Exposure of the elderly to potential nephrotoxic drug combinations in Belgium

Interpretable prediction of drug-drug interactions via text embedding in biomedical literature

Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicting the risk of DDIs is crucial for ensuring safe drug use, particularly by identifying the types of DDIs and the mechanisms involved. Therefore, this study used biomedical literature to proposed hierarchical attention-based deep learning models to predict DDIs and their types. The proposed model consists of two components: drug embedding and DDI prediction. The drug embedding module extracts representation vectors that effectively capture drug properties using sentence and sequence embedding methods. For sentence embedding, a pre-trained biomedical language model is used to map drug-related sentences into vector space. For sequence embedding, sentence embedding vectors are sequentially fed into bidirectional long short-term memory with a hierarchical attention network, enabling the analysis of sentences relevant to DDI prediction while accounting for the order of the sentences. Finally, DDI prediction is performed using a deep neural network based on the sequence embedding vectors of a drug pair. Our model achieved high performances in the accuracy (0.85-0.90), AUROC (0.98-0.99), and AUPR (0.63-0.95) performance across 164 DDI types. Additionally, the proposed model showed improvements in up to 11 % in AUROC, and 8 % in AUPR. Furthermore, model interprets predictions by leveraging attention mechanisms and drug similarity. The results indicated that the model considered various factors beyond similarity to predict DDIs. These findings may help prevent unforeseen medical accidents and reduce healthcare costs by predicting detailed drug interaction types.

Nephrotoxic drug burden and predictors of exposure among patients with renal impairment in Ethiopia: A multi-center study

Background

Nephrotoxic drugs may hasten the decline in kidney function and worsen the progression of renal impairment as a result; these drugs should be avoided or used with caution in patients with pre-existing renal insufficiency. The purpose of this study was to assess the burden of nephrotoxic medication use and its predictors among patients with underlying renal impairment.

Methods

A multicenter, institution-based, cross-sectional study was conducted from May 30, 2021 to July 30, 2021, at medical wards. Renal impaired patients admitted during the data collection period who took at least one medication were enrolled in the study. A simple random sampling technique was used to select the study participants. Data was collected through an interview and a medical card review. Both bivariable and multivariable binary logistic regression analyses were fitted to identify factors associated with nephrotoxic drug use.

Results

Among the 422 participants, more than half of them (53.6 %) were male. The mean patient's age was 47.5 (±16.7) years. A total of 1310 drugs were prescribed for 422 patients with renal impairment, of which 80.15 % were nephrotoxic. Nephrotoxic drugs were prescribed for 66.4 % of patients. The burden of nephrotoxic medication prescription was significantly associated with variables like the presence of comorbidity (AOR = 6.31, 95 % CI: 2.01-19.79), the number of medications prescribed (AOR = 1.43, 95 % CI: 1.05-1.93), and the age of participants (AOR = 1.12, 95 % CI: 1.07-1.17).

Conclusion

The present study demonstrated that two-third of the patients with renal impairment were exposed to nephrotoxic medications. Furosemide, Enalapril, and vancomycin were the most frequently prescribed nephrotoxic medications. The study suggests that prescribers need to give special attention to older patients who have underlying renal insufficiency, a comorbid condition, and polypharmacy regarding exposure to contraindicated nephrotoxic medication.

Fluorescence-Based High-Throughput Assays for Investigating Cytochrome P450 Enzyme-Mediated Drug-Drug Interactions

The cytochrome P450 enzymes (CYPs), a group of heme-containing enzymes, catalyze oxidative metabolism of a wide range of drugs and xenobiotics, as well as different endogenous molecules. Strong inhibition of human CYPs is the most common cause of clinically associated pharmacokinetic drug-drug/herb-drug interactions (DDIs/HDIs), which may result in serious adverse drug reactions, even toxicity. Accurate and rapid assessing of the inhibition potentials on CYP activities for therapeutic agents is crucial for the prediction of clinically relevant DDIs/HDIs. Over the past few decades, significant efforts have been invested into developing optical substrates for the human CYPs, generating a variety of powerful tools for high-throughput assays to detect CYP activities in biologic specimens and for screening of CYP inhibitors. This minireview focuses on recent advances in optical substrates developments for human CYPs, as well as their applications in screening CYP inhibitors and DDIs/HDIs studies. The examples for rational design and optimization of highly specific optical substrates for the target CYP enzyme, as well as applications in investigating CYP-mediated DDIs, are illustrated. Finally, the challenges and future perspectives in this field are proposed. Collectively, this review summarizes the reported optical-based biochemical assays for highly efficient CYP activities detection, which strongly facilitated the discovery of CYP inhibitors and the investigations on CYP-mediated DDIs. SIGNIFICANCE STATEMENT: Optical substrates for cytochrome P450 enzymes (CYPs) have emerged as powerful tools for the construction of high-throughput assays for screening of CYP inhibitors. This mini-review covers the advances and challenges in the development of highly specific optical substrates for sensing human CYP isoenzymes, as well as their applications in constructing fluorescence-based high-throughput assays for investigating CYP-mediated drug-drug interactions.

