Population pharmacokinetic and pharmacodynamic modeling of MNRP1685A in cynomolgus monkeys using two-target quasi-steady-state (QSS) model

Yan, Xin; Xiang, Hong; Jin, Denise; Theil, Frank‐Peter; Joshi, Amita; Damico‐Beyer, Lisa A.; Bai, Shuang

doi:10.1007/s10928-012-9244-6

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…5B), in cynomolgus monkey studies, the increase of total cNRP1 seems to be driven by the accumulation of drug-cNRP1 complex (17). To better use total cNRP1 as a surrogate marker for receptor occupancy in humans, a mechanistic PK/PD model (2-target QSS model) was applied to predict the profiles of free receptors both the membrane mNRP1 and cNRP1.…”

Section: Resultsmentioning

confidence: 99%

“…Because MNRP1685A binds to both mNRP1 and cNRP1, the 2-target quasi-steady-state (QSS) model (16) has been shown to adequately describe the free drug MNRP1685A and total cNRP1 concentration-time profiles in cynomolgus monkeys (17). In this model, MNRP1685A distributes to both central and peripheral compartments, with parallel linear and nonlinear clearance of free drug, and binds to free cNRP1 in the central compartment.…”

Section: Mnrp1685a/cnrp1 Pk/pd Analysismentioning

confidence: 99%

See 1 more Smart Citation

Pharmacokinetic and Pharmacodynamic Analysis of Circulating Biomarkers of Anti-NRP1, a Novel Antiangiogenesis Agent, in Two Phase I Trials in Patients with Advanced Solid Tumors

Yan

et al. 2012

Clinical Cancer Research

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Purpose: MNRP1685A is a monoclonal antibody to neuropilin-1 (NRP1). We evaluated blood-based pharmacodynamic biomarkers of MNRP1685A in two phase I studies to assess exposure/response relationships to inform target dose and regimen selection.Experimental Design: The phase I studies evaluated escalating doses of MNRP1685A as a single agent or in combination with bevacizumab. Plasma placental growth factor (PlGF), VEGF, and circulating NRP1 (cNRP1) were evaluated at multiple time points using meso-scale discovery (MSD) assays and ELISA, respectively. Plasma PlGF was also measured in a phase I/II trial of bevacizumab in metastatic breast cancer (AVF0776). The association between PlGF and MNRP1685A dose was described by a sigmoid E max model. cNRP1 and MNRP1685A PK profiles were described using a two-target quasi-steady state (QSS) model.Results: A dose-and time-dependent increase in plasma PlGF and cNRP1 was observed in all patients treated with MNRP1685A. PK/PD analysis showed that bevacizumab and MNRP1685A had an additive effect in elevating PlGF. Predictions based on the two-target QSS model showed that the free drug concentration to maintain greater than 90% saturation of membrane NRP1 (mNRP1) and cNRP1 is about 8 mg/mL.Conclusion: These data show that MNRP1685A inhibits the VEGF pathway in humans as assessed by an increase in plasma PlGF. MNRP1685A seems to enhance bevacizumab-mediated VEGF pathway blockade, as showed by an increase in the magnitude of PlGF elevation when combined with bevacizumab. PK/PD analysis of biomarkers in the phase I population allowed identification of doses at which apparent maximal pathway modulation was observed.

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Section: Resultsmentioning

confidence: 99%

Section: Mnrp1685a/cnrp1 Pk/pd Analysismentioning

confidence: 99%

Pharmacokinetic and Pharmacodynamic Analysis of Circulating Biomarkers of Anti-NRP1, a Novel Antiangiogenesis Agent, in Two Phase I Trials in Patients with Advanced Solid Tumors

Yan

et al. 2012

Clinical Cancer Research

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“…Indeed, in cynomolgus monkeys, the pharmacokinetics of MNRP1685A an anti-neuropilin-1 (NRP1) mAb, was described using a simplified TMDD model with drug binding to both membrane and circulating antigen targets and was shown to be less influenced by circulating than membrane antigen mass. [170] 6 Conclusions Among factors of variability of the pharmacokinetics of therapeutic antibodies, antigen mass has been one of the most frequently reported, in more than half of pharmacokinetic modelling studies. Antigen mass influence may result in different pharmacokinetic profiles for different antibodies in different diseases, and was described using a number of modelling strategies.…”

