Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases

Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors

Objective: This study assessed the patterns and clinical significance of potential drug-drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018-2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann-Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to 'aspirin + captopril' combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information.

Survey of Potential Drug Interactions, Use of Non-Medical Health Products, and Immunization Status among Patients Receiving Targeted Therapies

In recent years, several changes have occurred in the management of chronic immunological conditions with the emerging use of targeted therapies. This two-phase cross-sectional study was conducted through structured in-person interviews in 2018-2019 and 2022. Additional data sources included ambulatory medical records and the itemized reimbursement reporting interface of the National Health Insurance Fund. Drug interactions were analyzed using the UpToDate Lexicomp, Medscape drug interaction checker, and Drugs.com databases. The chi-square test was used, and odds ratios (ORs) were calculated. In total, 185 patients participated. In 53% of patients (n = 53), a serious drug-drug interaction (DDI) was identified (mean number: 1.07 ± 1.43, 0-7), whereas this value was 38% (n = 38) for potential drug-supplement interactions (mean number: 0.58 ± 0.85, 0-3) and 47% (n = 47) for potential targeted drug interactions (0.72 ± 0.97, 0-5) in 2018. In 2022, 78% of patients (n = 66) were identified as having a serious DDI (mean number: 2.27 ± 2.69, 0-19), 66% (n = 56) had a potential drug-supplement interaction (mean number: 2.33 ± 2.69, 0-13), and 79% (n = 67) had a potential targeted drug interactions (1.35 ± 1.04, 0-5). Older age (>60 years; OR: 2.062), female sex (OR: 3.387), and polypharmacy (OR: 5.276) were identified as the main risk factors. Screening methods and drug interaction databases do not keep pace with the emergence of new therapeutics.

Potentially Inappropriate Medication: A Pilot Study in Institutionalized Older Adults

Institutionalized older adults often face complex medication regimens, increasing their risk of adverse drug events due to polypharmacy, overprescribing, medication interactions, or the use of Potentially Inappropriate Medications (PIM). However, data on medication use and associated risks in this population remain scarce. This pilot study aimed to characterize the sociodemographic, clinical and pharmacotherapeutic profiles, and the use of PIM among institutionalized elders residing in Residential Structures for Elderly People (ERPI) in the Faro municipality, located in the Portuguese region of the Algarve. We conducted a cross-sectional study in a non-randomized sample of 96 participants (mean age: 86.6 ± 7.86 years) where trained researchers reviewed medication profiles and identified potentially inappropriate medications using the EU(7)-PIM list. Over 90% of participants exhibited polypharmacy (≥5 medications), with an average of 9.1 ± 4.15 medications per person. About 92% had potential drug interactions, including major and moderate interactions. More than 86% used at least one potentially inappropriate medication, most commonly central nervous system drugs. This pilot study demonstrates that institutionalized older adults may be at high risk of potential medication-related problems. Implementing comprehensive medication review programs and promoting adapted prescribing practices are crucial to optimize medication use and improve the well-being of this vulnerable population.

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Polypharmacy in Multiple Sclerosis: Prevalence, Risks, and Mitigation Strategies

PURPOSE OF REVIEW

Polypharmacy, the use of ≥ 5 medications, is common in people with multiple sclerosis and is associated with negative outcomes. The use of multiple medications is common for symptom management in people with multiple sclerosis, but risks drug-drug interactions and additive side effects. Multiple sclerosis providers should therefore focus on the appropriateness and risks versus benefits of pharmacotherapy in each patient. This review describes the prevalence and risks associated with polypharmacy in people with multiple sclerosis and offers strategies to identify and mitigate inappropriate polypharmacy.

RECENT FINDINGS

Research in people with multiple sclerosis has identified risk factors and negative outcomes associated with polypharmacy. Medication class-specific investigations highlight their contribution to potentially inappropriate polypharmacy in people with multiple sclerosis. People with multiple sclerosis are at risk for inappropriate polypharmacy. Multiple sclerosis providers should review medications and consider their appropriateness and potential for deprescribing within the context of each patient.

Frequencies of Combined Dysfunction of Cytochromes P450 2C9, 2C19, and 2D6 in an Italian Cohort: Suggestions for a More Appropriate Medication Prescribing Process

Improper drug prescription is a main cause of both drug-related harms (inefficacy and toxicity) and ineffective spending and waste of the healthcare system's resources. Nowadays, strategies to support an improved, informed prescription process may benefit from the adequate use of pharmacogenomic testing. Using next-generation sequencing, we analyzed the genomic profile for three major cytochromes P450 (CYP2C9, CYP2C19, CYP2D6) and studied the frequencies of dysfunctional isozymes (e.g., poor, intermediate, or rapid/ultra-rapid metabolizers) in a cohort of 298 Italian subjects. We found just 14.8% of subjects with a fully normal set of cytochromes, whereas 26.5% of subjects had combined cytochrome dysfunction (more than one isozyme involved). As improper drug prescription is more frequent, and more burdening, in polytreated patients, since drug-drug interactions also cause patient harm, we discuss the potential benefits of a more comprehensive PGX testing approach to support informed drug selection in such patients.

Prescribed Versus Taken Polypharmacy and Drug-Drug Interactions in Older Cardiovascular Patients during the COVID-19 Pandemic: Observational Cross-Sectional Analytical Study

The study aimed to assess clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019-2022). Medscape Drug Interaction Checker was used to identify pairwise drug-drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, the total number of DDIs was significantly higher among the prescribed medications (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among the prescribed and taken medications, respectively (p < 0.05). Median numbers of DDIs between the prescribed vs. taken medications were Me = 2, IQR 0-7 vs. Me = 3, IQR 0-7 per record, respectively. Prevalence of polypharmacy was significantly higher among the prescribed medications compared with that among the taken drugs (p < 0.05). Women were taking significantly more drugs and had higher prevalence of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, implement patient engagement in reporting adverse drug reactions, and provide genetic profiling of patients to avoid potentially serious DDIs.