Selective LC-MS/MS determination of citalopram enantiomers and application to a pharmacokinetic evaluation of generic and reference formulations.

Two methods using LC-MS/MS were validated to quantify citalopram (CTP) racemate [(R/S)-CTP] and the enantiomers (R)-CTP and (S)-CTP in human plasma, respectively. Paroxetine hydrochloride was used as the internal standard, and samples were extracted by protein precipitation with acetonitrile. The non-enantioselective method was conducted using a C18 column, and the mobile phase consisted of water for solvent A and acetonitrile for solvent B, both with 0.1% formic acid. For the chiral method, an analytical column Lux Cellulose-1 was used. Mobile phase A was composed of water with 0.025% of formic acid and 0.05% of diethylamine, and mobile phase B consisted of acetonitrile:2-propanol (95:5, v/v). No significant matrix effects were observed at the retention times of analytes and internal standard. The mean recovery was 89%, and the assays were linear in the concentration range of 1-50 and 5-30 ng/mL for the non-enantioselective and enantioselective methods, respectively. The intra- and inter-day precisions of both methods were less than 12.30%, and the accuracies were less than 12.13%. The validated methods were successfully applied to a pharmacokinetic study in which 20-mg CTP tablets were administered to healthy volunteers, and their plasma levels were monitored over time in a bioequivalence study. HIGHLIGHTS: Simple and rapid LC-MS/MS method for the quantification of citalopram and its enantiomers in human plasma. Both methods were demonstrated to be selective, reliable, and sensitive. Both methods have sufficient sensitivity to quantify the steady state through concentrations already reported for citalopram and escitalopram. Validated method presented in this study can be suitably applied to pharmacokinetic studies involving citalopram and escitalopram. Bland-Altman analysis suggested that non-enantioselective and enantioselective methods can be applied in pharmacokinetic studies.

Citalopram and Cannabidiol: In Vitro and In Vivo Evidence of Pharmacokinetic Interactions Relevant to the Treatment of Anxiety Disorders in Young People.

BACKGROUND: Cannabidiol (CBD), a major nonintoxicating constituent of cannabis, exhibits anxiolytic properties in preclinical and human studies and is of interest as a novel intervention for treating anxiety disorders. Existing first-line pharmacotherapies for these disorders include selective serotonin reuptake inhibitor and other antidepressants. Cannabidiol has well-described inhibitory action on cytochrome P450 (CYP450) drug-metabolizing enzymes and significant drug-drug interactions (DDIs) between CBD and various anticonvulsant medications (eg, clobazam) have been described in the treatment of epilepsy. Here, we examined the likelihood of DDIs when CBD is added to medications prescribed in the treatment of anxiety. METHODS: The effect of CBD on CYP450-mediated metabolism of the commonly used antidepressants fluoxetine, sertraline, citalopram, and mirtazapine were examined in vitro. Cannabidiol-citalopram interactions were also examined in vivo in patients (n = 6) with anxiety disorders on stable treatment with citalopram or escitalopram who received ascending daily doses of adjunctive CBD (200-800 mg) over 12 weeks in a recent clinical trial. RESULTS: Cannabidiol minimally affected the metabolism of sertraline, fluoxetine, and mirtazapine in vitro. However, CBD significantly inhibited CYP3A4 and CYP2C19-mediated metabolism of citalopram and its stereoisomer escitalopram at physiologically relevant concentrations, suggesting a possible in vivo DDI. In patients on citalopram or escitalopram, the addition of CBD significantly increased citalopram plasma concentrations, although it was uncertain whether this also increased selective serotonin reuptake inhibitor-mediated adverse events. CONCLUSIONS: Further pharmacokinetic examination of the interaction between CBD and citalopram/escitalopram is clearly warranted, and clinicians should be vigilant around the possibility of treatment-emergent adverse effects when CBD is introduced to patients taking these antidepressants.

Plasma Concentrations and Cardiovascular Effects of Citalopram Enantiomers After Oral Versus Infusion Citalopram Therapy in Dextromethorphan-Mephenytoin-Phenotyped Patients With Major Depression.

