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

Potential adverse events associated with sphingosine-1-phosphate (S1P) receptor modulators in patients with multiple sclerosis: an analysis of the FDA adverse event reporting system (FAERS) database

Objective

Sphingosine-1-phosphate receptor (S1PR) modulators have recently attracted increasing attention for the treatment of multiple sclerosis (MS). Despite their preference in the clinic, multiple adverse events (AEs) continue to be reported every year. This study aimed to investigate the potential AEs as well as related important medical events (IMEs) signal associated with S1PR modulators, including fingolimod, siponimod and ozanimod in a real-world study using the FDA Adverse Event Reporting System (FAERS) database.

Methods

All data were collected from the FAERS database, spanning from the fourth quarter of 2010(2010Q4) to the second quarter of 2023 (2023Q2). Potential AE and IME signals of S1PR modulators were identified based on a disproportionality analysis using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and the bayesian confidence propagation neural network of information components (IC).

Results

Overall, 276,436 reports of fingolimod, 20,972 reports of siponimod and 10,742 reports of ozanimod were analyzed from the FAERS database. Among reports, females were more prone to develop AEs (73.71% for females vs. 23.21% for males), and more than 50% of patients suffered from AEs were between 18 and 64 years. Subsequently, we investigated the top 20 AEs associated with the signal strength of S1PR modulators at the preferred term (PT) level, and identified 31 (8 vs. 11 vs. 12, respectively) unlabeled risk signals such as thrombosis, uterine disorder and reproductive system and breast disorders. Furthermore, we discovered that the S1PR modulator reported variations in the possible IMEs, and that the IMEs associated with ocular events were reported frequently. It's interesting to note that infection and malignancy are prominent signals with both fingolimod and siponimod in the top 20 PTs related to mortality reports.

Conclusion

The present investigation highlights the possible safety risks associated with S1PR modulators. The majority of AEs are generally consistent with previous studies and are mentioned in the prescribing instructions, however, several unexpected AE signals have also been observed. Ozanimod showed the lowest signal intensity and a better safety profile than the other S1PR modulators. Due to the short marketing time of drugs and the limitations of spontaneous reporting database, further research is required to identify potential AEs related to S1PR modulators.

Managing multiple sclerosis in individuals aged 55 and above: a comprehensive review

Multiple Sclerosis (MS) management in individuals aged 55 and above presents unique challenges due to the complex interaction between aging, comorbidities, immunosenescence, and MS pathophysiology. This comprehensive review explores the evolving landscape of MS in older adults, including the increased incidence and prevalence of MS in this age group, the shift in disease phenotypes from relapsing-remitting to progressive forms, and the presence of multimorbidity and polypharmacy. We aim to provide an updated review of the available evidence of disease-modifying treatments (DMTs) in older patients, including the efficacy and safety of existing therapies, emerging treatments such as Bruton tyrosine kinase (BTKs) inhibitors and those targeting remyelination and neuroprotection, and the critical decisions surrounding the initiation, de-escalation, and discontinuation of DMTs. Non-pharmacologic approaches, including physical therapy, neuromodulation therapies, cognitive rehabilitation, and psychotherapy, are also examined for their role in holistic care. The importance of MS Care Units and advance care planning are explored as a cornerstone in providing patient-centric care, ensuring alignment with patient preferences in the disease trajectory. Finally, the review emphasizes the need for personalized management and continuous monitoring of MS patients, alongside advocating for inclusive study designs in clinical research to improve the management of this growing patient demographic.

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.

Knowledge of Drug–Food Interactions Among Healthcare Professionals Working in Public Hospitals in Ethiopia

Background

Drug–food interactions can result in unfavorable outcomes during the treatment of patients. Healthcare professionals (HCPs) should advise patients on drug–food interactions. Knowledge of such interactions is crucial to avoid their occurrence. However, there is no information regarding the knowledge of HCPs about drug–food interactions in Harari Regional State.

Objective

To assess knowledge of drug–food interactions and associated factors among HCPs working in public hospitals in Harari Regional State, Eastern Ethiopia from April 15 to May 15, 2022.

Methods

A cross-sectional study was conducted in public hospitals in Harari Regional State, Eastern Ethiopia, among 251 HCPs. After stratification was done based on profession (pharmacists, nurses, and doctors), the sample size was proportionally allocated for the respective groups. Data were collected using a standardized self-administered questionnaire, entered into Epi-Data 3.1 and analyzed using Statistical Package for Social Sciences 26.0. Descriptive statistics were used to summarize variables. Multivariable logistic regression was done to determine factors associated with knowledge of drug–food interactions. P < 0.05 was used to declare significant association.

Results

Among the HCPs who completed the questionnaire, 56 (22.3%), 36 (14.3%), and 159 (63.3%) were doctors, pharmacists, and nurses, respectively. The majority of the HCPs were males (174 (69.3%)). The mean age of the HCPs was 27.6±3.8. The mean knowledge score±SD of the HCPs was 28.6±6.6 out of an overall score of 59. The HCPs poorly identified drug–food interactions and the correct administration time of drugs relative to meals. Being a pharmacist (AOR: 2.8, CI: 1.3–6.4, p-value=0.012), and working at a tertiary hospital (AOR: 3.9, CI: 2.1–7.3, p-value <0.001), were associated with higher knowledge of drug-food interactions.

Conclusion

The HCPs in this study had inadequate knowledge of drug–food interactions. Thus, additional educational courses and training should be provided in order to improve knowledge regarding drug-food interaction.

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.