Partial derivative—Based sensitivity analysis of models describing target-mediated drug disposition

Abraham, Anson K.; Krzyzanski, Wojciech; Mager, Donald E.

doi:10.1208/aapsj0902020

Cited by 28 publications

(23 citation statements)

References 13 publications

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“…But, the WSSR values from the rapid binding TMDD and M–M model fittings were demonstrated to be very similar over a wide range of k int and k deg (from 0.4 to 50). This reflects a previous finding that simulated TMDD signature profiles are not sensitive to changes in k int or k deg at early sampling times [25]. With a relative small total target baseline, k int < k deg will likely result in only a moderate increase in R tot .…”

Section: Resultssupporting

confidence: 83%

Selection between Michaelis–Menten and target-mediated drug disposition pharmacokinetic models

Yan

Mager

Krzyzanski

2009

J Pharmacokinet Pharmacodyn

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Target-mediated drug disposition (TMDD) models have been applied to describe the pharmacokinetics of drugs whose distribution and/or clearance are affected by its target due to high binding affinity and limited capacity. The Michaelis–Menten (M–M) model has also been frequently used to describe the pharmacokinetics of such drugs. The purpose of this study is to investigate conditions for equivalence between M–M and TMDD pharmacokinetic models and provide guidelines for selection between these two approaches. Theoretical derivations were used to determine conditions under which M–M and TMDD pharmacokinetic models are equivalent. Computer simulations and model fitting were conducted to demonstrate these conditions. Typical M–M and TMDD profiles were simulated based on literature data for an anti-CD4 monoclonal antibody (TRX1) and phenytoin administered intravenously. Both models were fitted to data and goodness of fit criteria were evaluated for model selection. A case study of recombinant human erythropoietin was conducted to qualify results. A rapid binding TMDD model is equivalent to the M–M model if total target density Rtot is constant, and RtotKD/(KD + C)2 ≪ 1 where KD represents the dissociation constant and C is the free drug concentration. Under these conditions, M–M parameters are defined as: Vmax = kintRtotVc and Km = KD where kint represents an internalization rate constant, and Vc is the volume of the central compartment. Rtot is constant if and only if kint = kdeg, where kdeg is a degradation rate constant. If the TMDD model predictions are not sensitive to kint or kdeg parameters, the condition of RtotKD/(KD + C)2 ≪ 1 alone can preserve the equivalence between rapid binding TMDD and M–M models. The model selection process for drugs that exhibit TMDD should involve a full mechanistic model as well as reduced models. The best model should adequately describe the data and have a minimal set of parameters estimated with acceptable precision.

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Section: Resultssupporting

confidence: 83%

Selection between Michaelis–Menten and target-mediated drug disposition pharmacokinetic models

Yan

Mager

Krzyzanski

2009

J Pharmacokinet Pharmacodyn

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“…The relatively high variability for this estimate is likely related to the use of different methods for assessment of binding affinity (e.g., surface plasmon resonance technology, [39] or cell binding assays [40]). Given the known impact of K d on the terminal elimination profile for drugs demonstrating TMD [51], it is likely that uncertainty in K d is a contributing factor to the simulated uncertainty in terminal portions of the predicted concentration versus time profiles.…”