BACKGROUND: Authors compared plasma concentrations of citalopram (CIT) enantiomers and their metabolites in patients with depression administered either intravenously (IV) or as oral racemic CIT. Then, plasma concentrations were related to the metabolism of probes used for phenotyping patients with depression for CYP2C19 and CYP2D6 activity and cardiovascular functions. METHODS: Dextromethorphan-mephenytoin-phenotyped patients with depression were administered racemic CIT (days 1 and 2: 20 mg/d; days 3-10: 40 mg/d) either orally or as a slow-drop infusion for 10 days and were then orally administered the drug for another 32 days. Blood probes were collected at the time of minimal and maximal concentrations on day 10, immediately before and 2 hours after drug administration, and on days 21 and 42. Plasma CIT and its metabolites were assayed by stereoselective high-performance liquid chromatography. RESULTS: The following concentrations (ng/mL) were noted in the group receiving active IV infusion (IV-POS group, n = 27) of racemic CIT on day 10, before drug administration: escitalopram (S-CIT): 24 ± 10.2; R-citalopram (R-CIT): 45 ± 14.5; S-desmethyl-CIT: 13 ± 4.4; and R-desmethyl-CIT: 17 ± 8.2. In patients receiving oral administration (POS-POS group, n = 25), the values were 30 ± 12.7, 51 ± 17.4, 13 ± 4.6, and 17 ± 7.9 ng/mL, respectively. In the IV-POS group, 3 patients were poor dextromethorphan (CYP2D6) metabolizers; in the POS-POS group, one was a poor mephenytoin (CYP2C19) metabolizer. On day 10, before CIT treatment, S/R-CIT and S/R-mephenytoin ratios were significantly correlated, determined at baseline. Overall, CIT reduced the heart rate but did not significantly modify QTc. No relationship was found between any cardiovascular parameters and pharmacokinetic and pharmacogenetic data. CONCLUSIONS: Owing to CIT's high bioavailability, the plasma concentrations of its enantiomers remained largely independent on the administration route. CYP2C19 preferentially demethylated S-CIT after CIT therapy.

Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression.

In clinical practice, an antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We predicted the transcriptomic profile of citalopram remitters by performing an in silico transcriptomic-wide association study on STAR*D GWAS data (N = 1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity-map database. Spearman correlation, Pearson correlation, and the Kolmogorov-Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p value for each rank by using a permutation procedure. The drugs with the top ranks were those having a high positive correlation with the expression profiles of remitters and that may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p = 0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second-highest ranked antidepressants, respectively (p = 0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters' and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements, it can be applicable at the individual level to tailor treatment prescription.

Comparison of Bioavailability and Bioequivalence of Generic and Brand Name Formulations of Escitalopram Oxalate Tablets in Healthy Chinese Population Under Fasting and Fed Conditions.

PURPOSE: This study compared the bioequivalence of two formulations of escitalopram oxalate 20 mg tablets in terms of bioavailability and tolerability in healthy Chinese male and female subjects. PATIENTS AND METHODS: A randomized, single-blind, two-period, two-sequence crossover study was performed under fasting and fed conditions, with a 21-day washout period. In total, 24 healthy subjects (18 males and 6 females) were enrolled in the fasting test and the fed test, respectively. Blood samples were collected over 168 h post-dose in each period. The concentrations of escitalopram in plasma were determined by high-performance liquid chromatography coupled with a tandem mass spectrometry. Pharmacokinetic parameters used for bioequivalence assessment were determined from the drug concentration data using noncompartmental analysis. RESULTS: All subjects showed good medication compliance. The 90% confidence intervals (CIs) for the geometric mean ratios of AUC0-t, AUC0-∞, and Cmax were within the bioequivalence acceptance criteria (80.00% to 125.00%). Adverse events were recorded and no deaths or serious adverse events (SAEs) occurred. CONCLUSION: Escitalopram oxalate 20 mg tablets produced in China were bioequivalent to the reference formulation (Lexapro®) in healthy Chinese male and female subjects under fasting and fed conditions.

Toxicity Profile, Pharmacokinetic, and Drug-Drug Interaction Study of Citalopram and Sertraline Following Oral Delivery in Rat: An LC-MS/MS Method for the Simultaneous Determination in Plasma.

