Cytochrome P450 (CYP450) Interactions Involving Atypical Antipsychotics are Common in Community-Dwelling Older Adults Treated for Behavioral and Psychological Symptoms of Dementia

The impact of adjunctive aripiprazole on QT interval: A 12-week open label study in patients on olanzapine, clozapine or risperidone

OBJECTIVE

To evaluate the effect of adjunct aripiprazole on QT of patients clinically stabilized on atypical antipsychotics.

METHODS

The dataset was from an open-label 12-week prospective trial that evaluated adjunctive use of 5 mg/day of aripiprazole on metabolic profile in patients with schizophrenia, or schizoaffective disorder stabilized on olanzapine, clozapine, or risperidone. Bazett-corrected QT (QTc) was manually calculated from ECGs measured at baseline (before aripiprazole) and week 12, by two doctors blind to the diagnosis and atypical antipsychotic. The change in QTc (∆QTc: baseline QTc-week 12 QTc) and the number of participants in normal, borderline, prolonged, and pathological groups after 12 weeks were analyzed.

RESULTS

Fifty-five participants, mean age of 39.3 (SD 8.2) years, were analyzed. The ∆QTc after 12 weeks was 5.9 ms (p = 0.143) for the whole sample; 16.4 ms (p = 0.762), 3.7 ms (p = 0.480) and 0.5 ms (p = 0.449), for the clozapine, risperidone and olanzapine group, respectively. There was no significant statistical difference comparing the change in QTc overall, and between atypical antipsychotic groups, when evaluating from baseline to endpoint. However, stratifying the sample based on sex-dependent QTc cut-offs showed a 45% decrease in abnormal QTc readings (p = 0.049) after aripiprazole initiation; 20 subjects had abnormal QTc at baseline, while only 11 subjects had abnormal QTc at 12 weeks. 25.5% of participants showed a reduction in at least one QTc severity group, while 65.5% had no change and 9.0% worsened in QTc group, after 12 weeks of adjunct aripiprazole.

CONCLUSION

Low-dose adjunctive aripiprazole did not prolong QTc in patients stabilized on either olanzapine, risperidone, or clozapine. More controlled studies evaluating the QTc effect of adjunctive aripiprazole should be done to confirm and support these findings.

Prevalence of Drug-Drug Interactions in Older Community-Dwelling Individuals: A Systematic Review and Meta-analysis

BACKGROUND

Drug-drug interactions (DDIs) can lead to medication-related harm, and the older population is at greatest risk. We conducted a systematic review and meta-analysis to estimate DDI prevalence and identify common DDIs in older community-dwelling adults.

METHODS

PubMed and EMBASE were searched for observational studies published between 01/01/2010 and 10/05/2021 reporting DDI prevalence in community-dwelling individuals aged ≥ 65 years. Nursing home and inpatient hospital studies were excluded. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. Meta-analysis was performed using a random-effects model with logit transformation. Heterogeneity was evaluated using Cochran's Q and I². DDI prevalence and 95% confidence intervals (CIs) are presented. All analyses were performed in R (version 4.1.2).

RESULTS

There were 5144 unique articles identified. Thirty-three studies involving 17,011,291 community-dwelling individuals aged ≥ 65 years met inclusion criteria. Thirty-one studies reported DDI prevalence at the study-participant level, estimates ranged from 0.8% to 90.6%. The pooled DDI prevalence was 28.8% (95% CI 19.3-40.7), with significant heterogeneity (p < 0.10; I² = 100%; tau² = 2.13) largely explained by the different DDI identification methods. Therefore, 26 studies were qualitatively synthesised and seven studies were eligible for separate meta-analyses. In a meta-analysis of three studies (N = 1122) using Micromedex^®, pooled DDI prevalence was 57.8% (95% CI 52.2-63.2; I² = 69.6%, p < 0.01). In a meta-analysis of two studies (N = 809,113) using Lexi-Interact^®, pooled DDI prevalence was 30.3% (95% CI 30.2-30.4; I² = 6.8%). In a meta-analysis of two studies (N = 947) using the 2015 American Geriatrics Society Beers criteria^®, pooled DDI prevalence was 16.6% (95% CI 5.6-40.2; I² = 97.5%, p < 0.01). Common DDIs frequently involved cardiovascular drugs, including ACE inhibitor-potassium-sparing diuretic; amiodarone-digoxin; and amiodarone-warfarin.

CONCLUSIONS

DDIs are prevalent among older community-dwelling individuals; however, the methodology used to estimate these events varies considerably. A standardised methodology is needed to allow meaningful measurement and comparison of DDI prevalence.

Neuropharmacological effect of risperidone: From chemistry to medicine

As the second-oldest atypical antipsychotic, risperidone has a long history of off-label usage for treating behavioural and psychological signs and symptoms of dementia (BPSD), such as agitation, aggressiveness, and psychosis. Risperidone has been shown in several trials to have a statistically significant benefit when used in a therapeutic context. Several lines of evidence suggest a possible role of risperidone via the antagonistic effect of Dopamine D2 and 5HT-receptor in different neurological diseases like cognitive dysfunction of schizophrenia, neuroinflammation, Huntington's disease, and sleep cycle management. Therefore, the pharmacological interactions of risperidone in all these diseases were investigated. Some reports on the use of risperidone in the treatment of dopaminergic psychosis have been slightly conflicting. However, more research is needed to evaluate the role of risperidone in the treatment of these neurological diseases.

