Pharmacokinetic patterns of risperidone-associated adverse drug reactions

Relationship between plasma risperidone concentrations and clinical features in chronic schizophrenic patients in China

BACKGROUND

Prior studies have noted great variability in the plasma levels of risperidone (RIS). Plasma concentrations of RIS and its active moiety are highly variable and depend on absorption, metabolism, and other predictors of metabolic dysregulation; however, these factors are poorly understood and the association between metabolic change and change in psychopathology is uncertain.

AIM

To ascertain the characteristics of chronic schizophrenic patients treated with RIS, and to assess their relationship with plasma RIS levels.

METHODS

This was a descriptive cross-sectional study of 50 patients with a diagnosis of schizophrenic psychosis treated with RIS in a psychiatric service. The plasma concentrations of RIS and its metabolite 9-hydroxyrisperidone were determined by high performance liquid chromatography. The patients' demographic and clinical characteristics, and psychopathologies were assessed, and the associations between clinical variables and plasma levels of RIS were explored.

RESULTS

Male patients received higher doses of RIS than female ones, but plasma concentrations of RIS and risperidone + 9-hydroxyrisperidone (active moiety) were higher in female patients. Age and the mean scores of the general psychopathology subscale of the Positive and Negative Syndrome Scale (PANSS) were significantly positively correlated with plasma concentrations of risperidone + 9-hydroxyrisperidone adjusted for weight and dose in all 50 subjects. In male subjects, we found a statistically significant positive correlation between the concentrations of risperidone + 9-hydroxyrisperidone in plasma/(dose × kg) and age, mean PANSS negative subscale scores, mean PANSS general psychopathology subscale scores, and mean PANSS total scores.

CONCLUSION

Long-term use of RIS should be closely monitored in older patients and females to minimize the risk of high concentrations which could induce side effects.

Evidence for Therapeutic Drug Monitoring of Atypical Antipsychotics

Second-generation antipsychotics (SGAs), also known as atypical antipsychotics, are a newer class of antipsychotic drugs used to treat schizophrenia, bipolar disorder, and related psychiatric conditions. The plasma concentration of antipsychotic drugs is a valid measure of the drug at its primary target structure in the brain, and therefore determines the efficacy and safety of these drugs. However, despite the well-known high variability in pharmacokinetics of these substances, psychiatric medication is usually administered in uniform dosage schedules. Therapeutic drug monitoring (TDM), as the specific method that can help personalised medicine in dose adjustment according to the characteristics of the individual patient, minimizing the risk of toxicity, monitoring adherence, and increasing cost-effectiveness in the treatment, thus seems to be an elegant tool to solve this problem. Non-response to therapeutic doses, uncertain adherence to medication, suboptimal tolerability, or pharmacokinetic drug-drug interactions are typical indications for TDM of SGAs. This review aims to summarize an overview of the current knowledge and evidence of the possibilities to tailor the dosage of selected SGAs using TDM, including the necessary pharmacokinetic parameters for personalised pharmacotherapy.

Dose-Related Reference Range as a Tool in Therapeutic Drug Monitoring

BACKGROUND

Therapeutic drug monitoring (TDM) aims to individualize drug therapy. This systematic review provides a state-of-the-art overview of the benefits of adding the dose-related reference range (DRR) as a second reference range to the set of tools used by TDM for measurement and evaluation. It discusses alternative pharmacokinetic approaches for individualization of drug therapy.

METHODS

Literature was searched in PubMed. Textbooks provided Bateman transformations for calculating expected drug concentrations at various times after drug application in "normal patients," that is, the population of phase II clinical trials. The review compiles conditions and prerequisites for these transformations to be valid.

RESULTS

Relating a measured drug concentration to the orienting therapeutic reference range provides pharmacodynamic information for improving the benefit-to-risk ratio of desired drug effects versus adverse drug effects. The discriminating DRR considers a patient's individual pharmacokinetic situation. DRR is statistically based on the pharmacokinetic parameters total clearance, time to reach maximal concentrations, and elimination half-life. Relating the measured drug concentration to a range rather than a particular value, DRR determines if individual patients do or do not belong to the population of "normal patients." Once a patient is identified to be outside the population of "normal patients," the clinical-pharmacological TDM report elaborates the cause. It consists of the measured value, the TDM 9-field-board, the elimination pathways table, and a medication recommendation taking into account clinical information. The internet-based platform KONBEST supports editing of the clinical-pharmacological TDM report. It is personally signed and send to the therapist.

