Clinical Pharmacokinetics and Pharmacodynamics of Atezolizumab in Metastatic Urothelial Carcinoma

Stroh, Mark; Winter, Helen; Marchand, Mathilde; Claret, Laurent; Eppler, Stephen; Ruppel, Jane; Abidoye, Oyewale O.; Teng, SL; Lin, WT; Dayog, S; Bruno, René; Jin, Juebin; Girish, Sandhya

doi:10.1002/cpt.587

Cited by 120 publications

(187 citation statements)

References 12 publications

(26 reference statements)

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“…All covariates included in the final PopPK model for 23 mAbs, including the PK structural model, were summarized from the literature review (Table ) . The effect of covariates was evaluated specifically for the linear portion of CL for mAbs exhibiting linear or parallel linear and nonlinear CL.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology

Bajaj

Suryawanshi

Roy

et al. 2019

Brit J Clinical Pharma

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Aims The development of monoclonal antibodies (mAbs) requires an understanding of the interindividual variability (IIV) in pharmacokinetics (PK) at the population level facilitated by population PK (PopPK) modelling. However, there is no clear rationale for selecting which covariates to screen during PopPK model development. Here, we compare the effect of covariates on PK parameters for mAbs in oncology and identify the most commonly used covariates affecting PK parameters. Methods All 25 mAbs approved for therapeutic use in oncology until December 2017 by the Food and Drug Administration and the European Medicines Agency were selected for study. Literature searches revealed 23 available PopPK models for these mAbs. To understand the magnitude and types of covariate effect on PK parameters, all covariates included in the final PopPK model for each mAb were summarized. Results The most commonly identified covariates were baseline body weight (BW; 17 mAbs), baseline serum albumin (8 mAbs), and sex (7 mAbs) on clearance; and BW (16 mAbs) and sex (12 mAbs) on central volume of distribution. A reduced PopPK model was developed for nivolumab and ipilimumab using these covariates, and the percentage of explained IIV from the reduced model (20.3% and 16.8%, respectively) was compared with that from the full model (24.5% and 27.9%, respectively). Conclusions This analysis provides a uniform platform for selecting covariates and suggests that the effect of BW, albumin and sex should be included during the development of PopPK models for mAbs in oncology. The reduced model was able to explain IIV to a similar extent as the full model.

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Section: Resultsmentioning

confidence: 99%

Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology

Bajaj

Suryawanshi

Roy

et al. 2019

Brit J Clinical Pharma

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“…7 The population PK of the IgG1 antibody atezolizumab (approved at 1,200 mg q3w) in hematologic and solid malignancies was described in a recent report. 8 The PK was linear over a wide dose range. Population PK indicated several statistically significant covariates (body weight, sex, anti-drug antibody (ADA), albumin, and tumor burden), none of which would require dose adjustment.…”

Section: Study Highlightsmentioning

confidence: 96%

“…The population PK of the IgG1 antibody atezolizumab (approved at 1,200 mg q3w) in hematologic and solid malignancies was described in a recent report . The PK was linear over a wide dose range.…”

mentioning

confidence: 99%

Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status

Baverel¹,

Dubois²,

Jin³

et al. 2018

Clin Pharma and Therapeutics

119

152

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The objectives of this analysis were to develop a population pharmacokinetics (PK) model of durvalumab, an anti‐PD‐L1 antibody, and quantify the impact of baseline and time‐varying patient/disease characteristics on PK. Pooled data from two studies (1,409 patients providing 7,407 PK samples) were analyzed with nonlinear mixed effects modeling. Durvalumab PK was best described by a two‐compartment model with both linear and nonlinear clearances. Three candidate models were evaluated: a time‐invariant clearance (CL) model, an empirical time‐varying CL model, and a semimechanistic time‐varying CL model incorporating longitudinal covariates related to disease status (tumor shrinkage and albumin). The data supported a slight decrease in durvalumab clearance with time and suggested that it may be associated with a decrease in nonspecific protein catabolic rate among cancer patients who benefit from therapy. No covariates were clinically relevant, indicating no need for dose adjustment. Simulations indicated similar overall PK exposures following weight‐based and flat‐dosing regimens.

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“…Since techniques of tumor size measurements and their target-antigen density are very different between type of cancer, it is difficult to deliver a global message on the influence of tumor size on mAb pharmacokinetics, even if 7 publications reported and increase in mAb clearance with pre-therapeutic tumour size (table 1): -breast cancer size for trastuzumab [94] and trastuzumab emtansine [131,133] ; -bladder urothelial carcinoma size for atezolizumab [134] ; -size of various solid tumors for olaratumumab [135] and pembrolizumab [136] ; -and the number of extrahepatic metastases for bevacizumab [130] .…”

Section: Influence Of Tumor Sizementioning

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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Clinical Pharmacokinetics and Pharmacodynamics of Atezolizumab in Metastatic Urothelial Carcinoma

Cited by 120 publications

References 12 publications

Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology

Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology

Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status

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

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