Associations between model-predicted rivaroxaban exposure and patient characteristics and efficacy and safety outcomes in patients with non-valvular atrial fibrillation.

Rivaroxaban exposure and patient characteristics may affect the rivaroxaban benefit-risk balance. This study aimed to quantify associations between model-predicted rivaroxaban exposure and patient characteristics and efficacy and safety outcomes in patients with non-valvular atrial fibrillation (NVAF), using data from the phase 3 ROCKET AF trial (NCT00403767). In ROCKET AF, 14,264 patients with NVAF were randomized to rivaroxaban (20 mg once daily [OD], or 15 mg OD if creatinine clearance was 30-49 mL/min) or dose-adjusted warfarin (median follow-up: 707 days); rivaroxaban plasma concentration was measured in a subset of 161 patients. In this post hoc exposure-response analysis, a multivariate Cox model was used to correlate individual predicted rivaroxaban exposures and patient characteristics with time-to-event efficacy and safety outcomes in 7061 and 7111 patients, respectively. There was no significant association between model-predicted rivaroxaban trough plasma concentration (Ctrough) and efficacy outcomes. Creatinine clearance and history of stroke were significantly associated with efficacy outcomes. Ctrough was significantly associated with the composite of major or non-major clinically relevant (NMCR) bleeding (hazard ratio [95th percentile vs. median]: 1.26 [95% confidence interval 1.13-1.40]) but not with major bleeding alone. The exposure-response relationship for major or NMCR bleeding was shallow with no clear threshold for an acceleration in risk. History of gastrointestinal bleeding had a greater influence on safety outcomes than Ctrough. These results support fixed rivaroxaban 15 mg and 20 mg OD dosages in NVAF. Therapeutic drug monitoring is unlikely to offer clinical benefits in this indication beyond evaluation of patient characteristics.

Prospective dose selection and acceleration of paliperidone palmitate 3-month formulation development using a pharmacometric bridging strategy.

AIMS: To prospectively select the dose of the paliperidone palmitate 3-month (PP3M) formulation, using a pharmacometric bridging strategy based on the paliperidone palmitate 1-month (PP1M) formulation previously approved for schizophrenia treatment. METHODS: Pharmacokinetic (PK) data from a 6-month interim analysis of a single dose PP3M Phase I clinical trial was integrated with a previously developed PP1M population-PK model. The model was updated to incorporate formulation as a covariate on absorption parameters and to explore the most critical design element of the Phase III study: the PP1M-to-PP3M dose multiplier for patients switching formulations. Plasma paliperidone concentrations were measured at predetermined intervals during Phase III, enabling comparison of the multiple-dose PK between PP1M and PP3M. Exposure matching was assessed graphically to determine whether paliperidone plasma concentrations from the two formulations overlapped. RESULTS: Prospective steady-state PK simulations revealed that a 3.5 multiple of the PP1M dose would yield a corresponding PP3M dose with comparable exposure. The prospective pharmacometric simulation and observed Phase III PK data agreed closely. Phase III results confirmed the hypothesis that efficacy of PP3M was noninferior to that of PP1M. The similarity in exposures between the two formulations was likely a key determinant of the equivalent efficacy between the two products observed in the Phase III study. CONCLUSIONS: Successful prospective PP3M Phase III clinical trial dose selection was achieved through the use of pharmacometric bridging, without conducting a Phase II study and using only limited Phase I data for PP3M. We estimate that this strategy reduced development time by 3-5 years and may be applicable to other drug development projects.

Model-Informed Clinical Development of Once-Every-6-Month Injection of Paliperidone Palmitate in Patients with Schizophrenia: A Pharmacometric Bridging Approach (Part I).