The burden of depression and other mental disorders is on the rise globally, and successful treatment sometimes requires modifications of drugs and/or dose regimens. The development of novel analytical methods for the determination of antidepressant drugs in biological fluids is thus urgently required. Herein, a sensitive, robust, and rapid liquid chromatographic coupled tandem mass spectrometric method was developed and validated for the determination of citalopram (CIT) and sertraline (SER) in rat plasma after oral administration. The analytes of interest and internal standard (duloxetine (DUL)) were extracted from 100 µL of plasma with solid-phase extraction on an Oasis HLB cartridge followed by the separation with gradient elution with water containing 0.1% formic acid and acetonitrile on an Agilent Eclipse Plus ODS (4.6 × 100 mm, 3.5 µm) column at flow rate 0.2 mL min-1. The triple quadrupole mass spectrometry was applied via electrospray ionization source for detection. The fragmentation pattern of the protonated CIT, SER, and DUL was elucidated using multiple reaction monitoring of the transitions of m/z 325.2 to 109, 306.1 to 158.9, and 298.1 to 154.1 as [M + H]+ for CIT, SER, and DUL, respectively. The proposed method has been validated as per US-FDA bioanalytical guidelines in terms of linearity, accuracy, precision, matrix effects, stability, selectivity, and recovery. The method was linear over the concentration range of 1-2000 and 1-1000 ng mL-1 with the lower limit of detection of 0.12 and 0.19 ng mL-1 for CIT and SER, respectively. The interday and intraday precisions and accuracy expressed by the relative standard deviation and the relative standard error were both lower than 11.1% and 2.1%, respectively. The proposed method was successfully applied for the pharmacokinetics and drug monitoring studies of CIT and SER in rat plasma after a single oral dose of 120 mg kg-1 of CIT and SER. Coadministration of SER with CIT has affected the peak plasma concentrations, maximum plasma concentration time, area under the concentration-time curve, and oral clearance of CIT. Molecular modeling study showed that SER could competitively inhibit CYP2D6, the main enzyme involved in CIT metabolism. A possible drug-drug interaction in psychiatric patients undergoing chronic SER and CIT treatment is therefore worthy of more attention in an effort to avoid side effects and serotonin syndrome.

Escitalopram in Adolescents With Generalized Anxiety Disorder: A Double-Blind, Randomized, Placebo-Controlled Study.

BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are commonly used to treat pediatric anxiety disorders, including generalized anxiety disorder (GAD); however, their efficacy and tolerability are difficult to predict. This study evaluated the efficacy and tolerability of escitalopram in adolescents with GAD (DSM-IV-TR) and the impact of variants in HTR2A and serotonin transporter (SLC6A4) genes and cytochrome P450 2C19 (CYP2C19) phenotypes on response as well as CYP2C19 phenotype on escitalopram pharmacokinetics from February 2015 through November 2018. METHODS: Patients were treated with escitalopram (forced titration to 15 mg/d, then flexible titration to 20 mg/d) (n = 26, mean ± SD age: 14.8 ± 1.7 years) or placebo (n = 25, mean ± SD age: 14.9 ± 1.6 years) for 8 weeks. Outcomes were the change in scores on the Pediatric Anxiety Rating Scale (PARS) and Clinical Global Impressions (CGI) scales as well as vital signs and adverse events. Plasma escitalopram and desmethylcitalopram area under the curve during 24 hours (AUC0-24) and maximum concentration (Cmax) were determined and compared across CYP2C19 phenotypes. RESULTS: Escitalopram was superior to placebo for mean ± SD baseline-to-endpoint change in PARS (-8.65 ± 1.3 vs -3.52 ± 1.1, P = .005) and CGI scores, and increasing CYP2C19 metabolism was associated with decreases in escitalopram Cmax (P = .07) and AUC0-24 (P < .05). Vital signs, corrected QT interval, and adverse events were similar in patients who received escitalopram and placebo. CONCLUSIONS: Escitalopram reduces anxiety symptoms, and pharmacogenetics variables influence the trajectory and magnitude of improvement. Variation in CYP2C19 metabolism accounts for significant differences in escitalopram pharmacokinetics, raising the possibility that CYP2C19 phenotype should be considered when prescribing escitalopram. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02818751.

Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder.

Pharmacogenomic tests used to guide clinical treatment for major depressive disorder (MDD) must be thoroughly validated. One important assessment of validity is the ability to predict medication blood levels, which reflect altered metabolism. Historically, the metabolic impact of individual genes has been evaluated; however, we now know that multiple genes are often involved in medication metabolism. Here, we evaluated the ability of individual pharmacokinetic genes (CYP2C19, CYP2D6, CYP3A4) and a combinatorial pharmacogenomic test (GeneSight Psychotropic®; weighted assessment of all three genes) to predict citalopram/escitalopram blood levels in patients with MDD. Patients from the Genomics Used to Improve DEpression Decisions (GUIDED) trial who were taking citalopram/escitalopram at screening and had available blood level data were included (N=191). In multivariate analysis of the individual genes and combinatorial pharmacogenomic test separately (adjusted for age, smoking status), the F statistic for the combinatorial pharmacogenomic test was 1.7 to 2.9-times higher than the individual genes, showing that it explained more variance in citalopram/escitalopram blood levels. In multivariate analysis of the individual genes and combinatorial pharmacogenomic test together, only the combinatorial pharmacogenomic test remained significant. Overall, this demonstrates that the combinatorial pharmacogenomic test was a superior predictor of citalopram/escitalopram blood levels compared to individual genes.

A population pharmacokinetic model for escitalopram and its major metabolite in depressive patients during the perinatal period: Prediction of infant drug exposure through breast milk.

BACKGROUND AND OBJECTIVES: Escitalopram (SCIT) is frequently prescribed to breastfeeding women. Available information on SCIT excretion into breast milk is based on heterogeneous and incomplete data. A population pharmacokinetic model that aimed to better characterize maternal and infant exposure to SCIT and its metabolite was developed. METHODS: The study population was composed of women treated by SCIT or racemic citalopram and enrolled in the multicenter prospective cohort study SSRI-Breast Milk study (ClinicalTrial.gov NCT01796132). A joint structural model was first built for SCIT and S-desmethylcitalopram (SDCIT) in plasma using NONMEM and the milk-to-plasma ratio (MPR) was estimated by adding the drug breast milk concentrations. The effect of different influential covariates was tested and the average drug exposure with variability through breastfeeding was predicted under various conditions by simulation. RESULTS: The study enrolled 33 patients treated with SCIT or racemic citalopram who provided 80 blood and 104 milk samples. Mean MPR for both parent drug and metabolite was 1.9. Increased milk fat content was significantly associated with an increased drug transfer into breast milk (+28% for SCIT and +18% for SDCIT when fat amount doubles from 3.1 to 6.2 g/100 mL). Simulations suggested that an exclusively breastfed infant would ingest daily through breast milk 3.3% of the weight-adjusted maternal SCIT dose on average. CONCLUSION: The moderate between-subject variability in milk concentration of SCIT and the limited exposure to escitalopram through breast milk observed provide reassurance for treated mothers of breastfed healthy infants.

Physiologically Based Pharmacokinetic Approach Can Successfully Predict Pharmacokinetics of Citalopram in Different Patient Populations.

A physiologically based pharmacokinetic model (PBPK) was built for citalopram using Simcyp-based absorption, distribution, metabolism, and excretion simulator. Various physicochemical properties of citalopram were obtained from the published literature. The in vitro-in vivo extrapolation method was used to predict clearance in humans from recombinant enzyme data. Tissue distribution was predicted using parameter estimation function to fit the developed model to the observed concentration-versus-time data using nonlinear mixed-effects modeling approach. The model was verified by comparing the PBPK-based predictions with the observed pharmacokinetic (PK) profiles of citalopram in 26 clinical studies across a dose range of 10 to 60 mg. The predicted PK parameters of citalopram after intravenous dosing were within the -10% to 22% of the corresponding PK parameters obtained from the studies with quantified data sets. Most of the predicted PK parameters of citalopram after single-dose oral administration were within the 70%-130% range of the corresponding PK parameters obtained from observed data from 8 studies. After multidose oral administration, percentage error of Cmax and AUC was between -21% and 25% and -31% and 21%, respectively. Most of the observed data were within the 5th and 95th percentile interval of the variability around the predicted plasma concentrations. With the established model, the PK profiles in geriatric populations, populations with cytochrome P450 (CYP) 2C19 and/or 2D6 extensive metabolizers or poor metabolizers were predicted, and the predictions were in good agreement with the observed data. The model developed is robust to represent the absorption and disposition of citalopram and can predict the impact of patient covariates, such as age and genetic polymorphism of CYP2C19 and CYP2D6, on exposure of citalopram.

