Polypharmacy in chronic neurological diseases: Multiple sclerosis, dementia and Parkinson's disease

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.

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.

Comorbidity among inpatients with dementia: a preliminary cross-sectional study in West China

OBJECTIVE

To investigate comorbidities among hospitalized patients with dementia.

METHOD

Data were extracted from the discharge records in our hospital. Comorbidities based on ICD-10 were selected from the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). The distributions of these comorbidities were described in dementia inpatients and age- and sex-matched nondementia controls, as well as in inpatients with Alzheimer's disease and vascular dementia. A logistic regression model was applied to identify dementia-specific morbid conditions.

RESULTS

A total of 3355 patients with dementia were included, with a majority of 1503 (44.8%) having Alzheimer's disease, 395 (11.8%) with vascular dementia, and 441 (13.1%) with mixed dementia. The mean number of comorbidities was 3.8 in dementia patients (vs. 2.9 in controls). The most prevalent comorbidities in inpatients with dementia compared with those without dementia were cerebral vascular disease (73.0% vs. 35.9%), hypertension (62.8% vs. 56.2%), and peripheral vascular disease (53.7% vs. 31.2%). Comorbidities associated with dementia included epilepsy (OR 4.8, 95% CI 3.5-6.8), cerebral vascular disease (OR 4.1, 95% CI 3.7-4.5), depression (OR 4.0, 95% CI 3.2-5.0), uncomplicated diabetes (OR 1.5, 95% CI 1.4-1.7), peripheral vascular disease (OR 1.8, 95% CI 1.6-2.0), rheumatoid arthritis collagen vascular disease (OR 1.7, 95% CI 1.3-2.3), and anemia (OR 1.2, 95% CI 1.04-1.3). Some comorbidities suggested a protective effect against dementia. They were hypertension (OR 0.8, 95% CI 0.7-0.9), COPD (OR 0.6, 95% CI 0.5-0.6), and solid tumor without metastasis (OR 0.4, 95% CI 0.3-0.4). Vascular dementia has more cardiovascular and cerebrovascular comorbidities than Alzheimer's disease.

CONCLUSION

Patients with dementia coexisted with more comorbidities than those without dementia. Comorbidities (esp. cardio-cerebral vascular risks) in patients with vascular dementia were more than those in patients with AD. Specifically, vascular and circulatory diseases, epilepsy, diabetes and depression increased the risk of dementia.

Metformin Adherence Reduces the Risk of Dementia in Patients With Diabetes: A Population-based Cohort Study

OBJECTIVE

Metformin is widely used as the first-line drug for type 2 diabetes mellitus and has numerous benefits apart from lowering blood glucose. However, metformin-retained regimen is challenged by newly launching, powerful glucose-lowering antiglycemic agents. This population-based cohort study examined the association between metformin adherence and the risk of dementia and Parkinson's disease (PD).

METHODS

Diabetic patients with metformin-included combination antiglycemic therapy were identified from the National Health Insurance Research Database and categorized into metformin-adherent and -nonadherent groups according to the medical record of the first year prescription. Patients contraindicated with metformin, severe diabetic complications, and poor drug compliance were excluded. The study outcome was the diagnosis of dementia or PD.

RESULTS

A total of 31 384 matched pairs were included after using propensity score matching and both groups were followed up for an average of 5 years. Metformin adherence was associated with a significantly lower risk of dementia (adjusted hazard risk ratio = 0.72, P < .001) but not PD (adjusted hazard risk ratio = 0.97, P = .825). Subgroup analysis revealed that the risk of dementia was significantly reduced in metformin-adherent patients, both male and female, aged >65 or ≤ 65 years, and with or without concurrent insulin treatment. This effect was not influenced by concurrent insulin treatment, which may eliminate the bias caused by the severity of diabetes mellitus.

CONCLUSION

Despite the launching of numerous new oral antiglycemic agents, metformin may provide further benefit on lowering risk of dementia beyond conventional glycemic control according to the real-world evidence.

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.

Oral health in patients with neurodegenerative and cerebrovascular disease: a retrospective study

PURPOSE

This retrospective study investigates oral health and oral care in patients with neurodegenerative and cerebrovascular diseases (CVDs) treated in a dental facility for people with disabilities.

METHODS

Oral health indices decayed, missing, and filled teeth (DMFT), periodontal screening index (PSI), treatment spectrum, and oral hygiene were evaluated in 152 patients with multiple sclerosis, Parkinson's disease, dementia, and CVD and 30 controls. Regression analyses identified group differences and influencing factors on DMFT.