BACKGROUND AND OBJECTIVE: A model-informed drug development (MIDD) approach was implemented for paliperidone palmitate (PP) 6-month (PP6M) clinical development, using pharmacokinetics and pharmacokinetic/pharmacodynamic model-based simulations. METHODS: PP6M pharmacokinetics were simulated by extending the PP 3-month (PP3M) pharmacokinetic model to account for increased injection volume, and hence dose. Contribution of the MIDD approach to the design of the pivotal PP6M phase-3 study (PP6M/PP3M noninferiority study, NCT03345342) investigating schizophrenia relapse rates was twofold: (1) PP6M dose selection, and (2) hypothesis generation that lower trough concentrations (Ctrough) associated with PP6M, relative to PP3M, were not associated with lower efficacy, which was to be evaluated in the phase-3 study. Moreover, accompanied by an intense sampling scheme to adequately characterize paliperidone pharmacokinetics and to elucidate the potential relationship between concentration and safety/efficacy, the bridging strategy eliminated the need for additional phase-1/phase-2 clinical studies. RESULTS: Using a MIDD bridging strategy, PP6M doses were selected that, compared with PP3M, were expected to have a similar range of exposures and a noninferior relapse rate and safety profile. Clinical data from PP6M/PP3M noninferiority study confirmed that PP6M, compared with PP3M, had a similar range of exposures (T'jollyn et al. in Eur J Drug Metab Pharmacokinet 2024), as well as a noninferior relapse rate and safety profile (this manuscript). CONCLUSIONS: Consistency of the MIDD approach with observed clinical outcomes confirmed the hypothesis that lower Ctrough did not lead to increased relapse rates at the doses administered. Although higher paliperidone peak concentrations are achieved with corresponding doses of PP6M relative to PP3M in the phase-3 clinical study, types and incidences of treatment-related adverse events were comparable between PP6M and PP3M groups and no new safety concerns emerged for PP6M (Najarian et al. in Int J Neuropsychopharmacol 25(3):238-251, 2022).

Model-Informed Clinical Development of 6-Monthly Injection of Paliperidone Palmitate in Patients with Schizophrenia: Dosing Strategies Guided by Population Pharmacokinetic Modeling and Simulation (Part II).

BACKGROUND AND OBJECTIVE: Paliperidone palmitate 6-month (PP6M) intramuscular (IM) injection is the longest-acting treatment available for patients with schizophrenia. A population pharmacokinetic (popPK) modeling and simulation approach was deployed to inform dosing strategies for PP6M. METHODS: The extensive analysis database included 15,932 paliperidone samples from 700 patients receiving gluteal paliperidone palmitate 3-month (PP3M) or PP6M injections in the double-blind phase of a phase-3 noninferiority study (NCT03345342). Exposure parameters for paliperidone appeared to increase dose-proportionally within each dosing schedule (PP3M/PP6M). The range of paliperidone exposures after IM administration of PP6M overlaps with that of corresponding doses of oral paliperidone extended release, PP 1-month (PP1M), and PP3M. Model-based simulations were performed to investigate paliperidone exposures in different PP6M dosing scenarios and relevant subpopulations. RESULTS: A dosing window of ≤ 2 weeks earlier and ≤ 3 weeks later than the target 6-month interval for maintenance treatment with PP6M dosing maintains paliperidone exposures at levels that are not expected to substantially impact its safety and efficacy. For missed-dose scenarios, tailored re-initiation regimens are proposed that should be applied before resuming PP6M maintenance dosing. Regarding subpopulations, PP6M 700 mg eq. is the highest dose recommended in mild renal-impairment patients; the paliperidone pharmacokinetics after PP6M administration is not affected by sex, body mass index, or age in a clinically meaningful way. CONCLUSION: Paliperidone concentration-time profiles after PP6M and PP3M dosing were adequately described by the popPK model. Model-based simulation results provide guidance for clinicians on initiating PP6M therapy, transitioning between paliperidone formulations, the dosing windows to use for maintenance dosing, and managing missed PP6M doses.

Mixed-effects beta regression for modeling continuous bounded outcome scores using NONMEM when data are not on the boundaries.