Dual Roles for the TSPYL Family in Mediating Serotonin Transport and the Metabolism of Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder.

We previously reported that testis-specific Y-encoded-like protein (TSPYLs) are transcription regulators for CYP3A4, CYP2C9, and CYP2C19. Here, we observed dual roles for TSPYLs in mediating serotonin transport and the metabolism of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD). The widely prescribed SSRIs, citalopram, and escitalopram are metabolized mainly by CYP2C19. The TSPYL1 rs3828743 single nucleotide polymorphism (SNP), which decreases its suppression of CYP2C19 expression, was associated with rapid escitalopram metabolism and worse treatment response in the Mayo PGRN-AMPS clinical trial. We also found that TSPYLs can regulate expression of the serotonin transporter protein, SLC6A4, and, in turn, serotonin transport into cells. The SNPs in tight linkage disequilibrium with the TSPYL1 rs10223646 SNP were significantly correlated with baseline severity of depression in patients with MDD in the Sequenced Treatment Alternatives to Relieve Depression and International SSRI Pharmacogenomics Consortium clinical trials. Our findings suggest that genetic variation in TSPYL genes may be novel indicators for baseline severity of depression and SSRI poor response.

Assessing pharmacokinetic differences in Caucasian and East Asian (Japanese, Chinese and Korean) populations driven by CYP2C19 polymorphism using physiologically-based pharmacokinetic modelling.

Understanding the influence of ethnicity on drug exposure is key to patient safety and could minimize repetitive clinical studies. This analysis aimed to evaluate the ability of physiologically-based pharmacokinetic modelling to predict exposure of CYP2C19 substrates (lansoprazole, (es)citalopram, voriconazole) across Caucasian and East Asian populations. CYP2C19 abundance levels in Japanese and Chinese populations have been re-assessed based on clinical evidence. Model performance in each population was evaluated by predicted-over-observed AUC ratios and comparison of observed data with simulated plasma concentration profiles. Exposures in 84.4% (76 out of 90) of the clinical studies were predicted within 1.5-fold of observed values. The reported concentration-time profiles were well-captured within the 90% prediction intervals. With specified CYP2C19 phenotype, PBPK modelling is capable to predict systemic exposure of drugs largely metabolized by CYP2C19 in different ethnic populations. This study demonstrated PBPK modelling can be applied to assess genotype-dependent exposure difference across ethnicities.

Sex-, feeding-, and circadian time-dependency of P-glycoprotein expression and activity - implications for mechanistic pharmacokinetics modeling.

P-glycoprotein (P-gp) largely influences the pharmacokinetics (PK) and toxicities of xenobiotics in a patient-specific manner so that personalized drug scheduling may lead to significant patient's benefit. This systems pharmacology study investigated P-gp activity in mice according to organ, sex, feeding status, and circadian time. Sex-specific circadian changes were found in P-gp ileum mRNA and protein levels, circadian amplitudes being larger in females as compared to males. Plasma, ileum and liver concentrations of talinolol, a pure P-gp substrate, significantly differed according to sex, feeding and circadian timing. A physiologically-based PK model was designed to recapitulate these datasets. Estimated mesors (rhythm-adjusted mean) of ileum and hepatic P-gp activity were higher in males as compared to females. Circadian amplitudes were consistently higher in females and circadian maxima varied by up to 10 h with respect to sex. Fasting increased P-gp activity mesor and dampened its rhythm. Ex-vivo bioluminescence recordings of ileum mucosae from transgenic mice revealed endogenous circadian rhythms of P-gp protein expression with a shorter period, larger amplitude, and phase delay in females as compared to males. Importantly, this study provided model structure and parameter estimates to refine PK models of any P-gp substrate to account for sex, feeding and circadian rhythms.

