Journal of Pharmacokinetics and Pharmacodynamics最新文献

Modeling interoccasion variability (IOV) of pharmacokinetic parameters is challenging in sparse study designs. We conducted a simulation study with stochastic simulation and estimation (SSE) to evaluate the influence of IOV (25, 75%CV) from numerous perspectives (power, type I error, accuracy and precision of parameter estimates, consequences of neglecting an IOV, capability to detect the 'correct' IOV). To expand the scope from modeling-related aspects to clinical trial practice, we investigated the minimal sample size for IOV detection and calculated areas under the concentration-time curve (AUC) derived from models containing IOV and mis-specified models. The power to correctly detect an IOV increased from one to three occasions (OCC) and the type I error rate to falsely include an IOV was not elevated. Two sampling schemes were compared (with/without trough sample) and including a trough sample resulted in better performance throughout the different evaluations in this simulation study. Parameters were estimated more precisely when more OCCs were included and IOV was of high effect size. Neglecting an IOV that was truly present had a high impact on bias and imprecision of the parameter estimates, mostly on interindividual variabilities and residual error. To reach a power of ≥ 95% in all scenarios when sampling in three OCCs between 10 and 50 patients were required in the investigated setting. AUC calculations with mis-specified models revealed a distorted AUC distribution as IOV was not considered.

About 15-20% of women experience postnatal depression and may seek advice about medication use whilst breastfeeding. Venlafaxine is a potent and selective neuronal serotonin-norepinephrine reuptake inhibitor indicated for treating major depressive disorders. The drug is mainly metabolised by cytochrome P450 2D6 (CYP2D6) to its active metabolite O-desmethylvenlafaxine (ODV), with small contributions from CYP2C9 and CYP2C19. Subsequently, the formed ODV undergoes CYP3A4- and UGT-mediated metabolism and renal excretion. A physiologically based pharmacokinetic (PBPK) model describing the disposition of both venlafaxine and ODV was developed. Consistent with observed data, simulations showed that exposure of the combined active moieties (venlafaxine plus ODV) was similar for both CYP2D6 extensive (EM) and poor metaboliser (PM) subjects. Clinical lactation data for venlafaxine were available from several studies but CYP genotypes were not recorded. Interestingly, based on simulated exposures in breast milk, the estimated average relative infant daily dose (RIDD) ranged from 3.8% for all EMs to 7.6% for all PMs of CYP2D6, CYP2C9 and CYP2C19. Furthermore, simulations in breastfed infants indicated that both CYP polymorphisms and enzyme ontogenies contribute to the significant variability that is observed clinically but the combined exposures of venlafaxine and ODV remain below the thresholds that have been reported for adverse events in adults and children. The data generated here add to the existing knowledge base and can help clinicians and their patients make a more informed decision on the use of venlafaxine during breastfeeding.

Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear correlation structures, which simple descriptions like multivariate Gaussian distributions fail to represent. Prominent non-Gaussian frameworks for covariate distribution modelling are copula-based models and models based on multiple imputation by chained equations (MICE). While both frameworks have already found applications in the life sciences, a systematic investigation of their goodness-of-fit to the theoretical underlying distribution, indicating strengths and weaknesses under different conditions, is still lacking. To bridge this gap, we thoroughly evaluated covariate distribution models in terms of Kullback-Leibler (KL) divergence, a scale-invariant information-theoretic goodness-of-fit criterion for distributions. Methodologically, we proposed a new approach to construct confidence intervals for KL divergence by combining nearest neighbour-based KL divergence estimators with subsampling-based uncertainty quantification. In relevant data sets of different sizes and dimensionalities with both continuous and discrete covariates, non-Gaussian models showed consistent improvements in KL divergence, compared to simpler Gaussian or scale transform approximations. KL divergence estimates were also robust to the inclusion of latent variables and large fractions of missing values. While good generalization behaviour to new data could be seen in copula-based models, MICE shows a trend for overfitting and its performance should always be evaluated on separate test data. Parametric copula models and MICE were found to scale much better with the dimension of the dataset than nonparametric copula models. These findings corroborate the potential of non-Gaussian models for modelling realistic life science covariate distributions.

