J Pharmacokinet Pharmacodyn
· 2023 Dec · PMID 37095406
·
Full text
Comparator arms in randomized clinical trials may be impractical and/or unethical to assemble in rare diseases. In the absence of comparator arms, evidence generated from external control studies has been used to support...Comparator arms in randomized clinical trials may be impractical and/or unethical to assemble in rare diseases. In the absence of comparator arms, evidence generated from external control studies has been used to support successful regulatory submissions and health technology assessments (HTA). However, conducting robust and rigorous external control arm studies is challenging and despite all efforts, residual biases may remain. As a result, regulatory and HTA agencies may request additional external control analyses so that decisions may be made based upon a body of supporting evidence.This paper introduces external control studies and provides an overview of the key methodological issues to be considered in the design of these studies. A series of case studies are presented in which evidence derived from one or more external controls was submitted to regulatory and HTA agencies to provide support for the consistency of findings.
J Pharmacokinet Pharmacodyn
· 2023 Aug · PMID 37083930
·
Full text
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'). W...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' and to facilitate the comparison to 'scm'. 'Frem' 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', 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' 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.
Llanos-Paez C, Ambery C, Yang S
… +3 more, Beerahee M, Plan EL, Karlsson MO
J Pharmacokinet Pharmacodyn
· 2023 Aug · PMID 36947282
·
Full text
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 publish...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 (FEV) 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 FEV 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 FEV and mean annual exacerbation rate.
J Pharmacokinet Pharmacodyn
· 2023 Aug · PMID 36944853
·
Full text
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 ph...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.
Methylphenidate (MPH) is a psychostimulant which inhibits the uptake of dopamine and norepinephrine transporters, DAT and NET, and is mostly used to treat Attention Deficit/Hyperactivity Disorder. The current dose optimi...Methylphenidate (MPH) is a psychostimulant which inhibits the uptake of dopamine and norepinephrine transporters, DAT and NET, and is mostly used to treat Attention Deficit/Hyperactivity Disorder. The current dose optimization is done through titration, a cumbersome approach for patients. To assess the therapeutic performance of MPH regimens, we introduce an in silico framework composed of (i) a population pharmacokinetic model of MPH, (ii) a pharmacodynamic (PD) model of DAT and NET occupancy, (iii) a therapeutic box delimited by time and DAT occupancy, and (iv) a performance score computation. DAT occupancy data was digitized (n = 152) and described with Emax models. NET occupancy was described with a KPD model. We used this integrative framework to simulate the performance of extended-release (18-99 mg) and tid MPH regimens (25-40 mg). Early blood samples of MPH seem to lead to higher DAT occupancy, consistent with an acute tolerance observed in clinical rating scales. An Emax model with a time-dependent tolerance was fitted to available data to assess the observed clockwise hysteresis. Peak performance is observed at 63 mg. While our analysis does not deny the existence of an acute tolerance, data precision in terms of formulation and sampling times does not allow a definite confirmation of this phenomenon. This work justifies the need for a more systematic collection of DAT and NET occupancy data to further investigate the presence of acute tolerance and assess the impact of low MPH doses on its efficacy.
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...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= 0.130 vs. 0.0551 week, p-value < 0.0001), but similar for both groups (k = 0.0624 vs.0.0563 week, 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.
Monoclonal antibodies, endogenous IgG, and serum albumin bind to FcRn in the endosome for salvaging and recycling after pinocytotic uptake, which prolongs their half-life. This mechanism has been broadly recognized and i...Monoclonal antibodies, endogenous IgG, and serum albumin bind to FcRn in the endosome for salvaging and recycling after pinocytotic uptake, which prolongs their half-life. This mechanism has been broadly recognized and is incorporated in currently available PBPK models. Newer types of large molecules have been designed and developed, which also bind to FcRn in the plasma space for various mechanistic reasons. To incorporate FcRn binding affinity in PBPK models, binding in the plasma space and subsequent internalisation into the endosome needs to be explicitly represented. This study investigates the large molecules model in PK-Sim and its applicability to molecules with FcRn binding affinity in plasma. With this purpose, simulations of biologicals with and without plasma binding to FcRn were performed with the large molecule model in PK-Sim. Subsequently, this model was extended to ensure a more mechanistic description of the internalisation of FcRn and the FcRn-drug complexes. Finally, the newly developed model was used in simulations to explore the sensitivity for FcRn binding in the plasma space, and it was fitted to an in vivo dataset of wild-type IgG and FcRn inhibitor plasma concentrations in Tg32 mice. The extended model demonstrated a strongly increased sensitivity of the terminal half-life towards the plasma FcRn binding affinity and could successfully fit the in vivo dataset in Tg32 mice with meaningful parameter estimates.
