Steven Xu

Abiraterone Acetate to Lower Androgens in Women With Classic 21-Hydroxylase Deficiency

CONTEXT Chronic supraphysiological glucocorticoid therapy controls the androgen excess of 21-hydroxylase deficiency (21OHD) but contributes to the high prevalence of obesity, glucose intolerance, and reduced bone mass in these patients. Abiraterone acetate (AA) is a prodrug for abiraterone, a potent CYP17A1 inhibitor used to suppress androgens in the treatment of prostate cancer. OBJECTIVE The objective of the study was to test the hypothesis that AA added to physiological hydrocortisone and 9α-fludrocortisone acetate corrects androgen excess in women with 21OHD without causing hypertension or hypokalemia. DESIGN This was a phase 1 dose-escalation study. SETTING The study was conducted at university clinical research centers. PARTICIPANTS We screened 14 women with classic 21OHD taking hydrocortisone 12.5-20 mg/d to enroll six participants with serum androstenedione greater than 345 ng/dL (>12 nmol/L). INTERVENTION AA was administered for 6 days at 100 or 250 mg every morning with 20 mg/d hydrocortisone and 9α-fludrocortisone acetate. MAIN OUTCOME MEASURE The primary endpoint was normalization of mean predose androstenedione on days 6 and 7 (< 230 ng/dL [<8 nmol/L)] in greater than 80% of participants. Secondary end points included serum 17-hydroxyprogesterone and testosterone (T), electrolytes, plasma renin activity, and urine androsterone and etiocholanolone glucuronides. RESULTS With 100 mg/d AA, mean predose androstenedione fell from 764 to 254 ng/dL (26.7-8.9 nmol/L). At 250 mg/d AA, mean androstenedione normalized in five participants (83%) and decreased from 664 to 126 ng/dL (23.2-4.4 nmol/L), meeting the primary end point. Mean androstenedione declined further during day 6 to 66 and 38 ng/dL (2.3 and 1.3 nmol/L) at 100 and 250 mg/d, respectively. Serum T and urinary metabolites declined similarly. Abiraterone exposure was strongly negatively correlated with mean androstenedione. Hypertension and hypokalemia were not observed. CONCLUSION AA 100-250 mg/d added to replacement hydrocortisone normalized several measures of androgen excess in women with classic 21OHD and elevated serum androstenedione.

Population pharmacokinetics and pharmacodynamics of rivaroxaban in patients with acute coronary syndromes

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Population pharmacokinetics and pharmacodynamics of rivaroxaban have been characterized in healthy subjects and in patients with total venous thromboembolism, deep vein thrombosis or atrial fibrillation. WHAT THIS STUDY ADDS • This article is the first description of the population pharmacokinetics (PK) and pharmacodynamics (PD) of rivaroxaban in patients with acute coronary syndrome (ACS). It is the largest population pharmacokinetic and pharmacodynamic study on rivaroxaban conducted to date (n= 2290). The PK and PK-PD relationship of rivaroxaban in patients with ACS were similar to those in other patient populations. In addition, model-based simulations showed that the influence of renal function and age on the exposure to rivaroxaban in the ACS population were similar to the findings from Phase 1 special population studies. These findings suggest that rivaroxaban has highly predictable PK-PD and may provide a consistent anticoagulant effect across the studied patient populations, which allows an accurate prediction of the dose to control anticoagulation optimally. AIMS The aim of this analysis was to use a population approach to facilitate the understanding of the pharmacokinetics and pharmacodynamics of rivaroxaban in patients with acute coronary syndrome (ACS) and to evaluate the influence of patient covariates on the exposure of rivaroxaban in patients with ACS. METHODS A population pharmacokinetic model was developed using pharmacokinetic samples from 2290 patients in Anti-Xa Therapy to Lower Cardiovascular Events in Addition to Standard Therapy in Subjects with Acute Coronary Syndrome Thrombolysis in Myocardial Infarction 46. The relationship between pharmacokinetics and the primary pharmacodynamic end point, prothrombin time, was evaluated. RESULTS The pharmacokinetics of rivaroxaban in patients with ACS was adequately described by an oral one-compartment model. The estimated absorption rate, apparent clearance and volume of distribution were 1.24 h(-1) (interindividual variability, 139%), 6.48 l h(-1) (31%) and 57.9 l (10%), respectively. Simulations indicate that the influences of renal function, age and bodyweight on exposure in ACS patients are consistent with the findings in previous Phase 1 studies. Rivaroxaban plasma concentrations exhibit a close-to-linear relationship with prothrombin time in the ACS population, with little interindividual variability. The estimated pharmacokinetic and pharmacodynamic parameters for the ACS patients were comparable to those for venous thromboembolism prevention, deep vein thrombosis and atrial fibrillation patients. CONCLUSIONS The similarity in pharmacokinetics/pharmacodynamics of rivaroxaban among different patient populations and the low interindividual variability in the exposure-prothrombin time relationship indicate that the anticoagulant effect of rivaroxaban is highly predictable and consistent across all the patient populations studied.

