Lack of pharmacologic interaction between paroxetine and alprazolam at steady state in healthy volunteers

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

A 6-week randomized controlled trial with 4-week follow-up of acupuncture combined with paroxetine in patients with major depressive disorder

Acupuncture possesses the antidepressant potential. In this 6-week randomized controlled trial with 4-week follow-up, 160 patients with major depressive disorder (MDD) were randomly assigned to paroxetine (PRX) alone (n = 48) or combined with 18 sessions of manual acupuncture (MA, n = 54) or electrical acupuncture (EA, n = 58). Treatment outcomes were measured mainly using the 17-item Hamilton Depression Rating Scale (HAMD-17), Self-rating Depression Scale (SDS), clinical response and remission rates. Average PRX dose taken and proportion of patients who required an increased PRX dose due to symptom aggravation were also obtained. Both additional MA and EA produced a significantly greater reduction from baseline in score on HAMD-17 and SDS at most measure points from week 1 through week 6 compared to PRX alone. The clinical response was markedly greater in MA (69.8%) and EA (69.6%) groups than the group treated with PRX alone (41.7%, P = 0.004). The proportion of patients who required an increase dose of PRX due to symptom aggravation was significantly lower with MA (5.7%) and EA (8.9%) than PRX alone (22.9%, P = 0.019). At 4 weeks follow-up after completion of acupuncture treatment, patients with EA, but not MA, continued to show significantly greater clinical improvement. Incidence of adverse events was not different in the three groups. Our study indicates that acupuncture can accelerate the clinical response to selective serotonin reuptake inhibitors (SSRIs) and prevent the aggravation of depression. Electrical acupuncture may have a long-lasting enhancement of the antidepressant effects (Trial Registration: ChiCTR-TRC-08000278).

Understanding the pharmacokinetics of anxiolytic drugs

INTRODUCTION

Anxiety disorders are considered the most common mental disorders and they can increase the risk for comorbid mood and substance use disorders, significantly contributing to the global burden of disease. For this reason, anxiolytics are the most prescribed psychoactive drugs, particularly in the Western world.

AREAS COVERED

This review aims to analyze pharmacokinetic profile, plasma level variations so as the metabolism, interactions and possible relation to clinical effect of several drugs which are used primarily as anxiolytics. The drugs analyzed include benzodiazepines, anticonvulsants (pregabalin, gabapentin), buspirone, β-blockers and antihistamines (hydroxyzine). Regarding the most frequently used anxiolytic benzodiazepines, data on alprazolam, bromazepam, chlordesmethyldiazepam, chlordiazepoxide, clotiazepam, diazepam, etizolam, lorazepam, oxazepam, prazepam and clonazepam have been detailed.

EXPERT OPINION

There is a need for a more balanced assessment of the benefits and risks associated with benzodiazepine use, particularly considering pharmacokinetic profile of the drugs to ensure that patients, who would truly benefit from these agents, are not denied appropriate treatment. An optimal pharmacological approach involving an integrative pharmacokinetic and pharmacodynamic optimization strategy would ensure better treatment and personalization of anxiety disorders. So it would be desirable for the development of new anxiolytic drug(s) that are more selective, fast acting and free from the unwanted effects associated with the traditional benzodiazepines as tolerance or dependence.

Clinically relevant drug interactions in anxiety disorders

OBJECTIVE

Certain drugs used in the treatment of patients with anxiety disorders can interact with other psychotropic drugs and with pharmacological treatments for physical illnesses. There is a need for an updated comparative review of clinically relevant drug interactions in this area.

DESIGN

Relevant literature on drug interactions with medications used in the treatment of anxiety disorders was identified through a search in MEDLINE and EMBASE.