CONCLUSIONS

The DRR embedded into a clinical-pharmacological TDM report allows adjusting a patient's medication to the patient's individual needs (individualization of drug therapy).

Clinical response in patients treated with once-monthly paliperidone palmitate: analysis of a therapeutic drug monitoring (TDM) database

To investigate pharmacokinetic correlates of clinical response in patients treated with once-monthly paliperidone palmitate (PP1M) injections at steady state. Plasma concentrations and dose-adjusted-plasma concentrations (C/D) of paliperidone from a naturalistic therapeutic drug monitoring (TDM) database were compared between responders and non-responders using the Clinical Global Impressions-Improvement scale (CGI-I) ratings. Analyses were based on the non-parametric Mann-Whitney U test and the Pearson Chi-squared test (χ²) with a significance level of 0.05. Subgroup analyses were performed separately in patients with schizophrenia spectrum, schizoaffective disorders and bipolar disorders. Comparing 93 responders with 80 non-responders, we detected no significant differences in the proportion of females, age, and body mass index (p's ranging 0.18-0.83); there were more smokers in the group of non-responders (p = 0.04), which also included more patients with bipolar disorders (p = 0.014). Despite the lack of differences for prescribed PP1M doses and dose intervals (p = 0.42 and p = 0.11, respectively), non-responders had higher paliperidone plasma concentrations and C/D levels (p = 0.033 and p = 0.021, respectively). Subgroup analyses did not yield differences for paliperidone plasma and C/D levels between non-responders and responders with schizophrenia spectrum (p = 0.099 and p = 0.14, respectively) and bipolar disorders (p = 0.95 and p = 0.75, respectively); dose-adjusted plasma concentrations were higher in non-responders compared to responders with schizoaffective disorders (p = 0.039), while no differences were reported for plasma levels (p = 0. 15). Our results show that paliperidone plasma concentrations over injection doses may be associated with patterns of clinical response suggesting potential utility of TDM as part of PP1M-based maintenance treatment.

Pharmacokinetic Correlates of Once-Monthly Paliperidone Palmitate-Related Adverse Drug Reactions

OBJECTIVE

The objective of this study was to investigate associations between pharmacokinetic correlates and once-monthly paliperidone palmitate (PP1M)-related adverse drug reactions (ADRs).

METHODS

Plasma concentrations and dose-adjusted plasma concentrations ('concentration-by-dose' [C/D]) of paliperidone from a naturalistic therapeutic drug monitoring database of PP1M-treated patients were compared between patients with ADRs, classified according to the Udvalg for Kliniske Undersogelser side-effect rating scales categories, and patients without ADRs. Analyses included non-parametric tests and a logistic regression model with a significance level set at 0.05.

RESULTS

In 172 patients, we found no differences in sex, age, smoking, body mass index, PP1M dose, paliperidone plasma concentrations, and C/D values (p > 0.05) between 44 patients with and 128 patients without PP1M-related ADRs. We did not detect differences when specifying for different types of ADRs (p > 0.05). Injection intervals were shorter in patients with vs patients without ADRs (p = 0.03). The logistic regression did not report effects for sex, plasma concentrations, or C/D values (p > 0.05). Post hoc analyses in male patients receiving PP1M every 28 weeks reported higher paliperidone concentrations and C/D values in patients with vs without ADRs (p = 0.049 and p = 0.022). Within the group of male patients, we found an odds ratio of 3.07 for PP1M-associated ADRs in patients with C/D values above 7.7 (ng/mL)/(mg/day).

CONCLUSIONS

Our findings did not reveal distinct patterns of paliperidone concentrations in patients with PP1M-related ADRs. However, male patients receiving PP1M every 28 days with C/D values higher than 7.7 (ng/mL)/(mg/day) showed a higher risk for ADRs, implying that therapeutic drug monitoring may be useful in assessing the risk of PP1M-related ADRs.