Beta regression models have been recommended for continuous bounded outcome scores that are often collected in clinical studies. Implementing beta regression in NONMEM presents difficulties since it does not provide gamma functions required by the beta distribution density function. The objective of the study was to implement mixed-effects beta regression models in NONMEM using Nemes' approximation to the gamma function and to evaluate the performance of the NONMEM implementation of mixed-effects beta regression in comparison to the commonly used SAS approach. Monte Carlo simulations were conducted to simulate continuous outcomes within an interval of (0, 70) based on a beta regression model in the context of Alzheimer's disease. Six samples per subject over a 3 years period were simulated at 0, 0.5, 1, 1.5, 2, and 3 years. One thousand trials were simulated and each trial had 250 subjects. The simulation-reestimation exercise indicated that the NONMEM implementation using Laplace and Nemes' approximations provided only slightly higher bias and relative RMSE (RRMSE) compared to the commonly used SAS approach with adaptive Gaussian quadrature and built-in gamma functions, i.e., the difference in bias and RRMSE for fixed-effect parameters, random effects on intercept, and the precision parameter were <1-3 %, while the difference in the random effects on the slope was <3-7 % under the studied simulation conditions. The mixed-effect beta regression model described the disease progression for the cognitive component of the Alzheimer's disease assessment scale from the Alzheimer's Disease Neuroimaging Initiative study. In conclusion, with Nemes' approximation of the gamma function, NONMEM provided comparable estimates to those from SAS for both fixed and random-effect parameters. In addition, the NONMEM run time for the mixed beta regression models appeared to be much shorter compared to SAS, i.e., 1-2 versus 20-40 s for the model and data used in the manuscript.

Pharmacokinetic and pharmacodynamic modeling of opioid-induced gastrointestinal side effects in patients receiving tapentadol IR and oxycodone IR.

PURPOSE: To 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. METHODS: The 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. RESULTS: Risk 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. CONCLUSIONS: This 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.

Analysis of dose-response in flexible dose titration clinical studies.

Assessing dose-response from flexible-dose clinical trials (e.g., titration or dose escalation studies) is challenging and often problematic due to the selection bias caused by 'titration-to-response'. We investigate the performance of a dynamic linear mixed-effects (DLME) model and marginal structural model (MSM) in evaluating dose-response from flexible-dose titration clinical trials via simulations. The simulation results demonstrated that DLME models with previous exposure as a time-varying covariate may provide an unbiased and efficient estimator to recover exposure-response relationship from flexible-dose clinical trials. Although the MSM models with independent and exchangeable working correlations appeared to be able to recover the right direction of the dose-response relationship, it tended to over-correct selection bias and overestimated the underlying true dose-response. The MSM estimators were also associated with large variability in the parameter estimates. Therefore, DLME may be an appropriate modeling option in identifying dose-response when data from fixed-dose studies are absent or a fixed-dose design is unethical to be implemented.

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.

Population Pharmacokinetics of Esketamine Nasal Spray and its Metabolite Noresketamine in Healthy Subjects and Patients with Treatment-Resistant Depression.

