Abnormally low prolactin levels in schizophrenia patients after switching to aripiprazole in a randomized trial: a biomarker for rebound in psychotic symptoms?

Treatment of antipsychotic-induced hyperprolactinemia: an umbrella review of systematic reviews and meta-analyses

Background

Hyperprolactinemia is a common antipsychotic-induced adverse event in psychiatric patients, and the quality of clinical studies investigating the best treatments has varied. Thus, to better summarize the clinical evidence, we performed an umbrella review of overlapping systematic reviews and meta-analyses for the treatment of antipsychotic-induced hyperprolactinemia.

Methods

The PubMed, Cochrane Library, PsycINFO, Scopus and EMBASE were searched, and reviews and meta-analyses meeting our inclusion criteria were selected. Relevant data were extracted, and an umbrella review was conducted of all included meta-analyses. The quality of included meta-analyses was assessed by using PRISMA scores and AMSTAR 2 quality evaluation. Finally, the clinical evidence for appropriate treatments was summarized and discussed.

Results

Five meta-analyses published between 2013 and 2020 met the requirements for inclusion in this umbrella review. The PRISMA scores of the included meta-analyses ranged from 19.5-26. AMSTAR 2 quality evaluation showed that 2 of the 5 included meta-analyses were of low quality and 3 were of very low quality. The included meta-analyses provide clinical evidence that adding aripiprazole or a dopamine agonist can effectively and safely improve antipsychotic-induced hyperprolactinemia. Two meta-analyses also showed that adjunctive metformin can reduce serum prolactin level, but more clinical trials are needed to confirm this finding.

Conclusion

Adjunctive dopamine agonists have been proven to be effective and safe for the treatment of antipsychotic-induced hyperprolactinemia. Among the researched treatments, adding aripiprazole may be the most appropriate.

Partial agonists of dopamine receptors: clinical effects and dopamine receptor interactions in combining aripiprazole with a full antagonist in treating psychosis

Partial agonists of dopamine receptors are used in combination with full antagonists in treating psychosis, either to mitigate side-effects or in the hope of increasing effectiveness. We examine how combinations may affect the occupancy of D2/D3 dopamine receptors and explore how these can explain the outcomes in the light of the dopamine hypothesis of psychosis. The combinations considered here are from published studies combining aripiprazole with amisulpride, with risperidone in people with hyperprolactinaemia and with olanzapine to mitigate weight gain. We discuss possible worsening of symptoms by the addition of a partial agonist or switching. We also examine the potentially adverse interaction with a full antagonist such as haloperidol given during a subsequent relapse to control severe agitation.

Co-prescription of aripiprazole on prolactin levels in long-term hospitalized chronic schizophrenic patients with co-morbid type 2 diabetes: A retrospective clinical study

BACKGROUND

One of the most frequent side effects of atypical antipsychotics is hyperprolactinemia (HPRL), and metformin or aripiprazole co-prescription is regarded as an effective therapy option for reducing prolactin (PRL) levels. However, whether either of the two drugs can reduce PRL levels in patients with long-term hospitalized chronic schizophrenia with co-morbid type 2 diabetes (T2DM) has not been adequately reported.

METHODS

In our study, long-term hospitalized chronic schizophrenia patients with co-T2DM who were prescribed olanzapine or risperidone as the primary antipsychotic medication were enrolled. A total of 197 of these cases with co-prescribed aripiprazole were set up as the study group (co-Ari group), and the other 204 cases without co-prescribed aripiprazole were set up as the control group (non-Ari group). The two groups' variations in each target parameter were compared, and the variables affecting PRL levels were examined.

RESULTS

Compared to the non-Ari group, fasting blood glucose (FBG), blood uric acid (UA), total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels were significantly higher in the co-Ari group, but there was no difference in PRL levels. Co-prescribing aripiprazole had no impact on PRL levels in all patients with co-T2DM, and aripiprazole dose had no impact on PRL levels in the clinical subgroup of the co-Ari group.

CONCLUSION

Aripiprazole not only worsened the severity of index disturbances associated to metabolism in long-term hospitalized chronic schizophrenia patients with co-T2DM on metformin-based hypoglycemic medications but also failed to lower PRL levels.