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL⁻¹ g⁻¹ day), as illustrated by a minimal bias (mean relative error (%) =  + 3%), good precision (MAE = 8.7 μg mL⁻¹ g⁻¹ day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions.

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R ² (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.

Dose-Corrected Serum Concentrations and Metabolite to Parent Compound Ratios of Venlafaxine and Risperidone from Childhood to Old Age

OBJECTIVE

Comparative pharmacokinetic data about the antidepressant venlafaxine (VEN) and the antipsychotic drug risperidone (RIS) over the lifespan and especially in children and adolescents is lacking. This is the first cross-sectional study that aimed to investigate differences in dose-corrected serum concentrations (CDs) and metabolite to parent compound ratios (MPRs) of VEN and RIS across the lifespan.

METHODS

Patients treated with VEN and RIS at the University Hospital of Würzburg, Germany were included in the study. Serum level determinations were performed during clinical routine care. Patients with CYP2D6 influencing co-medication were excluded from analyses.

RESULTS

In 953 patients (12-93 years) treated with VEN and 552 patients (7-92 years) treated with RIS, children/adolescents (<18 years) showed 11% and 19%, and 44% and 42% lower CDs of the active moieties (CDs_AM) of VEN and RIS than adults and elderly (≥60 years) (Kruskal-Wallis tests; p ≤ 0.001). However, when CDs were normalized to body weight, a different pattern emerged. Gender differences, with higher CDs_AM in females were present in adults and elderlies but not in children/adolescents. No gender- or age-dependent difference in MPRs was found; however, 80% of MPRs of RIS in children/adolescents were below the range of "normal" CYP2D6 function for adults.

CONCLUSIONS

We suggest a higher clearance as a reason for lower CDs_AM of VEN and RIS in children/adolescents compared to adults/elderlies. Metabolism of VEN or RIS by CYP2D6, characterized by MPRs, was not associated with age. However, MPRs of RIS were lower in children/adolescents, possibly due to a higher renal clearance of 9-OH-risperidone.

Lack of Smoking Effects on Pharmacokinetics of Oral Paliperidone-analysis of a Naturalistic Therapeutic Drug Monitoring Sample

INTRODUCTION

Major smoking effects have been reported for a series of psychotropic agents, mainly including substrates of CYP450 1A2, although smoking may also affect alternative metabolic pathways. To our knowledge, smoking effects on paliperidone pharmacokinetics have not been assessed yet.

METHODS

We compared plasma concentrations of paliperidone as well as dose-corrected-plasma concentrations (C/D) from a naturalistic database between smokers and nonsmokers using nonparametrical tests, such as the Mann-Whitney U-test (MWU). Additionally, we compared light and heavy smokers with nonsmokers separately.

RESULTS

Comparing 55 smokers with 37 nonsmokers treated with oral paliperidone, no differences in the percentage of females, age, body weight, body mass index, and daily paliperidone dose were reported (p=0.709 for χ², p=0.26, p=0.38, p=0.67, and p=0.8 for MWU). No differences were detected in plasma concentrations or C/D values (p=0.50 and p=0.96 for MWU). Likewise, differences in daily dose, plasma concentrations, or C/D values were not significant between light smokers (n=17) and nonsmokers (p=0.61, p=0.81, and p=0.33 for MWU) or heavy smokers (n=22) and nonsmokers (p=0.874, p=0.38, and p=0.59; MWU in all cases).

DISCUSSION

Paliperidone is not affected by smoking, and paliperidone dose-adjustments in smokers may not be necessary. This may be seen as an essential difference to risperidone, whose cytochrome-mediated metabolism might be affected by smoking.

Effects of the Proton Pump Inhibitors Omeprazole and Pantoprazole on the Cytochrome P450-Mediated Metabolism of Venlafaxine

BACKGROUND AND OBJECTIVE: An increasing trend in prescribing proton pump inhibitors (PPIs) inevitably increases the risk of unwanted drug-drug interactions (DDIs). The aim of this study was to uncover pharmacokinetic interactions between two PPIs-omeprazole and pantoprazole-and venlafaxine.