BACKGROUND: Esketamine nasal spray is approved for treatment-resistant depression. OBJECTIVE: The objective of this study was to characterize the pharmacokinetics of esketamine and noresketamine in healthy subjects and patients with treatment-resistant depression. METHODS: Esketamine and noresketamine were measured in > 9000 plasma samples collected from 820 individuals who received esketamine by the intranasal, intravenous, and oral routes. An open linear model for esketamine (three compartments) and noresketamine (two compartments) that included a hepato-portal compartment was developed using NONMEM® VII. The effects of covariates on esketamine pharmacokinetics and a model evaluation were performed using conventional methods. RESULTS: The fraction of a 28-mg intranasal dose absorbed through the nasal cavity (FRn) is 54% (100% of this fraction is completely absorbed); the remaining 46% is swallowed and undergoes intestinal and first-pass metabolism and 18.6% of the swallowed dose reaches the systemic circulation. The absolute bioavailability of 56 and 84 mg of intranasal esketamine is 54 and 51%, respectively. Esketamine volume at steady state and clearance were 752 L and 114 L/h, respectively. Noresketamine volume at steady state and apparent clearance were 185 L and 38 L/h, respectively. Relative to non-Asian subjects, Asian subjects showed a 64.0 and 19.4% decrease in the esketamine elimination rate constant and noresketamine apparent clearance, respectively. Japanese subjects exhibited a 34% increase in FRn vs other races. Hepatic blood flow decreased by 21.9 L/h for each decade in age in subjects aged > 60 years. These changes resulted in esketamine and noresketamine maximum concentration and area under the concentration-time curve after 24 h post-dose values that were up to 36% higher than those observed in other races or in younger adult subjects. CONCLUSIONS: Esketamine and noresketamine pharmacokinetics was successfully characterized in healthy subjects and patients with treatment-resistant depression. The model quantified esketamine absolute nasal and oral bioavailability, its hepatic flow-limited clearance and biotransformation to the major metabolite noresketamine, and the influence of intrinsic and extrinsic factors on esketamine pharmacokinetics. Clinical trials registration numbers of the studies included in the analysis: ESKETINTRD1001 (NCT01780259), ESKETINTRD1002 (NCT01980303), ESKETINTRD1003 (NCT02129088), ESKETINTRD1008 (NCT02846519), ESKETINTRD1009 (NCT02343289), ESKETINTRD1010 (NCT02568176), ESKETINTRD1012 (NCT02345148), 54135419TRD1015 (NCT02682225), ESKETINTRD2003 (NCT01998958), ESKETINSUI2001 (NCT02133001), ESKETINTRD3001 (NCT02417064), ESKETINTRD3002 (NCT02418585), and ESKETINTRD3005 (NCT02422186).

Population pharmacokinetics of doripenem based on data from phase 1 studies with healthy volunteers and phase 2 and 3 studies with critically ill patients.

A population pharmacokinetic model of doripenem was constructed using data pooled from phase 1, 2, and 3 studies utilizing nonlinear mixed effects modeling. A 2-compartment model with zero-order input and first-order elimination best described the log-transformed concentration-versus-time profile of doripenem. The model was parameterized in terms of total clearance (CL), central volume of distribution (V(c)), peripheral volume of distribution (V(p)), and distribution clearance between the central and peripheral compartments (Q). The final model was described by the following equations (for jth subject): CL(j) (liters/h) = 13.6.(CL(CR)(j)/98 ml/min)(0.659).(1 + CL(race)(j) [0 for Caucasian]); V(c)(j) (liters) = 11.6.(weight(j)/73 kg)(0.596); Q(j) (liters/h) = 4.74.(weight(j)/73)(1.06); and V(p)(j) (liters) = 6.04.(CL(CR)(j)/98 ml/min)(0.417).(weight(j)/73 kg)(0.840).(age(j)/40 years)(0.307). According to the final model, population mean parameter estimates and interindividual variability (percent coefficient of variation [% CV]) for CL (liters/h), V(c) (liters), V(p) (liters), and Q (liters/h) were 13.6 (19%), 11.6 (19%), 6.0 (25%), and 4.7 (42%), respectively. Residual variability, estimated using three separate additive residual error models, was 0.17 standard deviation (SD), 0.55 SD, and 0.92 SD for phase 1, 2, and 3 data, respectively. Creatinine clearance was the most significant predictor of doripenem clearance. Mean Bayesian clearance was approximately 33%, 55%, and 76% lower for individuals with mild, moderate, or severe renal impairment, respectively, than for those with normal renal function. The population pharmacokinetic model based on healthy volunteer data and patient data informs us of doripenem disposition in a more general population as well as of the important measurable intrinsic and extrinsic factors that significantly influence interindividual pharmacokinetic differences.