with COVID-19 treatments, except lopinavir/ritonavir.

CONCLUSIONS

Clinicians prescribing antidepressants, mood stabilizers/anticonvulsants, and benzodiazepines, should be aware of the probable risk of drug-drug interaction with COVID-19 medications and may benefit from heeding these recommendations for use to ensure patient safety.

Drug-drug interactions between COVID-19 treatments and antipsychotics drugs: integrated evidence from 4 databases and a systematic review

RATIONALE

Management of anxiety, delirium, and agitation cannot be neglected in coronavirus disease (COVID-19). Antipsychotics are usually used for the pharmacological management of delirium, and confusion and behavioral disturbances. The concurrent use of treatments for COVID-19 and antipsychotics should consider eventual drug-drug interactions OBJECTIVE: To systematically review evidence-based available on drug-drug interactions between COVID-19 treatments and antipsychotics.

EVIDENCE REVIEW

Three databases were consulted: Lexicomp® Drug Interactions, Micromedex® Solutions Drugs Interactions, and Liverpool© Drug Interaction Group for COVID-19 therapies. To acquire more information on QT prolongation and Torsade de Pointes (TdP), the CredibleMeds® QTDrugs List was searched. The authors made a recommendation agreed to by consensus. Additionally, a systematic review of drug-drug interactions between antipsychotics and COVID-19 treatment was conducted.

RESULTS

The main interactions between COVID-19 drugs and antipsychotics are the risk of QT-prolongation and TdP, and cytochromes P450 interactions. Remdesivir, baricinitib, and anakinra can be used concomitantly with antipsychotics without risk of drug-drug interaction (except for hematological risk with clozapine and baricinitib). Favipiravir only needs caution with chlorpromazine and quetiapine. Tocilizumab is rather safe to use in combination with antipsychotics. The most demanding COVID-19 treatments for coadministration with antipsychotics are chloroquine, hydroxychloroquine, azithromycin, and lopinavir/ritonavir because of the risk of QT prolongation and TdP and cytochromes interactions. The systematic review provides highly probable drug interaction between lopinavir/ritonavir plus quetiapine and ritonavir/indinavir plus risperidone.

CONCLUSIONS

Clinicians prescribing antipsychotics should be aware of the likely risk of drug-drug interaction with COVID-19 medication and may benefit from taking into account present recommendations of use to preserve patient safety.

Free software to analyse the clinical relevance of drug interactions with antiretroviral agents (SIMARV®) in patients with HIV/AIDS

BACKGROUND

Highly active antiretroviral therapy has extended the expected lifespan of patients with HIV/AIDS. However, the therapeutic benefits of some drugs used simultaneously with highly active antiretroviral therapy may be adversely affected by drug interactions.

OBJECTIVE

The goal was to design and develop a free software to facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS.

METHODS

A comprehensive Medline/PubMed database search of drug interactions was performed. Articles that recognized any drug interactions in HIV disease were selected. The publications accessed were limited to human studies in English or Spanish, with full texts retrieved. Drug interactions were analyzed, assessed, and grouped into four levels of clinical relevance according to gravity and probability. Software to systematize the information regarding drug interactions and their clinical relevance was designed and developed.

RESULTS

Overall, 952 different references were retrieved and 446 selected; in addition, 67 articles were selected from the citation lists of identified articles. A total of 2119 pairs of drug interactions were identified; of this group, 2006 (94.7%) were drug-drug interactions, 1982 (93.5%) had an identified pharmacokinetic mechanism, and 1409 (66.5%) were mediated by enzyme inhibition. In terms of clinical relevance, 1285 (60.6%) drug interactions were clinically significant in patients with HIV (levels 1 and 2). With this information, a software program that facilitates identification and assessment of the clinical relevance of antiretroviral drug interactions (SIMARV^®) was developed.

CONCLUSIONS

A free software package with information on 2119 pairs of antiretroviral drug interactions was designed and developed that could facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS.