METHODS

A therapeutic drug monitoring database contained plasma concentrations of venlafaxine and its active metabolite O-desmethylvenlafaxine. We considered three groups: a group of patients who received venlafaxine without confounding medications (non-PPI group, n = 906); a group of patients who were comedicated with omeprazole (n = 40); and a group of patients comedicated with pantoprazole (n = 40). Plasma concentrations of venlafaxine, O-desmethylvenlafaxine and active moiety (venlafaxine + O-desmethylvenlafaxine), as well as dose-adjusted plasma concentrations, were compared using non-parametrical tests.

RESULTS

Daily doses of venlafaxine did not differ between groups (p = 0.949). The Mann-Whitney U test showed significantly higher plasma concentrations of active moiety, as well as venlafaxine and O-desmethylvenlafaxine, in both PPI groups [p = 0.023, p = 0.011, p = 0.026, +29% active moiety, +27% venlafaxine, +36% O-desmethylvenlafaxine (pantoprazole); p = 0.003, p = 0.039 and p < 0.001, +36% active moiety, +27% venlafaxine, +55% O-desmethylvenlafaxine (omeprazole)]. Significantly higher concentration-by-dose (C/D) values for venlafaxine and active moiety were detected in the pantoprazole group (p = 0.013, p = 0.006, respectively), while in the omeprazole group, C/D ratios for all three parameters-venlafaxine, O-desmethylvenlafaxine and active moiety-were significantly higher (p = 0.021, p < 0.001 and p < 0.001, respectively).

CONCLUSIONS

Significantly higher plasma concentrations for all parameters (venlafaxine, O-desmethylvenlafaxine, active moiety) suggest clinically relevant inhibitory effects of both PPIs, most likely on the cytochrome P450 (CYP) 2C19-mediated metabolism of venlafaxine. The findings might be the result of different degrees of CYP2C19 involvement, therefore the inhibition of CYP2C19 by both PPIs may lead to an increased metabolism via CYP2D6 to O-desmethylvenlafaxine.

Effect of fluvoxamine augmentation and smoking on clozapine serum concentrations

BACKGROUND

Clozapine (CLZ) is metabolized via cytochrome P450 CYP1A2 to N-desmethylclozapine (NCLZ). Smoking induces CYP1A2 thereby increasing clozapine metabolism whereas fluvoxamine inhibits CYP1A2. Studies suggest that the beneficial effect of fluvoxamine augmentation in raising serum clozapine concentrations also occurs when serum concentrations are low due to smoking. Yet, little is known about the influence of fluvoxamine augmentation on clozapine serum concentrations in smoking versus non-smoking patients.

METHODS

A TDM database was analyzed. Serum concentrations of CLZ, NCLZ, dose-adjusted serum concentrations (C/D) and metabolite-to-parent ratios (MPR) were compared using non-parametrical tests in four groups: clozapine-monotherapy in non-smokers (V_NS, n = 28) and smokers (V_S, n = 43); combined treatment with clozapine and fluvoxamine in non-smokers (V_NS+F, n = 11) and smokers (V_S+F, n = 43).

RESULTS

The CLZ monotherapy smoking group showed lower values of C/D CLZ of -38.6% (p < 0.001), C/D NCLZ -35.6% (p < 0.001) and a higher MPR (p = 0.021) than in the non-smoking group. The combination of CLZ and fluvoxamine in non-smoking patients led to higher C/D values: C/D CLZ +117.9% (p < 0.001), C/D NCLZ +60.8% (p = 0.029) while the MPR did not differ between groups (p = 0.089). Changes were comparable to fluvoxamine augmentation in the smoking group with increased C/D CLZ of +120.1% (p < 0.001), C/D NCLZ of +85.8% (p < 0.001) and lower MPR (p = 0.006).

CONCLUSIONS

Smoking in clozapine monotherapy reduced median dose-adjusted serum concentrations more than a third. Combined treatment with fluvoxamine and clozapine led to higher median C/D values in both, smokers and non-smokers. The opposing effects of CYP1A2 induction by smoking and inhibition by fluvoxamine on clozapine serum concentrations balanced out.

