The Development of Practice Recommendations for Drug-Disease Interactions by Literature Review and Expert Opinion

Preventable serious drug-disease interactions of reserve antibiotics

INTRODUCTION

Antibiotics that are used exclusively in hospital settings and reserved for treating infections caused by multidrug-resistant or extended-resistant bacterial pathogens are referred to as 'reserved' antibiotics. The purpose of this review article is to provide a better understanding of the risks associated with serious interactions between reserved antibiotics and various diseases, as well as to present key strategies for their prevention.

AREAS COVERED

The literature search was conducted in the MEDLINE, SCOPUS, EBSCO, and GOOGLE SCHOLAR databases without any restrictions on time or language. Only clinical studies, observational human studies, case reports, and case series that reported serious drug-disease interactions were considered.

EXPERT OPINION

Knowledge of the interactions between reserve antibiotics and diseases, that have actually occurred and then been described in the medical literature, is crucial to the safe treatment of critically ill patients with infections caused by multidrug-resistant bacterial strains. Introducing into routine practice the checking of possible interactions with diseases that a patient suffers from, strict monitoring of changes in the function of the excretory organs (kidneys and liver), and measuring the concentration of drugs in the plasma will reduce the possibility of adverse drug-disease interactions.

Physicians' and pharmacists' perspective on clarity and clinical relevance of absolute contraindications in "Summaries of Product Characteristics"

AIMS

Previous work has identified several limitations in "Summaries of Product Characteristics" (SmPCs), which are associated with risks for patients. The aim of this study was to evaluate pharmacists' and physicians' interpretation of contraindications in SmPCs and reasons for their nonadherence in clinical routine.

METHODS

For 20 commonly missed or ignored absolute contraindications, an anonymous online survey providing 24 clinical example cases (one or two per contraindication) for physicians and pharmacists was developed. Experts in medication safety were asked whether the respective case fulfilled the definition of the contraindication in the SmPC: (a) formally, irrespective of the clinical relevance of the contraindication (17 cases), and (b) whether the contraindication was deemed clinically relevant in each respective case (24 cases).

RESULTS

Twenty-seven pharmacists and 27 physicians completed the survey. For only one case (1/17; 5.8%) did all experts agree on the same answer option regarding the formal fulfilment of a given contraindication statement. Experts gave heterogeneous answers regarding the interpretation of a contraindication. For instance, among 10 predefined answer options for the contraindication "active liver disease" in the SmPC of simvastatin, every option was selected by at least six experts. In 17/24 (70.8%) clinical example cases a majority of experts agreed on the clinical relevance of a given contraindication. Key reasons for nonadherence to contraindications were "patient monitoring possible", "lack of alternative treatment" and "acute/severe situations".

CONCLUSIONS

Experts' disagreement on the interpretation of contraindications in SmPCs using clinical example cases indicates that further efforts are needed to improve their usability in clinical routine.

Association of Drug-Disease Interactions with Mortality or Readmission in Hospitalised Middle-Aged and Older Adults: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVE

Multimorbidity is common in hospitalised adults who are at increased risk of inappropriate prescribing including drug-disease interactions. These interactions occur when a medicine being used to treat one condition exacerbates a concurrent medical condition and may lead to adverse health outcomes. The aim of this review was to examine the association between drug-disease interactions and the risk of mortality and readmission in hospitalised middle-aged and older adults.

METHODS

A systematic review was conducted on drug-disease interactions in hospitalised middle-aged (45-64 years) and older adults (≥65 years). The study protocol was prospectively registered with PROSPERO (Registration Number: CRD42022341998). Drug-disease interactions were defined as a medicine being used to treat one condition with the potential to exacerbate a concurrent medical condition or that were inappropriate based on a comorbid medical condition. Both observational and interventional studies were included. The outcomes of interest were mortality and readmissions. The databases searched included MEDLINE, CINAHL, EMBASE, Web of Science, SCOPUS and the Cochrane Library from inception to 12 July, 2022. A meta-analysis was performed to pool risk estimates using the random-effects model.

RESULTS

A total of 563 studies were identified and four met the inclusion criteria. All were observational studies in older adults, with no studies identified in middle-aged adults. Most of the studies were at risk of bias because of an inadequate adjustment for covariates and a lack of clarity around individuals lost to follow-up. There were various definitions of drug-disease interactions within these four studies. Two studies assessed drugs that were contraindicated based on renal function, one assessed an individual drug-disease combination, and one was based on the clinical judgement of a pharmacist. There were two studies that showed an association between drug-disease interactions and the outcomes of interest. One reported that the use of diltiazem in patients with heart failure was associated with an increased risk of readmissions. The second reported that the use of medicines contraindicated according to renal function were associated with increased risk of all-cause mortality and a composite of mortality and readmission. Three of the studies (total study population = 5705) were amenable to a meta-analysis, which showed no significant association between drug-disease interactions and readmissions (odds ratio = 1.0, 95% confidence interval 0.80-1.38).