The time course of biomarkers (e.g., acute phase proteins) are typically described using days relative to events of interest, such as surgery or birth, without specifying the sample time. This limits their use as they may change rapidly during a single day. We investigated strategies to impute missing clock times, using procalcitonin for population modelling as the motivating example. 1275 procalcitonin concentrations from 282 neonates were available with dates but not sample times (Scenario 0). Missing clock times were imputed using a random uniform distribution under three scenarios: (1) minimum sampling intervals (8-12 h); (2) procalcitonin concentrations increase for postnatal days 0-1 then decrease; (3) standard sampling practice at the study hospital. Unique datasets (n = 100) were created with scenario-specific imputed clock times. Procalcitonin was modelled for each scenario using the same non-linear mixed effects model using NONMEM. Scenarios were evaluated by the NONMEM objective function value compared to Scenario 0 (∆OFV) and with visual predictive checks. Scenario 3, based on standard sampling practice at the study hospital, was the best imputation procedure with an improved objective function value compared to Scenario 0 (∆OFV: -62.6). Scenario 3 showed a shorter lag time between the birth event and the procalcitonin concentration increase (average: 12.0 h, 95% interval: 9.7 to 14.3 h) compared to other scenarios (averages: 15.3 to 18.7 h). A methodology for selecting imputation strategies for clock times was developed. This may be applied to other problems where clock times are missing.

Pharmacokinetics (PK)/pharmacodynamics (PD) modeling and simulation is crucial for optimizing antimicrobial dosing. This study assessed covariate impact on PK variability and identified scenarios where fixing the covariate with median value proves effective PK/PD simulations for antibiotics with population PK (popPK) model including only one covariate effect. Three published popPK models were employed, with creatinine clearance (CRCL) identified as a covariate on clearance (CL) for meropenem and tobramycin, and total body weight (WT) associated with the volume of distributions for daptomycin. Given a fixed dose for Meropenem (1000 mg), and a WT based dose for tobramycin (7 mg/kg) and daptomycin (6 mg/kg), PK/PD simulation outcomes (e.g., percentage of PK/PD target attainment (PTA) and toxicity risk) were compared between fixed covariate-based and covariate distribution-based approaches. Covariate impact on PK was assessed through deterministic simulation using outer bounds of covariate versus simulation using median covariate value, with an overlap ratio calculated the percentage of overlapped area under concentration-time curve (AUC) between these two simulation approaches. Meropenem and tobramycin simulations showed a broader PK profiles and distinct PTA distribution with sampled covariate distribution, while daptomycin simulations exhibited consistency in PK/PD characteristics. CRCL had a relative strong impact on meropenem and tobramycin PK, while a weak impact of WT on daptomycin PK was observed from extensive overlap in simulated PK curves, with an overlap ratio of 99.5%. Regarding a weak covariate impact on PK with high overlap ratio, sampling from covariate distribution may not significantly enhance simulation performance, fixing covariate with median value could be efficient for antibiotic PK/PD simulations.

Identification of covariates that can explain sources of variability among individuals in pharmacometric models is key, as it can lead to patient-subgrouping or patient-specific dosing strategies. Common recommendations propose to limit the covariate-parameters relationships to be tested to those that are scientifically plausible, a process called covariate "scope reduction". We investigated the possible impact of scope reduction on model parameters estimated with misspecified models in terms of omission bias (when a relevant covariate is not included in a model) and inclusion bias (when a non-relevant covariate is included). One-hundred datasets were simulated with a rich-sampling design using 8 variations of a one-compartment model with first-order absorption, having clearance (CL), volume of distribution (V), and absorption rate constant (Ka) as parameters, and body weight (WT) as covariate. Parameters were estimated using 14 models that included the covariate using fixed-effects (FEM) and 2 full random-effects models (FREM), with combinations of covariate-parameter relationships and IIV correlations. Estimated parameters were compared to the parameter values used for simulations in terms of accuracy (bias) and precision. Results showed that, in misspecified FEMs, covariate coefficients and IIV parameters were sensitive to omission bias. Conversely, misspecified covariate models did not introduce inclusion bias since the impact of a non-relevant covariate was estimated, as expected, to values close to zero, and in these cases FREM performed better than FEM. In conclusion, while inclusion bias does not seem to be an issue in misspecified models, the risk of introducing omission bias in parameter estimates should be kept in mind when considering covariate scope reduction when covariate models are implemented using fixed effects.