How to Treat Hypertension in Venlafaxine-Medicated Patients-Pharmacokinetic Considerations in Prescribing Amlodipine and Ramipril

BACKGROUND

Amlodipine (AMLO) and ramipril (RAMI) belong to the most prescribed drugs in patients with hypertension, a condition also encountered in depression. Venlafaxine may worsen hypertension because of noradrenergic properties. Although of special clinical relevance, data on pharmacokinetic interactions between AMLO, RAMI, and venlafaxine (VEN) are lacking.

METHODS

Two TDM databases consisting of plasma concentrations of VEN and its active metabolite O-desmethylvenlafaxine (ODVEN) were analyzed. We considered a group of patients comedicated with AMLO, VAMLO (n = 22); a group comedicated with RAMI, VRAMI (n = 20); and a 4:1 control group age matched to the VAMLO group receiving VEN without confounding medications, V0 (n = 88). Plasma concentrations of VEN, ODVEN, and active moiety, AM (VEN + ODVEN); metabolic ratio (ODVEN/VEN); and dose-adjusted plasma concentrations (C/D) were compared using nonparametric tests.

RESULTS

Groups did not differ in daily VEN dose, age, or sex. The metabolic ratio (ODVEN/VEN) was lower in the AMLO group (P = 0.029), whereas the RAMI group showed lower values for ODVEN (P = 0.029). All other parameters showed no significant differences.

CONCLUSIONS

Significantly lower values for the metabolic ratio in the AMLO group are unlikely to be explained by cytochrome P450 (CYP) 3A4 and weak CYP2D6 inhibition by AMLO. Other factors such as differences in CYP2D6 polymorphisms and metabolizer status may better explain the findings. Ramipril showed modest effects with changes in ODVEN concentrations that did not remain significant after dose-adjusted comparisons.

Reduced clearance of venlafaxine in a combined treatment with quetiapine

Venlafaxine and the atypical antipsychotic quetiapine are often administered concomitantly. Both drugs share several metabolic hepatic pathways. However, pharmacokinetic interactions between venlafaxine and quetiapine have not been studied yet. A therapeutic drug monitoring database containing serum concentrations of venlafaxine (VEN) and its active metabolite O-desmethylvenlafaxine (ODVEN) was analyzed. Two groups of patients were compared: venlafaxine monotherapy V₀ (n = 153) and co-medication with quetiapine, V_QUE (n = 71). Serum concentrations of VEN, ODVEN, and active moiety, AM (VEN + ODVEN), metabolite to parent compound ratio (ODVEN/VEN) and dose adjusted serum concentrations were compared using non-parametrical tests without information on CYP2D6 genotype. The two groups did not differ in terms of the daily dosage of venlafaxine, age, or sex. Median serum concentrations in the quetiapine group showed significantly, 15.8% and 29.3% higher values for AM and ODVEN (p = 0.002, Cohen's d = 0,41; p = 0.003, d = 0,44), respectively. Dose adjusted serum concentrations of active moiety and ODVEN revealed comparable differences (p = 0.038, d = 0,32; p = 0.015, d = 0,28) with significantly higher values in the co-medicated group. Significantly higher values for ODVEN and AM suggest a reduced clearance of ODVEN and active moiety when quetiapine is co-administered. This may be a consequence of a reduced metabolism of venlafaxine to the inactive metabolite N-desmethylvenlafaxine via CYP3A4, the main metabolizing enzyme for quetiapine, and a shift towards a higher proportion of the active metabolite ODVEN. Therapeutic drug monitoring is recommended in the case of co-medication to ensure clinical efficacy and patient safety. Although the increase of AM is moderate, we consider it relevant for clinicians given the prevalence of concomitant medication of quetiapine and venlafaxine.

Antidepressant polypharmacy and the potential of pharmacokinetic interactions: Doxepin but not mirtazapine causes clinically relevant changes in venlafaxine metabolism

BACKGROUND

To uncover pharmacokinetic interactions between venlafaxine and doxepin or mirtazapine in a naturalistic sample.