RESULTS

Patients with neurodegenerative or CVD had a significantly higher DMFT (21.2 ± 5.8 vs. 18.3 ± 5.9), more decayed teeth (4.3 ± 4.8 vs. 1 ± 1.9), fewer filled teeth (7.9 ± 5.5 vs. 11 ± 5.6), and a higher number of surgical (39.5% vs. 20%) treatments but significantly less conservative (49.3% vs. 73.3%) and prosthetic (15.1% vs. 56.7%) treatments than the control group (p< 0.05). The frequency of toothbrushing and the use of an electric toothbrush were related to lower DMFT in patients with neurodegenerative and CVD. Smoking was associated with higher DMFT.

CONCLUSIONS

Poor oral health was found in all individuals with disabilities, suggesting that limitations in oral care attributed to aging and neurological disorders negatively affect oral health. Oral rehabilitation of patients with disabilities requires awareness of oral health limitations and early intervention through dental care. Implications for rehabilitationPoor oral health and oral hygiene is common among older people with disabilities.To optimize oral rehabilitation of patients with disabilities, early intervention, individualized treatment plans and an adapted time frame for dental treatment are required.Education of dentists, caregivers, and family members is essential for oral rehabilitation and improvement of oral hygiene in patients with disabilities.

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

Polypharmacy (PP) is a common problem in modern medicine, especially known to affect patients with chronic diseases such as multiple sclerosis (MS). With an increasing number of drugs taken, the risk of potential drug–drug interactions (pDDIs) is rising. This study aims to assess the prevalence and clinical relevance of polypharmacy and pDDIs in patients with MS. Pharmacological data of 627 patients with MS were entered into two drug–drug-interaction databases to determine the number and severity of pDDIs for each patient. The patients were divided into those with and without PP (total PP and prescription medication PP (Rx PP)). Of the 627 patients included, 53.3% and 38.6% had total PP and Rx PP, respectively. On average, every patient took 5.3 drugs. Of all patients, 63.8% had at least one pDDI with a mean of 4.6 pDDIs per patient. Less than 4% of all pDDIs were moderately severe or severe. Medication schedules should be checked for inappropriate medication and for possible interacting drugs to prevent pDDIs. Physicians as well as pharmacists should be more sensitive towards the relevance of pDDIs and know how they can be detected and avoided.

Treatment patterns in pediatric patients with multiple sclerosis in Germany-a nationwide claim-based analysis

BACKGROUND

The manifestation of multiple sclerosis (MS) in childhood and adolescence occurs in 3%-5% of all MS cases. However, the immunomodulatory and symptomatic treatment options in this population group are still limited.

OBJECTIVE

We aimed to elucidate the prescription frequency of medications used in pediatric patients with multiple sclerosis (PwMS) compared with the general population, considering the entire spectrum of medications prescribed.

METHODS

Based on nationwide outpatient drug prescription data and statutory health insurance (SHI) physicians' claims data from 2018, we conducted a population-based cross-sectional study in Germany. Children and adolescents aged ⩽17 years (n = 11,381,939) diagnosed with MS (n = 613), and a matched (age, sex, and health insurance sector) control group (n = 6130) were included. The prescription prevalence was measured as the proportion of MS patients with ⩾1 prescription.

RESULTS

Of the 613 pediatric PwMS with a median age of 16 years, 403 (65.7%) were female. For 15 out of the 18 different active agents analyzed, PwMS had a significantly higher prescription prevalence than the control group (Fisher's exact test: p ⩽ 0.037). The most frequently prescribed drugs in PwMS were ibuprofen (28.4%; anti-inflammatory drug), cholecalciferol (23.0%; vitamin D3), and interferon beta-1a (21.5%; disease-modifying drug, DMD). The proportions of DMD prescriptions and antibiotic prescriptions were higher among PwMS aged 15-17 years than among those ⩽14 years (DMD: 43.4% vs 34.2%, p = 0.05; antibiotic: 34.1% vs 24.8%, p = 0.031). In contrast, younger PwMS were more likely to receive a prescription for anti-inflammatory/anti-rheumatic drugs (36.6% vs 26.5%, p = 0.02).

CONCLUSION

Our study analyzing real-world medication data showed that interferon beta, anti-inflammatory drugs, and vitamins play an essential role in the treatment of pediatric PwMS. Future research should evaluate longitudinal treatment patterns of pediatric PwMS, paying particular attention to the time of diagnosis, time of first DMD initiation, and therapy switches.