AZD7648 is a potent inhibitor of DNA-dependent protein kinase (DNA-PK), which is part of the non-homologous end-joining DNA repair pathway. When combined with the PARP inhibitor olaparib, AZD7648 shows robust combination activity in pre-clinical ATM-knockout mouse xenograft models. To understand the combination activity of AZD7648 and olaparib, we developed a semi-mechanistic pharmacokinetic/pharmacodynamic (PK-PD) model that incorporates the mechanism of action for each drug which links to proliferating, quiescent, and dying cell states with an additional Allee effect-like term to account for the non-linear growth and regression observed at low cell densities. Model parameters were fitted to training data sets that contained continuous treatment of either monotherapy or the combination. The observed interaction of AZD7648 on olaparib PK was incorporated in the PK-PD model by an effect function specific for each of the drug's MoA and was found essential to quantify drug effects at high dose levels of combination treatments. The model was able to adequately describe the observed efficacy for both monotherapy and sustained regressions in combination groups, mainly driven by maintaining a > 2:1 AUC ratio of apoptotic:proliferating cell fractions. We found that this model was suitable for forecasting intermittent dosing schedules a priori and resulted in accurate predictions when compared to xenograft efficacy data, without the need for extra, descriptive terms to describe supra-additive effects under combined dose regimes. This model provides quantitative understanding on the combination effect of AZD7648 and olaparib and allows for the exploration of the full exposure landscape without the need to experimentally test all scenarios. Furthermore, the model can be utilized to assess what exposures would be necessary in the clinic by linking it to observed or predicted human PK exposures. The model suggests 64.9 uM olaparib is sufficient to achieve tumor stasis in the absence of AZD7648, while the combination of AZD7648 and olaparib only requires plasma concentrations of 20.2 uM AZD7648 and 19.9 uM olaparib at steady-state to achieve the same effect.

Plasma pharmacokinetic (PK) profiles often do not resemble the PK within the central nervous system (CNS) because of blood-brain-border (BBB) processes, like active efflux by P-glycoprotein (P-gp). Methods to predict CNS-PK are therefore desired. Here we investigate whether in vitro apparent permeability (P_app) and corrected efflux ratio (ER_c) extracted from literature can be repurposed as input for the LeiCNS-PK3.4 physiologically-based PK model to confidently predict rat brain extracellular fluid (ECF) PK of P-gp substrates. Literature values of in vitro Caco-2, LLC-PK1-mdr1a/MDR1, and MDCKII-MDR1 cell line transport data were used to calculate P-gp efflux clearance (CL_Pgp). Subsequently, CL_Pgp was scaled from in vitro to in vivo through a relative expression factor (REF) based on P-gp expression differences. BrainECF PK was predicted well (within twofold error of the observed data) for 2 out of 4 P-gp substrates after short infusions and 3 out of 4 P-gp substrates after continuous infusions. Variability of in vitro parameters impacted both predicted rate and extent of drug distribution, reducing model applicability. Notably, use of transport data and in vitro P-gp expression obtained from a single study did not guarantee an accurate prediction; it often resulted in worse predictions than when using in vitro expression values reported by other labs. Overall, LeiCNS-PK3.4 shows promise in predicting brainECF PK, but this study highlights that the in vitro to in vivo translation is not yet robust. We conclude that more information is needed about context and drug dependency of in vitro data for robust brainECF PK predictions.

Brain amyloid beta neuritic plaque accumulation is associated with an increased risk of progression to Alzheimer's disease (AD) [Pfeil, J., et al. in Neurobiol Aging 106: 119-129, 2021]. Several studies estimate rates of change in amyloid plaque over time in clinically heterogeneous cohorts with different factors impacting amyloid plaque accumulation from ADNI and AIBL [Laccarino, L., et al. in Annals Clin and Trans Neurol 6: 1113 1120, 2019, Vos, S.J., et al. in Brain 138: 1327-1338, 2015, Lim, Y.Y., et al. in Alzheimer's Dementia 9: 538-545, 2013], but there are no reports using non-linear mixed effect model for amyloid plaque progression over time similar to that existing of disease-modifying biomarkers for other diseases [Cook, S.F. and R.R. Bies in Current Pharmacol Rep 2: 221-230, 2016, Gueorguieva, I., et al. in Alzheimer's Dementia 19: 2253-2264, 2023]. This study describes the natural progression of amyloid accumulation with population mean and between-participant variability for baseline and intrinsic progression rates quantified across the AD spectrum. 1340 ADNI participants were followed over a 10-year period with ¹⁸F-florbetapir PET scans used for amyloid plaque detection. Non-linear mixed effect with stepwise covariate modelling (scm) was used. Change in natural amyloid plaque levels over 10 year period followed an exponential growth model with an intrinsic rate of approx. 3 centiloid units/year. Age, gender, APOE4 genotype and disease stage were important factors on the baseline in the natural amyloid model. In APOE4 homozygous carriers mean baseline amyloid was increased compared to APOE4 non carriers. These results demonstrate natural progression of amyloid plaque in the continuum of AD.