Section: Antigenic Targets Present In Several Tissuesmentioning

confidence: 99%

Influence of Antigen Mass on the Pharmacokinetics of Therapeutic Antibodies in Humans

et al. 2018

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Therapeutic antibodies are increasingly used to treat various diseases, including neoplasms and chronic inflammatory diseases. Antibodies exhibit complex pharmacokinetic properties, notably due to the influence of antigen mass, i.e. the amount of antigenic targets to which the monoclonal antibody binds specifically. This review focuses on the influence of antigen mass on the pharmacokinetics of therapeutic antibodies quantified by pharmacokinetic modelling in humans. Out of 159 pharmacokinetic studies, 85 reported an influence of antigen mass. This influence led to nonlinear elimination decay in 50 publications which was described using target-mediated drug disposition (TMDD) or derived models, as quasi-steady-state, irreversible binding and Michaelis-Menten models. In 35 publications, the pharmacokinetics was apparently linear and the influence of antigen mass was described as covariate of pharmacokinetic parameters. If some reported covariates, such as circulating antigen concentration or tumor size, are likely to be correlated to antigen mass, others, such as disease activity or disease type, may contain little information on the amount of antigenic targets. In some cases, antigen targets exist in different forms, notably in the circulation and expressed at cell surface. The influence of antigen mass should be soundly described during the early clinical phases of drug development. To maximize therapeutic efficacy, sufficient antibody doses should be administered to ensure the saturation of antigen targets by therapeutic antibody in all patients. If necessary, antigen mass should be taken into account in routine clinical practice. Key points-Current knowledge on the pharmacokinetics of monoclonal antibodies (mAbs) states that higher antigen amount was associated with mAb concentrations and higher mAb clearance. -Beacause of antigen mass, mAb pharmacokinetics may display nonlinear elimination shape. The influence of antigen mass on mAb pharmacokinetics is described using target-mediated drug disposition (TMDD) model, or approximations of this model. Antigen mass influence may be quantified using covariates. -Current mAb clinical development and use in clinical practice may be improved by optimization of dose, which should ensure the saturation of antigen targets in all patients.3

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“…The model successfully described the PK time-course profiles of several mAbs, such as anti-IL6 32 and anti-neuropilin-1. 33 Mathematical equations for the QE and QSS models are identical, except for their corresponding constants K D and K ss , with K ss D K D C K int /K on . Additionally, it has been demonstrated that when the amount of the drug significantly exceeds the amount of the target, models with Michaelis-Menten elimination may be sufficient to describe TMDD processes.…”

Section: Monoclonal Antibody Dispositionmentioning

confidence: 99%

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

Ait‐Oudhia¹,

Ovacik²,

Mager³

2016

mAbs

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Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro-and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients. KEYWORDSBoolean network analysis; In vitro cell-based models; In vivo physiologically-based pharmacokinetic models; mechanistic pharmacodynamic models; target mediated drug disposition; translational systems pharmacology

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Population pharmacokinetic and pharmacodynamic modeling of MNRP1685A in cynomolgus monkeys using two-target quasi-steady-state (QSS) model

Cited by 8 publications

References 18 publications

Pharmacokinetic and Pharmacodynamic Analysis of Circulating Biomarkers of Anti-NRP1, a Novel Antiangiogenesis Agent, in Two Phase I Trials in Patients with Advanced Solid Tumors

Pharmacokinetic and Pharmacodynamic Analysis of Circulating Biomarkers of Anti-NRP1, a Novel Antiangiogenesis Agent, in Two Phase I Trials in Patients with Advanced Solid Tumors

Influence of Antigen Mass on the Pharmacokinetics of Therapeutic Antibodies in Humans

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

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