RESULTS

Drug interactions involving medications used to treat anxiety disorders may be pharmacokinetic, such as enzyme inhibition or induction in the cytochrome P450 system and transporter-mediated drug interactions, or pharmacodynamic, such as additive effects in causing drowsiness or additive effects at neurotransmitter receptors. Certain selective serotonin reuptake inhibitors (fluoxetine, fluvoxamine, and paroxetine) are particularly liable to be potentially involved in untoward pharmacokinetic interactions.

CONCLUSIONS

The potential for drug interactions with medications used in anxiety disorders should be the cause of clinical concern, particularly in elderly individuals. However, the liability for harmful drug interactions may be anticipated, and the risk reduced. Although not all interactions are clinically relevant, careful monitoring of clinical response and possible interactions is essential.

Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods

The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.

Interaction study of the combined use of paroxetine and fosamprenavir-ritonavir in healthy subjects

Human immunodeficiency virus-infected patients have an increased risk for depression. Despite the high potential for drug-drug interactions, limited data on the combined use of antidepressants and antiretrovirals are available. Theoretically, ritonavir-boosted protease inhibitors may inhibit CYP2D6-mediated metabolism of paroxetine. We wanted to determine the effect of fosamprenavir-ritonavir on paroxetine pharmacokinetics and vice versa and to evaluate the safety of the combination. Group A started with 20 mg paroxetine every day for 10 days; after a wash-out period of 16 days, subjects received paroxetine (20 mg every day) plus fosamprenavir-ritonavir (700/100 mg twice a day) from days 28 to 37. Group B received the regimens in reverse order. On days 10 and 37, pharmacokinetic curves were recorded. Twenty-six healthy subjects (18 females, 8 males) were included. Median (range) age and weight were 44.4 (18.2 to 64.3) years and 68.8 (51.0 to 89.4) kg. Three subjects were excluded (two because of adverse events; one for nonadherence). Addition of fosamprenavir-ritonavir to paroxetine resulted in a significant decrease in paroxetine exposure: the geometric mean ratios (90% confidence intervals) of paroxetine plus fosamprenavir-ritonavir to paroxetine alone were 0.45 (0.41 to 0.49) for the area under the concentration-time curve from 0 to 24 h (AUC(0-24)), 0.49 (0.45 to 0.53) for the maximum concentration of the drug in plasma (C(max)), and 0.75 (0.71 to 0.80) for the apparent elimination half-life (t(1/2)). The free fraction of paroxetine showed a median (interquartile range) increase of 30% (18 to 42%) after the addition of fosamprenavir-ritonavir. The AUC(0-12), C(max), C(min), and t(1/2) of amprenavir and ritonavir were similar to those of historical controls. No serious adverse events occurred. Fosamprenavir-ritonavir reduced total paroxetine exposure by 55%. This is partly explained by protein displacement of paroxetine. We think that this interaction is clinically relevant and that titration to a higher dose of paroxetine may be necessary to accomplish the needed antidepressant effect.

Mechanism-Based Inactivation of Human Cytochrome P450 Enzymes and the Prediction of Drug-Drug Interactions

The ability to use vitro inactivation kinetic parameters in scaling to in vivo drug-drug interactions (DDIs) for mechanism-based inactivators of human cytochrome P450 (P450) enzymes was examined using eight human P450-selective marker activities in pooled human liver microsomes. These data were combined with other parameters (systemic C(max), estimated hepatic inlet C(max), fraction unbound, in vivo P450 enzyme degradation rate constants estimated from clinical pharmacokinetic data, and fraction of the affected drug cleared by the inhibited enzyme) to predict increases in exposure to drugs, and the predictions were compared with in vivo DDIs gathered from clinical studies reported in the scientific literature. In general, the use of unbound systemic C(max) as the inactivator concentration in vivo yielded the most accurate predictions of DDI with a mean -fold error of 1.64. Abbreviated in vitro approaches to identifying mechanism-based inactivators were developed. Testing potential inactivators at a single concentration (IC(25)) in a 30-min preincubation with human liver microsomes in the absence and presence of NADPH followed by assessment of P450 marker activities readily identified those compounds known to be mechanism-based inactivators and represents an approach that can be used with greater throughput. Measurement of decreases in IC(50) occurring with a 30-min preincubation with liver microsomes and NADPH was also useful in identifying mechanism-based inactivators, and the IC(50) measured after such a preincubation was highly correlated with the k(inact)/K(I) ratio measured after a full characterization of inactivation. Overall, these findings support the conclusion that P450 in vitro inactivation data are valuable in predicting clinical DDIs that can occur via this mechanism.