Data Sharing and Privacy in Pharmaceutical Studies

Adverse drug events have been a long-standing concern for the wide-ranging harms to public health, and the substantial disease burden. The key to diminish or eliminate the impacts is to build a comprehensive pharmacovigilance system. Application of the "big data" approach has been proved to assist the detection of adverse drug events by involving previously unavailable data sources and promoting health information exchange. Even though challenges and potential risks still remain. The lack of effective privacy-preserving measures in the flow of medical data is the most important Accepted: one, where urgent actions are required to prevent the threats and facilitate the construction of pharmacovigilance systems. Several privacy protection methods are reviewed in this article, which may be helpful to break the barrier.

Deep learning for drug–drug interaction extraction from the literature: a review

Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Drug management in acute kidney disease - Report of the Acute Disease Quality Initiative XVI meeting

AIMS

To summarize and extend the main conclusions and recommendations relevant to drug management during acute kidney disease (AKD) as agreed at the 16^th Acute Disease Quality Initiative (ADQI) consensus conference.

METHODS

Using a modified Delphi method to achieve consensus, experts attending the 16^th ADQI consensus conference reviewed and appraised the existing literature on drug management during AKD and identified recommendations for clinical practice and future research. The group focussed on drugs with one of the following characteristics: (i) predominant renal excretion; (ii) nephrotoxicity; (iii) potential to alter glomerular function; and (iv) presence of metabolites that are modified in AKD and may affect other organs.

RESULTS

We recommend that medication reconciliation should occur at admission and discharge, at AKD diagnosis and change in AKD phase, and when the patient's condition changes. Strategies to avoid adverse drug reactions in AKD should seek to minimize adverse events from overdosing and nephrotoxicity and therapeutic failure from under-dosing or incorrect drug selection. Medication regimen assessment or introduction of medications during the AKD period should consider the nephrotoxic potential, altered renal and nonrenal elimination, the effects of toxic metabolites and drug interactions and altered pharmacodynamics in AKD. A dynamic monitoring plan including repeated serial assessment of clinical features, utilization of renal diagnostic tests and therapeutic drug monitoring should be used to guide medication regimen assessment.

CONCLUSIONS

Drug management during different phases of AKD requires an individualized approach and frequent re-assessment. More research is needed to avoid drug associated harm and therapeutic failure.

Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of >90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD.

[Analgesics in geriatric patients. Adverse side effects and interactions]

Pain is a widespread symptom in clinical practice. Older adults and chronically ill patients are particularly affected. In multimorbid geriatric patients, pharmacological pain treatment is an extension of a previously existing multimedication. Besides the efficacy of pain treatment, drug side effects and drug-drug interactions have to be taken into account to minimize the health risk for these patients. Apart from the number of prescriptions, the age-related pharmacokinetic and pharmacodynamic changes significantly increase the risk among older adults. The use of non-steroidal anti-inflammatory drugs (NSAID) is widespread but NSAIDs have the highest risk of adverse drug reactions and drug interactions. In particular, the gastrointestinal, cardiovascular, renal and coagulation systems are affected. Apart from the known toxic effect on the liver (in high doses), paracetamol (acetaminophen) has similar risks although to a lesser degree. According to current data, metamizol is actually better than its reputation suggests. The risk of potential drug interactions seems to be low. Apart from the risk of sedation in combination with other drugs, tramadol and other opioids can induce the serotonin syndrome. Among older adults, especially in the case of polypharmacy, an individualized approach should be considered instead of sticking to the pain management recommended by the World Health Organization (WHO) in order to minimize drug-drug interactions and adverse drug reactions.

Angiotensin-converting enzyme inhibitors-induced angioedema treated by C1 esterase inhibitor concentrate (Berinert®): about one case and review of the therapeutic arsenal

C1 esterase inhibitor (Berinert®) is generally used to treat severe attack of hereditary angioedema. We describe here the case of a patient who presented with a severe angioedema induced by angiotensin-converting enzyme inhibitors (ACEIs) endangering her life. It could be successfully treated with that medicine.

A determination of the current causes of hyperkalaemia and whether they have changed over the past 25 years

OBJECTIVE

Hyperkalaemia is a potentially lethal electrolyte disorder. The objective of this study was to determine if the causes of hyperkalaemia-related visits to the emergency department (ED) have changed since 25 years.

METHODS

All patients presenting to the ED with hyperkalaemia between January 2009 and August 2011 were included in this retrospective, single-centre study. Patients were divided into one of these three categories: mild (5·2≤ K(+)<5·8 mEq/l), moderate (5·8≤K(+)<7·0 mEq/l) or severe hyperkalaemia (K(+)≥7·0 mEq/l). The causes of hyperkalaemia were divided into three groups: renal failure (RF), potassium-increasing drugs (PIDs) or others.