Multilevel Pharmacological Effects of Antipsychotics in Potential Glioblastoma Treatment

Glioblastoma Multiforme (GBM) is a debilitating type of brain cancer with a high mortality rate. Despite current treatment options such as surgery, radiotherapy, and the use of temozolomide and bevacizumab, it is considered incurable. Various methods, such as drug repositioning, have been used to increase the number of available treatments. Drug repositioning is the use of FDA-approved drugs to treat other diseases. This is possible because the drugs used for this purpose have polypharmacological effects. This means that these medications can bind to multiple targets, resulting in multiple mechanisms of action. Antipsychotics are one type of drug used to treat GBM. Antipsychotics are a broad class of drugs that can be further subdivided into typical and atypical classes. Typical antipsychotics include chlorpromazine, trifluoperazine, and pimozide. This class of antipsychotics was developed early on and primarily works on dopamine D2 receptors, though it can also work on others. Olanzapine and Quetiapine are examples of atypical antipsychotics, a category that was created later. These medications have a high affinity for serotonin receptors such as 5- HT2, but they can also act on dopamine and H1 receptors. Antipsychotic medications, in the case of GBM, also have other effects that can affect multiple pathways due to their polypharmacological effects. These include NF-B suppression, cyclin deregulation, and -catenin phosphorylation, among others. This review will delve deeper into the polypharmacological, the multiple effects of antipsychotics in the treatment of GBM, and an outlook for the field's future progression.

Clinically Significant Cytochrome P450-Mediated Drug-Drug Interactions in Children Admitted to Intensive Care Units

OBJECTIVES

Children admitted to intensive care units (ICUs) often require multiple medications due to the complexity and severity of their disease, which put them at an increased risk for drug interactions. This study examined cytochrome P450-mediated drug-drug interactions (DDIs) based on the Pediatric Intensive Care (PIC) database, with the aim of analyzing the incidence of clinically significant potential drug-drug interactions (pDDIs) and exploring the occurrence of actual adverse reactions.

METHODS

The Lexicomp database was used to screen cytochrome P450-mediated DDI pairings with good levels of reliability and clear clinical phenotypes. Patients exposed to the above drug pairs during the same period were screened in the PIC database. The incidence of clinically significant pDDIs was calculated, and the occurrence of adverse reactions was explored based on laboratory measurements.

RESULTS

In total, 84 (1.21%) of 6920 children who used two or more drugs were exposed to at least one clinically significant pDDI. All pDDIs were based on CYP3A4, with nifedipine + voriconazole (39.60%) being the most common drug pair, and the most frequent being the J02 class of drugs. Based on laboratory measurements, 15 adverse reactions were identified in 12 patients.

CONCLUSIONS

Clinically significant cytochrome P450-mediated pDDIs existed in the children admitted to ICUs, and some of the pDDIs led to adverse clinical outcomes. The use of clinical decision support systems can guide clinical medication use, and clinical monitoring of patients' needs has to be enhanced.

Effectiveness of caregiver non-pharmacological interventions for behavioural and psychological symptoms of dementia: An updated meta-analysis

BACKGROUND

The behavioural and psychological symptoms of dementia (BPSD) have significant negative effects on the health of both patients with dementia and their caregivers. However, the reported effectiveness of non-pharmacological interventions targeting caregivers of patients with dementia for BPSD is inconsistent.

METHODS

We systematically searched the databases PubMed, PsycINFO, CINAHL, Embase, Cochrane Library and four Chinese databases from 2010 through April 2021. The Standardised mean difference (SMD) was calculated using random-effects models. Risk of bias in individual studies was assessed using Cochrane Collaboration's tool, and the certainty of evidence was assessed using the five GRADE criteria.

RESULTS

Thirty-one randomised controlled trials (RCTs) involving 3501 dyads were included. The meta-analysis indicated that non-pharmacological interventions showed small but significant effects on both BPSD in patients with dementia (SMD = -0.14; 95% CI, -0.22 to -0.06; P = 0.001) and caregiver reactions to BPSD (SMD = -0.16; 95% CI, -0.25 to -0.07; P = 0.001). The effect sizes of BPSD in patients at follow-up (SMD = -0.24; 95% CI, -0.38 to -0.09; P = 0.002) were larger than those at post-test. Tailored interventions were associated with more substantial reductions in BPSD in patients (SMD = -0.24; 95% CI, -0.37 to -0.11; P < 0.001) than standardised interventions (SMD = -0.07; 95% CI, -0.18 to 0.04; P = 0.218).

CONCLUSIONS

Non-pharmacological interventions targeting caregivers have the potential to reduce BPSD in patients with dementia and improve caregiver negative reactions to BPSD. Moreover, tailored interventions seemed to be more effective in reducing BPSD, and more significant improvements in BPSD may be observed in long-term follow-up.

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.