Journal of Pharmacokinetics and Pharmacodynamics最新文献

The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.

The main objective of this manuscript was to validate the ability of the monoclonal antibody physiologically-based pharmacokinetic (PBPK) model to predict tissue concentrations of antibodies in the human. To accomplish this goal, preclinical and clinical tissue distribution and positron emission tomography imaging data generated using zirconium-89 (⁸⁹Zr) labeled antibodies were obtained from the literature. First, our previously published translational PBPK model for antibodies was expanded to describe the whole-body biodistribution of ⁸⁹Zr labeled antibody and the free ⁸⁹Zr, as well as residualization of free ⁸⁹Zr. Subsequently, the model was optimized using mouse biodistribution data, where it was observed that free ⁸⁹Zr mainly residualizes in the bone and the extent of antibody distribution in certain tissues (e.g., liver and spleen) may be altered by labeling with ⁸⁹Zr. The mouse PBPK model was scaled to rat, monkey, and human by simply changing the physiological parameters, and a priori simulations performed by the model were compared with the observed PK data. It was found that model predicted antibody PK in majority of the tissues in all the species superimposed over the observed data, and the model was also able to predict the PK of antibody in human tissues reasonably well. As such, the work presented here provides unprecedented evaluation of the antibody PPBK model for its ability to predict tissue PK of antibodies in the clinic. This model can be used for preclinical-to-clinical translation of antibodies and for prediction of antibody concentrations at the site-of-action in the clinic.

Population pharmacokinetics (popPK) of bempedoic acid and the popPK/pharmacodynamic (popPK/PD) relationship between bempedoic acid concentrations and serum low-density lipoprotein cholesterol (LDL-C) from baseline were characterized. A two-compartment disposition model with a transit absorption compartment and linear elimination best described bempedoic acid oral pharmacokinetics (PK). Multiple covariates, including renal function, sex, and weight, had statistically significant effects on the predicted steady-state area under the curve. Mild (estimated glomerular filtration rate (eGFR) 60 to < 90 mL/min vs. ≥ 90 mL/min) and moderate (eGFR 30 to < 60 mL/min vs. ≥ 90 mL/min) renal impairment, female sex, low (< 70 kg vs. 70-100 kg) and high (> 100 kg vs. 70-100 kg) body weight were predicted to have a 1.36-fold (90% confidence interval (CI) 1.32, 1.41), 1.85-fold (90% CI 1.74, 2.00), 1.39-fold (90% CI 1.34, 1.47), 1.35-fold (90% CI 1.30, 1.41), and 0.75-fold (90% CI 0.72, 0.79) exposure difference relative to their reference populations, respectively. An indirect response model described changes in serum LDL-C with a model-predicted 35% maximal reduction and bempedoic acid IC₅₀ of 3.17 µg/mL. A 28% reduction from LDL-C baseline was predicted for a steady-state average concentration of 12.5 µg/mL after bempedoic acid (180 mg/day) dosing, accounting for approximately 80% of the predicted maximal LDL-C reduction. Concurrent statin therapy, regardless of intensity, reduced the maximal effect of bempedoic acid but resulted in similar steady-state LDL-C levels. While multiple covariates had statistically significant effects on PK and LDL-C lowering, none were predicted to warrant bempedoic acid dose adjustment.

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in C_max for apixaban and a 45% decrease in AUC and a 25% decrease in C_max for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.

The International Society of Pharmacometrics (ISoP) Mentorship Program (IMP) aims to help professionals at all career stages to transition into the pharmacometrics field, move to a different role/area within pharmacometrics, or expand their skillsets. The program connects mentees at various stages of their careers with mentors based on established criteria for mentor-mentee matching. Pairing mentees with appropriate mentors ensures strong alignment between mentees' interests and mentors' expertise as this is critical to the success and continuation of the relationship between the mentor and mentee. Once mentors and mentees are connected, they are strongly encouraged to meet at least once per month for an hour. The mentor and mentee have the freedom to tailor their sessions to their liking, including frequency, duration, and topics they choose to focus on. Mentees are encouraged to clearly define their goals to help direct their mentor-mentee relationship and conversations. Mentees and mentors alike are given the opportunity to provide feedback about the program to the ISoP Education Committee through surveys and testimonials. Due to the program's infancy, structured guidelines for mentor-mentee sessions are still being developed and instituted using the program evaluation described in this paper.

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique ('scm') was compared to full random effects modelling ('frem'). We evaluated the power to identify a 'true' covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20-500) and covariate correlations (0-90% cov-corr). The PsN 'frem' routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as 'frem_posthoc' and to facilitate the comparison to 'scm'. 'Frem_posthoc' had a higher power to detect the true covariate with lower bias in small n studies compared to 'scm', applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For 'frem_posthoc', power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step 'frem' models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final 'scm' model precision obtained using common settings. We conclude that 'frem_posthoc' is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV₁) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV₁ data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV₁ and mean annual exacerbation rate.

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (k_g= 0.130 vs. 0.0551 week^-1, p-value < 0.0001), but similar for both groups (k_g = 0.0624 vs.0.0563 week^-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

Fractional differential equations (FDEs), i.e. differential equations with derivatives of non-integer order, can describe certain experimental datasets more accurately than classic models and have found application in pharmacokinetics (PKs), but wider applicability has been hindered by the lack of appropriate software. In the present work an extension of NONMEM software is introduced, as a FORTRAN subroutine, that allows the definition of nonlinear mixed effects (NLME) models with FDEs. The new subroutine can handle arbitrary user defined linear and nonlinear models with multiple equations, and multiple doses and can be integrated in NONMEM workflows seamlessly, working well with third party packages. The performance of the subroutine in parameter estimation exercises, with simple linear and nonlinear (Michaelis-Menten) fractional PK models has been evaluated by simulations and an application to a real clinical dataset of diazepam is presented. In the simulation study, model parameters were estimated for each of 100 simulated datasets for the two models. The relative mean bias (RMB) and relative root mean square error (RRMSE) were calculated in order to assess the bias and precision of the methodology. In all cases both RMB and RRMSE were below 20% showing high accuracy and precision for the estimates. For the diazepam application the fractional model that best described the drug kinetics was a one-compartment linear model which had similar performance, according to diagnostic plots and Visual Predictive Check, to a three-compartment classic model, but including four less parameters than the latter. To the best of our knowledge, it is the first attempt to use FDE systems in an NLME framework, so the approach could be of interest to other disciplines apart from PKs.