Prevalence and Severity of Potential Drug–Drug Interactions in Patients with Multiple Sclerosis with and without Polypharmacy

The Role of Clinical Pharmacists in Patient-Centric Comprehensive Multiple Sclerosis Care

BACKGROUND

Individuals with multiple sclerosis (MS) may experience a variety of visible and invisible symptoms and, as they age, comorbidities related and unrelated to their MS. This can result in a complex medication regimen that includes disease-modifying therapies, symptom management drugs, and prescriptions for other comorbid disorders.

METHODS

We reviewed the existing literature to discover how to optimally integrate neurology clinical pharmacists into the MS care team and how clinical pharmacists can directly support both providers and patients through their expertise in pharmacology and medication management.

RESULTS

With approaches founded on a shared decision-making process alongside neurology providers, patients, and care partners, clinical pharmacists can help meet the complex challenges of MS care in a variety of ways. Especially within MS clinics, they are well positioned to enhance current neurology practices given their extensive training in comprehensive medication management and their ability to identify nuances in medication management to promote pharmacovigilance and patient-centered care.

CONCLUSIONS

Neurology clinical pharmacists bring multifaceted medication management and patient counseling and education skills to the MS care team and can support the shared decision-making process by serving as an accessible resource for patients and clinicians. By building trusted partnerships between neurology providers and clinical pharmacists, MS care teams can achieve effective and efficient patient care. Future research should compare clinical and patient-reported outcomes between patients receiving standard care and those receiving multidisciplinary, pharmacist-integrated care.

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.

Depression and Anxiety in Association with Polypharmacy in Patients with Multiple Sclerosis

Polypharmacy (intake of ≥5 drugs) is an important issue for patients with chronic diseases such as multiple sclerosis (MS). We aimed to assess the prevalence of polypharmacy with regard to the severity of anxiety/depression and to comorbidities. Therefore, 374 MS patients from two German neurological sites were examined for drug burden, comorbidities, disability level and psychopathological measures capturing depression and anxiety using the Hospital Anxiety and Depression Scale (HADS-A and HADS-D). We found that patients with a higher HADS-D score take more medication (r = 0.217, p < 0.001). Furthermore, patients with higher depression severity were more likely to show polypharmacy (p < 0.001). These differences were not significant for anxiety. (p = 0.413). Regarding the frequency of ≥1 comorbidities, there were no significant differences between patients with different HADS-A (p = 0.375) or HADS-D (p = 0.860) severity levels, whereas the concrete number of comorbidities showed a significant positive linear correlation with HADS-A (r = 0.10, p = 0.045) and HADS-D scores (r = 0.19, p < 0.001). In conclusion, symptoms of depression pose a relevant issue for MS patients and are correlated with polypharmacy and comorbidities. Anxiety is not correlated with polypharmacy but with the frequency of several comorbidity groups in MS patients.

Factors associated with a basic common drug-drug interaction knowledge among emergency department medical personnel

Background

Drug-drug interactions (DDIs) are common but less concerning in clinical practice of time-sensitive situations. We aimed to identify factors associated with a basic common DDI knowledge among an emergency physician (EP), an emergency medicine resident (EMR), and an emergency care nurse (ECN).

Methods

This was a prospective cross-sectional study. EP, EMR, and ECN did the examination (multiple-choice questions, 40 points) about common DDI. Prespecified factors associated with examination scores were profession, longer emergency medicine experience, pharmacological training, last advanced cardiovascular life support (ACLS) training, DDI checker book, and application user experience. The outcome was an examination score to evaluate the ability of DDI knowledge. Univariable and multivariable means regressions were used.

Results

A total of 244 participants were enrolled. Factors associated with high examination score were EP (unadjusted mean difference 3.3 points, 95% confidence interval [CI] 2.1 to 4.5, p < 0.001), EMR (2.1, 95% CI 0.7 to 3.5, p 0.005) compared to ECN. Last ACLS training within 2 years (3.7, 95% CI 0.7 to 6.6, p 0.015), 2–4 years (3.4, 95% CI 0.4 to 6.5, p 0.027), and ≥4 years (4.4, 95% CI 1.2 to 7.6, p 0.007) were higher score than no ACLS training. Moreover, the DDI checker application experience user (1.7, 95% CI 0.6 to 2.8, p 0.003) also had a high score compared to the non-experienced user. After adjustment for all factors, EP (adjusted mean difference 3.3 points, 95% CI 1.8 to 4.7, p < 0.001), EMR (2.5, 95% CI 0.6 to 4.3, p 0.010) were higher scores compared to ECN. Meanwhile, the last ACLS training ≥4 years (3.3, 95% CI 0.1 to 6.6, p 0.042) was a higher score than no ACLS training.

Conclusion

EP, EMR, and the last ACLS training ≥4 years were associated with higher DDI knowledge than ECN and no ACLS training, respectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40360-022-00623-0.

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

Background: Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. Objective: To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. Methods: The databases Stockley's, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. Results: The most different pDDIs were identified using MediQ (n = 1,161), followed by Drugs.com (n = 923) and Stockley's (n = 706). The proportion of pDDIs classified as severe was much higher for Stockley's (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley's and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. Conclusions: The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.

Associated factors of potential drug-drug interactions and drug-food interactions in patients with multiple sclerosis

Background:

Multiple sclerosis (MS) is the most common immune-mediated demyelinating disease in younger adults. Patients with MS (PwMS) are vulnerable to the presence of potential drug–drug interactions (pDDIs) and potential drug–food interactions (pDFIs) as they take numerous medications to treat MS, associated symptoms and comorbidities. Knowledge about pDDIs and pDFIs can increase treatment success and reduce side effects.

Objective:

We aimed at determining the frequency and severity of pDDIs and pDFIs in PwMS, with regard to polypharmacy.

Methods:

In the cross-sectional study, we analysed pDDIs and pDFIs of 627 PwMS aged ⩾18 years. Data collection was performed through patient record reviews, clinical examinations and structured patient interviews. pDDIs and pDFIs were identified using two DDI databases: Drugs.com Interactions Checker and Stockley’s Interactions Checker.

Results:

We identified 2587 pDDIs (counted with repetitions). Of 627 PwMS, 408 (65.1%) had ⩾ 1 pDDI. Polypharmacy (concomitant use of ⩾ 5 drugs) was found for 334 patients (53.3%). Patients with polypharmacy (Pw/P) were found to have a 15-fold higher likelihood of having ⩾ 1 severe pDDI compared with patients without polypharmacy (Pw/oP) (OR: 14.920, p < 0.001). The most frequently recorded severe pDDI was between citalopram and fingolimod. Regarding pDFIs, ibuprofen and alcohol was the most frequent severe pDFI.

Conclusion:

Pw/P were particularly at risk of severe pDDIs. Age and educational level were found to be factors associated with the occurrence of pDDIs, independent of the number of medications taken. Screening for pDDIs/pDFIs should be routinely done by the clinical physician to increase drug safety and reduce side effects.