RESULTS

Overall, 139 patients with hyperkalaemia were included in the study (mean K(+) of 6·2 mEq/l): 35% with mild, 49% with moderate and 16% with severe hyperkalaemia. Eighty-three per cent of patients (n = 115) had RF with creatinine levels ≥1·25 mg/dl or estimated glomerular filtration rate (eGFR) levels ≤60 ml/min/1·73 m(2). Serum potassium levels were significantly related with creatinine and eGFR values (P<0·001). The severity of hyperkalaemia was significantly related with creatinine levels ≥1·25 mg/dl (P = 0·002) and eGFR values ≤60 ml/min/1·73 m(2) (P = 0·005). Seventy-five per cent of patients (n = 105) were taking PIDs. Potassium levels were significantly related with PIDs (P<0·001), in particularly spironolactone (P = 0·001) and angiotensin-converting enzyme inhibitors (P = 0·008). The category 'others' included 7% of patients (n = 10).

CONCLUSIONS

RF (83%) and PIDs (75%) remain common causes of hyperkalaemia. Hyperkalaemia is significantly related with four variables: creatinine levels, spironolactone, ACEIs and beta-blocker intake. The causes of hyperkalaemia have not changed in recent years.

ACE inhibitor and angiotensin receptor-II antagonist prescribing and hospital admissions with acute kidney injury: a longitudinal ecological study

Background

ACE Inhibitors (ACE-I) and Angiotensin-Receptor Antagonists (ARAs) are commonly prescribed but can cause acute kidney injury (AKI) during intercurrent illness. Rates of hospitalization with AKI are increasing. We aimed to determine whether hospital AKI admission rates are associated with increased ACE-I/ARA prescribing.

Methods and Findings

English NHS prescribing data for ACE-I/ARA prescriptions were matched at the level of the general practice to numbers of hospital admissions with a primary diagnosis of AKI. Numbers of prescriptions were weighted for the demographic characteristics of general practices by expressing prescribing as rates where the denominator is Age, Sex, and Temporary Resident Originated Prescribing Units (ASTRO-PUs). We performed a mixed-effect Poisson regression to model the number of admissions for AKI occurring in each practice for each of 4 years from 1/4/2007.

From 2007/8-2010/11, crude AKI admission rates increased from 0.38 to 0.57 per 1000 patients (51.6% increase), and national annual ACE-I/ARA prescribing rates increased by 0.032 from 0.202 to 0.234 (15.8% increase). There was strong evidence (p<0.001) that increases in practice-level prescribing of ACE-I/ARA over the study period were associated with an increase in AKI admission rates. The increase in prescribing seen in a typical practice corresponded to an increase in admissions of approximately 5.1% (rate ratio = 1.051 for a 0.03 per ASTRO-PU increase in annual prescribing rate, 95%CI 1.047-1.055). Using the regression model we predict that 1,636 (95%CI 1,540-1,780) AKI admissions would have been avoided if prescribing rates were at the 2007/8 level, equivalent to 14.8% of the total increase in AKI admissions.

Conclusion

In this ecological analysis, up to 15% of the increase in AKI admissions in England over a 4-year time period is potentially attributable to increased prescribing of ACE-I and ARAs. However, these findings are limited by the lack of patient level data such as indication for prescribing and patient characteristics.

Concurrent use of diuretics, angiotensin converting enzyme inhibitors, and angiotensin receptor blockers with non-steroidal anti-inflammatory drugs and risk of acute kidney injury: nested case-control study

OBJECTIVES

To assess whether a double therapy combination consisting of diuretics, angiotensin converting enzyme inhibitors, or angiotensin receptor blockers with addition of non-steroidal anti-inflammatory drugs (NSAIDs) and the triple therapy combination of two of the aforementioned antihypertensive drugs to which NSAIDs are added are associated with an increased risk of acute kidney injury.

DESIGN

Retrospective cohort study using nested case-control analysis.

SETTING

General practices contributing data to the UK Clinical Practice Research Datalink linked to the Hospital Episodes Statistics database.

PARTICIPANTS

A cohort of 487,372 users of antihypertensive drugs.

MAIN OUTCOME MEASURES

Rate ratios with 95% confidence intervals of acute kidney injury associated with current use of double and triple therapy combinations of antihypertensive drugs with NSAIDs.

RESULTS

During a mean follow-up of 5.9 (SD 3.4) years, 2215 cases of acute kidney injury were identified (incidence rate 7/10,000 person years). Overall, current use of a double therapy combination containing either diuretics or angiotensin converting enzyme inhibitors or angiotensin receptor blockers with NSAIDs was not associated with an increased rate of acute kidney injury. In contrast, current use of a triple therapy combination was associated with an increased rate of acute kidney injury (rate ratio 1.31, 95% confidence interval 1.12 to 1.53). In secondary analyses, the highest risk was observed in the first 30 days of use (rate ratio 1.82, 1.35 to 2.46).

CONCLUSIONS

A triple therapy combination consisting of diuretics with angiotensin converting enzyme inhibitors or angiotensin receptor blockers and NSAIDs was associated with an increased risk of acute kidney injury. The risk was greatest at the start of treatment. Although antihypertensive drugs have cardiovascular benefits, vigilance may be warranted when they are used concurrently with NSAIDs.

Recent Publications on Medications and Pharmacy

Hospital Pharmacy presents this feature to keep pharmacists abreast of new publications in the medical/pharmacy literature. Articles of interest regarding a broad scope of topics are abstracted monthly.