Section: Discussionmentioning

confidence: 99%

Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer

Abuqayyas

Balthasar

2012

J Pharmacokinet Pharmacodyn

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This investigation evaluated the utility of a physiologically based pharmacokinetic (PBPK) model, which incorporates model parameters representing key determinants of monoclonal antibody (mAb) target-mediated disposition, to predict, a priori, mAb disposition in plasma and in tissues, including tumors that express target antigens. Monte Carlo simulation techniques were employed to predict the disposition of two mAbs, 8C2 (as a non-binding control mouse IgG1 mAb) and T84.66 (a high-affinity murine IgG1 anti-carcinoembryonic antigen mAb), in mice bearing no tumors, or bearing colorectal HT29 or LS174T xenografts. Model parameters were obtained or derived from the literature. 125I-T84.66 and 125I-8C2 were administered to groups of SCID mice, and plasma and tissue concentrations were determined via gamma counting. The PBPK model well-predicted the experimental data. Comparisons of the population predicted versus observed areas under the plasma concentration versus time curve (AUC) for T84.66 were 95.4 ± 67.8 versus 84.0 ± 3.0, 1,859 ± 682 versus 2,370 ± 154, and 5,930 ± 1,375 versus 5,960 ± 317 (nM × day) at 1, 10, and 25 mg/kg in LS174T xenograft-bearing SCID mice; and 215 ± 72 versus 233 ± 30, 3,070 ± 346 versus 3,120 ± 180, and 7,884 ± 714 versus 7,440 ± 626 in HT29 xenograft-bearing mice. Model predicted versus observed 8C2 plasma AUCs were 312.4 ± 30 versus 182 ± 7.6 and 7,619 ± 738 versus 7,840 ± 24.3 (nM × day) at 1 and 25 mg/kg. High correlations were observed between the predicted median plasma concentrations and observed median plasma concentrations (r2 = 0.927, for all combinations of treatment, dose, and tumor model), highlighting the utility ofthe PBPK model forthe a priori prediction of in vivo data.

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“…The imprecision in the K D estimate is not unexpected given that accurate estimation of this parameter requires the implementation of very intensive PK sampling, in particular at concentration below the K D . The region of drug concentrations lower than K D is the most informative for the identifiability of the K D parameter (30). However, it is also very close to the limit of quantification.…”

Section: Discussionmentioning

confidence: 99%

Pharmacodynamics-Mediated Drug Disposition (PDMDD) and Precursor Pool Lifespan Model for Single Dose of Romiplostim in Healthy Subjects

Wang

Krzyzanski

Doshi

et al. 2010

AAPS J

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Abstract. The objective of this study was to characterize the pharmacokinetics and pharmacodynamics (PK-PD) of romiplostim after single-dose administration in healthy subjects. The mean serum romiplostim concentrations (PK data) and mean platelet counts (PD data) collected from 32 subjects receiving a single intravenous (0.3, 1 and 10 μg/kg) or subcutaneous (0.1, 0.3, 1, and 2 μg/kg) dose were fitted simultaneously to a mechanistic PK-PD model based on pharmacodynamics-mediated drug disposition (PDMDD) and a precursor pool lifespan concept. The two-compartment PK model incorporated receptor-mediated endocytosis and linear mechanisms as parallel elimination pathways. The maximal concentration of receptors (assumed to be proportional to the platelet count), the equilibrium dissociation constant, and the first-order internalization rate constant for endocytosis of the drug-receptor complex were 0.022 fg/platelet, 0.131 ng/mL, and 0.173 h −1 , respectively. Romiplostim concentration stimulates the production of platelet precursors via the Hill function, where the SC 50 was 0.052 ng/mL and S max was 11.2. The estimated precursor cell and platelet lifespans were 5.9 and 10.5 days, respectively. Model-based simulations revealed that the romiplostim exposure and the platelet response are both dependent on the dose administered and the baseline platelet counts. Also, weekly dosing produced a sustained PD response while dosing intervals ≥2 weeks resulted in fluctuating platelet counts. Thus, the mechanistic PK-PD model was suitable for describing the romiplostim PK-PD interplay (PDMDD), the dose-dependent platelet stimulation, and the lifespans of thrombopoietic cell populations.

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Partial derivative—Based sensitivity analysis of models describing target-mediated drug disposition

Cited by 28 publications

References 13 publications

Selection between Michaelis–Menten and target-mediated drug disposition pharmacokinetic models

Selection between Michaelis–Menten and target-mediated drug disposition pharmacokinetic models

Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer

Pharmacodynamics-Mediated Drug Disposition (PDMDD) and Precursor Pool Lifespan Model for Single Dose of Romiplostim in Healthy Subjects

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