METHODS

A therapeutic drug monitoring database containing plasma concentrations of venlafaxine (VEN) and its active metabolite O-desmethylvenlafaxine (ODVEN) was analyzed. We included 1067 of 1594 patients in the analysis. Three study groups were considered; a group of patients under venlafaxine without confounding medications, V₀ (n = 905), a group of patients co-medicated with doxepin, V_DOX (n = 25) and a second group, co-medicated with mirtazapine, V_MIR, n = 137. Plasma concentrations of VEN, ODVEN and the clinically relevant active moiety, sum of venlafaxine and O-desmethylvenlafaxine (ODVEN) (AM), as well as dose-adjusted plasma concentrations (C/D) were compared.

RESULTS

Median concentrations in the doxepin group showed 57.7% and 194.4% higher values for AM and VEN respectively; these differences were statistically significant (p < 0.001 for AM and p = 0.002 for VEN). Similar differences were detected for C/D concentrations of active moiety and VEN (p < 0.001 and p = 0.001) with higher values also in the doxepin group. The ratios ODVEN/VEN were lower in the doxepin group (p < 0.001). A co-medication with mirtazapine did not cause any changes in venlafaxine metabolism.

CONCLUSIONS

Higher concentrations for VEN and AM imply an inhibiting effect of doxepin on the metabolism of venlafaxine, although the huge variability of concentrations has to be taken into account. It is recommended to monitor plasma concentrations in combination treatment to avoid problems in safety and efficacy.

LIMITATIONS

Despite the large size of our study sample, the naturalistic nature of this data may arise some concerns of information bias potentially resulting from non-standardized data recording.

Pharmacokinetics of risperidone in different application forms - Comparing long-acting injectable and oral formulations

We aimed to explore the differences in the pharmacokinetics of risperidone between oral and long-acting injectable (LAI) formulations using a large database of therapeutic drug monitoring (TDM). Plasma concentrations of risperidone (RIS), its active metabolite (9-OH-RIS) and the active moiety (AM) (RIS+9-OH-RIS), their concentration-to-dose (C/D) ratios and ratio of RIS/9-OH-RIS (an index of CYP2D6 metabolic activity) were used to compare patients receiving risperidone orally (n = 851) and those treated with LAI RIS (n = 63). Patients taking CYP inducers or inhibitors or with liver/renal impairment were eliminated. Our study demonstrated that patients on LAI RIS, despite slightly higher RIS doses in the oral group, showed no significant differences in total AM or 9-OH-RIS. Conversely, RIS concentration, RIS C/D ratio and total C/D ratio were slightly higher in the LAI RIS group, reaching significance due to the large sample size. More importantly, the median ratio of RIS/9-OH-RIS was 0.52 in LAI RIS vs. 0.25 in the oral group, providing a significant difference (p < 0.001). After controlling for confounding factors, we replicated the RIS/9-OH-RIS ratio increases in patients with LAI RIS, probably reflecting a decrease in first-pass metabolism. More studies are required to establish the clinical use of TDM for patients on LAI RIS.

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring

Background

Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.

Methods and findings

We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.

Conclusions

This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Effect of smoking on risperidone pharmacokinetics - A multifactorial approach to better predict the influence on drug metabolism

PURPOSE

To disentangle an association between tobacco smoking, smoking habits and pharmacokinetic patterns such as plasma concentrations of risperidone (RIS), its active metabolite 9-hydroxyrisperidone (9-OH-RIS) and the active moiety, AM, (RIS+9-OH-RIS) in a naturalistic sample.

METHODS

Plasma concentrations, dose adjusted plasma concentrations (C/D) of RIS, 9-OH-RIS and AM in patients out of a therapeutic drug monitoring (TDM) database were compared between smokers (n=401) and non-smokers (n=292).

RESULTS

Daily dosage of risperidone differed significantly with smokers receiving higher doses than patients in the control group (p=0.001). No differences were detected in plasma concentrations of the active moiety, RIS and 9-OH-RIS (p=0.8 for AM, p=0.646 for RIS and p=0.538 for 9-OH-RIS). However, dose corrected concentrations (C/D) of metabolite (C/D 9-OH-RIS) and active moiety (C/D AM) differed between significantly between groups (p=0.002 and p=0.007). After stratifying smokers to a group of moderate smokers (<20cigarettes/day) (RS1, n=109) and a group of heavy smokers (≥20cigarettes/day) (RS2, n=135), the comparison between non-smokers and both groups only showed lower values of C/D for 9-OH-RIS (p=0.011) for the group of moderate smokers while other pharmacokinetic parameters did not differ.

