Estimating the effects of co-medications on plasma olanzapine concentrations by using a mixed model

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.

Drug-drug interactions between antiseizure medications and antipsychotic medications: a narrative review and expert opinion

INTRODUCTION

Antiseizure medications (ASMs) and antipsychotic drugs are frequently coadministered with the potential for drug-drug interactions. Interactions may either be pharmacokinetic or pharmacodynamic, resulting in a decrease or increase in efficacy and/or an increase or decrease in adverse effects.

AREAS COVERED

The clinical evidence for pharmacokinetic and pharmacodynamic interactions between ASMs and antipsychotics is reviewed based on the results of a literature search in MEDLINE conducted in April 2023.

EXPERT OPINION

There is now extensive published evidence for the clinical importance of interactions between ASMs and antipsychotics. Enzyme-inducing ASMs can decrease blood concentrations of many of the antipsychotics. There is also evidence that enzyme-inhibiting ASMs can increase antipsychotic blood concentrations. Similarly, there is limited evidence showing that antipsychotic drugs may affect the blood concentrations of ASMs through pharmacokinetic interactions. There is less available evidence for pharmacodynamic interactions, but these can also be important, as can displacement from protein binding. The lack of published evidence for an interaction should not be interpreted as meaning that the given interaction does not occur; the evidence is building continually. There is no substitute for careful patient monitoring and sound clinical judgment.

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.

Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

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.

Coffee and cigarette smoking interactions with lamotrigine

The objective of this analysis was to determine possible interactions between lamotrigine (LTG) and coffee or cigarette use. As part of the statistical analysis of factors influencing LTG pharmacokinetics (PK) in the Equigen chronic dose study, we collected prospective data from enrolled patients on their use of coffee and cigarettes. Subjects were part of a crossover replication study of generic LTG products with rigorous blood sampling and were instructed to not change their typical consumption of these products for the duration of the study. A total of 35 subjects were enrolled, with 33 subjects having sufficient data for analysis. Higher consumption of coffee was associated with a significantly lower area under the curve (AUC) and maximum concentration (Cmax) of lamotrigine (LTG). Higher cigarette use did not result in a significant change in AUC or Cmax. Coffee, but not cigarette use, either induces LTG metabolism or inhibits LTG absorption.

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.

High Doses of Drugs Extensively Metabolized by CYP3A4 Were Needed to Reach Therapeutic Concentrations in Two Patients Taking Inducers

INTRODUCTION

In the last 20 years of clinical practice, the senior author has identified these 2 rare cases in which the patients needed extremely high doses of drugs metabolized by CYP3A4 to reach and maintain serum therapeutic concentrations.

METHODS

The high metabolic ability of these 2 patients was demonstrated by the low concentration-to-dose ratios (C/D ratios) of several drugs metabolized by CYP3A4.

RESULTS

Case 1 was characterized by a history of high carbamazepine doses (up to 2,000mg/day) and needed 170 mg/day of diazepam in 2 days to cooperate with dental cleaning. The high activity of the CYP3A4 isoenzyme was manifested by fast metabolism for quetiapine and diazepam, which took more than 1 year to normalize after the inducer, phenytoin, was stopped. Case 2 was also very sensitive to CYP3A4 inducers as indicated by very low C/D ratios for carbamazepine, risperidone and paliperidone. The carbamazepine (2,800 mg/day) and risperidone (20 mg/day) dosages for this second patient are the highest doses ever seen for these drugs by the senior author. Risperidone induction appeared to last for many months and metabolism was definitively normal 3 years after stopping carbamazepine. On the other hand, olanzapine C/D ratios were normal for induction.

CONCLUSIONS

The literature has never described similar cases of very high doses of drugs metabolized by CYP3A4. We speculate that these 2 patients may have unusual genetic profiles at the nuclear receptor levels; these receptors regulate induction of drugs.

Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

The estimation of carry-over effects is a difficult problem in the design and analysis of clinical trials of treatment sequences including cross-over trials. Except for simple designs, carry-over effects are usually unidentifiable and therefore nonestimable. Solutions such as imposing parameter constraints are often unjustified and produce differing carry-over estimates depending on the constraint imposed. Generalized inverses or treatment-balancing often allow estimating main treatment effects, but the problem of estimating the carry-over contribution of a treatment sequence remains open in these approaches. Moreover, washout periods are not always feasible or ethical. A common feature of designs with unidentifiable parameters is that they do not have design matrices of full rank. Thus, we propose approaches to the construction of design matrices of full rank, without imposing artificial constraints on the carry-over effects. Our approaches are applicable within the framework of generalized linear mixed-effects models. We present a new model for the design and analysis of clinical trials of treatment sequences, called Antichronic System, and introduce some special sequences called Skip Sequences. We show that carry-over effects are identifiable only if appropriate Skip Sequences are used in the design and/or data analysis of the clinical trial. We explain how Skip Sequences can be implemented in practice, and present a method of computing the appropriate Skip Sequences. We show applications to the design of a cross-over study with 3 treatments and 3 periods, and to the data analysis of the STAR*D study of sequences of treatments for depression.

Prolactin levels: sex differences in the effects of risperidone, 9-hydroxyrisperidone levels, CYP2D6 and ABCB1 variants

AIM

The role of sex on the association of plasma prolactin levels with risperidone (R) and 9-hydroxyrisperidone (9-OHR) concentrations is investigated.

METHODS

Plasma R and prolactin concentrations, CYP2D6 and exon 21 and 26 ABCB1 gene variants were studied in 110 patients.

RESULTS

In females, a 1 ng/ml increase in R levels was associated with a significant 1.02% increase in prolactin levels. In males, a 1 ng/ml increase in 9-OHR levels was associated with a significant 1.18% increase in prolactin levels. ABCB1 haplotype 12 had significant but opposite effects in males and females. In the combined sample, 9-OHR, but not R levels had significant effects on prolactin levels.

CONCLUSION

Genes had sex-specific effects on risperidone-associated prolactin elevations.

Estimating individual benefits of medical or behavioral treatments in severely ill patients

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models

We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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.

Meta-analysis: the effects of smoking on the disposition of two commonly used antipsychotic agents, olanzapine and clozapine

OBJECTIVE

To clarify the effects of smoking on the disposition of two commonly used antipsychotics, olanzapine and clozapine, and to create standards to adjust the doses of these drugs in clinical practice based on the smoking status.

DESIGN

A meta-analysis was conducted by searching MEDLINE, Scopus and the Cochrane Library for relevant prospective and retrospective studies.

INCLUDED STUDIES

We included the studies that investigated the effects of smoking on the concentration to dose (C/D) ratio of olanzapine or clozapine.

PRIMARY OUTCOME MEASURE

The weighted mean difference was calculated using a DerSimonian-Laird random effects model, along with 95% CI.

RESULTS

Seven association studies, comprising 1094 patients (652 smokers and 442 non-smokers) with schizophrenia or other psychiatric disorders, were included in the meta-analysis of olanzapine. The C/D ratio was significantly lower in smokers than in non-smokers (p<0.00001), and the mean difference was -0.75 (ng/mL)/(mg/day) (95% CI -0.89 to -0.61). Therefore, it was estimated that if 10 and 20 mg/day of olanzapine would be administered to smokers, about 7 and 14 mg/day, respectively, should be administered to non-smokers in order to obtain the equivalent olanzapine concentration. Four association studies of clozapine were included in the meta-analysis of clozapine, comprising 196 patients (120 smokers and 76 non-smokers) with schizophrenia or other psychiatric disorders. The C/D ratio was significantly lower in smokers than in non-smokers (p<0.00001), and the mean difference was -1.11 (ng/mL)/(mg/day) (95% CI -1.53 to -0.70). Therefore, it was estimated that if 200 and 400 mg/day of clozapine would be administered to smokers, about 100 and 200 mg/day, respectively, should be administered to non-smokers.