Citalopram overdose and severe serotonin syndrome in an intermediate metabolizing patient.

INTRODUCTION: Citalopram is a selective serotonin reuptake inhibitor used for treatment of depression. Metabolism is primarily through CYP3A4 and CYP2C19; activity of the latter can vary depending on genetics. Although rare after single agent exposure, large citalopram ingestions can lead to serotonin syndrome. We report a case of citalopram overdose in an intermediate CYP2C19 metabolizer complicated by severe serotonin syndrome. CASE DETAILS: A 25-year-old female presented after intentional citalopram overdose with seizures, tachycardia, persistent neuromuscular findings, and severe hyperthermia requiring aggressive sedation and cooling. Protracted symptoms required critical care services throughout a 14 day hospital stay despite traditional treatment of serotonin syndrome. Pharmacogenomic studies revealed intermediate CYP2C19 metabolism which reduces citalopram inactivation and may cause increased levels and toxicity. DISCUSSION: In the majority of serotonin syndrome cases, symptoms resolve rapidly after treatment initiation and discontinuation of the offending agents. Severe cases are typically associated with ingestion of multiple serotonergic agents. Our patient had severe toxicity after single agent ingestion. Pharmacogenetic testing identified abnormal CYP2C19 activity and previous cases have associated enzyme dysfunction and citalopram toxicity. CONCLUSION: Citalopram overdose may be associated with severe serotonin syndrome and further investigation is warranted to understand the impact of enzyme genotype on toxicity.

Smoking Is Associated With Lower Dose-Corrected Serum Concentrations of Escitalopram.

BACKGROUND: Tobacco smoking rates in depressive patients are higher compared with the general population. Smoking was demonstrated to accelerate the metabolism of different drugs metabolized by CYP1A2, but possibly also by CYP2C19 and CYP3A4. The principle aim of the present investigation from 2015 to 2018 was to determine the differences in the pharmacokinetics of escitalopram between smokers and nonsmokers. METHODS: A group of nonsmokers (n = 88) was compared with smokers (n = 36), both receiving escitalopram, using the Mann-Whitney U test. Linear regression analysis was used to account for the impact of escitalopram dose, age, and sex in addition to smoking on the steady-state serum concentration of escitalopram. RESULTS: Smokers received by mean 17.6% higher doses of escitalopram (P = 0.026) but showed 31.9% lower serum concentrations (P = 0.031). To control for confounders, linear regression analysis showed that dose (P < 0.001), sex (P = 0.03), and smoking tobacco (P = 0.027) did significantly influence serum concentrations of escitalopram with higher levels in women and nonsmokers. CONCLUSIONS: Notwithstanding higher daily doses, smokers had significantly lower serum concentrations of escitalopram. In concordance with previous results, besides CYP1A2, a possible induction of CYP2C19 and CYP3A4 by tobacco smoke, resulting in lower serum concentrations of escitalopram in smokers than in nonsmokers, is suggested. Therefore, to provide personalized therapy, clinicians should consider smoking status and inform patients on the interactions of smoking and escitalopram metabolism.

Escitalopram population pharmacokinetics in people living with human immunodeficiency virus and in the psychiatric population: Drug-drug interactions and probability of target attainment.