CONCLUSIONS

Apart from the well-known induction of CYP1A2 activity by polycyclic aromatic hydrocarbons, smoking might exert an effect on other CYP isoenzymes as well. A possible interpretation proposes a slight inducing effect of smoking on risperidone metabolism most likely via CYP3A4.

Clinical response in a risperidone-medicated naturalistic sample: patients' characteristics and dose-dependent pharmacokinetic patterns

The purpose of this study was to disentangle an association between plasma concentrations of risperidone (RIS), its active metabolite 9-hydroxyrisperidone (9-OH-RIS) and the active moiety, AM (RIS + 9-OH-RIS), and clinical response in a naturalistic sample. Plasma concentrations of RIS, 9-OH-RIS and AM in patients out of a therapeutic drug monitoring (TDM) database were compared between responders (n = 64) and non-responders (n = 526) using the Clinical Global Impressions (CGI) Scale. Daily dosage of risperidone did not differ between responders and non-responders. Differences for active moiety plasma levels between the two groups did not reach statistical significance. However, responders showed lower plasma concentrations of the parent compound RIS as well as lower metabolic ratios RIS/9-OH-RIS than non-responders (p = 0.017 and p = 0.034). These differences did not remain after controlling for age and baseline symptoms. Furthermore, the cohort was split into two subgroups based on the daily dosage: patients under high (≥6 mg/day) (R _H, n = 187) and patients under lower dosages (<6 mg) (R _L, n = 403) of risperidone. Differences between responders and non-responders after controlling for demographic and clinical characteristics remained only for plasma concentrations of active moiety in the lower-dose medicated groups; non-responders showed higher active moiety plasma concentrations than responders. Understanding the mechanisms involved and factors associated with the clinical response in patients medicated with antipsychotics is of great interest. Our data imply that clinical response to an antipsychotic treatment cannot be attributed to a single pharmacokinetic pattern. It seems to be rather a complex patchwork of influencing factors such as demographic and clinical characteristics as well as the metabolizer status as surrogate of CYP activity. It seems that the ratio between RIS and 9-OH-RIS may play a crucial role in mediating the clinical effect.

Cytochrome P450-mediated interaction between perazine and risperidone: implications for antipsychotic polypharmacy

BACKGROUND

Although clinically widespread, scientific evidence for antipsychotic polypharmacy is still limited. Combining different drugs increases the potential for drug-drug interactions, enhancing the risk of adverse drug reactions. We aimed to unravel the potential pharmacokinetic interactions between risperidone (RIS) and perazine.

METHODS

Using a therapeutic drug monitoring database containing plasma concentrations of RIS and its active metabolite [9-hydroxyrisperidone (9-OH-RIS)], we considered two groups: a group of patients under antipsychotic monotherapy with RIS (n = 40) and a group of patients that was comedicated with perazine (n = 16). Groups were matched for demographic characteristics and daily dosage of RIS. Plasma concentrations, concentrations corrected for the dose (C/D) of RIS, 9-OH-RIS and the active moiety (RIS + 9-OH-RIS), as well as the metabolic ratios of concentrations of 9-OH-RIS/RIS, were compared using nonparametric tests.

RESULTS

All parameters other than plasma concentrations and the C/D ratio of 9-OH-RIS differed between groups. Median values for plasma concentrations of the active moiety and C/D of the active moiety were higher in the perazine group (P < 0.001 and P < 0.001, respectively). Differences were driven by variations in the plasma concentrations and C/D of RIS, which were higher in the perazine group (P < 0.001 and P < 0.001, respectively). Metabolic ratios were lower in the perazine group (P = 0.003).

DISCUSSION

The coadministration of perazine in RIS-medicated patients leads to significantly higher plasma concentrations and C/D values of RIS and its active moiety, and a lower metabolic ratio, reflecting the cytochrome P450 (CYP) 2D6 phenotype. We suggest that the mechanism underlying the effect of perazine on RIS metabolism is based on an inhibition of CYP2D6 and CYP3A4 activity.

