Valproic Acid Significantly Lowers Serum Concentrations of Olanzapine—An Interaction Effect Comparable With Smoking

Population pharmacokinetics of olanzapine in pediatric patients with psychiatric disorders

OBJECTIVE

To develop and validate a population pharmacokinetic (PPK) model of oral olanzapine in pediatric Chinese patients in order to individualize therapy in this population.

METHODS

A total of 897 serum concentrations from 269 pediatric patients taking oral olanzapine (ages 8-17 years) were collected. Demographic parameters, biological characteristics and concomitant medications were investigated as covariates. The data were analyzed using a nonlinear mixed-effects modeling approach. Bootstrapping (1000 runs), normalized prediction distribution error (NPDE), and external validation of 62 patients were employed. Simulations were performed to explore the individualized dosing regimens in various situations.

RESULTS

The one-compartment model with first-order absorption and elimination had an apparent clearance (CL/F) of 10.38 L/h, a distribution volume (V/F) of 9.41 L/kg and an absorption rate constant (Ka) fixed at 0.3 h-1. The equation was CL∕F (L∕h) = 10.38 × (body weight∕60)0.25 ×1.33 (if male) × 0.71 (if co-occurrence of infection) × 0.51 (if co-therapy with fluvoxamine) × 1.27 (if co-therapy with sertraline) × 1.43 (if co-therapy with valproate). The final model had satisfactory stability, robustness, and predictive ability. The results from a simulation suggested the oral olanzapine doses required for male and female pediatric patients weighing between 40 and 60 kg without co-medication were 10-15 mg/day and 7.5-10 mg/day, respectively, and dosage adjustments should be based on sex and body weight; and co-administrated with valproate, sertraline, or fluvoxamine.

CONCLUSION

This model may help individualize optimum dosing of oral olanzapine for pediatric patients.

Therapeutic Drug Monitoring of Olanzapine: Effects of Clinical Factors on Plasma Concentrations in Psychiatric Patients

BACKGROUND

Therapeutic drug monitoring (TDM) is strongly recommended for olanzapine due to its high pharmacokinetic variability. This study aimed to investigate the impact of various clinical factors on olanzapine plasma concentrations in patients with psychiatric disorders.

METHODS

The study used TDM data from the PsyMetab cohort, including 547 daily dose-normalized, steady-state, olanzapine plasma concentrations (C:D ratios) from 248 patients. Both intrinsic factors (eg, sex, age, body weight) and extrinsic factors (eg, smoking status, comedications, hospitalization) were examined. Univariate and multivariable, linear, mixed-effects models were employed, with a stepwise selection procedure based on Akaike information criterion to identify the relevant covariates.

RESULTS

In the multivariable model (based on 440 observations with a complete data set), several significant findings emerged. Olanzapine C:D ratios were significantly lower in smokers (β = -0.65, P < 0.001), valproate users (β = -0.53, P = 0.002), and inpatients (β = -0.20, P = 0.025). Furthermore, the C:D ratios decreased significantly as the time since the last dose increased (β = -0.040, P < 0.001). The male sex had a significant main effect on olanzapine C:D ratios (β = -2.80, P < 0.001), with significant interactions with age (β = 0.025, P < 0.001) and body weight (β = 0.017, P = 0.011). The selected covariates explained 30.3% of the variation in C:D ratios, with smoking status accounting for 7.7% and sex contributing 6.9%. The overall variation explained by both the fixed and random parts of the model was 67.4%. The model facilitated the prediction of olanzapine C:D ratios based on sex, age, and body weight.

CONCLUSIONS

The clinical factors examined in this study, including sex, age, body weight, smoking status, and valproate comedication, remarkably influence olanzapine C:D ratios. Considering these factors, in addition to TDM and the clinical situation, could be important for dose adjustment.

