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  • Population-based meta-analysis of bortezomib exposure–response relationships in multiple myeloma patients
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2020-01-14
    Li Zhang, Donald E. Mager

    Bortezomib (Velcade®) is a reversible proteasome inhibitor that shows potent antineoplastic activity, by inhibiting the constitutively increased proteasome activity in myeloma cells, and is approved as a first-line therapy for multiple myeloma (MM). Although clinically successful, bortezomib exhibits a relatively narrow therapeutic index and can induce dose-limiting toxicities such as thrombocytopenia. This study aims to develop a quantitative and predictive pharmacodynamic model to investigate bortezomib dosing-regimens in a rational and efficient manner. Mean temporal profiles of bortezomib pharmacokinetics, proteasome activity, M-protein concentrations, and platelet counts following bortezomib monotherapy were extracted from published clinical studies. A population–based meta-analysis of bortezomib anti-myeloma activity and thrombocytopenia was conducted sequentially with a Stochastic Approximation Expectation Maximization algorithm in Monolix. The final pharmacodynamic model integrates drug-target interactions and cell signaling dynamics with temporal biomarkers of clinical efficacy and toxicity. Bortezomib pharmacokinetics, disease progression, and platelet dynamic profiles were well characterized in MM patients, and a local sensitivity analysis of the final model suggests that increased proteasome concentration could ultimately attenuate bortezomib antineoplastic activity in MM patients. In addition, model simulations confirm that a once-weekly dosing schedule represents an optimal therapeutic regimen with comparable antineoplastic activity but significantly reduced risk of thrombocytopenia. In conclusion, a pharmacodynamic model was successfully developed, which provides a quantitative, mechanism-based platform for probing bortezomib dosing-regimens. Further research is needed to determine whether this model could be used to individualize bortezomib regimens to maximize antineoplastic efficacy and minimize thrombocytopenia during MM treatment.

  • Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2020-01-06
    Clémence Rigaux, Bernard Sébastien

    Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks’ study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.

  • The non-compartmental steady-state volume of distribution revisited
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2020-01-03
    Wojciech Jawień, Jan Kobierski

    The lack in the literature of a simple, yet general and complete derivation of the widely used equation for non-compartmental calculation of steady-state volume of distribution is pointed out. It is demonstrated that the most frequently cited references contain an overly simplified explanation. The logical gap consists in doubly defining the same quantities without a proof the definitions are equivalent. Two alternative solutions are proposed: analytical derivation and hydrodynamic analogy. It is shown, that the problem can be analyzed in a purely macroscopic framework by utilizing the integral mean value of the function, without the need to resort to statistical distributions.

  • Comparison of the gamma-Pareto convolution with conventional methods of characterising metformin pharmacokinetics in dogs
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-12-21
    Carl A. Wesolowski, Surajith N. Wanasundara, Paul S. Babyn, Jane Alcorn

    A model was developed for long term metformin tissue retention based upon temporally inclusive models of serum/plasma concentration (\( C \)) having power function tails called the gamma-Pareto type I convolution (GPC) model and was contrasted with biexponential (E2) and noncompartmental (NC) metformin models. GPC models of \( C \) have a peripheral venous first arrival of drug-times parameter, early \( C \) peaks and very slow washouts of \( C \). The GPC, E2 and NC models were applied to a total of 148 serum samples drawn from 20 min to 72 h following bolus intravenous metformin in seven healthy mongrel dogs. The GPC model was used to calculate area under the curve (AUC), clearance (\( CL \)), and functions of time, f(t), for drug mass remaining (M), apparent volume of distribution (\(V_{d}\)), as well as \(t_{1/2}\ f(t)\) for \( C \), \( M \) and \(V_{d}\). The GPC models of \( C \) yielded metformin \( CL \)-values that were 84.8% of total renal plasma flow (RPF) as estimated from meta-analysis. The GPC \( CL \)-values were significantly less than the corresponding NC and E2 \( CL \)-values of 104.7% and 123.7% of RPF, respectively. The GPC plasma/serum only model predicted 78.9% drug \( M \) average urinary recovery at 72 h; similar to prior human urine drug \( M \) collection results. The GPC model \(t_{1/2}\) of \( M \), \( C \) and \(V_d\), were asymptotically proportional to elapsed time, with a constant limiting \(t_{1/2}\) ratio of M/C averaging 7.0 times, a result in keeping with prior simultaneous \( C \) and urine \( M \) collection studies and exhibiting a rate of apparent volume growth of \(V_d\) that achieved limiting constant values. A simulated constant average drug mass multidosing protocol exhibited increased \(V_d\) and \(t_{1/2}\) with elapsing time, effects that have been observed experimentally during same-dose multidosing. The GPC heavy-tailed models explained multiple documented phenomena that were unexplained with lighter-tailed models.

  • Optimization of clinical dosing schedule to manage neutropenia: learnings from semi-mechanistic modeling simulation approach
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-12-18
    Yue Guo, Nahor Haddish-Berhane, Hong Xie, Daniele Ouellet

    Neutropenia is a common side-effect of oncology drugs. We aimed to study the impact of exposure and dosing schedule on neutropenia to guide selection of dosing schedules that minimize neutropenia potential while maintaining the desired minimum concentration (Cmin) required for target engagement. Dose, frequency and PK parameters were chosen for five hypothetical drugs of various half-lives to (1) achieve same exposure with continuous dosing and evaluate impact of 4 intermittent dosing schedules; and (2) achieve same nadir for continuous and intermittent dosing and evaluate impact on % time above Cmin, a surrogate assumed to indicate target engagement. Absolute neutrophil count (ANC) profiles were simulated using Friberg model, a widely used semi-mechanistic myelosuppression model, assuming drug concentration directly reduce the proliferation rate of stem cells and progenitor cells in proliferation compartment. The correlations between different PK measures and neutropenia metrics were explored. In (1), when the same daily dose was used, intermittent schedules offered better management of ANC nadir. The reduced average drug exposure with intermittent dosing led to lower% time above Cmin. In (2), when the dose was adjusted to achieve the same nadir, drugs with moderate half-life (8–48 h) showed similar % time above Cmin regardless of schedule, while continuous dosing was better for a short half-life (4 h). Area under the concentration curve (AUC) was highly correlated with neutropenia. In summary, continuous dosing, with the dose selected correctly, is most effective to maintain % time above Cmin while providing similar tolerability as intermittent dosing with a higher dose. But dose interruptions could be required to manage individual toxicities. Intermittent schedules, on the other hand, allow recovery of ANC, enabling more orderly schedules.

  • Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-08-08
    Carrie German, Minu Pilvankar, Andrzej Przekwas

    The primary goal of this work was to develop a computational tool to enable personalized prediction of pharmacological disposition and associated responses for opioids and antidotes. Here we present a computational framework for physiologically-based pharmacokinetic (PBPK) modeling of an opioid (morphine) and an antidote (naloxone). At present, the model is solely personalized according to an individual’s mass. These PK models are integrated with a minimal pharmacodynamic model of respiratory depression induction (associated with opioid administration) and reversal (associated with antidote administration). The model was developed and validated on human data for IV administration of morphine and naloxone. The model can be further extended to consider different routes of administration, as well as to study different combinations of opioid receptor agonists and antagonists. This work provides the framework for a tool that could be used in model-based management of pain, pharmacological treatment of opioid addiction, appropriate use of antidotes for opioid overdose and evaluation of abuse deterrent formulations.

  • Correction to: Bayesian approach to investigate a two-state mixed model of COPD exacerbations
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-11-15
    Anna Largajolli, Misba Beerahee, Shuying Yang

    The article [Bayesian approach to investigate a two-state mixed model of COPD exacerbations], written by [Anna Largajolli, Misba Beerahee, Shuying Yang], was originally published electronically on the publisher’s internet portal (currently SpringerLink) on [13 June 2019] without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on [November 2019] to © The Author(s) [2019] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

  • Population pharmacokinetics and covariate analysis of Sym004, an antibody mixture against the epidermal growth factor receptor, in subjects with metastatic colorectal cancer and other solid tumors
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-11-02
    Lene Alifrangis, Rik Schoemaker, Niels J. Skartved, Rikke Hald, Clara Montagut, Scott Kopetz, Josep Tabernero, Michael Kragh, Janet R. Wade

    Sym004 is an equimolar mixture of two monoclonal antibodies, futuximab and modotuximab, which non-competitively block the epidermal growth factor receptor (EGFR). Sym004 has been clinically tested for treatment of solid tumors. The present work characterizes the non-linear pharmacokinetics (PK) of Sym004 and its constituent antibodies and investigates two types of covariate models for interpreting the interindividual variability of Sym004 exposure. Sym004 serum concentration data from 330 cancer patients participating in four Phase 1 and 2 trials (n = 247 metastatic colorectal cancer, n = 87 various types advanced solid tumors) were pooled for non-linear mixed effects modeling. Dose regimens of 0.4–18 mg/kg Sym004 dosed by i.v. infusion weekly or every 2nd week were explored. The PK profiles for futuximab and modotuximab were parallel, and the parameter values for their population PK models were similar. The PK of Sym004 using the sum of the serum concentrations of futuximab and modotuximab was well captured by a 2-compartment model with parallel linear and saturable, Michaelis–Menten-type elimination. The full covariate model including all plausible covariates included in a single step showed no impact on Sym004 exposure of age, Asian race, renal and hepatic function, tumor type and previous anti-EGFR treatments. The reduced covariate model contained statistically and potentially clinically significant influences of body weight, albumin, sex and baseline tumor size. Population PK modeling and covariate analysis of Sym004 were feasible using the sum of the serum concentrations of the two constituent antibodies. Full and reduced covariate models provided insights into which covariates may be clinically relevant for dose modifications and thus may need further exploration.