Population Pharmacokinetics of Tapentadol Immediate Release (IR) in Healthy Subjects and Patients with Moderate or Severe Pain

BackgroundTapentadol is a new, centrally active analgesic agent with two modes of action — ώ, opioid receptor agonism and norepinephrine reuptake inhibition — and the immediate-release (IR) formulation is approved in the US for the relief of moderate to severe acute pain. The aims of this analysis were to develop a population pharmacokinetic model to facilitate the understanding of the pharmacokinetics of tapentadol IR in healthy subjects and patients following single and multiple dosing, and to identify covariates that might explain variability in exposure following oral administration.MethodsThe analysis included pooled data from 11 385 serum pharmacokinetic samples from 1827 healthy subjects and patients with moderate to severe pain. Population pharmacokinetic modelling was conducted using nonlinear mixed-effects modelling (NONMEM®) software to estimate population pharmacokinetic parameters and the influence of the subjects’ demographic characteristics, clinical laboratory chemistry values and disease status on these parameters. Simulations were performed to assess the clinical relevance of the covariate effects on tapentadol exposure.ResultsA two-compartment model with zero-order release followed by first-order absorption and first-order elimination best described the pharmacokinetics of tapentadol IR following oral administration. The interindividual variability (coefficient of variation) in apparent oral clearance (CL/F) and the apparent central volume of distribution after oral administration were 30% and 29%, respectively. An additive error model was used to describe the residual variability in the log-transformed data, and the standard deviation values were 0.308 and 0.314 for intensively and sparsely sampled data, respectively. Covariate analysis showed that sex, age, bodyweight, race, body fat, hepatic function (using total bilirubin and total protein as surrogate markers), health status and creatinine clearance were statistically significant factors influencing the pharmacokinetics of tapentadol. Total bilirubin was a particularly important factor that influenced CL/F, which decreased by more than 60% in subjects with total bilirubin greater than 50 ώmol/L.ConclusionsThe population pharmacokinetic model for tapentadol IR identified the relationship between pharmacokinetic parameters and a wide range of covariates. The simulations of tapentadol exposure with identified, statistically significant covariates demonstrated that only hepatic function (as characterized by total bilirubin and total protein) may be considered a clinically relevant factor that warrants dose adjustment. None of the other covariates are of clinical relevance, nor do they necessitate dose adjustment.

Pharmacokinetic and Pharmacodynamic Modeling of Opioid-Induced Gastrointestinal Side Effects in Patients Receiving Tapentadol IR and Oxycodone IR

ABSTRACTPurposeTo understand the relationship between the risk of opioid-related gastrointestinal adverse effects (AEs) and exposure to tapentadol and oxycodone as well as its active metabolite, oxymorphone, using pharmacokinetic/pharmacodynamic models.MethodsThe analysis was based on a study in patients with moderate-to-severe pain following bunionectomy. Population PK modeling was conducted to estimate population PK parameters for tapentadol, oxycodone, and oxymorphone. Time to AEs was analyzed using Cox proportional-hazards models.ResultsRisk of nausea, vomiting, and constipation significantly increased with exposure to tapentadol or oxycodone/oxymorphone. However, elevated risk per drug exposure of AEs for tapentadol was ~3–4 times lower than that of oxycodone, while elevated AE risk per drug exposure of oxycodone was ~60 times lower than that for oxymorphone, consistent with reported in vitro receptor binding affinities for these compounds. Simulations show that AE incidence following administration of tapentadol IR is lower than that following oxycodone IR intake within the investigated range of analgesic noninferiority dose ratios.ConclusionsThis PK/PD analysis supports the clinical findings of reduced nausea, vomiting and constipation reported by patients treated with tapentadol, compared to patients treated with oxycodone.

Shrinkage in Nonlinear Mixed-Effects Population Models: Quantification, Influencing Factors, and Impact

Shrinkage of empirical Bayes estimates (EBEs) of posterior individual parameters in mixed-effects models has been shown to obscure the apparent correlations among random effects and relationships between random effects and covariates. Empirical quantification equations have been widely used for population pharmacokinetic/pharmacodynamic models. The objectives of this manuscript were (1) to compare the empirical equations with theoretically derived equations, (2) to investigate and confirm the influencing factor on shrinkage, and (3) to evaluate the impact of shrinkage on estimation errors of EBEs using Monte Carlo simulations. A mathematical derivation was first provided for the shrinkage in nonlinear mixed effects model. Using a linear mixed model, the simulation results demonstrated that the shrinkage estimated from the empirical equations matched those based on the theoretically derived equations. Simulations with a two-compartment pharmacokinetic model verified that shrinkage has a reversed relationship with the relative ratio of interindividual variability to residual variability. Fewer numbers of observations per subject were associated with higher amount of shrinkage, consistent with findings from previous research. The influence of sampling times appeared to be larger when fewer PK samples were collected for each individual. As expected, sample size has very limited impact on shrinkage of the PK parameters of the two-compartment model. Assessment of estimation error suggested an average 1:1 relationship between shrinkage and median estimation error of EBEs.