Quantifying drug-drug interactions in pharmaco-EEG

A drug interaction refers to an event in which the usual pharmacological effect of a drug is modified by other factors, most frequently additional drugs. When two drugs are administered simultaneously, or within a short time of each other, an interaction can occur that may increase or decrease the intended magnitude or duration of the effect of one or both drugs. Drugs may interact on a pharmaceutical, pharmacokinetic or pharmacodynamic basis. Pharmacodynamic interactions arise when the alteration of the effects occurs at the site of action. This is a wide field where not only interactions between different drugs are considered but also drug and metabolites (midazolam/alpha-hydroxy-midazolam), enantiomers (ketamine), as well as phenomena such as tolerance (nordiazepam) and sensitization (diazepam). Pharmacodynamic interactions can result in antagonism or synergism and can originate at a receptor level (antagonism, partial agonism, down-regulation, up-regulation), at an intraneuronal level (transduction, uptake), or at an interneuronal level (physiological pathways). Alternatively, psychotropic drug interactions assessed through quantitative pharmaco-EEG can be viewed according to the broad underlying objective of the study: safety-oriented (ketoprofen/theophylline, lorazepam/diphenhydramine, granisetron/haloperidol), strictly pharmacologically-oriented (benzodiazepine receptors), or broadly neuro-physiologically-oriented (diazepam/buspirone). Methodological issues are stressed, particularly drug plasma concentrations, dose-response relationships and time-course of effects (fluoxetine/buspirone), and unsolved questions are addressed (yohimbine/caffeine, hydroxizyne/alcohol).

Sleep laboratory study on single and repeated dose effects of paroxetine, alprazolam and their combination in healthy young volunteers

AIMS

To evaluate the potential interaction of 20 mg paroxetine and 1 mg alprazolam (early morning once-daily administration) on polysomnographic (PSG) sleep and subjective sleep and awakening quality, both after a single intake and after reaching a steady-state concentration.

METHODS

Twenty-two (11 for the PSG) healthy young volunteers of both sexes with no history of sleep disturbances (Pittsburgh Sleep Quality Index <5) participated in a double-blind, double-dummy, placebo-controlled, repeated-dose, 4-period, cross-over study. All volunteers received all 4 treatment sequences: paroxetine-alprazolam placebo (PAP); paroxetine placebo-alprazolam (PPA); paroxetine-alprazolam (PA), and paroxetine placebo-alprazolam placebo (PLA), in a randomized order. Each treatment was administered over 15 consecutive days, with a treatment-free interval of 7 days prior to the subsequent study period. In each experimental period, one PSG sleep study was performed on the 1st night (single-dose effects) and another study was performed on the 15th night (repeated-dose effects). Additionally, two other PSG studies were assessed: an adaptation recording, and a control night recording. All-night PSG recordings were obtained following standard procedures. Each 30-second period was scored according to the criteria of Rechtschaffen and Kales by means of an automatic sleep analysis system: Somnolyzer 24x7. A self-rating scale for sleep and awakening quality and early morning behavior was completed no later than 15 min after awakening over the 15 days of each experimental intervention. General lineal models (treatment/time) were applied separately to each variable.

