Journal of Pharmacokinetics and Pharmacodynamics最新文献

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.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5 mg once daily for moderate patients and 6.25 mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Sepiapterin is an exogenously synthesized new chemical entity that is structurally equivalent to endogenous sepiapterin, a biological precursor of tetrahydrobiopterin (BH₄), which is a cofactor for phenylalanine hydroxylase. Sepiapterin is being developed for the treatment of hyperphenylalaninemia in pediatric and adult patients with phenylketonuria (PKU). This study employed concentration-QT interval analysis to assess QT prolongation risk following sepiapterin treatment. Data from three phase 1 studies and one phase 3 study were pooled for this analysis. Pediatric and adult PKU patients ≥ 2 years received multiple doses at 60 mg/kg and adult healthy volunteers received a single or multiple doses at 20 or 60 mg/kg. Time-matched triplicate ECG measurements and plasma samples for pharmacokinetic analysis were collected. Prespecified linear mixed models relating ΔQTcF to concentrations of sepiapterin and the major active circulating metabolite BH₄ were developed for the analysis. The analysis demonstrated that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing at the highest therapeutic dose, 60 mg/kg/day. The final model showed a marginal but negligible QTcF reduction with increasing sepiapterin and BH₄ concentrations. The effect on ΔQTcF was estimated to -2.72 [-3.72, -1.71] and - 1.25 [-2.75, 0.25] ms at mean baseline adjusted BH₄ C_max of 332 ng/mL (therapeutic exposure) and 675 ng/mL (supratherapeutic exposure) at dose 60 mg/kg, respectively, in PKU patients with food and in healthy volunteers with a high fat diet. Various covariates, such as clinical study ID, age, sex, food effect, race, body weight, and disease status, on QTcF interval were investigated and were found insignificant, except for food effect and age. This study concludes that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing up to 60 mg/kg/day, and BH₄ and sepiapterin concentrations minimally affect ΔQTcF after adjustment for time, sex, and meal.

P-glycoprotein (P-gp) is a key efflux transporter and may be involved in drug-drug interactions (DDIs) at the blood-brain barrier (BBB), which could lead to changes in central nervous system (CNS) drug exposure. Morphine is a P-gp substrate and therefore a potential victim drug for P-gp mediated DDIs. It is however unclear if P-gp inhibitors can induce clinically relevant changes in morphine CNS exposure. Here, we used a physiologically-based pharmacokinetic (PBPK) model-based approach to evaluate the potential impact of DDIs on BBB transport of morphine by clinically relevant P-gp inhibitor drugs.The LeiCNS-PK3.0 PBPK model was used to simulate morphine distribution at the brain extracellular fluid (brain_ECF) for different clinical intravenous dosing regimens of morphine, alone or in combination with a P-gp inhibitor. We included 34 commonly used P-gp inhibitor drugs, with inhibitory constants and expected clinical P-gp inhibitor concentrations derived from literature. The DDI impact was evaluated by the change in brain_ECF exposure for morphine alone or in combination with different inhibitors. Our analysis demonstrated that P-gp inhibitors had a negligible effect on morphine brain_ECF exposure in the majority of simulated population, caused by low P-gp inhibition. Sensitivity analyses showed neither major effects of increasing the inhibitory concentration nor changing the inhibitory constant on morphine brain_ECF exposure. In conclusion, P-gp mediated DDIs on morphine BBB transport for the evaluated P-gp inhibitors are unlikely to induce meaningful changes in clinically relevant morphine CNS exposure. The developed CNS PBPK modeling approach provides a general approach for evaluating BBB transporter DDIs in humans.