CONCLUSIONS

We suggest that the doses of olanzapine and clozapine should be reduced by 30% and 50%, respectively, in non-smokers compared with smokers in order to obtain an equivalent olanzapine or clozapine concentration.

Clinically relevant interactions between newer antidepressants and second-generation antipsychotics

INTRODUCTION

Combinations of newer antidepressants and second-generation antipsychotics (SGAs) are frequently used by clinicians. Pharmacokinetic drug interaction (PK DI) and poorly understood pharmacodynamic (PD) drug interaction (PD DI) can occur between them.

AREAS COVERED

This paper comprehensively reviews PD DI and PK DI studies.

EXPERT OPINION

More PK DI studies are needed to better establish dose correction factors after adding fluoxetine and paroxetine to aripiprazole, iloperidone and risperidone. Further PK DI studies and case reports are also needed to better establish the need for dose correction factors after adding i) fluoxetine to clozapine, lurasidone, quetiapine and olanzapine; ii) paroxetine to olanzapine; iii) fluvoxamine to asenapine, aripiprazole, iloperidone, lurasidone, olanzapine, quetiapine and risperidone; iv) high sertraline doses to aripiprazole, clozapine, iloperidone and risperidone: v) bupropion and duloxetine to aripiprazole, clozapine, iloperidone and risperidone; and vi) asenapine to paroxetine and venlafaxine. Possible beneficial PD DI effects occur after adding SGAs to newer antidepressants for treatment-resistant major depressive and obsessive-compulsive disorders. The lack of studies combining newer antidepressants and SGAs in psychotic depression is worrisome. PD DIs between newer antidepressants and SGAs may be more likely for mirtazapine and bupropion. Adding selective serotonin reuptake inhibitors and SGAs may increase QTc interval and may very rarely contribute to torsades de pointes.

Clinically significant drug interactions with atypical antipsychotics

Atypical antipsychotics [also known as second-generation antipsychotics (SGAs)] have become a mainstay therapeutic treatment intervention for patients with schizophrenia, bipolar disorders and other psychotic conditions. These agents are commonly used with other medications--most notably, antidepressants and antiepileptic drugs. Drug interactions can take place by various pharmacokinetic, pharmacodynamic and pharmaceutical mechanisms. The pharmacokinetic profile of each SGA, especially with phase I and phase II metabolism, can allow for potentially significant drug interactions. Pharmacodynamic interactions arise when agents have comparable receptor site activity, which can lead to additive or competitive effects without alterations in measured plasma drug concentrations. Additionally, the role of drug transporters in drug interactions continues to evolve and may effect both pharmacokinetic and pharmacodynamic interactions. Pharmaceutical interactions occur when physical incompatibilities take place between agents prior to drug absorption. Approximate therapeutic plasma concentration ranges have been suggested for a number of SGAs. Drug interactions that markedly increase or decrease the concentrations of these agents beyond their ranges can lead to adverse events or diminished clinical efficacy. Most clinically significant drug interactions with SGAs occur via the cytochrome P450 (CYP) system. Many but not all drug interactions with SGAs are identified during drug discovery and pre-clinical development by employing a series of standardized in vitro and in vivo studies with known CYP inducers and inhibitors. Later therapeutic drug monitoring programmes, clinical studies and case reports offer methods to identify additional clinically significant drug interactions. Some commonly co-administered drugs with a significant potential for drug-drug interactions with selected SGAs include some SSRIs. Antiepileptic mood stabilizers such as carbamazepine and valproate, as well as other antiepileptic drugs such as phenobarbital and phenytoin, may decrease plasma SGA concentrations. Some anti-infective agents such as protease inhibitors and fluoroquinolones are of concern as well. Two additional important factors that influence drug interactions with SGAs are dose and time dependence. Smoking is very common among psychiatric patients and can induce CYP1A2 enzymes, thereby lowering expected plasma levels of certain SGAs. It is recommended that ziprasidone and lurasidone are taken with food to promote drug absorption, otherwise their bioavailability can be reduced. Clinicians must be aware of the variety of factors that can increase the likelihood of clinically significant drug interactions with SGAs, and must carefully monitor patients to maximize treatment efficacy while minimizing adverse events.

