Background: Gender differences in treatment response rates for patients treated with antipsychotics are known. However, the literature lacks a pharmacodynamic model to allow for gender-based clinical trial simulations from modelling parameters for Olanzapine and dopamine D2 receptor occupancy. Thus, the primary aim of this analysis is to test and quantify the effect of gender on the pharmacodynamics of Olanzapine. Methods: Population pharmacodynamic modelling was performed using non-linear mixed effects modelling in MONOLIX, while the Clinical Trial Simulations were performed using R for statistical programming. The pharmacometric analysis is based on a pooled data approach from three clinical studies where patients were diagnosed with schizophrenia and one clinical study where the patients were diagnosed with bipolar disorder. Results: Olanzapine D2RO was modelled using an E_max model in a study population of 70 patients. Population pharmacodynamic parameters were estimated to be: E_max = 85.6% (RSE = 3%), ED50-Men = 5.15 mg/day (RSE = 14) and ED50-Women = 2.38 mg/day (RSE = 34%), with the p-value = 0.037 for the gender-stratified ED50 results. Conclusion: The pharmacometrics analysis and model-based dosing simulations suggest that, in order to achieve 70% D2RO, women require a 10 mg/day dose and men require approximately a 20 mg/day dose of Olanzapine. Further, clinical implications exist suggesting that clinicians should factor patient gender when considering both a starting dose, as well a, a maintenance dose for patients prescribed Olanzapine due to quantifiable gender-differences of striatal dopamine D2 receptor occupancy.

Background: Selective serotonin reuptake inhibitors (SSRIs) are among the most widely prescribed class of drugs in the practice of psychiatry. Cytochrome P450 (CYP) 2C19 and CYP2D6 are established as clinically relevant drug metabolizing enzymes (DMEs) that influence the pharmacokinetics of SSRIs and may either be grouped as being primarily metabolized by CYP2C19 or CYP2D6. The aim of this study is to test the hypothesis that the primary drug metabolizing pathway for SSRI antidepressants are associated with adverse drug reactions (ADRs) related to physiological modulation of organs with the highest gene tissue expression. Methods: Post-marketing ADR cases were obtained from the United States Food and Drug Administration’s Adverse Events Reporting System from each of the four quarters for the years 2016 and 2017. Cases were grouped based on one of two primary pharmacokinetic pharmacogenomic pathway biomarkers CYP2C19 and CYP2D6. Citalopram, escitalopram, and sertraline were grouped as CYP2C19 substrates and fluvoxamine, fluoxetine, and paroxetine as CYP2D6 substrates. Logistic regression was computed for the reported SSRI ADRs associated with one of two aforementioned DMEs. All data homogenization and computations were performed in R for statistical programming. Results: The most commonly reported ADR among the SSRIs was anxiety (n = 3,332). The top two ADRs associated with SSRIs metabolized by CYP2D6 are: nightmare (n = 983) reporting odds-ratio (OR) = 4.37 (95% confidence interval (CI) [3.67–5.20]) and panic attack (n = 1,243) OR = 2.43 (95% CI [2.11–2.79]). Contrastingly, the top two ADRs for CYP2C19 metabolized SSRIs are: electrocardiogram QT prolonged (n = 351) OR = 0.18 (95% CI [0.13–0.24]) and small for dates baby (n = 306) OR = 0.19 (95% CI [0.14–0.26]). The study tested and produced 40 statistically significant CYP2C19- and CYP2D6-biased ADRs. In overall context, the results suggest that CYPC19 SSRI substrates are associated with ADRs related to modulation of the autonomic nervous system, seizure, pain, erectile-dysfunction, and absorption. Contrastingly, CYP2D6 SSRI substrates are associated with ADRs related to nightmares, withdrawal syndrome, and de-realization of cognitive processes. The results of this study may aid as guidance to optimize drug selection in psychopharmacology.

Objective: Antipsychotic compounds are known to induce sedation somnolence and have expanded clinical indications beyond schizophrenia to regulatory approval in bipolar disorder, treatment-resistant depression, and is being repurposed in infectious diseases and oncology. However, the medical sciences literature lacks a comprehensive association between sedation and somnolence among a wide-range of antipsychotic compounds. The objective of this study is to assess the disproportionality of sedation and somnolence among thirty-seven typical and atypical antipsychotics.Materials and Methods: Patient adverse drug reactions (ADR) cases were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) between January 01, 2004 and September 30, 2020 for a wide-array of clinical indications and off-label use of antipsychotics. An assessment of disproportionality were based on cases of sedation and somnolence and calculated using the case/non-case methodology. Statistical analysis resulting in the reporting odds-ratio (ROR) with corresponding 95% confidence intervals (95% CI) were conducted using the R statistical programming language.Results: Throughout the reporting period, there were a total of 9,373,236 cases with 99,251 specific ADRs reporting sedation and somnolence. Zuclopenthixol (n = 224) ROR = 13.3 (95% CI, 11.6–15.3) was most strongly associated of sedation and somnolence and haloperidol decanoate long-acting injection (LAI) was not statistically associated sedation and somnolence. Further, among atypical antipsychotic compounds, tiapride and asenapine were the top two compounds most strongly associated with sedation and somnolence. Comprehensively, the typical antipsychotics ROR = 5.05 (95%CI, 4.97–5.12) had a stronger association with sedation and somnolence when compared to atypical antipsychotics ROR = 4.65 (95%CI, 4.47–4.84).Conclusion: We conducted a head-to-head comparison of thirty-seven antipsychotics and ranked the compounds based on the association of sedation and somnolence from ADR data collected throughout 16 years from the FAERS. The results are informative and with recent interests in repurposing phenothiazine antipsychotics in infectious disease and oncology provides an informative assessment of the compounds during repurposing and in psychopharmacology.

Background: We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Decision Tree and Random Forest machine learning algorithms in R. Results: Using quantitative gene expression values reported from patient blood samples, the RBPMS2 and LILRA5 genes classify male lithium responders with an area under the receiver operator characteristic curve (AUROC) of 0.92 and the ABRACL, FHL3, and NBPF14 genes classify female lithium responders AUROC of 1. A Decision Tree rule for establishing male versus female samples, using gene expression values were found to be: if RPS4Y1 ≥ 9.643, patient is a male and if RPS4Y1 < 9.643, patient is female with a probability=100%. Conclusions: We developed a pre-treatment gender- and gene-expression-based predictive model selective for classifying male lithium responders with a sensitivity of 96% using 2-genes and female lithium responders with sensitivity=92% using 3-genes.

Recent meta-analyses and publications over the past 15 years have provided evidence showing there are considerable gender differences in the pharmacokinetics of metoprolol. Throughout this time, there have not been any research articles proposing a gender stratified dose-adjustment resulting in an equivalent total drug exposure. Metoprolol pharmacokinetic data was obtained from a previous publication. Data was modeled using nonlinear mixed effect modeling using the MONOLIX software package to quantify metoprolol concentration–time data. Gender-stratified dosing simulations were conducted to identify equivalent total drug exposure based on a 100 mg dose in adults. Based on the pharmacokinetic modeling and simulations, a 50 mg dose in adult women provides an approximately similar metoprolol drug exposure to a 100 mg dose in adult men.