  • Cardiac risk assessment based on early Phase I data and PK-QTc analysis is concordant with the outcome of thorough QTc trials: an assessment based on eleven drug candidates
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-10-30
    Puneet Gaitonde, Yeamin Huh, Borje Darpo, Georg Ferber, Günter Heimann, James Li, Kaifeng Lu, Bernard Sebastien, Kuenhi Tsai, Steve Riley

    Cardiac safety assessment is a key regulatory requirement for almost all new drugs. Until recently, one evaluation aspect was via a specifically designated, expensive, and resource intensive thorough QTc study, and a by-time-point analysis using an intersection–union test (IUT). ICH E14 Q&A (R3) (http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf) allows for analysis of the PK-QTc relationship using early Phase I data to assess QTc liability. In this paper, we compared the cardiac risk assessment based on the early Phase I analysis with that from a thorough QTc study across eleven drug candidate programs, and demonstrate that the conclusions are largely the same. The early Phase I analysis is based upon a linear mixed effect model with known covariance structure (Dosne et al. in Stat Med 36(24):3844–3857, 2017). The treatment effect was evaluated at the supratherapeutic Cmax as observed in the thorough QTc study using a non-parametric bootstrap analysis to generate 90% confidence intervals for the treatment effect, and implementation of the standardized methodology in R and SAS software yielded consistent results. The risk assessment based on the concentration–response analysis on the early Phase I data was concordant with that based on the standard analysis of the thorough QTc study for nine out of the eleven drug candidates. This retrospective analysis is consistent with and supportive of the conclusion of a previous prospective analysis by Darpo et al. (Clin Pharmacol Ther 97(4):326–335, 2015) to evaluate whether C-QTc analysis can detect QTc effects in a small study with healthy subjects.

  • Impact of Phase 1 study design on estimation of QT interval prolongation risk using exposure–response analysis
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-10-29
    Nikolaos Tsamandouras, Sridhar Duvvuri, Steve Riley

    The International Council for Harmonisation (ICH) guidelines have been revised allowing for modeling of concentration-QT (C-QT) data from Phase I dose-escalation studies to be used as primary analysis for QT prolongation risk assessment of new drugs. This work compares three commonly used Phase I dose-escalation study designs regarding their efficiency to accurately identify drug effects on QT interval through C-QT modeling. Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. Clinical trial simulations were performed for each design and across different scenarios (e.g. different magnitudes of drug effect, QT variability), assuming a pre-specified linear mixed effect (LME) model for the relationship between drug concentration and change from baseline QT (ΔQT). Analyses suggest no systematic bias in either the predictions of placebo-adjusted ΔQT (ΔΔQT) or the LME model parameter estimates across all evaluated designs. Additionally, false negative rates remained similar and adequately controlled across all evaluated designs. However, compared to the crossover designs, the parallel design had significantly less power to correctly exclude a clinically significant QT effect, especially in the presence of substantial intercept inter-individual variability. In such cases, parallel design is associated with increased uncertainty around ΔΔQT prediction, mainly attributed to the uncertainty around the estimation of the treatment-specific intercept in the model. Throughout all the evaluated scenarios, the crossover design with interleaving cohorts had consistently the best performance characteristics. The results from this investigation will further facilitate informed decision-making during Phase I study design and the interpretation of the associated C-QT modeling output.

  • Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-10-26
    A. Brekkan, S. Jönsson, M. O. Karlsson, E. L. Plan

    Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.

  • Cabozantinib exposure–response analyses of efficacy and safety in patients with advanced hepatocellular carcinoma
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-10-21
    Linh Nguyen, Sunny Chapel, Benjamin Duy Tran, Steven Lacy

    Cabozantinib, a multi-kinase inhibitor, is approved in the United States and European Union for treatment of patients with hepatocellular carcinoma following prior sorafenib treatment. In the Phase III CELESTIAL trial, hepatocellular carcinoma patients receiving cabozantinib showed longer overall survival (OS) and progression-free survival (PFS) than those receiving placebo. The approved cabozantinib (Cabometyx®) dose is 60 mg once daily with allowable dose modifications to manage adverse events (AE). Time-to-event Cox proportional hazard exposure–response (ER) models were developed to characterize the relationship between predicted cabozantinib exposure and the likelihood of various efficacy and safety endpoints. The ER models were used to predict hazard ratios (HR) for efficacy and safety endpoints for starting doses of 60, 40, or 20 mg daily. Statistically significant relationships between cabozantinib exposure and efficacy and safety endpoints were observed. For efficacy endpoints, predicted HR were lower for OS and PFS at 40 and 60 mg relative to the 20 mg dose: HR for death (OS) are 0.84 (40 mg) and 0.70 (60 mg); HR for disease progression/death (PFS) are 0.73 (40 mg) and 0.62 (60 mg). For safety endpoints, predicted HR were lower for palmar-plantar erythrodysaesthesia (PPE), diarrhea, and hypertension at 20 or 40 mg relative to the 60 mg dose: HR for PPE are 0.31 (20 mg) and 0.66 (40 mg); HR for diarrhea are 0.61 (20 mg) and 0.86 (40 mg); HR for hypertension are 0.46 (20 mg) and 0.76 (40 mg). The rate of dose modifications was predicted to increase in patients with lower cabozantinib apparent clearance. OS and PFS showed the greatest benefit at the 60 mg dose. However, higher cabozantinib exposure was predicted to increase the likelihood of AE and subsequent dose reductions appeared to decrease these risks.

  • Pharmacokinetics-pharmacodynamics of sertraline as an antifungal in HIV-infected Ugandans with cryptococcal meningitis
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-10-04
    Ali A. Alhadab, Joshua Rhein, Lillian Tugume, Abdu Musubire, Darlisha A. Williams, Mahsa Abassi, Melanie R. Nicol, David B. Meya, David R. Boulware, Richard C. Brundage, ASTRO-CM Study Team

    The ASTRO-CM dose-finding pilot study investigated the role of adjunctive sertraline for the treatment of HIV-associated cryptococcal meningitis in HIV-infected Ugandan patients. The present study is a post hoc pharmacokinetic-pharmacodynamic analysis of the ASTRO-CM pilot study to provide insight into sertraline exposure–response–outcome relationships. We performed a population pharmacokinetic analysis using sertraline plasma concentration data and correlated various predicted PK-PD indices with the percentage change in log10 CFU/mL from baseline. Sertraline clearance was 1.95-fold higher in patients receiving antiretroviral (ART), resulting in 49% lower drug exposure. To quantify the clinical benefit of sertraline, we estimated rates of fungal clearance from cerebrospinal fluid (CSF) of ASTRO-CM patients using Poisson model and compared the clearance rates to a historical control study (COAT) in which patients received standard Cryptococcus therapy of amphotericin B (0.7–1.0 mg/kg per day) and fluconazole (800 mg/day) without sertraline. Adjunctive sertraline significantly increased CSF fungal clearance rate compared to COAT trial and sertraline effect was dose-independent with no covariate found to affect fungal clearance including ART. Study findings suggest sertraline response could be mediated by different mechanisms than directly inhibiting the initiation of protein translation as previously suggested; this is supported by the prediction of unbound sertraline concentrations is unlikely to reach MIC concentrations in the brain. Study findings also recommend against the use of higher doses of sertraline, especially those greater than the maximum FDA-approved daily dose (200 mg/day), since they unlikely provide any additional benefits and come with greater costs and risk of adverse events.

  • Correction to: Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-07-13
    Pierre Chelle, Cindy H. T. Yeung, Santiago Bonanad, Juan Cristóbal Morales Muñoz, Margareth C. Ozelo, Juan Eduardo Megías Vericat, Alfonso Iorio, Jeffrey Spears, Roser Mir, Andrea Edginton

    The article Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients, written by Pierre Chelle, Cindy H. T. Yeung, Santiago Bonanad, Juan Cristóbal Morales Muñoz, Margareth C. Ozelo, Juan Eduardo Megías Vericat, Alfonso Iorio, Jeffrey Spears, Roser Mir, Andrea Edginton, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 21 May 2019 without open access.

  • Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-05-21
    Pierre Chelle, Cindy H. T. Yeung, Santiago Bonanad, Juan Cristóbal Morales Muñoz, Margareth C. Ozelo, Juan Eduardo Megías Vericat, Alfonso Iorio, Jeffrey Spears, Roser Mir, Andrea Edginton

    Fanhdi/Alphanate is a plasma derived factor VIII concentrate used for treating hemophilia A, for which there has not been any dedicated model describing its pharmacokinetics (PK). A population PK model was developed using data extracted from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project. WAPPS-Hemo provided individual PK profiles for hemophilia patients using sparse observations as provided in routine clinical care by hemophilia centers. Plasma factor activity measurements and covariate data from hemophilia A patients on Fanhdi/Alphanate were extracted from the WAPPS-Hemo database. A population PK model was developed using NONMEM and evaluated for suitability for Bayesian forecasting using prediction-corrected visual predictive check (pcVPC), cross validation, limited sampling analysis and external evaluation against a population PK model developed on rich sampling data. Plasma factor activity measurements from 92 patients from 12 centers were used to derive the model. The PK was best described by a 2-compartment model including between subject variability on clearance and central volume, fat free mass as a covariate on clearance, central and peripheral volumes, and age as covariate on clearance. Evaluations showed that the developed population PK model could predict the PK parameters of new individuals based on limited sampling analysis and cross and external evaluations with acceptable precision and bias. This study shows the feasibility of using real-world data for the development of a population PK model. Evaluation and comparison of the model for Bayesian forecasting resulted in similar results as a model developed using rich sampling data.

  • A quantitative systems pharmacology model of colonic motility with applications in drug development
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-08-20
    Raibatak Das, Lucia Wille, Liming Zhang, Chunlin Chen, Wendy Winchester, Jangir Selimkhanov, Jill Wykosky, Joshua F. Apgar, John M. Burke, Mark Rogge, Fei Hua, Majid Vakilynejad

    We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.

  • A modeling and simulation-based assessment of the impact of confounding factors on the readout of a sildenafil survival trial in pulmonary arterial hypertension
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-09-20
    Pascal Chanu, Xiang Gao, Rene Bruno, Laurent Claret, Lutz Harnisch

    Sildenafil (REVATIO®) was approved for the treatment of adult Pulmonary Arterial Hypertension (PAH) in the US and the EU. A pediatric study has been performed and sildenafil was approved in the EU for pediatric PAH. The long-term extension of this study revealed good survival but also an increased mortality with the high dose of sildenafil compared to lower doses. As a consequence, FDA required Pfizer to evaluate REVATIO®’s effect on the risk of death in adults with PAH. Following FDA’s rationale a survival model was developed to characterize the exposure–mortality relationship and assess its potential impact on an ongoing survival trial in adults in the context of confounding factors. Clinical trial simulations were performed to assess the design of the survival trial in adults (AFFILIATE, NCT02060487), expected to last approximately 8 years according to both assumptions: absence or presence of an exposure–mortality relationship and to quantify the impact of confounding factors on its readout. Simulations showed that the trial would be robust in most conditions. But its interpretation will depend on the number of confounding factors such as additional treatments attempting to control disease progression.

  • Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-05-24
    Daniela J. Conrado, Jane Larkindale, Alexander Berg, Micki Hill, Jackson Burton, Keith R. Abrams, Richard T. Abresch, Abby Bronson, Douglass Chapman, Michael Crowther, Tina Duong, Heather Gordish-Dressman, Lutz Harnisch, Erik Henricson, Sarah Kim, Craig M. McDonald, Stephan Schmidt, Camille Vong, Xiaoxing Wang, Brenda L. Wong, Florence Yong, Klaus Romero, the Duchenne Muscular Dystrophy Regulatory Science Consortium (D-RSC)

    Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

  • FDA’s Office of Orphan Products Development: providing incentives to promote the development of products for rare diseases
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-07-05
    Soumya Patel, Katherine I. Miller Needleman

    There are nearly 30 million Americans that suffer from at least one of the more than 7000 rare diseases identified to date. Therapies for treating, preventing, or diagnosing rare diseases have been limited due to various reasons. Incentives are provided to sponsors in an effort to promote the development of therapies for rare diseases and to encourage the availability of therapeutically superior drugs or biologics. This paper will discuss the mission of the Office of Orphan Products Development within the Food and Drug Administration (FDA), the specific programs within the office and the relation to incentives provided, achievements of the programs, and continued challenges in rare disease product development.

  • Orphan drug development: the increasing role of clinical pharmacology
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-07-23
    Mariam A. Ahmed, Malek Okour, Richard Brundage, Reena V. Kartha

    Over the last few decades there has been a paradigm shift in orphan drug research and development. The development of the regulatory framework, establishment of rare disease global networks that support drug developments, and advances in technology, has resulted in tremendous growth in orphan drug development. Nevertheless, several challenges during orphan drug development such as economic constraints; insufficient clinical information; fewer patients and thus inadequate power; etc. still exist. While the standard regulatory requirements for drug approval stays the same, applications of scientific judgment and regulatory flexibility is significantly important to help meeting some of the immense unmet medical need in rare diseases. Clinical pharmacology presents a vital role in accelerating orphan drug development and overcoming some of these challenges. This review highlights the critical contributions of clinical pharmacology in orphan drug development; for example, dose finding, optimizing clinical trial design, indication expansion, and population extrapolation. Examples of such applications are reviewed in this article.

  • Development and evaluation of a generic population pharmacokinetic model for standard half-life factor VIII for use in dose individualization
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-05-18
    Alanna McEneny-King, Pierre Chelle, Gary Foster, Arun Keepanasseril, Alfonso Iorio, Andrea N. Edginton

    Hemophilia A is a rare bleeding disorder resulting from a lack of functional factor VIII (FVIII). Therapy consists of replacement with exogenous FVIII, but is complicated by high inter-patient variability. A population pharmacokinetics (PopPK) approach can facilitate the uptake of an individualized approach to hemophilia therapy. We developed a PopPK model using data from seven brands of standard half-life FVIII products. The final model consists of a 2-compartment structure, with a proportional residual error model and between-subject variability on clearance and central volume. Fat-free mass, age, and brand were found to significantly affect pharmacokinetic (PK) parameters. Internal and external evaluations found that the model is fit for Bayesian forecasting and capable of predicting PK for brands not included in the modelling dataset, and useful for determining individualized prophylaxis regimens for hemophilia A patients.

  • Modeling and simulation of the modified Rankin Scale and National Institutes of Health Stroke Scale neurological endpoints in intracerebral hemorrhage
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-08-29
    Rik Schoemaker, Satyaprakash Nayak, Lutz O. Harnisch, Mats O. Karlsson, VISTA-ICH Collaboration

    Intracerebral hemorrhage (ICH) is a form of stroke characterized by uncontrolled bleeding into the parenchyma of the brain. There is no approved therapy for ICH and it is associated with very poor neurological outcomes with around half of subjects dying within 1 month and most subjects showing complete or partial disability. A key challenge is to identify subjects who could benefit from intervention using characteristics such as baseline hemorrhage volume and the increase in hemorrhage volume in the first few hours, which have been correlated with final outcomes in ICH. Combined longitudinal models were developed to describe stroke scales using categorical data (Modified Rankin Scale, mRS), continuous bounded data (National Institutes of Health Stroke Scale, NIHSS), and time to death. Covariate effects for baseline hematoma volume and maximum increase in hematoma volume were incorporated to assess the improvement in outcome when hematoma volume increase would be reduced by a potential treatment. The combined model provided an adequate description of stroke scales, with patients split into a Non-survival and a High-survival sub-population, and dropout due to death was well described by a constant hazard survival model. Models were compared indicating that the combined mRS/NIHSS model provided the most information, followed by the NIHSS-only model, and the mRS-only model, and finally the traditional statistical analysis on dichotomized response at 90 days. Simulations showed that substantial reductions in hematoma volume increase were required to increase the probability of a favorable outcome.

  • A physiologically-motivated model of cystic fibrosis liquid and solute transport dynamics across primary human nasal epithelia
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-09-07
    Florencio Serrano Castillo, Carol A. Bertrand, Michael M. Myerburg, Monica E. Shapiro, Timothy E. Corcoran, Robert S. Parker

    Cystic fibrosis (CF) disease is caused by mutations affecting the gene coding for the cystic fibrosis transmembrane conductance regulator (CFTR), an anion channel expressed in the mucosal side of epithelial tissue. In the airway, dysfunctional CFTR results in a transepithelial osmotic imbalance leading to hyperabsorption of airway surface liquid mucostasis, chronic inflammation, and eventual respiratory failure. Human nasal epithelial cell cultures from healthy and CF donors were used to perform studies of liquid and solute transport dynamics at an air/liquid interface in order to emulate the in vivo airway. Then, these results were used to inform a quantitative systems pharmacology model of airway epithelium describing electrically and chemically driven transcellular ionic transport, contributions of both convective and diffusive paracellular solute transport, and osmotically driven transepithelial water dynamics. Model predictions showed CF cultures, relative to non-CF ones, have increased apical and basolateral water permeabilities, and increase paracellular permeability and transepithelial chemical driving force for a radiolabeled tracer used to track small molecule absorption. These results provide a computational platform to better understand and probe the mechanisms behind the liquid hyperabsorption and small molecule retention profiles observed in the CF airway.

