Epidemiologic investigation of the relative clearance of haloperidol by mixed-effect modeling using routine clinical pharmacokinetic data in Japanese patients

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.

Inherent correlation between dose and clearance in therapeutic drug monitoring settings: possible misinterpretation in population pharmacokinetic analyses

During the course of therapeutic drug monitoring (TDM), doses are adjusted to attain a target concentration range and a correlation between clearance (CL) and dose is introduced. In population pharmacokinetic analyses of such TDM data, CL has frequently been modeled as a function of dose. This paper demonstrates by simulation methodology that the TDM process does indeed introduce a correlation between dose and CL which can be interpreted as a nonlinearity. Using literature values of carbamazepine pharmacokinetics, three steady-state concentrations were simulated following a standard 1000 mg total daily dose (TDD) regimen in 100 in silico subjects. A set of clinical rules was established to adjust the TDD based on these three concentrations, as might be done in the clinical setting. Another set of concentrations using these TDM-derived TDDs for each subject (600-1600 mg) was simulated. A standard population pharmacokinetic analysis of the post-TDM data was conducted using NONMEM. This process was replicated 100 times to estimate the type II error rate. When CL was modeled without TDD, plots of WRES versus PRED demonstrated a clear pattern, as did the delta plots of CL (CL minus TVCL) versus TDD, suggesting the covariate TDD should be incorporated into the model. After TDD was included in the model for CL, the objective function value decreased by an average of 75.7 (p < 0.001). In addition, the inter-individual variability in CL expressed as a coefficient of variation decreased by an average of 9.9% and the diagnostic plots improved. Although CL was simulated to be independent of TDD, it was identified as an important covariate using standard approaches in a simulated TDM setting in 100% of the replicated simulation studies. The TDM process introduces a correlation between CL and TDD that can be misinterpreted as nonlinearity in the system.

Interactions between Antiepileptic and Antipsychotic Drugs

Antiepileptic and antipsychotic drugs are often prescribed together. Interactions between the drugs may affect both efficacy and toxicity. This is a review of human clinical data on the interactions between the antiepileptic drugs carbamazepine, valproic acid (sodium valproate), vigabatrin, lamotrigine, gabapentin, topiramate, tiagabine, oxcarbazepine, levetiracetam, pregabalin, felbamate, zonisamide, phenobarbital and phenytoin with the antipsychotic drugs risperidone, olanzapine, quetiapine, clozapine, amisulpride, sulpiride, ziprasidone, aripiprazole, haloperidol and chlorpromazine; the limited information on interactions between antiepileptic drugs and zuclopenthixol, periciazine, fluphenazine, flupenthixol and pimozide is also presented. Many of the interactions depend on the induction or inhibition of the cytochrome P450 isoenzymes, but other important mechanisms involve the uridine diphosphate glucuronosyltransferase isoenzymes and protein binding. There is some evidence for the following effects. Carbamazepine decreases the plasma concentrations of both risperidone and its active metabolite. It also decreases concentrations of olanzapine, clozapine, ziprasidone, haloperidol, zuclopenthixol, flupenthixol and probably chlorpromazine and fluphenazine. Quetiapine increases the ratio of carbamazepine epoxide to carbamazepine and this may lead to toxicity. The data on valproic acid are conflicting; it may either increase or decrease clozapine concentrations, and it appears to decrease aripiprazole concentrations. Chlorpromazine possibly increases valproic acid concentrations. Lamotrigine possibly increases clozapine concentrations. Phenobarbital decreases clozapine, haloperidol and chlorpromazine concentrations. Phenytoin decreases quetiapine, clozapine, haloperidol and possibly chlorpromazine concentrations. There are major gaps in the data. In many cases there are no published clinical data on interactions that would be predicted on theoretical grounds.

[Development of new chronopharmacotherapies based on biological rhythm]

The mammalian circadian pacemaker resides in the paired suprachiasmatic nuclei (SCN). Clock genes are the genes that control the circadian rhythms of physiology and behavior. The effectiveness and toxicity of many drugs vary depending on dosing time associated with 24-h rhythms of biochemical, physiological, and behavioral processes under the control of the circadian clock. However, many drugs are still administered without regard to the time of day. Identification of a rhythmic marker for selecting dosing time will lead to improved progress and diffusion of chronopharmacotherapy. The monitoring of rhythmic markers may be useful in choosing the most appropriate time of day for administration of drugs and may increase their therapeutic effects and/or reduce their side effects. On the other hand, several drugs can cause alterations in 24-h rhythms, leading to illness and altered homeostatic regulation. Here, we show the disruptive effect of interferon on the rhythm of locomotor activity, body temperature, and clock gene mRNA expression in the periphery and SCN. The alteration of the clock function, a new concept of adverse effects, can be overcome by devising a dosing schedule that minimizes adverse drug effects on clock function. Furthermore, to produce new rhythmicity by manipulating the conditions of living organs using rhythmic administration of altered feeding schedules or several drugs appears to lead to the new concept of chronopharmacotherapy. One approach to increasing the efficiency of pharmacotherapy is administering drugs at times during which they are best effective and/or tolerated.

Therapeutic drug monitoring databases for postmarketing surveillance of drug-drug interactions

Drug-drug interactions can be associated with patient morbidity due to either increased toxicity or a potentially ineffective concentration. Because interactions cannot always be anticipated during drug development and actual patients receiving a drug for therapeutic use often differ from those included in clinical trials, postmarketing surveillance is essential. Therapeutic drug monitoring (TDM) databases offer a unique opportunity in this respect. Prerequisites for TDM databases to provide valid information in a pharmacoepidemiological perspective include the following: precise description of exposure to the potentially interacting drugs; measurement of parent compound and active metabolites through accurate and precise analytical techniques; documentation of relevant patient characteristics that may act as confounding factors (e.g. gender, age, smoking habits); repeated assessments over time if possible; and sound pharmacokinetic framework for data selection, analysis and interpretation. The contribution of TDM to the documentation of drug-drug interactions takes advantage of different possible study designs, discussed on the basis of recently published studies. The single case report plays an important role as an alert signal. It is illustrated for a patient on long-term treatment, who displayed an unexpectedly high clozapine concentration after the introduction of ciprofloxacin comedication. The prospective on and off comedication panel study shows advantages in terms of carefully selected inclusion criteria and control of treatment modalities. A study of the thioridazine-fluvoxamine interaction is presented, with patients followed on thioridazine monotherapy, after introduction of fluvoxamine and after its discontinuation. The main advantage of the retrospective large-scale TDM database screen is representativeness of patients actually treated, whereas drawbacks are related to quality of data and suitability for valid interpretation. Such an approach is illustrated by a review of data collected over 10 years of routine TDM that allowed documenting induction of nortriptyline metabolism by carbamazepine and inhibition by several phenothiazines. Finally, population pharmacokinetics is well suited to observational data collected for TDM purpose, provided quality is ascertained. Focus is placed on interindividual variability and relationship between pharmacokinetic parameters and patient characteristics, including comedication. The population approach is discussed with respect to a study that documented a 32% increase of haloperidol clearance associated with anticonvulsant comedication, in addition to effects of age and bodyweight. Among factors to consider for improved effectiveness in the use of TDM databases for postmarketing surveillance of drug-drug interactions, integration of efficacy and safety data in future studies and communication of expert recommendations to prescribing physicians are essential.