Drug-drug conditioning between citalopram and haloperidol or olanzapine in a conditioned avoidance response model: implications for polypharmacy in schizophrenia

Patients with schizophrenia often have anxiety and depression, and thus are treated with multiple psychotherapeutic medications. This practice of polypharmacy increases the possibility for drug-drug interactions. However, the pharmacological and behavioral mechanisms underlying drug-drug interactions in schizophrenia remain poorly understood. In the present study, we adopted a preclinical approach and examined a less known behavioral mechanism, drug-drug conditioning (DDC) between haloperidol (a typical antipsychotic) or olanzapine (atypical antipsychotic) and citalopram (a selective serotonin reuptake inhibitor). A rat two-way conditioned avoidance response paradigm was used to measure antipsychotic activity and determine how DDC may alter the antipsychotic efficacy in this model. Following acquisition of the avoidance response, rats were then randomly assigned to receive vehicle, citalopram (10.0 mg/kg, intraperitoneally), haloperidol (0.05 mg/kg, subcutaneously), olanzapine (1.0 mg/kg, subcutaneously), combined haloperidol with citalopram, or combined olanzapine with citalopram treatment for seven avoidance test sessions. In comparison with antipsychotic treatment alone, combined treatment with citalopram potentiated the antiavoidance effect of olanzapine or haloperidol (to a lesser extent) during the seven drug-test sessions. In addition, repeated pairing of citalopram with haloperidol or olanzapine caused citalopram to show a newly acquired avoidance-disruptive effect. This effect was context specific because citalopram paired with haloperidol or olanzapine outside the avoidance testing context (i.e. home cages) did not show such an effect. These findings indicate that concurrent antidepressant and antipsychotic treatments may engender a DDC process that follows the general Pavlovian associative conditioning principles. They also indicate that adjunctive citalopram treatment may enhance the antipsychotic efficacy of haloperidol and olanzapine in the treatment of schizophrenia.

Drug dosage individualization based on a random-effects linear model

This article investigates drug dosage individualization when the patient population can be described with a random-effects linear model of a continuous pharmacokinetic or pharmacodynamic response. Specifically, we show through both decision-theoretic arguments and simulations that a published clinical algorithm may produce better individualized dosages than some traditional methods of therapeutic drug monitoring. Since empirical evidence suggests that the linear model may adequately describe drugs and patient populations, and linear models are easier to handle than the nonlinear models traditionally used in population pharmacokinetics, our results highlight the potential applicability of linear mixed models to dosage computations and personalized medicine.

Role of Statistical Random-Effects Linear Models in Personalized Medicine

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.

Interactions between antiepileptics and second-generation antipsychotics

INTRODUCTION

Pharmacokinetic and pharmacodynamic drug interactions (DIs) can occur between antiepileptics (AEDs) and second-generation antipsychotics (SGAPs). Some AED and SGAP pharmacodynamic mechanisms are poorly understood. AED-SGAP combinations are used for treating comorbid illnesses or increasing efficacy, particularly in bipolar disorder.

AREAS COVERED

This article provides a comprehensive review of the interactions between antiepileptics and second-generation antipsychotics. The authors cover pharmacokinetic AED-SGAP DI studies, the newest drug pharmacokinetics in addition to the limited pharmacodynamic DI studies.