Ruiz-Garcia A, Baverel P, Bottino D
… +12 more, Dolton M, Feng Y, González-García I, Kim J, Robey S, Singh I, Turner D, Wu SP, Yin D, Zhou D, Zhu H, Bonate P
J Pharmacokinet Pharmacodyn
· 2023 Jun · PMID 36870005
·
Full text
Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simula...Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.
T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The for...T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.
Brouwer AF, Lee GO, Schillinger RJ
… +4 more, Edwards CA, Van Wyk H, Yazbeck R, Morrison DJ
J Pharmacokinet Pharmacodyn
· 2023 Jun · PMID 36790613
·
Full text
Carbon stable isotope breath tests offer new opportunities to better understand gastrointestinal function in health and disease. However, it is often not clear how to isolate information about a gastrointestinal or metab...Carbon stable isotope breath tests offer new opportunities to better understand gastrointestinal function in health and disease. However, it is often not clear how to isolate information about a gastrointestinal or metabolic process of interest from a breath test curve, and it is generally unknown how well summary statistics from empirical curve fitting correlate with underlying biological rates. We developed a framework that can be used to make mechanistic inference about the metabolic rates underlying a C breath test curve, and we applied it to a pilot study of C-sucrose breath test in 20 healthy adults. Starting from a standard conceptual model of sucrose metabolism, we determined the structural and practical identifiability of the model, using algebra and profile likelihoods, respectively, and we used these results to develop a reduced, identifiable model as a function of a gamma-distributed process; a slower, rate-limiting process; and a scaling term related to the fraction of the substrate that is exhaled as opposed to sequestered or excreted through urine. We demonstrated how the identifiable model parameters impacted curve dynamics and how these parameters correlated with commonly used breath test summary measures. Our work develops a better understanding of how the underlying biological processes impact different aspect of C breath test curves, enhancing the clinical and research potential of these C breath tests.
'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address thi...'Are two populations the same or are they different' is a question that is often faced in clinical pharmacology trials e.g., a pharmacokinetic trial studying a particular drug in racially different groups. To address this question, concentration-time data were simulated from a reference and test population, where in the latter the clearance, sample size, and sampling design were systematically varied. It was of interest to determine whether the estimates of clearance from the two groups were the same or different. Two approaches were used to estimate the empirical Bayes estimates (EBEs) for clearance. One approach developed a population pharmacokinetic model for the reference population and the EBEs for the reference population were estimated from this model. The parameters of the reference population were fixed to their maximum likelihood estimates. The model was then applied to the test population dataset to estimate the EBEs of the test population using the MAXEVAL = 0 option in NONMEM. A second approach, the theta approach, combined the reference and test datasets into a single dataset and used population as a covariate in the model; the EBEs were estimated from this combined model. The power and type I error rate of each approach were calculated for each treatment combination using a variety of statistical tests to determine whether there was a difference in the distribution of the EBEs in the reference population compared to the test population. Our results suggest that either MAXEVAL or theta approaches can be used with informative sampling designs. In addition to reasonable power and type I error, both approaches gave almost identical results under a dense sampling design. To statistically compare the distribution of EBEs of pharmacokinetic parameters from a reference group to that of a test group, a T-test and DTS eCDF test are equally useful.
Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD mod...Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.
Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much pr...Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.
In a nonlinear mixed-effects modeling (NLMEM) approach of pharmacokinetic (PK) and pharmacodynamic (PD) data, two levels of random effects are generally modeled: between-subject variability (BSV) and residual unexplained...In a nonlinear mixed-effects modeling (NLMEM) approach of pharmacokinetic (PK) and pharmacodynamic (PD) data, two levels of random effects are generally modeled: between-subject variability (BSV) and residual unexplained variability (RUV). The goal of this simulation-estimation study was to investigate the extent to which PK and RUV model misspecification, errors in recording dosing and sampling times, and variability in drug content uniformity contribute to the estimated magnitude of RUV and PK parameter bias. A two-compartment model with first-order absorption and linear elimination was simulated as a true model. PK parameters were clearance 5.0 L/h; central volume of distribution 35 L; inter-compartmental clearance 50 L/h; peripheral volume of distribution 50 L. All parameters were assumed to have a 30% coefficient of variation (CV). One hundred in-silico subjects were administered a labeled dose of 120 mg under 4 sample collection designs. PK and RUV model misspecifications were associated with relatively larger increases in the magnitude of RUV compared to other sources for all levels of sampling design. The contribution of dose and dosing time misspecifications have negligible effects on RUV but result in higher bias in PK parameter estimates. Inaccurate sampling time data results in biased RUV and increases with the magnitude of perturbations. Combined perturbation scenarios in the studied sources will propagate the variability and accumulate in RUV magnitude and bias in PK parameter estimates. This work provides insight into the potential contributions of many factors that comprise RUV and bias in PK parameters.