Potential drug-drug interactions in hospitalised haematological patients

Background Frequently, haematological patients undergo highly complex and intensive treatment protocols, so a high risk of drug-drug interactions could be expected. Objectives To determine prevalence of clinically relevant drug-drug interactions, to identify the most frequent drug-drug interactions and associated risk factors. Methods A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient's treatment sheet was collected. Each medication list was screened through two databases: Thomson MicromedexTM and Drug Interaction FactsTM. All identified potential drug-drug interactions with a moderate or higher severity rating were recorded. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of drug-drug interactions. Multiple logistic regression models were used to identify risk factors associated with drug-drug interactions. Results A total of 2061 drug-drug interactions were detected in 317 treatment sheets from 58 patients. The prevalence of treatment sheets with drug-drug interactions by Micromedex and Drug Interaction Facts databases were 74.1% and 56.8%, respectively. Azole antifungals, immunosuppressive drugs, antiemetics, antidepressants, acid suppressants and corticosteroids were the most frequent involved drugs. In multivariate analysis, the main risk factor associated with increased odds for drug-drug interactions was a higher number of non-antineoplastic drugs. Conclusions The prevalence of drug-drug interactions was common, with immunosuppressant and azole antifungal agents being the most commonly involved drugs. The factor having the greatest influence on drug-drug interactions was a higher number of non-antineoplastic drugs.

Identification of potential clinically significant drug interactions in HIV-infected patients: a comprehensive therapeutic approach

OBJECTIVES

The aim of the study was to determine the prevalence of potential clinically significant drug interactions (CSDIs) in HIV-positive individuals and to identify associated risk factors.

METHODS

A cross-sectional study was conducted including all HIV-infected out-patients attending the Pharmacy Service of a regional reference hospital in Murcia, south-eastern Spain. The complete treatment was screened for possible CSDIs using the Spanish College of Pharmacists' online software resource, bot. Additionally, the severity level of the CSDIs involving antiretroviral (ARV) drugs was compared with that established in the specific antiretroviral database InteraccionesHIV.com. Multivariate logistic regression was used to identify associated risk factors.

RESULTS

Two hundred and sixty-eight patients were included in the study. A total of 292 potential drug interactions were identified, of which 102 (34.9%) were CSDIs, of which 52.9% involved ARV drugs. Seven therapeutic drug classes were involved in 75% of CSDIs (protease inhibitors, benzodiazepines, nonsteroidal anti-inflammatory drugs, nonnucleoside reverse transcriptase inhibitors, corticosteroids, antithrombotics and proton pump inhibitors). Factors independently associated with CSDIs were treatment with more than five drugs [odds ratio (OR) 15.1; 95% confidence interval (CI) 6.3-36.2], and treatment with a protease inhibitor (OR 5.3; 95% CI 2.4-11.74).

CONCLUSIONS

The findings of this study suggest that the prevalence of clinically relevant drug-drug interactions is high in HIV-infected patients, and could represent a major health problem. Awareness, recognition and management of drug interactions are important in optimizing the pharmaceutical care of HIV-infected patients and helping to prevent adverse events and/or loss of efficacy of the drugs administered.

Lessons learnt from the DDIExtraction-2013 Shared Task

The DDIExtraction Shared Task 2013 is the second edition of the DDIExtraction Shared Task series, a community-wide effort to promote the implementation and comparative assessment of natural language processing (NLP) techniques in the field of the pharmacovigilance domain, in particular, to address the extraction of drug-drug interactions (DDI) from biomedical texts. This edition has been the first attempt to compare the performance of Information Extraction (IE) techniques specific for each of the basic steps of the DDI extraction pipeline. To attain this aim, two main tasks were proposed: the recognition and classification of pharmacological substances and the detection and classification of drug-drug interactions. DDIExtraction 2013 was held from January to June 2013 and attracted wide attention with a total of 14 teams (6 of the teams participated in the drug name recognition task, while 8 participated in the DDI extraction task) from 7 different countries. For the task of the recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was an F1 of 65.1%. The results show advances in the state of the art and demonstrate that significant challenges remain to be resolved. This paper focuses on the second task (extraction of DDIs) and examines its main challenges, which have yet to be resolved.

Potentially inappropriate prescribing and drug-drug interactions among elderly Chinese nursing home residents in Macao

BACKGROUND

The ageing of the population has become a concern all over the world, including Macao. In general, older people are more prone to adverse drug events which can result from potentially inappropriate medication (PIM) use and drug-drug interactions (DDIs).

OBJECTIVE

This study was designed to evaluate the prevalence of PIM use and DDIs among elderly nursing home residents in Macao, and to find out the factors associated with these drug-related problems.

SETTING

This study was conducted in the largest nursing home in Macao, with a bed capacity of 168.

METHOD

All data of this cross-sectional study were collected from medical charts and medication administration records. PIM use was determined by the screening tool of older person's prescription (STOPP) criteria and potential DDIs were detected using the preset criteria of two compendia, Drug-Reax and Lexi-Interact. Multivariate logistic regression analysis was performed to identify the independent factors associated with each drug-related problem.

MAIN OUTCOME MEASURES

The proportions of elderly nursing home residents who regularly used PIMs and who were exposed to DDIs.