Pharmacokinetic Interactions Between Antiseizure and Psychiatric Medications

Antiseizure medications and drugs for psychiatric diseases are frequently used in combination. In this context, pharmacokinetic interactions between these drugs may occur. The vast majority of these interactions are primarily observed at a metabolic level and result from changes in the activity of the cytochrome P450 (CYP). Carbamazepine, phenytoin, and barbiturates induce the oxidative biotransformation and can consequently reduce the plasma concentrations of tricyclic antidepressants, many typical and atypical antipsychotics and some benzodiazepines. Newer antiseizure medications show a lower potential for clinically relevant interactions with drugs for psychiatric disease. The pharmacokinetics of many antiseizure medications is not influenced by antipsychotics and anxiolytics, while some newer antidepressants, namely fluoxetine, fluvoxamine and viloxazine, may inhibit CYP enzymes leading to increased serum concentrations of some antiseizure medications, including phenytoin and carbamazepine. Clinically relevant pharmacokinetic interactions may be anticipated by knowledge of CYP enzymes involved in the biotransformation of individual medications and of the influence of the specific comedication on the activity of these CYP enzymes. As a general rule, these interactions can be managed by careful evaluation of clinical response and, when indicated, individualized dosage adjustments guided by measurement of drugs serum concentrations, especially if pharmacokinetic interactions may cause any change in seizure control or signs of toxicity. Further studies are required to improve predictions of pharmacokinetic interactions between antiseizure medications and drugs for psychiatric diseases providing practical helps for clinicians in the clinical setting.

Effects of Age, Sex, and Comedication on the Plasma Concentrations of Olanzapine in Chinese Patients With Schizophrenia Based on Therapeutic Drug Monitoring Data

BACKGROUND

Olanzapine (OLA) is an atypical second-generation antipsychotic that exhibits significant pharmacokinetic variability. We retrospectively investigated the effects of age, sex, and specific comedications on OLA pharmacokinetics in Chinese patients with schizophrenia.

METHODS

Data on sex, age, and OLA dosage and steady-state plasma concentrations of 386 patients with schizophrenia (who have received OLA or a comedication of OLA with a psychotherapeutic drug) were collected and analyzed. The combined effects of dosage, age, sex, and comedication on OLA plasma levels were assessed via multiple linear regression analyses.

RESULTS

A daily dose of OLA was positively correlated with the drug's plasma concentrations. Overall, the OLA plasma concentrations and concentration-to-dose ratio (C/D) of the studied patients varied by 53.6- and 64.1-fold, achieving median values of 42.7 ng/mL and 2.73 (ng/mL)/(mg/d), respectively. Furthermore, a 1.27-fold higher estimated C/D in patients 60 years or older than in those younger than 60 years was identified. Female patients demonstrated a 33.6% higher C/D than in male patients. When coadministered with mood stabilizers (valproate or lithium), the median OLA C/D was 24.1% to 26.1% lower than that of OLA monotherapy. Interestingly, the OLA plasma concentration and C/D were not significantly affected by a comedication with aripiprazole, haloperidol, amisulpride, risperidone, clozapine, ziprasidone, citalopram, or buspirone.

CONCLUSIONS

The administered drug's dose was identified as an important determinant of the achieved OLA plasma concentration, with a positive correlation. The patients' sex and valproate (or lithium) comedication can significantly affect the C/D of OLA. Therapeutic drug monitoring should be routinely applied in cases of OLA-receiving patients with schizophrenia.

Published population pharmacokinetic models of valproic acid in adult patients: a systematic review and external validation in a Chinese sample of inpatients with bipolar disorder

BACKGROUND

This study reviewed all published valproic acid (VPA) population pharmacokinetic (PPK) models in adult patients and assessed them using external validation methods to determine predictive performance.