  • Mathematical modeling of the glucagon challenge test
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-09-30
    Saeed Masroor, Marloes G. J. van Dongen, Ricardo Alvarez-Jimenez, Koos Burggraaf, Lambertus A. Peletier, Mark A. Peletier

    A model for the homeostasis of glucose through the regulating hormones glucagon and insulin is described. It contains a subsystem that models the internalization of the glucagon receptor. Internalization is a mechanism in cell signaling, through which G-protein coupled receptors are taken from the surface of the cell to the endosome. The model is used to interpret data from a glucagon challenge test in which subjects have been under treatment with a novel glucagon receptor anti-sense drug which is aimed at reducing the number of receptors in the liver. It is shown how the receptor internalization results in tolerance of the blood glucose concentration to glucagon-induced hyperglycemia. We quantify the reduction of the number of receptors using the model and the data before and after treatment.

  • Tumor necrosis factor-mediated disposition of infliximab in ulcerative colitis patients
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-09-05
    Sophie E. Berends, Tamara J. van Steeg, Maurice J. Ahsman, Sharat Singh, Johannan F. Brandse, Geert R. A. M. D’Haens, Ron A. A. Mathôt

    Ulcerative Colitis (UC) is an inflammatory bowel disease typically affecting the colon. Patients with active UC have elevated tumor necrosis factor (TNF) concentrations in serum and colonic tissue. Infliximab is a monoclonal antibody directed against TNF and binds with high affinity. Target-mediated drug disposition (TMDD) is reported for monoclonal antibodies meaning that their pharmacokinetics are affected by high target affinity. Here, a TMDD model is proposed to describe the interaction between infliximab and TNF in UC patients. Data from 20 patients with moderate to severe UC was used. Patients received standard infliximab induction therapy (5 mg kg−1) at week 0, followed by infusions at week 2 and 6. IFX, anti-drug antibodies and TNF serum concentrations were measured at day 0 (1 h after infusion), 1, 4, 7, 11, 14, 18, 21, 28 and 42. A binding model, TMDD model, and a quasi-steady state (QSS) approximation were evaluated using nonlinear mixed effects modeling (NONMEM). A two-compartment model best described the concentration–time profiles of infliximab. Typical clearance of infliximab was 0.404 L day−1 and increased with the presence of anti-drug antibodies and with lower albumin concentrations. The TMDD-QSS model best described the pharmacokinetic and pharmacodynamics data. Estimate for TNF baseline (Bmax was 19.8 pg mL−1 and the dissociation constant (Kss) was 13.6 nM. This model could eventually be used to investigate the relationship between suppression of TNF and the response to IFX therapy.

  • Concentration–response modeling of ECG data from early-phase clinical studies to assess QT prolongation risk of contezolid (MRX-I), an oxazolidinone antibacterial agent
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-08-13
    Junzhen Wu, Kun Wang, Yuancheng Chen, Hong Yuan, Li Li, Jing Zhang

    The effects of contezolid (MRX-I, an oxazolidinone antibacterial agent) on cardiac repolarization were evaluated retrospectively using a population modeling approach in a Phase I study incorporating single ascending dose, multiple ascending dose, and food effect assessments. Linear mixed effect models were used to assess the relationships between MRX-I plasma concentrations and QT/QTc/∆QTc (baseline-adjusted), in which different correction methods for heart rate have been included. The upper bound of the one-sided 95% confidence interval (CI) for predicted ∆∆QTc was < 10 ms (ms) at therapeutic doses of MRX-I. Model performance/suitability was determined using diagnostic evaluations, which indicated rationality of one-stage concentration-QT model, as well as C-QT model suggested by Garnett et al. The finding demonstrated that MRX-I may have no clinical effects on the QT interval. Concentration-QT model may be an alternative to conventional thorough QT studies.

  • Journal editor's final report.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-11-14
    William J Jusko

  • 更新日期:2019-11-01
  • Bayesian approach to investigate a two-state mixed model of COPD exacerbations.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-06-15
    Anna Largajolli,Misba Beerahee,Shuying Yang

    Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6-12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect.

  • Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-06-23
    Shan Pan,Stephen B Duffull

    Physiologically based pharmacokinetic (PBPK) models are an important type of systems model used commonly in drug development before commencement of first-in-human studies. Due to structural complexity, these models are not easily utilised for future data-driven population pharmacokinetic (PK) analyses that require simpler models. In the current study we aimed to explore and automate methods of simplifying PBPK models using a proper lumping technique. A linear 17-state PBPK model for fentanyl was identified from the literature. Four methods were developed to search the optimal lumped model, including full enumeration (the reference method), non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA). For exploratory purposes, it was required that the total area under the fentanyl arterial concentration-time curve (AUC) between the lumped and original models differ by 0.002% at maximum. In full enumeration, a 4-state lumped model satisfying the exploratory criterion was found. In NARS, a lumped model with the same number of lumped states was found, requiring a large number of random samples. The scree plot provided a starting lumped model to NARS and the search completed within a short time. In SA, a 4-state lumped model was consistently delivered. In simplify an existing linear fentanyl PBPK model, SA was found to be robust and the most efficient and may be suitable for general application to other larger-scale linear systems. Ultimately, simplified PBPK systems with fundamental mechanisms may be readily used for data-driven PK analyses.

  • A translational platform PBPK model for antibody disposition in the brain.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-05-23
    Hsueh-Yuan Chang,Shengjia Wu,Guy Meno-Tetang,Dhaval K Shah

    In this manuscript, we have presented the development of a novel platform physiologically-based pharmacokinetic (PBPK) model to characterize brain disposition of mAbs in the mouse, rat, monkey and human. The model accounts for known anatomy and physiology of the brain, including the presence of distinct blood-brain barrier and blood-cerebrospinal fluid (CSF) barrier. CSF and interstitial fluid turnover, and FcRn mediated transport of mAbs are accounted for. The model was first used to characterize published and in-house pharmacokinetic (PK) data on the disposition of mAbs in rat brain, including the data on PK of mAb in different regions of brain determined using microdialysis. Majority of model parameters were fixed based on literature reported values, and only 3 parameters were estimated using rat data. The rat PBPK model was translated to mouse, monkey, and human, simply by changing the values of physiological parameters corresponding to each species. The translated PBPK models were validated by a priori predicting brain PK of mAbs in all three species, and comparing predicted exposures with observed data. The platform PBPK model was able to a priori predict all the validation PK profiles reasonably well (within threefold), without estimating any parameters. As such, the platform PBPK model presented here provides an unprecedented quantitative tool for prediction of mAb PK at the site-of-action in the brain, and preclinical-to-clinical translation of mAbs being developed against central nervous system (CNS) disorders. The proposed model can be further expanded to account for target engagement, disease pathophysiology, and novel mechanisms, to support discovery and development of novel CNS targeting mAbs.

  • Computer-assembled cross-species/cross-modalities two-pore physiologically based pharmacokinetic model for biologics in mice and rats.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2019-05-13
    Armin Sepp,Guy Meno-Tetang,Andrew Weber,Andrew Sanderson,Oliver Schon,Alienor Berges

    Two-pore physiologically-based pharmacokinetic (PBPK) models can be expected to describe the tissue distribution and elimination kinetics of soluble proteins, endogenous or dosed, as function of their size. In this work, we amalgamated our previous two-pore PBPK model for an inert domain antibody (dAb) in mice with the cross-species platform PBPK model for monoclonal antibodies described in literature into a unified two-pore platform that describes protein modalities of different sizes and includes neonatal Fc receptor (FcRn) mediated recycling. This unified PBPK model was parametrized for organ-specific lymph flow rates and the endosomal recycling rate constant using an extended tissue distribution time-course dataset that included an inert dAb, albumin and IgG in rats and mice. The model was evaluated by comparing the ab initio predictions for the tissue distribution and elimination properties of albumin-binding dAbs (AlbudAbsTM) in mice and rats with the experimental observations. Due to the large number of molecular species and reactions involved in large-scale PBPK models, we have also developed and deployed a MatlabTM script for automating the assembly of SimBiologyTM-based two-pore biologics PBPK models which drastically cuts the time and effort required for model building.