Block of Voltage-Gated Sodium Channels by Aripiprazole in a State-Dependent Manner

Aripiprazole is an atypical antipsychotic drug, which is prescribed for many psychiatric diseases such as schizophrenia and mania in bipolar disorder. It primarily acts as an agonist of dopaminergic and other G-protein coupled receptors. So far, an interaction with ligand- or voltage-gated ion channels has been classified as weak. Meanwhile, we identified aripiprazole in a preliminary test as a potent blocker of voltage-gated sodium channels. Here, we present a detailed analysis about the interaction of aripiprazole with the dominant voltage-gated sodium channel of heart muscle (hNa_v1.5). Electrophysiological experiments were performed by means of the patch clamp technique at human heart muscle sodium channels (hNa_v1.5), heterologously expressed in human TsA cells. Aripiprazole inhibits the hNa_v1.5 channel in a state- but not use-dependent manner. The affinity for the resting state is weak with an extrapolated K_r of about 55 µM. By contrast, the interaction with the inactivated state is strong. The affinities for the fast and slow inactivated state are in the low micromolar range (0.5–1 µM). Kinetic studies indicate that block development for the inactivated state must be described with a fast (ms) and a slow (s) time constant. Even though the time constants differ by a factor of about 50, the resulting affinity constants were nearly identical (in the range of 0.5 µM). Besides this, aripirazole also interacts with the open state of the channel. Using an inactivation deficit mutant, an affinity of about 1 µM was estimated. In summary, aripiprazole inhibits voltage-gated sodium channels at low micromolar concentrations. This property might add to its possible anticancer and neuroprotective properties.

Pharmacological treatment strategies for antipsychotic-induced hyperprolactinemia: a systematic review and network meta-analysis

Antipsychotic-induced hyperprolactinemia (AP-induced HPRL) occurs overall in up to 70% of patients with schizophrenia, which is associated with hypogonadism and sexual dysfunction. We summarized the latest evidence for the benefits of prolactin-lowering drugs. We performed network meta-analyses to summarize the evidence and applied Grading of Recommendations Assessment, Development, and Evaluation frameworks (GRADE) to rate the certainty of evidence, categorize interventions, and present the findings. The search identified 3,022 citations, 31 studies of which with 1999 participants were included in network meta-analysis. All options were not significantly better than placebo among patients with prolactin (PRL) less than 50 ng/ml. However, adjunctive aripiprazole (ARI) (5 mg: MD = -64.26, 95% CI = -87.00 to -41.37; 10 mg: MD = -59.81, 95% CI = -90.10 to -29.76; more than 10 mg: MD = -68.01, 95% CI = -97.12 to -39.72), switching to ARI in titration (MD = -74.80, 95% CI = -134.22 to -15.99) and adjunctive vitamin B6 (MD = -91.84, 95% CI = -165.31 to -17.74) were associated with significant decrease in AP-induced PRL among patients with PRL more than 50 ng/ml with moderated (adjunctive vitamin B6) to high (adjunctive ARI) certainty of evidence. Pharmacological treatment strategies for AP-induced HPRL depends on initial PRL level. No effective strategy was found for patients with AP-induced HPRL less than 50 ng/ml, while adjunctive ARI, switching to ARI in titration and adjunctive high-dose vitamin B6 showed better PRL decrease effect on AP-induced HPRL more than 50 ng/ml.

Identifying dopamine supersensitivity through a randomized controlled study of switching to aripiprazole from other antipsychotic agents in patients with schizophrenia

BACKGROUND

Aripiprazole has been reported to worsen psychotic symptoms when switching from other antipsychotics, possibly due to dopamine supersensitivity psychosis.

OBJECTIVE

This study aimed to explore the predictors and possible underlying mechanisms of aripiprazole-related psychotic exacerbation.

METHODS

We conducted an 8-week, open-label, randomized controlled study from October 2007 to September 2009, assigning patients with a primary diagnosis of schizophrenia or schizoaffective disorder to switch from other antipsychotics to aripiprazole with 2-week dual administration, and then to taper off the original agents in fast (n = 38, within 1 week) or slow (n = 41, within 4 weeks) strategies. Positive and Negative Syndrome Scale (PANSS) was examined at day 0, 7, 14, 28, 56. Aripiprazole-related exacerbation (ARE) was defined positive as a 2-point increase in delusion/hallucination dimension score within 28 days compared with baseline. Baseline demographic, clinical and intervention-related variables were compared between the ARE+ and ARE- groups.

RESULTS

Of the 79 randomized patients, 21 fulfilled the criteria of ARE+ , and 46 were classified as ARE-. Fourteen patients in the ARE+ group had worsening psychotic symptoms in the first and second weeks. Compared with the ARE- group, the ARE+ group had a higher baseline chlorpromazine equivalent dose (405.8 ± 225.8 mg vs 268.1 ± 165.4 mg, p = 0.007) and was associated with prescription of first-generation antipsychotics (p = 0.038).

CONCLUSIONS

A higher dose of original antipsychotics and prescription of first-generation antipsychotics may be associated with a higher risk of ARE. The underlying mechanism might be covert dopamine supersensitivity psychosis. These findings may help to identify high-risk patients and guide appropriate treatment strategies.

TRIAL REGISTRATION

ClinicalTrials.gov, identifier: NCT00545467.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients

BACKGROUND AND AIM

Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.

METHODS

Data were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.

RESULTS

The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.

CONCLUSIONS

Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.