RESULTS

A total of 114 elderly residents were eligible for PIM analysis. They consumed an average of 6.9 ± 3.1 different medications. About 46.5 % of them regularly used one or more PIMs. The prevalence of DDIs was 37.8 % among the 111 elderly residents who consumed at least two different medications. An increased number of drugs used was identified as the independent factor associated with PIM use and DDIs (p < 0.05). However, the use of STOPP-related PIMs did not appear to raise the likelihood of DDIs among the study population.

CONCLUSION

Both PIM use and DDIs are common among elderly nursing home residents in Macao. Further studies should be conducted to evaluate the clinical outcomes of pharmacist-led interventions for elderly residents in the local nursing home setting.

Extracting drug-drug interaction from the biomedical literature using a stacked generalization-based approach

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.

A single kernel-based approach to extract drug-drug interactions from biomedical literature

When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.

Prevalence of drug interactions in hospital healthcare

AIM OF THE REVIEW

To study the prevalence of drug interactions in hospital healthcare by reviewing literature.

METHOD

A review was carried out of studies written in Spanish and English on the prevalence of drug interactions in hospital care published in Pubmed between January 1990 and September 2008. The search strategy combined free text and MeSH terms, using the following keywords: "Drug interaction", "prevalence" and "hospital". For each article, we classified independent variables (pathology, age of population, whether patients were hospitalized or not, geographical location, etc.) and dependent variables (number of interactions per 100 patients studied, prevalence of patients with interactions, most common drug interactions, and others).

RESULTS

The search generated 436 articles. Finally, 47 articles were selected for the study, 3 provided results about drug interactions with real clinical consequences, 42 about potential interactions, and 2 described both. The prevalence of patients with interactions was between 15 and 45 % and the number of interactions per 100 patients was between 37 and 106, depending on the group of studies analyzed. There was a considerable increase in these rates in patients with heart diseases and elderly persons.

CONCLUSION

There is a large number of studies on the prevalence of drug interactions in hospitals but they report widely varying results. The prevalence is higher in patients with heart diseases and elderly people.

[Analysis of the drug interactions associated to domiciliary drug therapy in elderly hospitalized patients]

OBJECTIVE

To determine the prevalence of potentially relevant drug-drug interactions associated with chronic treatment of elderly patients over 64-years of age on hospital admission and the factors associated with an increased presence of these.

SUBJECTS AND METHODS

Cross-sectional observational study in a hospital referral area. All patients aged 65 or over admitted to the hospital in the last three months in 2009 were included. Based on the drug database of the General Council of Colleges of Pharmacy (BOT), drug-drug interactions and their potential clinical relevance were identified. To identify the variables associated with a higher prevalence of drug-drug interactions, analyses of correlation and of univariable linear regression and uni-and multivariable logistic regression analyses were performed using the SPSS, version 15.0.

RESULTS

We analyzed the drug prescription data of 382 patients, whose mean age was 7.7 years. A total of 45.3% of patients had comorbidities and 78.8% had taken 5 or more drugs. We identified 272 clinically relevant drug-drug interactions that involved 159 patients (41.6%). Seven pharmacological groups accounted for 80.6% of the drug-drug interactions. The variables that had a statistically significant association to a higher prevalence of relevant interactions were polypharmacy, respiratory insufficiency, and treatment with proton-pump inhibitors, vitamin K antagonists, diuretics or anti-platelet drugs.

CONCLUSIONS

A high prevalence of relevant drug-drug interactions was found in elderly hospitalized patients. Our findings suggest that prevention strategies should be implemented to avoid their associated adverse events, especially in high risk populations.

A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents

Background

A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The increasing volume of the scientific literature overwhelms health care professionals trying to be kept up-to-date with all published studies on DDI.

Methods

This paper describes a hybrid linguistic approach to DDI extraction that combines shallow parsing and syntactic simplification with pattern matching. Appositions and coordinate structures are interpreted based on shallow syntactic parsing provided by the UMLS MetaMap tool (MMTx). Subsequently, complex and compound sentences are broken down into clauses from which simple sentences are generated by a set of simplification rules. A pharmacist defined a set of domain-specific lexical patterns to capture the most common expressions of DDI in texts. These lexical patterns are matched with the generated sentences in order to extract DDIs.

Results

We have performed different experiments to analyze the performance of the different processes. The lexical patterns achieve a reasonable precision (67.30%), but very low recall (14.07%). The inclusion of appositions and coordinate structures helps to improve the recall (25.70%), however, precision is lower (48.69%). The detection of clauses does not improve the performance.