  • Population pharmacokinetic modeling of subcutaneously administered etanercept in patients with psoriasis.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-10-14
    Ivan Nestorov,Ralph Zitnik,Thomas Ludden

    The objective of this paper is to present a population PK model which adequately describes the time-concentration profiles of different doses of ctanercept (Enbrel) administered subcutaneously to subjects with moderate-to-severe psoriasis and to simulate the time courses of concentrations following 50 mg once weekly (QW) dosing. Pharmacokinetic (PK) data from three clinical studies with doses 25 mg QW 25 mg twice weekly (BIW) and 50 mg BIW, were used. A one-compartment model with gender, weight and time covariates on the apparent clearance and weight covariate on the apparent volume of distribution was developed. The population mean of the apparent steady state clearance in males was 0.129 l/h. compared to 0.148 l/h in females. The clearance varied with time being lower in the first 2 weeks of the therapy, increasing sharply during weeks 3-4. and converging gradually after that to its steady state level. The population mean of the apparent volume of distribution also varied with time and was 16.11 during week 1, 20.01 during weeks 2-4 and 22.51 after week 4. The population PK model adequately described the observed concentration-time profiles in subjects with psoriasis. Despite a somewhat different covariate set, the parameter estimates of the population PK model for etanercept are very similar between the psoriasis and rheumatoid arthritis populations. The population PK model was used to simulate the pharmacokinetic profiles after a novel 50 mg QW dosing regimen. The simulations show good agreement with the observed data from 84 subjects participating in a fourth study (50 mg QW dose) used as an external validation set. The simulations of the 50 mg QW and the 25 mg BIW dosing regimens show that there is a significant overlap between the profiles yielding similar steady state exposures with both dosing regimens. The latter is an indication that the respective efficacy and safety profiles after those two dosing regimens are likely to be similar.

  • Pharmacokinetic/pharmacodynamic modelling of GnRH antagonist degarelix: a comparison of the non-linear mixed-effects programs NONMEM and NLME.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-10-14
    Christoffer W Tornøe,Henrik Agersø,Henrik A Nielsen,Henrik Madsen,E Niclas Jonsson

    In this paper, the two non-linear mixed-effects programs NONMEM and NLME were compared for their use in population pharmacokinetic/pharmacodynamic (PK/PD) modelling. We have described the first-order conditional estimation (FOCE) method as implemented in NONMEM and the alternating algorithm in NLME proposed by Lindstrom and Bates. The two programs were tested using clinical PK/PD data of a new gonadotropin-releasing hormone (GnRH) antagonist degarelix currently being developed for prostate cancer treatment. The pharmacokinetics of intravenous administered degarelix was analysed using a three compartment model while the pharmacodynamics was analysed using a turnover model with a pool compartment. The results indicated that the two algorithms produce consistent parameter estimates. The bias and precision of the two algorithms were further investigated using a parametric bootstrap procedure which showed that NONMEM produced more accurate results than NLME together with the nlmeODE package for this specific study.

  • Modelling and simulation in the development and use of anti-cancer agents: an underused tool?
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-10-14
    Ferdinand Rombout,Leon Aarons,Mats Karlsson,Anthony Man,France Mentré,Peter Nygren,Amy Racine,Hans Schaefer,Jean-Louis Steimer,Iñaki Troconiz,Achiel van Peer,

    To help identify the role of modelling and simulation in the development of anti-cancer agents, their main advantages and the obstacles to their rational use, an expert meeting was organized by COST B15. This manuscript presents a synthesis of views expressed at that meeting and indicates future directions. The manuscript also shows some examples where modelling and simulation have proven to be of relevant value in the drug development process for anti-cancer agents.

  • 更新日期:2019-11-01
  • Grey-box modelling of pharmacokinetic/pharmacodynamic systems.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-01-27
    Christoffer W Tornøe,Judith L Jacobsen,Oluf Pedersen,Torben Hansen,Henrik Madsen

    Grey-box pharmacokinetic/pharmacodynamic (PK/PD) modelling is presented as a promising way of modelling PK/PD systems. The concept behind grey-box modelling is based on combining physiological knowledge along with information from data in the estimation of model parameters. Grey-box modelling consists of using stochastic differential equations (SDEs) where the stochastic term in the differential equations represents unknown or incorrectly modelled dynamics of the system. The methodology behind the grey-box PK/PD modelling framework for systematic model improvement is illustrated using simulated data and furthermore applied to Bergman's minimal model of glucose kinetics using clinical data from an intravenous glucose tolerance test (IVGTT). The grey-box estimates of the stochastic system noise parameters indicate that the glucose minimal model is too simple and should preferably be revised in order to describe the complicated in vivo system of insulin and glucose following an IVGTT.

  • A physiologically-based pharmacokinetic model of drug detoxification by nanoparticles.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-01-27
    Marissa S Fallon,Manoj Varshney,Donn M Dennis,Anuj Chauhan

    Nanoparticles (NPs) may be capable of reversing the toxic effects of drug overdoses in humans by adsorbing/absorbing drug molecules. This paper develops a model to include the kinetic effects of treating drug overdoses by NPs. Depending on the size and the nature of the NPs, they may either pass through the capillary walls and enter the tissue space or remain only inside the capillaries and other blood vessels: models are developed for each case. Furthermore, the time scale for equilibration between the NP and the blood will vary with the specific type of NP. The NPs may sequester drug from within the capillaries depending on whether this time scale is larger or smaller than the residence time of blood within the capillary. Models are developed for each scenario. The results suggest that NPs are more effective at detoxification if they are confined to the blood vessels and do not enter the tissues. The results also show that the detoxification process is faster if drug uptake occurs within the capillaries. The trends shown by the model predictions can serve as useful guides in the design of the optimal NP for detoxification.

  • Assessment of drug interactions relevant to pharmacodynamic indirect response models.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2005-01-27
    Justin Earp,Wojciech Krzyzanski,Abhijit Chakraborty,Miren K Zamacona,William J Jusko

    The assessment of drug interactions for a simple turnover system when the basic pharmacodynamic response is governed by indirect mechanisms was explored. This report describes a diverse array of possible in vivo pharmacodynamic effects from a combination of two drugs acting by similar or different indirect mechanisms. Various conditions of pharmacodynamic drug combinations were explored mathematically and by simulation: (a) interactions of two drugs acting simultaneously either on the production (k(in)) or on the dissipation (k(out)) processes controlling the in vivo response by competitive (four cases) or non-competitive interaction (six cases); and (b) combinations of two drugs acting on separate k(in) and k(out) processes simultaneously (four cases). A range of different combinations of drug doses was used. Plasma concentration time profiles were generated according to monoexponential disposition. Pharmacodynamic response profiles were simulated using the above conditions and characterized by descriptors such as Area Between Effect (and Baseline) Curve (ABEC) values. The interaction of agents by competitive mechanisms produced net responses that were additive in nature. Response profiles for non-competitive interactions on the same process were both antagonistic (for two drugs with effects that oppose each other) and synergistic (for two drugs that produce the same response). On the other hand, response signatures for agents acting non-competitively on the production and dissipation factors by opposing mechanisms (e.g. inhibiting k(in) plus stimulating k(out)) showed dramatic changes in net effects and produced apparent drug synergy. Net indirect response profiles for joint use of two or more drugs measured by ABEC values may look "additive", "antagonistic", or "synergistic" depending on doses, their intrinsic potencies, the nature of interaction (competitive or non-competitive) as well as their mechanisms of action. These models may help explain changes in pharmacologic responses to two agents in a more rational and mechanistic fashion than older empirical methods.

  • Statistical issues in a modeling approach to assessing bioequivalence or PK similarity with presence of sparsely sampled subjects.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-26
    Chuanpu Hu,Katy H P Moore,Yong H Kim,Mark E Sale

    Drug development at different stages may require assessment of similarity of pharmacokinetics (PK). The common approach for such assessment when the difference is drug formulation is bioequivalence (BE), which employs a hypothesis test based on the evaluation of a 90% confidence interval for the ratio of average pharmacokinetic (PK) parameters. The role of formulation effect in BE assessment is replaced by subject population in PK similarity assessment. The traditional approach for BE requires that the PK parameters, primarily AUC and Cmax, be obtained from every individual. Unfortunately in many clinical circumstances, some or even all of the individuals may be sparsely sampled, making the individual evaluation difficult. In such cases, using models, particularly population models, becomes appealing. However, conducting an appropriate statistical test based on population modeling in a form consistent, at least in principle, with traditional 90% confidence interval approach is not so straightforward as it may appear. This manuscript proposes one such approach that can be applied to sparse sampling situations. The approach aims to maintain, as much as possible, the appropriateness of the hypothesis test. It is applied to data from clinical studies to address a need in drug development for assessment of PK similarity in different populations.

  • Development of a whole body physiologically based model to characterise the pharmacokinetics of benzodiazepines. 1: Estimation of rat tissue-plasma partition ratios.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-26
    Ivelina Gueorguieva,Ivan A Nestorov,Susan Murby,Sophie Gisbert,Brent Collins,Kelly Dickens,Judith Duffy,Ziad Hussain,Malcolm Rowland

    Three methods for estimation of the equilibrium tissue-to-plasma partition ratios (Kp values) in the presence of tissue concentration time data have been investigated. These are the area method, the open loop (tissue specific) method and the whole body model(closed loop) method, each with different model assumptions. Additionally, multiple imputations, a technique for dealing with deficiencies in data sets (i.e., missing tissues) is used. The estimated Kp values by the three methods have been compared and the limitations and advantages of each approach drawn. The area method, which is essentially model free, gives only a crude estimate of Kp without making any statement of its uncertainty; whereas both the open and closed loop methods provide an estimate of this. The closed loop method, where the most assumptions are made, is the approach that gives the best overall estimates of Kp, which was confirmed by comparing the predicted concentration-time profiles with experimental data. Although the estimates from the closed loop method, as well as the other two methods, are conditioned on the data, they are the most reliable for both propagating parameter variability and uncertainty through a whole body physiologically based model, as well as for extrapolation to human. A series of benzodiazepines, namely alprazolam, chlordiazepoxide, clobazam, diazepam, flunitrazepam, midazolam and triazolam in rat is used as a case study in the current investigation.

  • Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM and NLMIXED.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-26
    Siv Jönsson,Maria C Kjellsson,Mats O Karlsson

    The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.

  • Modeling the short- and long-duration responses to exogenous levodopa and to endogenous levodopa production in Parkinson's disease.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-03
    Phylinda L S Chan,John G Nutt,Nicholas H G Holford

    Clinicians recognize levodopa has a short-duration response (measured in hr) and a long-duration response (measured in days) in Parkinson's disease. In addition there is a diurnal pattern of motor function with better function in the morning. Previous pharmacokinetic-pharmacodynamic modeling has quantified only the short-duration response. We have developed a pharmacokinetic-pharmacodynamic model for the short- and long-duration responses to exogenous levodopa and the effects of residual endogenous levodopa synthesis in patients with Parkinson's disease. Thirteen previously untreated (de novo) patients with Parkinson's disease and twelve patients who had received levodopa orally for 9.7+/-4.0 years (chronic) were investigated. A 2 hr IV infusion of levodopa with concomitant oral carbidopa was given on two occasions separated by 3 days with no levodopa in between. A two compartment pharmacokinetic model was used to fit plasma levodopa concentrations. A sigmoid Emax model was used to relate concentrations from endogenous and exogenous sources to tapping rate (a measure of motor response). A model incorporating three effect compartments (fast equilibration (half life, Teqf). slow equilibration (Teqs) and dopa synthesis (Teqd)), yielded the most descriptive model for levodopa pharmacokinetics and pharmacodynamics. Baseline tapping rate reflected endogenous levodopa synthesis and the long-duration response. Partial loss of the long-duration response during the 3 days without levodopa in the chronic group lowered baseline tapping (36+/-7%, mean+/-SEM) and increased maximum levodopa induced response above baseline (112+/-31%). The maximum levodopa induced response after the drug holiday is a result of lowered baseline tapping due to the loss of long-duration response and not due to a change in levodopa pharmacokinetics or pharmacodynamics.

  • Visualization-based analysis for a mixed-inhibition binary PBPK model: determination of inhibition mechanism.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-03
    Kristin K Isaacs,Marina V Evans,Thomas R Harris

    A physiologically based pharmacokinetic (PBPK) model incorporating mixed enzyme inhibition was used to determine the mechanism of metabolic interactions occurring during simultaneous exposures to the organic solvents chloroform and trichloroethylene (TCE). Visualization-based sensitivity and identifiability analyses of the model were performed to determine the conditions under which four inhibitory parameters describing inhibitor binding could be estimated. The sensitivity methods were used to reduce the 4-parameter estimation problem into two distinct 2-parameter problems. The inhibitory parameters were then estimated from multiple closed-chamber gas-uptake experiments using graphical methods. The estimated values of the four inhibitory parameters predicted that chloroform and TCE interact in a competitive manner. Based on the model analysis, we present recommendations for the design of experiments for determination of inhibition mechanism in binary chemical mixtures. We assert that a thorough analysis of the parameter-dependent sensitivity and identifiability characteristics can be used to plan efficient experimental protocols for the quantitative analysis of inhalation pharmacokinetics.

  • Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-11-03
    Ivelina I Gueorguieva,Ivan A Nestorov,Malcolm Rowland

    The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro-in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.

  • Integrated Population Pharmacokinetic Model of both cyclophosphamide and thiotepa suggesting a mutual drug-drug interaction.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-24
    Milly E de Jonge,Alwin D R Huitema,Sjoerd Rodenhuis,Jos H Beijnen

    PURPOSE/AIMS Cyclophosphamide (CP) and thiotepa (TT) are frequently administered simultaneously in high-dose chemotherapy regimens. The prodrug CP shows strong autoinduction resulting in increased formation of its activated metabolite 4-hydroxycyclophosphamide (4OHCP). TT inhibits this bioactivation of CP. Previously, we successfully modelled CP bioactivation and the effect of TT on the autoinduction. Recently we suggested that CP may also induce the conversion of TT in to its metabolite tepa (T). The aim of the current study was to investigate whether the influence of CP on TT metabolism can be described with a population pharmacokinetic model and whether this interaction can be incorporated in an integrated model describing both CP and TT pharmacokinetics. METHODS Plasma samples were collected from 49 patients receiving 86 courses of a combination of high-dose CP (4000 or 6000 mg/m2), TT (320 or 480 mg/m2) and carboplatin (1067 or 1600 mg/m2) given in short infusions during four consecutive days. For each patient, approximately 20 plasma samples were available per course. Concentrations of CP, 4OHCP, TT and T were determined using GC and HPLC. Kinetic data were processed using NONMEM. RESULTS The pharmacokinetics of TT and T were described with a two-compartment model. TT was eliminated through a non-inducible and an inducible pathway, the latter resulting information of T (ClindTT = 12.4 l/hr, ClnonindTT = 17.0 l/hr). Induction of TT metabolism was mediated by a hypothetical amount of enzyme, different from that involved in CP induction, whose amount increased with time in the presence of CP. The amount of enzyme followed a zero-order formation and a decrease with a first-order elimination rate constant of 0.0343 hr(-1) (t1/2 = 20 hr). This model was significantly better than a model lacking the induction by CP. The model was successfully incorporated into the previously published pharmacokinetic model for CP, and resulted in comparable parameter estimates for this compound and its metabolite 4OHCP. CONCLUSION The pharmacokinetics of TT, when administered in combination with CP, were successfully described. The model confirms induction of TT metabolism with time and it appears likely that CP is responsible for this phenomenon. The existence of a mutual pharmacokinetic interaction between CP and TT, as described in our integrated model, may be relevant in clinical practice.

  • Simulation of interaction between two non-depolarizing muscle relaxants: generation of an additive or a supra-additive neuromuscular block.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-24
    Vladimir Nigrovic,Anton Amann

    GOAL To examine in a model of neuromuscular transmission the interaction between two non-depolarizing muscle relaxants. An additive or a supra-additive interaction was evaluated as a function of the affinities of acetylcholine and the muscle relaxants for the two binding sites at a single receptor. METHODS Affinity of acetylcholine for site1 was postulated to be higher than for site2. Muscle relaxants may display a similar pattern of affinities, a higher affinity for site2, or the affinities may be identical. Receptors with both sites occupied by acetylcholine are activated and, if their concentration at an end plate surpasses the critical threshold, initiate contraction of the associated muscle fiber. The number of contracting muscle fibers determines twitch strength of the whole muscle. Neuromuscular block (NMB) = 1--twitch. NMB was simulated for muscle relaxants acting as single agents or in three types of combinations. (a) Complementary fractions of equieffective concentrations. (b) Variable combinations of equieffective concentrations producing NMB equal to NMB produced by the single muscle relaxant (isobolographic analysis), and (c) NMB-vs.-concentration relationship of single agents and of their combination in a fixed ratio. RESULTS Additive interaction was simulated for pairs of muscle relaxants displaying identical ratios of affinities. All other pairs produce a supra-additive interaction, more prominent, the more divergent the patterns of affinities. The slopes of NMB-vs.-concentration curves were steeper for supra-additive combinations than for single agents. CONCLUSION The simulations define conditions leading to additive or supra-additive interaction and suggest an experimental design suitable to test the results.