CONCLUSIONS

Few studies were identified examining the risk of drug-disease interactions and mortality and readmission in hospitalised adults. Most of the identified studies were at risk of bias. There is no universal accepted definition of drug-disease interactions in the literature. Further studies are needed to develop a standardised and accepted definition of these interactions to guide further research in this area.

Developing practical recommendations for drug-disease interactions in patients with hypertension

Background

Hypertension, a significant risk factor for cardiovascular diseases, demands proactive management as cardiovascular diseases remain the leading cause of death worldwide. Reducing systolic and diastolic blood pressure levels below recommended reference values of <140/90 mmHg can lead to a significant reduction of the risk of CVD and all-cause mortality. However, treatment of hypertension can be difficult and the presence of comorbidities could further complicate this treatment. Drugs used to manage these comorbidities may inadvertently have an impact on blood pressure, resulting in a phenomenon known as drug-disease interaction. This study aims to assess the safety of medication that can affect blood pressure in patients with hypertension and provide practical recommendations for healthcare professionals.

Methods

For the development of recommendations for the drug-disease interaction (DDSI) hypertension, a six-step plan that combined literature selection and multidisciplinary expert opinion was used. The process involved (1) defining the scope of the DDSI and selecting relevant drugs, (2) collecting evidence, (3) data-extraction, (4) reaching of expert consensus, (5) publication and implementation of the recommendations in healthcare systems and (6) updating the information.

Results

An increase of 10 mmHg in systolic blood pressure and 5 mmHg in diastolic blood pressure was defined as clinically relevant. Corticosteroids, danazol, and yohimbine caused a clinically relevant DDSI with hypertension. Several other drugs with warnings for hypertension in the official product information were assessed to have no clinically relevant DDSI due to minor influence or lack of data on blood pressure. Drugs with evidence for a relevant change in blood pressure which are prescribed under close monitoring of blood pressure according to clinical guidelines, were deemed to be not clinically relevant for signalling.

Conclusion

This study provides specific recommendations that can be implemented directly in clinical practice, for example, in clinical decision support systems, potentially resulting in safer drug use in patients with hypertension and better healthcare by reducing alert fatigue. Future research should focus on evaluating the effectiveness of implementation strategies and their impact on reducing unsafe use of medication in patients with hypertension.

MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug-Drug Interaction Events

Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug-drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods exhibit promising performance in DDI screening, their practical implementation faces two significant challenges: (i) the availability of comprehensive datasets to support clinical application, and (ii) the ability to infer DDI types for new drugs beyond the existing dataset coverage. To mitigate these challenges, we propose MM-GANN-DDI: a Multimodal Graph-Agnostic Neural Network for Predicting Drug-Drug Interaction Events. We first mine six drug modalities and incorporate a graph attention (GAT) mechanism to fuse these modalities with the topological features of the DDI graph. We further propose a novel graph neural network training mechanism called graph-agnostic meta-training (GAMT), which effectively leverages topological information from the DDI graph and efficiently predicts DDI types for new drugs beyond the available dataset. Specifically, GAMT samples meta-graphs from the original DDI graph, splitting them into support and query sets to simulate seen and unseen drugs. Two-level optimizations are applied to enhance the model's generalization capability. We evaluate our model on two datasets (DB-v1 and DB-v2) across three tasks. Our MM-GANN-DDI demonstrates competitive performance on all three tasks. Notably, in Task 2, which focuses on predicting DDI types for drugs outside the dataset, our proposed model outperforms other methods, exhibiting an improvement of 4.6 percentage points in AUPR on DB-v1 and 5.9 percentage points on DB-v2. Additionally, our model surpasses state-of-the-art methods and classic approaches in terms of accuracy, F1 score, precision, and recall. Ablation experiments provide further validation of the effectiveness of the proposed model design. Importantly, our model exhibits the potential to discover unobserved DDIs, demonstrating its practical application in clinical medication.

Implementation of a myasthenia gravis drug-disease interaction clinical decision support tool reduces prescribing of high-risk medications

INTRODUCTION/AIMS

High-risk medication exposure is a modifiable risk factor for myasthenic exacerbation and crisis. We evaluated whether real-time electronic clinical decision support (CDS) was effective in reducing the rate of prescribing potentially high-risk medications to avoid or use with caution in patients with myasthenia gravis.

METHODS

An expert panel reviewed the available drug-disease pairings and associated severity levels to activate the alerts for CDS. All unique alerts activated in both inpatient and outpatient contexts were analyzed over a two-year period. Clinical context, alert severity, medication class, and alert action were collected. The primary outcome was alert override rate. Secondary outcomes included the percentage of unique medication exposures avoided and predictors of alert override.