METHODS

Thirteen published PPK models (labeled with letters A to M) not restricted to children were identified in PubMed, Embase, and Web of Science databases. They were evaluated in a sample totaling 411 serum concentrations from 146 adult inpatients diagnosed with bipolar disorder in a Chinese hospital. Serum concentrations of VPA were analyzed by validated ultra-performance liquid chromatography-tandem mass spectrometry. Performance was assessed by 4 tests (prediction-based diagnostics, visual predictive checks, normalized prediction distribution error, and Bayesian forecasting).

RESULTS

Models K and L, developed in large samples of Chinese and Thai patients, showed good performance in our Chinese dataset. Models H and J demonstrated good performance in Tests 2 and 3 of the 4 tests, respectively. Another 7 models exhibited intermediate performance. The models with the worst performance, F and M, could not be improved by Bayesian forecasting.

CONCLUSION

In our validation study the most important factors contributing to good performance were absence of children, Asian ethnicity, one-compartment models and inclusion of body weight and VPA dose in previously published models.

Valproic acid autoinduction: a case-based review

Although valproic acid (VPA) induces the metabolism of multiple other drugs, the clinical reports of VPA autoinduction are rare. A comprehensive literature search yielded only one published case series, which provided the rationale to conduct a review of the published cases along with a new case of VPA autoinduction. Although there may be myriad of reasons for lack of published cases of VPA autoinduction, potential underreporting may be one of the core reasons. Lack of understanding into the highly complex metabolism of VPA may also make it difficult to recognize and report VPA autoinduction. However, it is important to mention that in addition to autoinduction increased elimination of VPA may be mediated by several pharmacokinetic (PK) factors, such as drug interactions, genetic polymorphisms of metabolic enzymes, and protein displacement reactions. As VPA is metabolized by multiple metabolic pathways, the risk for drug interactions is relatively high. There is also a growing evidence for high genetic inducibility of some enzymes involved in VPA metabolism. Protein displacement reactions with VPA increase the biologically active and readily metabolizable free fraction and pose a diagnostic challenge as they are usually not requested by most clinicians. Thus, monitoring of free fraction with total VPA levels may prevent clinically serious outcomes and optimize VPA treatment in clinically challenging patients. This case-based review compares the clinical data from three published cases and a new case of VPA autoinduction to enhance clinicians' awareness of this relatively rare but clinically relevant phenomenon along with a discussion of potential underlying mechanisms.

A Retrospective Analysis of Steady-State Olanzapine Concentrations in Chinese Patients Using Therapeutic Drug Monitoring: Effects of Valproate and Other Factors

BACKGROUND

The objective of this study was to investigate the serum concentrations of olanzapine in relation to age, sex, and other factors in Chinese patients aged between 10 and 90 years.

METHODS

Data for 884 olanzapine patients, deposited between 2016 and 2017, were retrieved from the therapeutic drug monitoring database of the Affiliated Brain Hospital of Guangzhou Medical University. The effects of covariates on serum olanzapine concentration, dose-normalized concentration (C/D ratio), and normalized concentration (C/D/weight) were investigated.

RESULTS

Generally, male patients had lower olanzapine concentration, C/D ratio, and C/D/weight than female patients (P < 0.001). Smoking and drinking reduced olanzapine concentration, C/D ratio, and C/D/weight (P < 0.001). Coadministration with valproate decreased olanzapine concentration, C/D ratio, and C/D/weight by about 16%, 30%, and 40%, respectively (P < 0.001). Patients younger than 60 years had higher olanzapine concentrations (P < 0.05) but lower C/D ratios and C/D/weight (P < 0.001) than patients older than 60 years. Age was correlated with olanzapine concentration (r = -0.082, P < 0.05), C/D ratio (r = 0.196, P < 0.001), and C/D/weight (r = 0.169, P < 0.001). Sample timing after dose and diagnostic factors also contributed to the olanzapine concentrations. Multiple linear regression analysis revealed significant influences of dosage, age, sex, valproate comedication, smoking, postdose interval, and schizophrenia (vs bipolar affective disorders) on serum olanzapine concentrations.