Impact of low percentage of data below the quantification limit on parameter estimates of pharmacokinetic models

The objectives of the simulation study were to evaluate the impact of BQL data on pharmacokinetic (PK) parameter estimates when the incidence of BQL data is low (e.g. ≤10%), and to compare the performance of commonly used modeling methods for handling BQL data such as data exclusion (M1) and likelihood-based method (M3). Simulations were performed by adapting the method of a recent publication by Ahn et al. (J Phamacokinet Pharmacodyn 35(4):401–421, 2008). The BQL data in the terminal elimination phase were created at frequencies of 1, 2.5, 5, 7.5, and 10% based on a one- and a two-compartment model. The impact of BQL data on model parameter estimates was evaluated based on bias and imprecision. The simulations demonstrated that for the one-compartment model, the impact of ignoring the low percentages of BQL data (≤10%) in the elimination phase was minimal. For the two-compartment model, when the BQL incidence was less than 5%, omission of the BQL data generally did not inflate the bias in the fixed-effect parameters, whereas more pronounced bias in the estimates of inter-individual variability (IIV) was observed. The BQL data in the elimination phase had the greatest impact on the volume of distribution estimate of the peripheral compartment of the two-compartment model. The M3 method generally provided better parameter estimates for both PK models than the M1 method. However, the advantages of the M3 over the M1 method varied depending on different BQL censoring levels, PK models and parameters. As the BQL percentages decreased, the relative gain of the M3 method based on more complex likelihood approaches diminished when compared to the M1 method. Therefore, it is important to balance the trade-off between model complexity and relative gain in model improvement when the incidence of BQL data is low. Understanding the model structure and the distribution of BQL data (percentage and location of BQL data) allows selection of an appropriate and effective modeling approach for handling low percentages of BQL data.

Clinical Implications of Complex Pharmacokinetics for Daratumumab Dose Regimen in Patients With Relapsed/Refractory Multiple Myeloma

New therapeutic strategies are urgently needed to improve clinical outcomes in patients with multiple myeloma (MM). Daratumumab is a first‐in‐class, CD38 human immunoglobulin G1κ monoclonal antibody approved for treatment of relapsed or refractory MM. Identification of an appropriate dose regimen for daratumumab is challenging due to its target‐mediated drug disposition, leading to time‐ and concentration‐dependent pharmacokinetics. We describe a thorough evaluation of the recommended dose regimen for daratumumab in patients with relapsed or refractory MM.

Modeling of Bounded Outcome Scores with Data on the Boundaries: Application to Disability Assessment for Dementia Scores in Alzheimer’s Disease

Mixed-effects beta regression (BR), boundary-inflated beta regression (ZOI), and coarsening model (CO) were investigated for analyzing bounded outcome scores with data at the boundaries in the context of Alzheimer’s disease. Monte Carlo simulations were conducted to simulate disability assessment for dementia (DAD) scores using these three models, and each set of simulated data were analyzed by the original simulation model. One thousand trials were simulated, and each trial contained 250 subjects. For each subject, DAD scores were simulated at baseline, 13, 26, 39, 52, 65, and 78 weeks. The simulation-reestimation exercise showed that all the three models could reasonably recover their true parameter values. The bias of the parameter estimates of the ZOI model was generally less than 1%, while the bias of the CO model was mainly within 5%. The bias of the BR model was slightly higher, i.e., less than or in the order of 20%. In the application to real-world DAD data from clinical studies, examination of prediction error and visual predictive check (VPC) plots suggested that both BR and ZOI models had similar predictive performance and described the longitudinal progression of DAD slightly better than the CO model. In conclusion, the investigated three modeling approaches may be sensible choices for bounded outcome scores with data on the edges. Prediction error and VPC plots can be used to identify the model with best predictive performance.

Modeling delayed drug effect using discrete-time nonlinear autoregressive models: a connection with indirect response models.

Indirect response (IDR) models have been widely applied to pharmacodynamic (PD) modeling, particularly when delayed response (hysteresis) is present. This paper proposes a class of nonlinear discrete-time autoregressive (AR) models with drug concentrations acting as a time-varying covariate on the asymptote parameter or the autocorrelation parameter of the AR models as an alternative modeling approach for delayed response data. The mathematical derivations revealed the inherent connection between IDR models and nonlinear AR models, and showed that the nonlinear AR models approximate the IDR models when the time interval between response data is small. Simulations demonstrate that the IDR models and the corresponding AR models produce similar temporal response profiles, and as the time interval decreased (i.e., more intensive sampling designs), the AR model based parameter estimates were more comparable to those estimated from the IDR models. In conjunction with mixed-effects modeling, the nonlinear AR models have been shown to well describe a set of simulated longitudinal PK/PD data for a clinical study. Further extensions of the proposed nonlinear AR models are warranted to model irregular and sparse PK/PD data.

Further Evaluation of Covariate Analysis using Empirical Bayes Estimates in Population Pharmacokinetics: the Perception of Shrinkage and Likelihood Ratio Test.

Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.