Clinical Dementia Rating-Sum of Boxes (CDR-SB) assessments from two Phase 3 studies (ENGAGE and EMERGE) of aducanumab in subjects with early Alzheimer's disease (AD) were pooled to develop an exposure-response (ER) model...Clinical Dementia Rating-Sum of Boxes (CDR-SB) assessments from two Phase 3 studies (ENGAGE and EMERGE) of aducanumab in subjects with early Alzheimer's disease (AD) were pooled to develop an exposure-response (ER) model. A linear model in the logit-transformed scaled CDR-SB best characterized the time profile for placebo- and aducanumab-treated subjects, with concentration as the exposure metric. The model allowed delineation of slow (4%), typical (86%), and fast (10%) progressing subpopulations in the data. The estimated drug effect on the disease progression rate was significant, 2.05 L/(g·year), with a 95% confidence interval (1.60, 2.50) that did not include zero. Following an evaluation of a series of ER model forms including differential drug and null effects either between the studies or among the three progression classes, the final ER model with a common (pooled) estimate of the drug effect between the studies and among the three progression classes was considered parsimonious. The final model provides supportive evidence that the two studies demonstrate a common intrinsic pharmacology. None of the identified covariates (Mini-Mental State Examination-BL score and Asian race) were clinically meaningful. Finally, simulations demonstrated that the intrinsic pharmacology remained consistent between the two Phase 3 studies.
Dosing requires consideration of diverse patient-specific factors affecting drug pharmacokinetics and pharmacodynamics. The available pharmacometric methods have limited capacity for modeling the inter-relationships and...Dosing requires consideration of diverse patient-specific factors affecting drug pharmacokinetics and pharmacodynamics. The available pharmacometric methods have limited capacity for modeling the inter-relationships and patterns of variability among physiological determinants of drug dosing (PDODD). To investigate whether generative adversarial networks (GANs) can learn a generative model from real-world data that recapitulates PDODD distributions. A GAN architecture was developed for modeling a PDODD panel comprised of: age, sex, race/ethnicity, body weight, body surface area, total body fat, lean body weight, albumin concentration, glomerular filtration rate (EGFR), urine flow rate, urinary albumin-to-creatinine ratio, alanine aminotransferase to alkaline phosphatase R-value, total bilirubin, active hepatitis B infection status, active hepatitis C infection status, red blood cell, white blood cell, and platelet counts. The panel variables were derived from National Health and Nutrition Examination Survey (NHANES) data sets. The dependence of GAN-generated PDODD on age, race, and active hepatitis infections was assessed. The continuous PDODD biomarkers had diverse non-normal univariate distributions and bivariate trend patterns. The univariate distributions of PDODD biomarkers from GAN simulations satisfactorily approximated those in test data. The joint distribution of the continuous variables was visualized using three 2-dimensional projection methods; for all three methods, the points from the GAN simulation random variate vectors were well dispersed amongst the test data. The age dependence trend patterns in GAN data were similar to those in test data. The histograms for R-values and EGFR from GAN simulations overlapped extensively with test data histograms for the Hispanic, White, African American, and Other race/ethnicity groups. The GAN-simulated data also mirrored the R-values and EGFR changes in active hepatitis C and hepatitis B infection. GANs are a promising approach for simulating the age, race/ethnicity and disease state dependencies of PDODD.
The Eleveld propofol pharmacokinetic (PK) model, which was developed based on a broad range of populations, showed greater bias (- 27%) in elderly subjects in a previous validation study conducted by Vellinga and colleag...The Eleveld propofol pharmacokinetic (PK) model, which was developed based on a broad range of populations, showed greater bias (- 27%) in elderly subjects in a previous validation study conducted by Vellinga and colleagues. We aimed to develop and externally validate a new PK-pharmacodynamic (PK-PD) model of propofol for elderly subjects. A population PK-PD model was constructed using propofol plasma concentrations and bispectral index (BIS) values that were obtained from 31 subjects aged 65 years older in previously published phase I studies. The predictive performance of the newly-developed PK-PD model (Choi model) was assessed in a separate Korean elderly population and compared with that of the Eleveld model. A three-compartment mammillary model using an allometric expression and a sigmoid E model well-described the time courses of propofol concentrations and BIS values. The V, V, V, Cl, Q, Q, E, E, Ce, γ, and k of a 60-kg subject were 8.36, 58.0, 650 L, 1.26, 0.917, 0.669 L/min, 92.1, 18.7, 2.21 μg/mL, 2.89, and 0.138 /min, respectively. In the Choi model and Eleveld model, pooled biases (95% CI) of the propofol concentration were 7.78 ( 3.09-12.49) and 16.70 (9.46-23.93) and pooled inaccuracies were 22.84 (18.87-26.81) and 24.85 (18.07-31.63), respectively. The Choi PK model was less biased than the Eleveld PK model in Korean elderly subjects (age range: 65.0-79.0 yr; weight range: 45.0-75.3 kg). Our results suggest that the Choi PK model, particularly, is applicable to target-controlled infusion in non-obese Korean elderly subjects.