CONCLUSIONS

The metabolism of olanzapine may be altered by several factors. Patients characterized with a combination of factors may benefit from therapeutic drug monitoring for the adjustment of olanzapine dose to minimize adverse reactions.

The Impact of Smoking, Sex, Infection, and Comedication Administration on Oral Olanzapine: A Population Pharmacokinetic Model in Chinese Psychiatric Patients

BACKGROUND AND OBJECTIVE: Prior olanzapine population pharmacokinetic (PPK) models have focused on the effects of sex and smoking on olanzapine clearance. This PPK model in Chinese adult psychiatric patients also investigated the influence of comedications and co-occurrence of infections on olanzapine clearance, and explored how to personalize oral olanzapine dosage in the clinical setting.

METHODS

A total of 1546 serum concentrations from 354 patients were collected in this study. A one-compartment model with first-order absorption was employed to develop the PPK model using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, co-occurrence of infection and concomitant medications (including dangguilonghui tablets, a Chinese herbal medicine for constipation). Bootstrap validation (1000 runs) and external validation of 50 patients were employed to evaluate the final model. Simulations were performed to explore the personalization of olanzapine dosing after stratification by sex, smoking, and comedication with valproate.

RESULTS

Typical estimates for the absorption rate constant (K_a), apparent clearance (CL/F), and apparent distribution volume (V/F) were 0.30 h^-1, 12.88 L/h, and 754.41 L, respectively. Olanzapine clearance was increased by the following variables: 1.23-fold by male sex, 1.23-fold by smoking, 1.23-fold by comedication with valproate, 1.16-fold by sertraline, and 2.01-fold by dangguilonghui tablets. Olanzapine clearance was decreased by the following variables: 0.75-fold by co-occurrence of infection, 0.70-fold by fluvoxamine, and 0.78-fold by perphenazine. The model evaluation indicated that the final model's performance was good, stable, and precise.

CONCLUSION

This study contributes to the personalization of oral olanzapine dosing, but further studies should be performed to verify the effects of infection and comedications, including valproate and dangguilonghui.

Pharmacokinetics and Bioequivalence of 2 Olanzapine Orally Disintegrating Tablet Products in Healthy Chinese Subjects Under Fed and Fasting Conditions

To assess the bioequivalence of two 5-mg olanzapine orally disintegrating tablet (ODT) products, 2 randomized, open-label, single-dose, 2-way crossover studies were carried out under fasting or fed conditions. Blood samples were collected at scheduled times according to the study protocol. Statistical analysis of area under the concentration-time curve from time 0 to 168 hours (AUC_0-t ), area under the curve from time zero to infinity (AUC_0-∞ ), and peak plasma concentration (C_max ) was conducted. Two formulations were considered bioequivalent if the 90% confidence intervals (CIs) of the geometric mean ratios for AUC_0-t, AUC_0-∞ , and C_max were within the range of 0.80-1.25. Adverse events were recorded and evaluated throughout the studies. A total of 48 subjects with 24 in each study completed the 2 studies. In fasted subjects, the 90%CIs for the test product versus the reference product were 97.28%-105.13% for AUC_0-t , 97.57%-105.54% for AUC_0-∞ , and 90.94%-103.97% for C_max . In fed subjects, the 90%CIs for AUC_0-t , AUC_0-∞ and C_max were 99.73%-122.63%, 99.56%-121.75%, and 99.46%-120.46%, respectively. No serious adverse events were reported in the studies. The reference and the test product of 5-mg olanzapine ODT show comparable pharmacokinetic profiles under both fed and fasted conditions and were considered bioequivalent.

The Effect of Valproic Acid on Olanzapine Serum Concentration: A Study Including 2791 Patients Treated With Olanzapine Tablets or Long-Acting Injections

BACKGROUND

The combination of olanzapine and valproic acid (VPA) is regularly prescribed in the treatment of bipolar or schizoaffective disorders. The VPA has been shown to reduce olanzapine concentration, but the mechanism behind this interaction remains unknown. We aimed to investigate the effect of VPA on olanzapine concentration during oral versus long-acting injectable (LAI) formulation in a real-life setting.