RESULTS

(1) No significant effects were observed in any sleep variables when control nights were compared with the 1st night with PLA. (2) Sleep continuity: After PAP a clear awakening effect was seen both in the first and second evaluations, mainly in wake time, movement time, number of awakenings and stage-1 duration. After PPA an evident hypnotic effect was observed on night 1. This effect was mainly observed in maintenance variables and slightly in sleep initiation variables; it had decreased by night 15. After PA an intermediate behavior in the variables related to sleep continuity was seen, highlighting the absence of the tolerance phenomenon observed when PPA was administered alone. (3) Sleep architecture: The most important effects in REM sleep were observed after PAP; an increase in REM latency and decreases in REM sleep. PAP also induced decreases in the number of non-REM and REM periods and increases in the average duration of non-REM periods and sleep cycles. PA presented a similar pattern to PAP, and PPA similar to PLA. In relation to non-REM sleep, PA showed more stage-2 and less slow-wave sleep (SWS). (4) Subjective perception: No significant differences were observed between treatments while they were being taken, but impairments in subjective sleep quality, awaking quality, latency and efficiency were seen, mainly after PA but also after PPA discontinuations.

CONCLUSION

The combination of PAP and PPA presented an intermediate pattern in relation to sleep continuity, with less awaking effect than PAP alone and less hypnotic effect than PPA alone, and without developing tolerance. The PAP and PPA combination also showed a similar effect to PAP on REM sleep and was the treatment with the longest stage 2 and shortest SWS. No subjective sleep and awakening effects were seen during drug intake but subjective withdrawal reports were seen after abrupt interruption. The high agreement rate for the epoch-by-epoch comparison between automatic and human scoring confirms the validity of the Somnolyzer 24x7 and thus facilitates sleep studies in neuropsychopharmacological research.

Fluoxetine during pregnancy: impact on fetal development

Women are at greatest risk of suffering from depression during the childbearing years and thus may either become pregnant while taking an antidepressant or may require a prescription for one during pregnancy. The antidepressant fluoxetine (FX) is a selective serotonin reuptake inhibitor (SSRI), which increases serotonin neurotransmission. Serotonin is involved in the regulation of a variety of physiological systems, including the sleep–wake cycle, circadian rhythms and the hypothalamic–pituitary–adrenal axis. Each of these systems also plays an important role in fetal development. Compared with other antidepressant drugs, the SSRIs, such as FX, have fewer side effects. Because of this, they are now frequently prescribed, especially during pregnancy. Clinical studies suggest poor neonatal outcome after exposure to FX in utero. Recent studies in the sheep fetus describe the physiological effects of in utero exposure to FX with an 8 day infusion during late gestation in the sheep. This is a useful model for determining the effects of FX on fetal physiology. The fetus can be studied for weeks in its normal intrauterine environment with serial sampling of blood, thus permitting detailed studies of drug disposition in both mother and fetus combined with monitoring of fetal behavioural state and cardiovascular function. Fluoxetine causes an acute increase in plasma serotonin levels, leading to a transient reduction in uterine blood flow. This, in turn, reduces the delivery of oxygen and nutrients to the fetus, thereby presenting a mechanism for reducing growth and/or eliciting preterm delivery. Moreover, because FX crosses the placenta, the fetus is exposed directly to FX, as well as to the effects of the drug on the mother. Fluoxetine increases high-voltage/non-rapid eye movement behavioural state in the fetus after both acute and chronic exposure and, thus, may interfere with normal fetal neurodevelopment. Fluoxetine also alters hypothalamic function in the adult and increases the magnitude of the prepartum rise in fetal cortisol concentrations in sheep. Fetal FX exposure does not alter fetal circadian rhythms in melatonin or prolactin. Studies of the effects of FX exposure on fetal development in the sheep are important in defining possible physiological mechanisms that explain human clinical studies of birth outcomes after FX exposure. To date, there have been insufficient longer-term follow-up studies in any precocial species of offspring exposed to SSRIs in utero. Thus, further investigation of the long-term consequences of in utero exposure to FX and other SSRIs, as well as the mechanisms involved, are required for a complete understanding of the impact of these agents on development. This should involve studies in both humans and appropriate animal models.