  • Power, selection bias and predictive performance of the Population Pharmacokinetic Covariate Model.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-24
    Jakob Ribbing,E Niclas Jonsson

    Identification and quantification of covariate relations is often an important part of population pharmacokinetic/pharmacodynamic (PK/PD) modelling. The covariate model is regularly built in a stepwise manner. With such methods, selection bias may be a problem if only statistically significant covariates are accepted into the model. Competition between multiple covariates may further increase selection bias, especially when there is a moderate to high correlation between the covariates. This can also result in a loss of power to find the true covariates. The aim of this simulation study was to investigate the effect on power, selection bias and predictive performance of the covariate model, when altering study design and system-related quantities. Data sets with 20-1000 subjects were investigated. Five covariates were created by sampling from a multivariate standard normal distribution. The true covariate was set up to have no, low, moderate and high correlation to the other four covariates, respectively. Data sets, in which each individual had two or three PK observations, were simulated using a one-compartment i.v. bolus model. The true covariate influenced clearance according to one of several magnitudes. Different magnitudes of residual error and inter-individual variability in the structural model parameters were also introduced to the simulation model. A total of 7400 replicate data sets were simulated independently for each combination of the above conditions. Models with one of the five simulated covariates influencing clearance and the model without any covariate were fitted to the data. The probability of selecting (according to a pre-specified P-value) the different covariates, along with the estimated covariate coefficient, was recorded. The results show that selection bias is very high for small data sets (< or = 50 subjects) simulated with a weak covariate effect. If selected under these circumstances, the covariate coefficient is on average estimated to be more than twice its true value, making the covariate model useless for predictive purposes. Surprisingly, even though competition from false covariates caused substantial loss in the power of selecting the true covariate, the already high selection bias increased only marginally. This means that the bias due to competition is negligible if statistical significance is also required for covariate selection. Bias and predictive performance are direct functions of power, only indirectly affected by study design and system-related quantities. Mainly because of selection bias, low-powered covariates can be expected to harm the predictive performance when selected. For the same reason these low-powered covariates may falsely appear to be clinically relevant when selected. If the aim of an analysis is predictive modelling, we do not recommend stepwise selection or significance testing of covariates to be performed on small or moderately sized data sets (<50-100 subjects).

  • A Bayesian approach to tracking patients having changing pharmacokinetic parameters.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-07
    David S Bayard,Roger W Jelliffe

    This paper considers the updating of Bayesian posterior densities for pharmacokinetic models associated with patients having changing parameter values. For estimation purposes it is proposed to use the Interacting Multiple Model (IMM) estimation algorithm, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and Maximum A-Posteriori (MAP) Bayesian estimation methods, which are presently used for posterior updating when pharmacokinetic parameters do not change. Both the MM and MAP Bayesian estimation methods are used in their sequential forms, to facilitate tracking of changing parameters. Results indicate that the IMM algorithm is well suited for tracking time-varying pharmacokinetic parameters in acutely ill and unstable patients, incurring only about half of the integrated error compared to the sequential MM and MAP methods on the same example.

  • Evaluation of type I error rates when modeling ordered categorical data in NONMEM.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-07
    Ulrika Wählby,Katalin Matolcsi,Mats O Karlsson,E Niclas Jonsson

    The development of non-linear mixed pharmacokinetic/pharmacodynamic models for continuous variables is usually guided by graphical assessment of goodness of fit and statistical significance criteria. The latter is usually the likelihood ratio test (LR). When the variable to be modeled is categorical, on the other hand, the available graphical methods are less informative and/or more complicated to use and the modeler needs to rely more heavily on statistical significance assessment in the model development. The aim of this study was to evaluate the type I error rates, obtained from using the LR test, for inclusion of a false parameter in a non-linear mixed effects model for ordered categorical data when modeling with NONMEM. Data with four ordinal categories were simulated from a logistic model. Two nested multinomial models were fitted to the data, the model used for simulation and a model containing one additional parameter. The difference in fit (objective function value) between models was calculated. Three types of models were explored; (i) a model without interindividual variability (IIV) where the addition of a parameter describing IIV was assessed, (ii) a model with IIV where the addition of a drug effect parameter (either categorical or continuous drug exposure measure) was evaluated, and (iii) a model including IIV and drug effect where the inclusion of a random effects parameter on the drug effect was assessed. Alterations were made to the simulation conditions, for example, varying the number of individuals and the size and distribution of the IIV, to explore potential influences on the type I error rate. The estimated type I error rate for inclusion of a false random effect parameter in model (i) and (iii) were, as expected, lower than the nominal. When the additional parameter was a fixed effects parameter describing drug effect (model(II)) the estimated type I error rates were in agreement with the nominal. None of the different simulation conditions tried changed this pattern. Thus, the LR test seems appropriate for judging the statistical significance of fixed effects parameters when modeling categorical data with NONMEM.

  • A population pharmacokinetic analysis of milrinone in pediatric patients after cardiac surgery.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-07
    James M Bailey,Timothy M Hoffman,David L Wessel,David P Nelson,Andrew M Atz,Anthony C Chang,Thomas J Kulik,Thomas L Spray,Akbar Akbary,Richard P Miller,Gil Wernovsky

    The purpose of this study was to ascertain the optimal pharmacokinetic model for milrinone in pediatric patients after cardiac surgery when milrinone was administered as a slow loading dose followed by a constant-rate infusion. The data used for pharmacokinetic analysis were collected in a prospective, randomized, placebo-controlled multi-center trial of milrinone as prophylaxis for the development of low cardiac output syndrome after surgery for repair of complex congenital cardiac defects. Two blood samples were randomly collected from each patient for determination of plasma milrinone concentrations with subsequent population pharmacokinetic modeling. The pharmacokinetics of milrinone in pediatric patients under 6 year's age were best described by a weight-normalized one compartment model after a slow loading dose followed by a constant-rate infusion. The volume of distribution was 482 ml kg(-1) and was independent of age. Clearance was a linear function of age given by Cl = 2.42 ml kg(-1) min(-1) [1 + 0.396*age].

  • Stereoselective hepatic disposition of model diastereomeric acyl glucuronides.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-07
    David M Shackleford,Roger L Nation,R W Milne,P J Hayball,Allan M Evans

    Numerous studies have previously been conducted with the impulse-response isolated perfused rat liver (IR-IPRL) to establish the role of both physiological and physicochemical factors in determining solutes' pattern of hepatic disposition, however the impact of optical isomerism on hepatic disposition has hardly been studied using this methodology. In this study, the IR-IPRL was used to assess the extent of stereoselectivity exhibited by the kinetic processes involved in the hepatic disposition of the diastereomeric acyl glucuronides of (R)- and (S)-2-phenylpropionic acid (i.e. (R)- and (S)-PPAG). Moment and model-dependent (distributed model and axial dispersion model) analyses were conducted of the hepatic outflow profiles generated upon bolus administration of (R)-(14)C-PPAG or (S)-(14)C-PPAG and 3H-Sucrose (used as a marker of the hepatic vascular space) into the portal inflow of isolated perfused livers of male Sprague-Dawley rats (n = 4). Significant differences between (R)- and (S)-PPAG were apparent in the pharmacokinetic parameters defining the total hepatic disposition of the two diastereomers, the most marked being the hepatic availabilities, where the value for (S)-PPAG (0.721 +/- 0.059) was significantly lower than that of (R)-PPAG (0.909 +/- 0.042). The distributed and axial dispersion model analyses suggested that the more extensive hepatic extraction of (S)-PPAG was (at least in part) due to the higher sinusoidal membrane permeability-surface area product (PS UPT) of the diastereomer, and this has been considered in light of the emerging evidence regarding the role of hepatocellular membrane transport mechanisms. Furthermore, given the potential immunogenicity of acyl glucuronides (through covalent binding to plasma and intracellular proteins), the results of this study suggest that diastereomeric glucuronides may exhibit differing toxicity due to differences in their access to intracellular proteins.

  • A method of obtaining starting values of k(in) and k(out) for the indirect response models.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-09-07
    Debu Mukherjee,Matthew M Hutmacher

    A method based on the multivariate technique known as principal component analysis is proposed to obtain starting values for the rate constants of indirect response models. The method is not iterative and only requires standard deviation calculations for two quantities, which are simple functions of the measured pharmacodynamic response. An algorithm is provided which can be implemented in a spreadsheet. The methodology is justified theoretically herein, nevertheless, two examples are provided to illustrate the method and demonstrate its viability.

  • Optimization of individual and population designs using Splus.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-03-06
    Sylvie Retout,France Mentré

    We address the problem of design optimization for individual and population pharmacokinetic studies. We develop Splus generic functions for pharmacokinetic design optimization: IFIM, a function for individual design optimization similar to the ADAPT II software, and PFIM_OPT, a function for population design optimization which is an extension of the Splus function PFIM for population design evaluation. Both evaluate and optimise designs using the Simplex algorithm. IFIM optimizes the sampling times in continuous intervals of times; PFIM_OPT optimizes either, for a given group structure of the population design, only the sampling times taken in some given continuous intervals or, both the sampling times and the group structure, performing then statistical optimization. A combined variance error model can be supplied with the possibility to include parameters of the error model as parameters to be estimated. The performance of the optimization with the Simplex algorithm is demonstrated with two pharmacokinetic examples: by comparison of the optimized designs to those of the ADAPT II software for IFIM, and to those obtained using a grid search or the Fedorov-Wynn algorithm for PFIM_OPT. The influence of the variance error model on design optimization was investigated. For a given total number of samples, different group structures of a population design are compared, showing their influence on the population design efficiency. The functions IFIM and PFIM_OPT offer new efficient solutions for the increasingly important task of optimization of individual or population pharmacokinetic designs.

