Population pharmacokinetic and covariate analysis of pertuzumab, a HER2-targeted monoclonal antibody, and evaluation of a fixed, non-weight-based dose in patients with a variety of solid tumors

Garg, Amit; Quartino, Angelica; Li, Jing; Jin, Jin; Wada, Daisuke; Li, Hanbin; Cortés, Javier; McNally, Virginia; Ross, Graham; Visich, Jennifer; Lum, Bert L.

doi:10.1007/s00280-014-2560-3

Cited by 51 publications

(66 citation statements)

References 39 publications

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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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“…Durvalumab CL decreased with increasing albumin level, and with decreased tumor burden. Hypoalbuminemia is a well‐known marker of cachexia, inflammatory conditions, and increased catabolic activity, and the impact of albumin on PK of monoclonal antibodies has been previously reported for infliximab, bevacizumab, ustekinumab, and pertuzumab . However, these interactions were only identified based on baseline albumin measures.…”

Section: Discussionmentioning

confidence: 98%

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

117

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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“…The pertuzumab serum concentration data collected in NeoSphere were analyzed using the published pertuzumab population PK model [14]. In this population PK model, pertuzumab PK was described by a two-compartment linear model with a clearance (CL), central volume of distribution (Vc), and terminal elimination half-life of 0.235 L/day, 3.11 L, and 18 days, respectively.…”

Section: Methodsmentioning

confidence: 99%

Pharmacokinetic and exposure–response analyses of pertuzumab in combination with trastuzumab and docetaxel during neoadjuvant treatment of HER2+ early breast cancer

Quartino¹,

Li²,

Jin³

et al. 2017

Cancer Chemother Pharmacol

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PurposeThe NeoSphere trial evaluated pertuzumab in the neoadjuvant setting [early breast cancer (EBC)] with pathological complete response (pCR) as the primary efficacy end point. This analysis of pertuzumab aimed to (1) compare its pharmacokinetics (PK) in patients with EBC versus advanced cancers, (2) to further evaluate PK drug–drug interactions (DDIs) when given in combination with trastuzumab, and (3) to assess the relationship between exposure and efficacy to assess the clinical dosing regimen in the EBC patients.MethodsPertuzumab serum concentration data from 180 patients in NeoSphere were compared to historical observations and potential DDI was assessed, by applying simulation techniques using a population PK model. The impact of pertuzumab exposure on pCR rate was evaluated using a logit response model (n = 88).ResultsThe observed PK matched the population PK model simulations, confirming that the PK in neoadjuvant EBC appear to be in agreement with the historical observations. No evidence of a DDI effect of trastuzumab or docetaxel on pertuzumab was observed supporting the doses when given in combination. In NeoSphere >90% of EBC patients achieved the non-clinical target serum concentration. There was no association between the pertuzumab serum concentration and pCR within the range observed in this study (20–100 μg/mL) supporting no dose adjustments needed for patients with lower exposure.ConclusionsThis analysis further supports the lack of DDI between the two therapeutic proteins and the appropriateness of the approved fixed non-body-weight-adjusted pertuzumab dose in the treatment of neoadjuvant EBC with pertuzumab in combination with trastuzumab and docetaxel.Electronic supplementary materialThe online version of this article (doi:10.1007/s00280-016-3218-0) contains supplementary material, which is available to authorized users.

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Population pharmacokinetic and covariate analysis of pertuzumab, a HER2-targeted monoclonal antibody, and evaluation of a fixed, non-weight-based dose in patients with a variety of solid tumors

Abstract: The fixed, non-weight-based dosing of pertuzumab, 840 mg loading dose followed by a 420 mg maintenance dose every 3 weeks, in patients with the solid tumors in this analysis is well supported by the population pharmacokinetic modeling and simulation results.

Cited by 51 publications

References 39 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

Pharmacokinetic and exposure–response analyses of pertuzumab in combination with trastuzumab and docetaxel during neoadjuvant treatment of HER2+ early breast cancer

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