Co-administration of sertraline and haloperidol

Pharmacokinetic drug interactions with oral haloperidol in adults: dose correction factors from a combined weighted analysis

INTRODUCTION

Pharmacokinetic (PK) drug-drug interactions (DDIs) of oral haloperidol, a first-generation antipsychotic, are systematically reviewed.

AREAS COVERED

After exclusions, the search for DDIs with oral haloperidol provided 47 articles as victim and 7 as perpetrator. Changes in mean haloperidol concentration-to-dose (C/D) ratios after weighting each study's size were used to calculate the effects of other drugs (inhibitors/inducers) on haloperidol. These changes of haloperidol C/D ratio were used to estimate dose-correction factors (<1 for inhibitors and >1 for inducers).

EXPERT OPINION

A box summarizes our recommendations for clinicians regarding our current knowledge of haloperidol PK DDIs, which will need to be updated as new information becomes available. Moderate to strong inducers (carbamazepine, phenobarbital, phenytoin, or rifampin) should be avoided since they required dose-correction factors of 2-5. Smoking appeared to be a weak inducer (dose-correction factor 1.2). Fluvoxamine, promethazine, and combinations of CYP3A4 and CYP2D6 inhibitors should be avoided. There are no long-term studies on fluoxetine to provide a dose correction factor. Limited information suggests that valproate may be an inhibitor (dose-correction factor 0.6). In most patients, haloperidol may not have clinically relevant effects as a perpetrator, but in vitro and clinical studies suggest it is a weak CYP2D6 inhibitor.

Combinatorial therapy discovery using mixed integer linear programming

MOTIVATION

Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction.

RESULTS

Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set.

AVAILABILITY

Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/.

CONTACT

zhandong.liu@bcm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Serum monitoring of antipsychotic drug levels during concomitant administration of sertraline and antipsychotic medication

OBJECTIVE

To assess whether pharmacokinetic drug interactions occur when sertraline is added to antipsychotic medications.

METHOD

Forty-eight patients with remitted DSM-IV schizophrenia and comorbid major depression were randomized to placebo for 6 weeks or sertraline 50 mg for 4 weeks followed by sertraline 50 mg to 100 mg for 2 weeks for nonresponders. Treatment with the patients' usual antipsychotic continued. Weekly clinical outcome assessments occurred for 6 weeks, and serum samples for drug monitoring were collected at Weeks 1, 5, and 6. Serum concentrations of sertraline and antipsychotics were measured with standard assays.

RESULTS

In both placebo- and sertraline-treated groups, most patients displayed minor fluctuations in antipsychotic serum levels over 6 weeks. There was no clinical evidence of drug interactions in the sertraline-treated group.

CONCLUSIONS

Clinically significant adverse effects did not occur despite variable antipsychotic serum levels with or without sertraline. Concern about pharmacokinetic interactions should not deter the use of sertraline for depression in individuals with schizophrenia.

Clinical pharmacokinetics of sertraline

Sertraline is a naphthalenamine derivative with the predominant pharmacological action of inhibiting presynaptic reuptake of serotonin from the synaptic cleft. It was initially marketed for the treatment of major depressive disorder and is now approved for the management of panic disorder, obsessive-compulsive disorder and post-traumatic stress disorder. Sertraline is slowly absorbed following oral administration and undergoes extensive first-pass oxidation to form N-desmethyl-sertraline, a weakly active metabolite that accumulates to a greater concentration in plasma than the parent drug at steady state. Sertraline is eliminated from the body by other metabolic pathways to form a ketone and an alcohol, which are largely excreted renally as conjugates. The elimination half-life of sertraline ranges from 22-36 hours, and once-daily administration is therapeutically effective. Steady-state plasma concentrations vary widely, up to 15-fold, in patients receiving usual antidepressant dosages between 50 and 150 mg/day. However, only sparse data have been published that support useful correlations between sertraline plasma concentrations and therapeutic or adverse effects to justify therapeutic drug monitoring. Sertraline has minimal inhibitory effects on the major cytochrome P450 enzymes, and few drug-drug interactions of clinical significance have been documented. Like other selective serotonin reuptake inhibitors, sertraline is well tolerated in therapeutic dosages and relatively safe in overdosage.

Clinically significant drug interactions with antidepressants in the elderly

Pharmacological treatment of depression in old age is associated with an increased risk of adverse pharmacokinetic and pharmacodynamic drug interactions. Elderly patients may have multiple disease states and, therefore, may require a variety of other drugs. In addition to polypharmacy, other factors such as age-related physiological changes, diseases, genetic constitution and diet may alter drug response and, therefore, predispose elderly patients to adverse effects and drug interactions. Antidepressant drugs currently available differ in their potential for drug interactions. In general, older compounds, such as tricyclic antidepressants (TCAs) and monoamine oxidase inhibitors (MAOIs), have a higher potential for interactions than newer compounds, such as selective serotonin reuptake inhibitors (SSRIs) and other relatively novel agents with a more specific mechanism of action. In particular, TCAs and MAOIs are associated with clinically significant pharmacodynamic interactions with many medications frequently prescribed to elderly patients. Moreover, TCAs may be susceptible to pharmacokinetic interactions when given in combination with inhibitors or inducers of the cytochrome P450 (CYP) isoenzymes involved in their metabolism. Because of a more selective mechanism of action, newer antidepressants have a low potential for pharmacodynamic drug interactions. However, the possibility of the serotonin syndrome should be taken into account when drugs affecting serotonergic transmission, such as SSRIs, venlafaxine or nefazodone, are coadministered with other serotonergic agents. Newer agents have a differential potential for pharmacokinetic interactions because of their selective effects on CYP isoenzymes. Within the group of SSRIs, fluoxetine and paroxetine are potent inhibitors of CYP2D6, while fluvoxamine predominantly affects CYP1A2 and CYP2C19 activity. Therefore, these agents should be closely monitored or avoided in elderly patients treated with substrates of these isoforms, especially those with a narrow therapeutic index. On the other hand, citalopram and sertraline have a low inhibitory activity on different drug metabolising enzymes and appear particularly suitable in an elderly population. Among other newer antidepressants, nefazodone is a potent inhibitor of CYP3A4 and its combination with substrates of this isoform should be avoided.