METHODS

From a therapeutic drug monitoring service, prescribed doses and serum concentrations from 2791 olanzapine-treated patients (9433 measurements) were included.

RESULTS

The number of patients on olanzapine-LAI treatment was 328, whereas 2463 were using oral olanzapine. The frequency of patients comedicated with VPA was 9.4% for olanzapine tablets and 5.8% for olanzapine-LAI. The VPA had no effect on olanzapine dose-adjusted concentrations in LAI users (1.6 vs 1.7 [ng/mL]/[mg/d]; P = 0.38), whereas in the oral group the dose-adjusted olanzapine concentration was lower in VPA users (2.2 vs 2.7 [ng/mL]/[mg/d]; P < 0.001). For smokers in the oral olanzapine group using VPA, 8.7% of the measurements were in the subtherapeutic range (<10 ng/mL) compared with 6.0% in nonusers (P = 0.003).

IMPLICATIONS

These findings show that the VPA-olanzapine interaction involves a presystemic mechanism and is therefore restricted to oral olanzapine treatment. For oral treatment of olanzapine, comedication with VPA implies a risk of insufficient effect, which may be of clinical relevance in smokers in particular. Thus, it is important to be aware of the interaction potential with VPA during oral olanzapine use, whereas for LAI-treated patients fewer precautions are required from a pharmacokinetic point of view.

Reduction in N-Desmethylclozapine Level Is Determined by Daily Dose But Not Serum Concentration of Valproic Acid-Indications of a Presystemic Interaction Mechanism

BACKGROUND

Valproic acid (VPA) is frequently used together with clozapine (CLZ) as mood-stabilizer or for the prevention of seizures in patients with psychotic disorders. VPA is known to reduce levels of the pharmacologically active CLZ-metabolite N-desmethylclozapine (N-DMC), but factors determining the degree of this interaction are unknown. Here, we investigated the relationship between VPA dose and serum concentration on N-DMC levels in a large patient population adjusting for sex, age, and smoking habits as covariates.

METHODS

A total of 763 patients with steady-state serum concentrations of CLZ and N-DMC concurrently using VPA (cases, n = 76) or no interacting drugs (controls, n = 687) were retrospectively included from a therapeutic drug monitoring service at Diakonhjemmet Hospital, Oslo, between March 2005 and December 2016. In addition to information about prescribed doses, age, sex, smoking habits, and use of other interacting drugs were obtained. The effects of VPA dose and serum concentration on dose-adjusted N-DMC levels were evaluated by univariate correlation and multivariate linear mixed-model analyses adjusting for covariates.

RESULTS

The dose-adjusted N-DMC levels were approximately 38% lower in VPA users (cases) versus nonusers (controls) (P < 0.001). Within the VPA cases, a negatively correlation between VPA dose and dose-adjusted N-DMC levels was observed with an estimated reduction of 1.42% per 100-mg VPA dose (P = 0.033) after adjusting for sex, age, and smoking. By contrast, there was no correlation between VPA serum concentration and dose-adjusted N-DMC levels (P = 0.873).

CONCLUSIONS

The study shows that VPA dose, not concentration, is of relevance for the degree of reduction in N-DMC level in clozapine-treated patients. Presystemic induction of UGT enzymes or efflux transporters might underlie the reduction in N-DMC level during concurrent use of VPA. Our findings indicate that a VPA daily dose of 1500 mg or higher provides a further 21% reduction in N-DMC concentration. This is likely a relevant change in the exposure of this active metabolite where low levels are associated with implications of CLZ therapy.