RESULTS

During the analysis period, 2817 unique alerts fired, representing 830 distinct patient-medication exposures for 577 unique patients. The overall alert override rate was 85% (80.3% for inpatient alerts and 95.8% for outpatient alerts). Of unique medication-patient exposures, 19% were avoided because of the alert. Assigned alert severity of "contraindicated" were less likely to be overridden (odds ratio [OR] 0.42, 95% confidence interval [CI] 0.32-0.56), as well as alerts activated during evening staffing (OR 0.69, 95% CI 0.55-0.87).

DISCUSSION

Implementation of a myasthenia gravis drug-disease interaction alert reduced overall patient exposure to potentially harmful medications by approximately 19%. Future optimization includes enhanced provider and pharmacist education. Further refinement of alert logic criteria to optimize medication risk reduction and reduce alert fatigue is warranted to support clinicians in prescribing and reduce electronic health record time burden.

Disease-Drug Interactions Requiring Special Attention

This short review addresses disease-drug interactions requiring special attention, namely interactions between common conditions and over-the-counter medication and interactions between rare conditions and drugs that are absolutely contraindicated. We specifically examine over-the-counter analgesics, antiemetics and drugs used to treat allergy symptoms and underlying disease conditions they can exacerbate. Resources for avoiding disease-drug interactions in patients with rare conditions, such as myasthenia gravis, glucose-6-phosphate deficiency, mitochondriopathies and long QT-syndrome are given. We also discuss methods for avoiding disease-drug interactions in clinical practice. These include awareness, regular diagnosis- and drug-history taking, consulting the product information, good communication between healthcare providers and patient education. Furthermore, pharmacovigilance activities help in the early identification and characterization of adverse drug reactions resulting from disease-drug interactions.

Management of drug-disease interactions: a best practice from the Netherlands

Background Drug-disease interactions are situations where pharmacotherapy may have a negative effect on patients' comorbidities. In these cases, it can be necessary to avoid that drug, adjust its dose or monitor therapy. In the Netherlands, pharmacists have developed a best practice how to systematically evaluate drug-disease interactions based on pharmacological considerations and implement recommendations for specific drug-disease interactions. Aim To describe the development of recommendations for drug-disease interactions and the implementation in prescribing and dispensing practice in the Netherlands. Setting Pharmacies and physicians' practices in primary care and hospitals in the Netherlands. Development A multi-disciplinary expert panel assessed if diseases had clinically relevant drug-disease interactions and evaluated drug-disease interactions by literature review and expert opinion, and subsequently developed practice recommendations. Implementation The recommendations were implemented in all clinical decision support systems in primary care and hospitals throughout the Netherlands. Evaluation Recommendations were developed for 57 diseases and conditions. Cardiovascular diseases have the most drug-disease interactions (n = 12, e.g. long QT-syndrome, heart failure), followed by conditions related to the reproductive system (n = 7, e.g. pregnancy). The number of drugs with recommendations differed between 6 for endometriosis and tympanostomy tubes, and up to 1171 in the case of porphyria or even all drugs for pregnancy. Conclusion Practice recommendations for drug-disease interactions were developed, and implemented in prescribing and dispensing practice. These recommendations support both pharmacists and physicians by signalling clinically relevant drug-disease interactions at point of care, thereby improving medication safety. This practice may be adopted and contribute to safer medication use in other countries as well.

Modelling bioactivities of combinations of whole extracts of edibles with a simplified theoretical framework reveals the statistical role of molecular diversity and system complexity in their mode of action and their nearly certain safety

Network pharmacology and polypharmacology are emerging as novel drug discovery paradigms. The many discovery, safety and regulatory issues they raise may become tractable with polypharmacological combinations of natural compounds found in whole extracts of edible and mixes thereof. The primary goal of this work is to get general insights underlying the innocuity and the emergence of beneficial and toxic activities of combinations of many compounds in general and of edibles in particular. A simplified model of compounds’ interactions with an organism and of their desired and undesired effects is constructed by considering the departure from equilibrium of interconnected biological features. This model allows to compute the scaling of the probability of significant effects relative to nutritional diversity, organism complexity and synergy resulting from mixing compounds and edibles. It allows also to characterize massive indirect perturbation mode of action drugs as a potential novel multi-compound-multi-target pharmaceutical class, coined Ediceuticals when based on edibles. Their mode of action may readily target differentially organisms’ system robustness as such based on differential complexity for discovering nearly certainly safe novel antimicrobials, antiviral and anti-cancer treatments. This very general model provides also a theoretical framework to several pharmaceutical and nutritional observations. In particular, it characterizes two classes of undesirable effects of drugs, and may question the interpretation of undesirable effects in healthy subjects. It also formalizes nutritional diversity as such as a novel statistical supra-chemical parameter that may contribute to guide nutritional health intervention. Finally, it is to be noted that a similar formalism may be further applicable to model whole ecosystems in general.