Conclusions

Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract DDI from texts. To the best of our knowledge, this work proposes the first integral solution for the automatic extraction of DDI from biomedical texts.

[A hazard scale for severe interactions: a tool for establishing prioritising strategies to improve the safety of the prescription in family medicine]

OBJECTIVE

To effectively locate the drugs most implicated in severe interactions as a basis of designing actions to improve patient safety in Primary Care.

DESIGN

Cross-sectional study of prescriptions using the Primary Care computerised medical records database (OMI-PC).

SETTING

Murcia (Spain) Health Areas I, VI, VII and IX (723,664 inhabitants).

PARTICIPANTS

There are 362,271 patients over 14 years-old available in the OMI-PC and are assigned to a doctor who uses the OMI-PC regularly.

MAIN MEASUREMENTS

We analysed the drugs that each patient could be taking, looking for severe interactions. We constructed a severe interaction hazard scale (e-PIG) calculating [1] the probability that a non-selected patient may be taking a particular drug and [2] the probability that a drug may produce a severe interaction. With this, we estimated the risk of producing a severe interaction for each drug, which was converted into a 5 point logarithmic scale.

RESULTS

We found 83,138 patients (22.9%) at risk (they took 2 or more drugs). We identified 466,940 prescriptions providing 939 drugs and 5,597 severe interactions (prevalence 5.8%). In these, 167 drugs were involved, of which e-PIG identified 5 (3%) with an extreme value: omeprazole, diazepam, acenocoumarol, ibuprofen and calcium.

CONCLUSIONS

e-PIG is a logarithmic expression of the risk that prescribing a particular drug may produce a severe interaction in a determined setting and time. Its monitoring could become a prioritisation element that may assist the design of strategies for improving the safety of the use of drugs.

Evaluation of three brands of drug interaction software for use in intensive care units

OBJECTIVE

To evaluate drug interaction software programs and determine their accuracy in identifying drug-drug interactions that may occur in intensive care units. Setting The study was developed in Brazil.

METHOD

Drug interaction software programs were identified through a bibliographic search in PUBMED and in LILACS (database related to the health sciences published in Latin American and Caribbean countries). The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting drug-drug interactions. The accuracy of the software programs identified was determined using 100 clinically important interactions and 100 clinically unimportant ones. Stockley's Drug Interactions 8th edition was employed as the gold standard in the identification of drug-drug interaction.

MAIN OUTCOME

Sensitivity, specificity, positive and negative predictive values.

RESULTS

The programs studied were: Drug Interaction Checker (DIC), Drug-Reax (DR), and Lexi-Interact (LI). DR displayed the highest sensitivity (0.88) and DIC showed the lowest (0.69). A close similarity was observed among the programs regarding specificity (0.88-0.92) and positive predictive values (0.88-0.89). The DIC had the lowest negative predictive value (0.75) and DR the highest (0.91).

CONCLUSION

The DR and LI programs displayed appropriate sensitivity and specificity for identifying drug-drug interactions of interest in intensive care units. Drug interaction software programs help pharmacists and health care teams in the prevention and recognition of drug-drug interactions and optimize safety and quality of care delivered in intensive care units.

Prevalence and typology of potential drug interactions occurring in primary care patients

PURPOSE

To investigate the prevalence and types of potential drug interactions in primary care patients to detect risky prescriptions as an essential condition to design intervention policies leading to an improvement in patient safety.

METHODS

Cross-sectional descriptive study.

SETTING

Two areas in Spain comprising 715,661 inhabitants.

PATIENTS

430,525 subjects with electronic medical records and assigned to a family doctor regularly updating them.

RESULTS

On a random day, 29.4% of the population was taking medication. Of these, 73.9% were at risk of suffering interactions, and these were found in 20.6% of them. The amount of interactions was higher among people with chronic conditions, the elderly, females and polymedicated patients. From the total of interactions, 55.1% belonged to the highest clinical relevance 'A' level, and 28.3% should have been avoided. The active ingredients primarily involved were hydrochlorothiazide and ibuprofen and, when focusing on those that should be avoided, omeprazole and acenocoumarol. The most frequent 'A' interaction that should be avoided was between non-conjugated excreted benzodiazepines and proton-pump inhibitors, followed by some NSAIDs and diuretics.

CONCLUSIONS

1 in 20 Spanish citizens is currently undergoing a potential drug interaction, including a high rate of clinically relevant ones that should be avoided. These results confirm the existence of a serious safety issue that should be approached and where all parties involved (physicians, health services, medical societies and patients) must do our bit to improve. Health services should foster the implementation of prescription alert systems linked with electronic medical records including clinical data.