Age Impacts Olanzapine Exposure Differently During Use of Oral Versus Long-Acting Injectable Formulations: An Observational Study Including 8,288 Patients

PURPOSE

Olanzapine is a commonly prescribed antipsychotic available as oral and long-acting injectable (LAI) formulations. Data are lacking on the use and safety of olanzapine-LAI in older patients. The aim of this study was to investigate the effect of increasing age on olanzapine exposure during oral versus LAI administration in a real-life setting.

METHODS

This observational study was based on routine therapeutic drug monitoring data collected during 2005-2017. As a measure of exposure, absolute concentrations and concentration/dose ratios of olanzapine were defined as outcome variables. Linear mixed-model analyzes were used to allow for inclusion of multiple samples per patient and adjustment for covariate effects.

RESULTS

Olanzapine concentrations and doses from 8,288 patients (21,378 measurements) were included. The number of patients on oral treatment was 7,893 (42%, 50 years or older), while 395 were using olanzapine-LAI (27%, 50 years or older). In contrast to oral use, where the dose-adjusted concentration of olanzapine increased significantly for patients 50 years or older (P < 0.001), increasing age had no effect on olanzapine concentration following LAI administration (P = 0.550). The effects of smoking habits and gender were equal in oral and olanzapine-LAI users.

CONCLUSION

While the dose-adjusted systemic exposure of olanzapine increases by age after oral administration, these novel findings from a large patient population show that systemic exposure of olanzapine-LAI is unaffected by age, probably due to the lacking influence of age-related changes in gastrointestinal absorption and/or presystemic metabolism. From a pharmacokinetic point of view, it is therefore no reason to restrict the use of olanzapine-LAI in older patients requiring long-term treatment.

Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation

AIM

The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S).

METHODS

A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment.

RESULTS

The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively.

CONCLUSIONS

Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.

Three patients needing high doses of valproic Acid to get therapeutic concentrations

Valproic acid (VPA) can autoinduce its own metabolism. Cases requiring VPA doses >4000 mg/day to obtain therapeutic plasma concentrations, such as these 3 cases, have never been published. Case 1 received VPA for seizures and schizophrenia and had >50 VPA concentrations in 4 years. A high dose of 5,250 mg/day of VPA concentrate was prescribed for years but this dose led to an intoxication when switched to the enterocoated divalproex sodium formulation, requiring a normal dose of 2000 mg/day. VPA metabolic capacity was significantly higher (t = −9.6; df = 6.3, p < 0.001) during the VPA concentrate therapy, possibly due to autoinduction in that formulation. Case 2 had VPA for schizoaffective psychosis with 10 VPA concentrations during an 8-week admission. To maintain a VPA level ≥50 μg/mL, VPA doses increased from 1500 to 4000 mg/day. Case 3 had tuberous sclerosis and epilepsy and was followed up for >4 years with 137 VPA concentrations. To maintain VPA concentrations ≥50 μg/mL, VPA doses increased from 3,375 to 10,500 mg/day. In Cases 2 and 3, the duration of admission and the VPA dose were strongly correlated (r around 0.90; p < 0.001) with almost no change after controlling for VPA concentrations, indicating progressive autoinduction that increased with time.

The effects of antiepileptic inducers in neuropsychopharmacology, a neglected issue. Part I: A summary of the current state for clinicians

The literature on inducers in epilepsy and bipolar disorder is seriously contaminated by false negative findings. This is part i of a comprehensive review on antiepileptic drug (AED) inducers using both mechanistic pharmacological and evidence-based medicine to provide practical recommendations to neurologists and psychiatrists concerning how to control for them. Carbamazepine, phenobarbital and phenytoin, are clinically relevant AED inducers; correction factors were calculated for studied induced drugs. These correction factors are rough simplifications for orienting clinicians, since there is great variability in the population regarding inductive effects. As new information is published, the correction factors may need to be modified. Some of the correction factors are so high that the drugs (e.g., bupropion, quetiapine or lurasidone) should not co-prescribed with potent inducers. Clobazam, eslicarbazepine, felbamate, lamotrigine, oxcarbazepine, rufinamide, topiramate, vigabatrin and valproic acid are grouped as mild inducers which may (i)be inducers only in high doses; (ii)frequently combine with inhibitory properties; and (iii)take months to reach maximum effects or de-induction, definitively longer than the potent inducers. Potent inducers, definitively, and mild inducers, possibly, have relevant effects in the endogenous metabolism of (i)sexual hormones, (ii) vitamin D, (iii)thyroid hormones, (iv)lipid metabolism, and (v)folic acid.