EXPERT OPINION

Dosing correction factors and measuring SGAP levels can help to compensate for the inductive properties of carbamazepine, phenytoin, phenobarbital and primidone. Further studies are needed to establish the clinical relevance of combining: i) AED strong inducers with amisulpride, asenapine, iloperidone, lurasidone and paliperidone; ii) valproate with aripiprazole, asenapine, clozapine and olanzapine; iii) high doses of oxcarbazepine (≥ 1500 mg/day) or topiramate (≥ 400 mg/day) with aripiprazole, lurasidone, quetiapine, risperidone, asenapine and olanzapine. Two pharmacodynamic DIs are beneficial: i) valproate-SGAP combinations may have additive effects in bipolar disorder, ii) combining topiramate or zonisamide with SGAPs may decrease weight gain. Three pharmacodynamic DIs contributing to decreased safety are common: sedation, weight gain and swallowing disturbances. A few AED-SGAP combinations may increase risk for osteoporosis or nausea. Three potentially lethal but rare pharmacodynamic DIs include pancreatitis, agranulocytosis/leukopenia and heat stroke. The authors believe that collaboration is needed from drug agencies and pharmaceutical companies, the clinicians using these combinations, researchers with expertise in meta-analyses, grant agencies, pharmacoepidemiologists and DI pharmacologists for future progression in this field.

Effect of valproate on olanzapine plasma concentrations in patients with bipolar or schizoaffective disorder

The effect of valproate on the steady-state plasma concentrations of olanzapine was investigated in 18 patients with bipolar or schizoaffective disorder. Additional valproate, at a dose ranging from 600 to 2000 mg/d, was administered for 4 weeks to patients stabilized on olanzapine (5-20 mg/d). During valproate coadministration, mean plasma olanzapine concentrations decreased significantly from 32.9 +/- 9.7 ng/mL at baseline to 27.4 +/- 9.8 ng/mL at week 2 (P = 0.02), and to 26.9 +/- 9.2 ng/mL at week 4 (P = 0.001). Smoking also decreased plasma olanzapine concentrations. Valproate coadministration with olanzapine was well tolerated and no patient showed a worsening of his or her psychopathological condition. These findings indicate that valproate, at doses of up to 2000 mg/d, is associated with a minimal, presumably not clinically significant, decrease in plasma olanzapine concentrations, possibly as a result of induction of olanzapine metabolism. New studies are needed to confirm that valproate could have mild inductive effects.

Practical guidelines for the use of new generation antipsychotic drugs (except clozapine) in adult individuals with intellectual disabilities

New generation antipsychotic (NGA) drugs introduced to the US market after clozapine (aripiprazole, olanzapine, paliperidone, quetiapine, risperidone, and ziprasidone) are frequently used in individuals with intellectual disabilities (ID). However, there is very limited research to fully establish evidence-based or personalized medicine approaches for their use in this population. These guidelines take a pragmatic approach to establishing frameworks for their use by utilizing the prescribing information and reviewing the available literature on other relevant neuropsychiatric disorders. In the absence of expert consensus guidance and well-controlled comparison trials, we present a set of guidelines to inform initiation, dosing and monitoring of use in adults. Further, in these guidelines we provide practical information on drug-drug interactions and adverse drug reactions, and a brief review of discontinuation syndromes, potential for abuse, use during pregnancy and cost considerations. We also provide drug utilization review forms for each NGA to facilitate implementation of these guidelines, these guidelines provide a practical and necessary resource for practitioners treating psychiatric disorders and challenging behaviors in adult individuals with ID.

Pharmacogenomics: the promise of personalized medicine for CNS disorders

This review focuses first on the concept of pharmacogenomics and its related concepts (biomarkers and personalized prescription). Next, the first generation of five DNA pharmacogenomic tests used in the clinical practice of psychiatry is briefly reviewed. Then the possible involvement of these pharmacogenomic tests in the exploration of early clinical proof of mechanism is described by using two of the tests and one example from the pharmaceutical industry (iloperidone clinical trials). The initial attempts to use other microarray tests (measuring RNA expression) as peripheral biomarkers for CNS disorders are briefly described. Then the challenge of taking pharmacogenomic tests (compared to drugs) into clinical practice is explained by focusing on regulatory oversight, the methodological/scientific issues concerning diagnostic tests, and cost-effectiveness issues. Current information on medicine-based evidence and cost-effectiveness usually focuses on average patients and not the outliers who are most likely to benefit from personalized prescription. Finally, future research directions are suggested. The future of 'personalized prescription' in psychiatry requires consideration of pharmacogenomic testing and environmental and personal variables that influence pharmacokinetic and pharmacodynamic drug response for each individual drug used by each patient.