  • Simultaneous vs. sequential analysis for population PK/PD data II: robustness of methods.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-03-06
    Liping Zhang,Stuart L Beal,Lewis B Sheinerz

    A model can be fit to joint PK/PD data (concentration and effect) either simultaneously or sequentially. The results of a companion paper suggested that when the data-analytic and true models agree, a particular sequential approach is computationally faster than the simultaneous one, yet produces hardly less precise PD parameter estimates, and for suitable designs, about as accurate PD standard error estimates. In this paper, we compare the performance of various methods for the case that the data-analytic model is misspecified. We illustrate these methods by applying them to a set of real data. Using NONMEM, population PK/PD observations were simulated under various study designs according to a one- or two-compartment PK model and direct Emax or sigmoid Emax model. A one-compartment PK model and Emax PD model were fit to the simulated observations by simultaneous and sequential methods. Predictive performance (interpolation and extrapolation) of PD and the type-I error rate of a likelihood ratio test are compared. The real data set consists of PK and (more frequent) PD observations after administration of the muscle relaxant vecuronium. When only the PK data-analytic model is misspecified, the simultaneous method has greater precision than the sequential methods. However a sequential method that uses a non-parametric PK model performs better than both other methods when PK model misspecification is severe. When the PD data-analytic model is misspecified, sequential and simultaneous methods perform similarly. The analysis of the real data shows that the PK fitted with the simultaneous method can be quite sensitive to PD model misspecification, yielding a possible diagnostic for this type of misspecification.

  • Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-03-06
    Liping Zhang,Stuart L Beal,Lewis B Sheiner

    Dose [-concentration]-effect relationships can be obtained by fitting a predictive pharmacokinetic (PK)-pharmacodynamic (PD) model to both concentration and effect observations. Either a model can befit simultaneously to all the data ("simultaneous" method), or first a model can befit to the PK data and then a model can be fit to the PD data, conditioning in some way on the PK data or on the estimates of the PK parameters ("sequential" method). Using simulated data, we compare the performance of the simultaneous method with that of three sequential method variants with respect to computation time, estimation precision, and inference. Using NONMEM, under various study designs, observations of one type of PK and one type of PD response from different numbers of individuals were simulated according to a one-compartment PK model and direct Emax PD model, with parameters drawn from an appropriate population distribution. The same PK and PD models were fit to these observations using simultaneous and sequential methods. Performance measures include computation time,fraction of cases for which estimates are successfully obtained, precision of PD parameter estimates, precision of PD parameter standard error estimates, and type-I error rates of a likelihood ratio test. With the sequential method, computation time is less, and estimates are more likely to be obtained. Using the First Order Conditional Estimation (FOCE) method, a sequential approach that conditions on both population PK parameter estimates and PK data, estimates PD parameters and their standard errors about as well as the "gold standard" simultaneous method, and saves about 40% computation time. Type-I error rates of likelihood ratio test for both simultaneous and sequential approaches are close to the nominal rates.

  • Estimation of Cmax and Tmax in populations after single and multiple drug administrations.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-02-24
    Laszlo Tothfalusi,Laszlo Endrenyi

    Following the oral administration of drugs, the plasma concentration generally reaches, in principle, a single, well-defined peak (Cmax) at the time of Tmax. A complication for the direct estimation of Cmax and Tmax is that measurements of concentrations are recorded only at discrete time points. Theoretical equations characterizing the population distribution of Cmax and Tmax are derived in relationship to the pharmacokinetic model, its parameters, their variabilities, and experimental errors. These equations can be solved by numerical integration. The resulting means, variances and other summary statistics of Cmax and Tmax are evaluated under various conditions involving single and multiple drug administrations. Results gained by the proposed numerical method agree closely with results gained by Monte-Carlo simulations. It is argued that the numerical method could be useful to study the statistical properties of the investigated measures and could, in some cases, provide a viable alternative to simulations. It is demonstrated that Cmax is estimated directly with positive bias, especially following repeated drug administrations. As a consequence, the recorded peak-trough fluctuation (PTF), measured in the steady state, can be excessively large (even by orders of magnitude) particularly when drug accumulation is high. These results have practical implications for the development of drugs and drug formulations.

  • Volterra series in pharmacokinetics and pharmacodynamics.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-02-24
    Davide Verotta

    Nonparametric black-box modeling has a long successful history of applications in pharmacokinetics (PK) (notably in deconvolution), but is rarely used in pharmacodynamics (PD). The main reason is associated with the fact that PK systems are often linear in respect to drug inputs, while the reverse is true for many PK/PD systems. In the PK/PD field existing non-parametric methods can deal with linear systems, but they cannot describe non-linear systems. Our purpose is to describe a novel implementation of a general nonparametric model which can represent non-linear systems, and in particular non-linear PK/PD systems, The model is based on a Volterra series, which is an integral series expansion of the response of a system in terms of its kernels and the inputs to the system. In PK we are familiar with the first term of the Volterra series, the convolution of the first kernel of the system (the so-called PK disposition function) with drug input rates. The main advantages of higher order Volterra representations is that they are general representations and can be used to describe and predict the response of an arbitrary (PK/ PD) system without any prior knowledge on the structure of the system. The main problem of the representation is that in a non-parametric representation of the kernels the number of parameters to be estimated grows geometrically with the order of the kernel. We developed a method to estimate the kernels in a Volterra-series which overcomes this problem. The method (i) is fully non-parametric (the kernels are represented using multivariate splines), (ii) is maximum-likelihood based, (iii) is adaptive (the order of the series and the dimensionality of each kernel is selected by the method), and (iv) allows for non-equispaced observations (thus allowing a reduction of the number of parameters in the representation, and the analysis of, e.g., PK/PD observations). The method is based on an adaptation of Friedmans's Multivariate Adaptive Regression Spline method. Examples demonstrate the possible application of the approach to the analysis of different PK/PD systems.

  • A two-part mixture model for longitudinal adverse event severity data.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2004-02-24
    Kenneth G Kowalski,Lynn McFadyen,Matthew M Hutmacher,Bill Frame,Raymond Miller

    We fit a mixed effects logistic regression model to longitudinal adverse event (AE) severity data (four-point ordered categorical response) to describe the dose-AE severity response for an investigational drug. The distribution of the predicted interindividual random effects (Bayes predictions) was extremely bimodal. This extreme bimodality indicated that biased parameter estimates and poor predictive performance were likely. The distribution's primary mode was composed of patients that did not experience an AE. Moreover, the Bayes predictions of these non-AE patients were nearly degenerative, i.e., the predictions were nearly identical. To resolve this extreme bimodality we propose using a two-part mixture modeling approach. The first part models the incidence of AE's, and the second part models the severity grade given the patient had an AE. Unconditional probability predictions are calculated by mixing the incidence and severity model probability predictions. We also report results of simulation studies, which assess the predictive and statistical (bias and precision) performance of our approach.

  • Reduction and lumping of physiologically based pharmacokinetic models: prediction of the disposition of fentanyl and pethidine in humans by successively simplified models.
    J. Pharmacokinet. Pharmacodyn. (IF 2.91) Pub Date : 2003-12-03
    Sven Björkman

    Physiologically based pharmacokinetic (PBPK) models can be used to predict drug disposition in humans from animal data and the influence of disease or other changes in physiology on the pharmacokinetics of a drug. The potential usefulness of a PBPK model must however be balanced against the considerable effort needed for its development. Proposed methods to simplify PBPK modeling include predicting the necessary tissue:blood partition coefficients (kp) from physicochemical data on the drug instead of determining them in vivo, formal lumping of model compartments, and replacing the various kp values of the organs and tissues by only two values, for "fat" and "lean" tissues, respectively. The aim of this study was to investigate the effects of simplifying complex PBPK models on their ability to predict drug disposition in humans. Arterial plasma concentration curves of fentanyl and pethidine were simulated by means of a number of successively reduced models. Median absolute prediction errors were used to evaluate the performance of each model, in relation to arterial plasma concentration data from clinical studies, and the Wilcoxon matched pairs test was used for comparison of predictions. An originally diffusion-limited model for fentanyl was simplified to perfusion-limitation, and this model was either lumped, reducing 11 organ/tissue compartments to six, or changed to a model based on only two kp values, those of fat (used for fat and lungs) and muscle (used for all other tissues). None of these simplifications appreciably changed the predictions of arterial drug concentrations in the 10 patients. Perfusion-limited models for pethidine were set up using either experimentally determined [Gabrielsson et al. 1986] or theoretically calculated [Davis and Mapleson 1993] kp values, and predictions using the former were found to be significantly better. Lumping of the models did not appreciably change the predictions; however, going from a full set of kp values to only two ("fat" and "lean") had an adverse effect. Using a kp for lungs determined either in rats or indirectly in humans [Persson et al. 1988], i.e., a total of three kp values, improved these predictions. In conclusion, this study strongly suggested that complex PBPK models for lipophilic basic drugs may be considerably reduced with marginal loss of power to predict standard plasma pharmacokinetics in humans. Determination of only two or three kp values instead of a "full" set can mean an important reduction of experimental work to define a basic model. Organs of particular pharmacological or toxicological interest should of course be investigated separately as needed. This study also suggests and applies a simple method for statistical evaluation of the predictions of PBPK models.

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上海纽约大学William Glover