A rational approach to employing high plasma levels of antipsychotics for violence associated with schizophrenia: case vignettes

Forensic psychiatric settings contain a high prevalence of treatment-resistant violent schizophrenia patients. Clozapine therapy has the most robust data for the management of violence in patients with schizophrenia, but for those who cannot tolerate or refuse clozapine, high-dose antipsychotic treatment to high achieve high plasma levels remains a viable option despite limited evidence for efficacy in controlled trials. This article enumerates rational guidelines for employing high plasma level strategies, emphasizing the appropriate interpretation of, and reaction to high plasma antipsychotic levels in these treatment resistant patients, and the need to push treatment to the limits of tolerability or clinical response.

Can valproic acid be an inducer of clozapine metabolism?

INTRODUCTION

Prior clozapine studies indicated no effects, mild inhibition or induction of valproic acid (VPA) on clozapine metabolism. The hypotheses that (i) VPA is a net inducer of clozapine metabolism, and (ii) smoking modifies this inductive effect were tested in a therapeutic drug monitoring study.

METHODS

After excluding strong inhibitors and inducers, 353 steady-state total clozapine (clozapine plus norclozapine) concentrations provided by 151 patients were analyzed using a random intercept linear model.

RESULTS

VPA appeared to be an inducer of clozapine metabolism since total plasma clozapine concentrations in subjects taking VPA were significantly lower (27% lower; 95% confidence interval, 14-39%) after controlling for confounding variables including smoking (35% lower, 28-56%).

DISCUSSION

Prospective studies are needed to definitively establish that VPA may (i) be an inducer of clozapine metabolism when induction prevails over competitive inhibition, and (ii) be an inducer even in smokers who are under the influence of smoking inductive effects on clozapine metabolism.

Interactions between valproic acid and quetiapine/olanzapine in the treatment of bipolar disorder and the role of therapeutic drug monitoring

OBJECTIVES

The anticonvulsant valproic acid and the atypical antipsychotics olanzapine and quetiapine provide synergistic mood-stabilising, antidepressant and antipsychotic activities in the treatment of bipolar and schizoaffective disorders. Existing literature shows that pharmacokinetic and pharmacodynamics drug-drug interactions (DDIs) possibly occur with the use of such a combination. Clinical reports of a possible interaction between the drugs leading to an increased risk of adverse drug reactions have also emerged. The main objective of this paper is to review the incidence of DDIs between the anticonvulsant and the antipsychotics, to postulate the possible mechanisms of the interaction and to establish whether certain target populations are at an increased susceptibility to such interactions. The usefulness of therapeutic drug monitoring (TDM) of the antipsychotics to monitor for an interaction was also assessed. A systematic database search was carried out using the search engine provided by PubMed using the following key words: olanzapine, quetiapine, valproic acid, pharmacokinetic drug-drug interaction, bipolar disorder, therapeutic drug monitoring.

KEY FINDINGS

Evidence of a possible clinically relevant DDI between valproic acid and both antipsychotics has been uncovered. A possible mechanism for the interactions has been postulated, and the importance of TDM has been discussed.

SUMMARY

Further research is required to determine whether DDIs occur with the concurrent use of valproic acid and olanzapine or quetiapine, and to investigate the potential of TDM as a clinical tool in improving pharmacotherapy and preventing toxicity.