Pharmacokinetic Drug-Drug Interactions of Mood Stabilizers and Risperidone in Patients Under Combined Treatment

BACKGROUND

The combination of anticonvulsant mood stabilizers with antipsychotic drugs may lead to clinically relevant drug-drug interactions. The objective of the study was to identify pharmacokinetic interactions of different mood stabilizers on the metabolism of risperidone (RIS) under natural conditions.

METHODS

A large therapeutic drug monitoring database containing plasma concentrations of RIS and its metabolite 9-hydroxy-RIS (9-OH-RIS) of 1,584 adult patients was analyzed. Four groups (n = 1,072) were compared: a control group without a potentially cytochrome interacting comedication (R0, n = 852), a group comedicated with valproate (VPA) (RVPA, n = 153), a group comedicated with lamotrigine (LMT) (RLMT, n = 46), and a group under concomitant medication with carbamazepine (CBZ) (RCBZ, n = 21). Dose-adjusted plasma concentrations (C/D ratio) for RIS, 9-OH-RIS and active moiety (AM) (RIS + 9-OH-RIS), as well as metabolic ratios (RIS/9-OH-RIS) were computed.

RESULTS

Groups did not differ with regard to the daily dosage (P = 0.46). Differences were detected for the distributions of the C/D ratios for RIS, 9-OH-RIS and AM (P = 0.003, P < 0.001 and P < 0.001, respectively). Differences remained significant after conducting a Bonferroni correction (P = 0.0125). Pairwise comparisons of the concomitant medication groups with the control group revealed significant differences; RIS C/D ratios were significantly higher in the VPA and the LMT group than in the control group (P = 0.013; P = 0.021). However, these differences did not remain significant after Bonferroni correction. In contrast, CBZ-treated patients showed lower dose-adjusted plasma concentrations of 9-OH-RIS (P < 0.001) as well as the AM (P < 0.001) than the control group; this difference survived the Bonferroni correction.

CONCLUSIONS

The data give evidence for pharmacokinetic interactions between RIS and different anticonvulsant mood stabilizers. Carbamazepine decreased serum concentrations of 9-OH-RIS and the AM when compared with the control group. In case of VPA and LMT, findings were less significant; hints for a weak RIS metabolism inhibition by LMT of unclear clinical significance were found.

Body mass index (BMI) but not body weight is associated with changes in the metabolism of risperidone; A pharmacokinetics-based hypothesis

OBJECTIVE

We sought to unravel the influence of body weight and body mass index (BMI), both consistently reported as pharmacokinetic relevant parameters, on metabolism of risperidone in a naturalistic sample.

METHODS

Conducting non parametrical tests we sought for correlations between plasma concentrations of RIS, 9-OH-RIS and AM and body weight and BMI in patients out of a therapeutic drug monitoring (TDM) database. Further, we stratified patients to three groups based upon BMI values and compared drug concentrations between groups.

RESULTS

Although body weight failed to correlate with pharmacokinetic parameters, BMI was positively correlated with plasma concentrations of the active metabolite (9-OH-RIS) (r_s=0.121, p=0.002) and active moiety (sum of RIS+9-OH-RIS) (r_s=0.128, p=0.001) as well as dose adjusted plasma concentrations of the active moiety (r_s=0.08, p=0.04). The comparison of pharmacokinetic parameters between different BMI groups yielded lower plasma concentrations of 9-OH-RIS in patients with low BMI (<20kg/m²) and higher plasma concentrations of the active moiety in obese patients (BMI ≥30kg/m²) when compared with the control group (30>BMI≥20kg/m²). By comparing low vs. high BMI patients, the latter group showed higher 9-OH-RIS plasma concentrations.

CONCLUSIONS AND LIMITATIONS

Considerable alterations in metabolism of risperidone were detected when comparing obese and cachectic patients with the control group in alignment with the positive correlation between BMI values and plasma concentrations of the active metabolite and active moiety as well as dose adjusted plasma concentrations of the active moiety. We suggest changes in CYP2D6 or CYP3A4 activity or differences in P-glycoprotein function in obese patients with greater BMI as a plausible mechanism underlying these alterations.