AIMS: The aims of this study were to characterize escitalopram pharmacokinetic profile, to identify factors influencing drug exposure, notably drug-drug interactions with antiretrovirals, and to simulate expected exposure under standard dosage regimen. METHODS: A population pharmacokinetic analysis was performed using NONMEM. A total of 159 plasma concentration measurements were obtained from 39 human immunodeficiency virus (HIV)-infected and 71 uninfected psychiatric patients. The influence of age, weight, sex, HIV and psychiatric cohorts, racemic citalopram treatment, and comedications on oral clearance was examined. Simulations served to calculate the percentage of patients expected to be under- or over-exposed, considering established therapeutic targets (15-80 ng/mL). RESULTS: A 1-compartment model with first-order absorption and elimination described the data adequately. The average escitalopram clearance and volume of distribution were 23.1 L/h (interindividual variability 51%), and 920 L, respectively. Escitalopram disposition did not differ between HIV-infected and uninfected patients, and was not affected by antiretroviral treatments. Coadministration of at least 1 proton-pump inhibitor (CYP2C19 inhibitor) modestly influenced escitalopram elimination (clearance decreased by 19%), with limited clinical relevance. Model-based simulations showed that, under a standard regimen of 10 mg once daily, a significant proportion of patients (56%) might be under-exposed. CONCLUSION: The variability in escitalopram disposition is large and poorly explained by demographic, clinical and environmental covariates, thus suggesting a role for dosage individualization based on therapeutic drug monitoring in case of poor clinical response. Escitalopram disposition is modestly impacted by comedications and therefore no a priori dosage adjustments are needed in patients receiving antiretroviral treatments, including boosted regimens.

Modeling the acute pharmacological response to selective serotonin reuptake inhibitors in human brain using simultaneous PET/MR imaging.

Pharmacological imaging of the effects of selective serotonin reuptake inhibitors (SSRI) may aid the clarification of their mechanism of action and influence treatment of highly prevalent neuropsychiatric conditions if the detected effects could be related to patient outcomes. In a randomized double-blind design, 38 healthy participants received a constant infusion of 8 mg citalopram or saline during either their first or second of two PET/MR scans. Resting-state functional MRI (fMRI) was acquired simultaneously with PET data on the binding of serotonin transporters (5-HTT) using [11C]DASB. Three different approaches for modeling of pharmacological fMRI response were tested separately. These relied on the use of regressors corresponding to (1) the drug infusion paradigm, (2) time courses of citalopram plasma concentrations and (3) changes in 5-HTT binding measured in each individual, respectively. Furthermore, the replication of results of a widely used model-free analysis method was attempted which assesses the deviation of signal in discrete time bins of fMRI data acquired after start of drug infusion. Following drug challenge, average 5-HTT occupancy was 69±7% and peak citalopram plasma levels were 111.8 ±â€¯21.1 ng/ml. None of the applied methods could detect significant differences in the pharmacological response between SSRI and placebo scans. The failed replication of SSRI effects reported in the literature despite a threefold larger sample size highlights the importance of appropriate correction for family-wise error in order to avoid spurious results in pharmacological imaging. This calls for the development of analysis methods which take regional specialization and the dynamics of brain activity into account.

Initial Development toward Non-Invasive Drug Monitoring via Untargeted Mass Spectrometric Analysis of Human Skin.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC-MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC-MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin.

Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.

We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine-learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN-AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Distribution of clomipramine, citalopram, midazolam, and metabolites in skeletal tissue after chronic dosing in rats.

In recent years, the use of skeletal tissue as an alternative matrix in forensic toxicology has received new interest. In cases where extreme decomposition has taken place, analysis of skeletal tissue is often the only option left. In this article, a fully validated method is presented and the distribution of clomipramine, citalopram, midazolam, and metabolites after chronically administration is examined within skeletal tissue. Rats were chronically dosed with respectively clomipramine, citalopram, or midazolam. Extracts were quantitatively analyzed using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Clomipramine, citalopram, and metabolites, respectively desmethylclomipramine and desmethylcitalopram are shown to be detectable in all bone types sampled. Midazolam and its metabolite α-OH-midazolam could not be detected. The absence of midazolam in extracts gives an indication that drugs with pKa values under physiological pH are badly or not incorporated in bone tissue. Bone and post-mortem blood concentrations were compared. A range of different bone types was compared and showed that the concentration is strongly dependent on the bone type. In concordance with previous publications, the humerus shows the highest drug levels. Skeletal tissue concentrations found ranged from 1.1 to 587.8 ng/g. Comparison of the same bone type between the different rats showed high variances. However, the drugs-metabolite ratio proved to have lower variances (<20%). Moreover, the drugs-metabolite ratio in the sampled bones is in close concordance to the ratios seen in blood within a rat. From this, we can assume that the drugs-metabolite ratio in skeletal tissue may prove to be more useful than absolute found concentration.