Exposure–response analyses of trastuzumab emtansine in patients with HER2-positive advanced breast cancer previously treated with trastuzumab and a taxane

Li, Chunze; Wang, Bei; Chen, Shang-Chiung; Wada, Russ; Lü, Dan; Wang, Xin; Polhamus, Daniel; French, Jonathan; Vadhavkar, Shweta; Strasak, Alexander; Smitt, Melanie C.; Joshi, Amita; Samant, Meghna; Quartino, Angelica; Jin, Jin; Girish, Sandhya

doi:10.1007/s00280-017-3440-4

Cited by 24 publications

(48 citation statements)

References 17 publications

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“…Among molecules discussed here it appears that exposure-safety analyses for pembrolizumab, nivolumab, durvalumab, and T-DM1 have not faced the same confounding issues as exposure-efficacy analyses. 15,20,[53][54][55][56][57][58] If patients with worsening disease status and prognostic factors are more likely to experience adverse events, and have a decreased drug exposure it could be thought that differences in prognostic factors can confound the exposure-safety relationship. The confounding, however, would contribute to an inverse E-R relationship rather than a positive relationship.…”

Section: Discussionmentioning

confidence: 99%

“…used CPH modeling and case-matching. 15 Case-matching analysis demonstrated a greater reduction of the HR. While the case-matching analysis had additional covariates included that could contribute to the reduction of HR the reduction can also be attributed to the limitation of CPH modeling where the structure of the hazards model equation imposes assumptions about the effect of covariates on the E-R relationship.…”

Section: Cph Modeling and Case Matchingmentioning

confidence: 95%

“…After adjusting for baseline risk factors in the analyses for both metrics the model-predicted Cmin 1st-dose and area under the curve at cycle 1 (AUC 1st-dose ) were significantly associated with OS though the observed Cmin 1st-dose and AUC 1st-dose were not. 15 Due to the limited understanding of this discrepancy a strong recommendation could not be made regarding the selection of observed or model-derived metrics, and it would be prudent for an E-R analysis to examine both. Upon examining both metrics if either of them is significantly associated with OS further investigation into the population PK model and E-R analysis is warranted.…”

Section: Pharmacokinetic Parametersmentioning

confidence: 99%

“…The method was more recently applied to E-R analysis for the first time by Yang et al and has since been used for E-R analyses of multiple oncology biologics such as T-DM1 and nivolumab. 2, 12,15 Case-matching analysis adjusts for confounding factors by balancing the distribution of baseline risk factors between the control and treatment groups prior to calculating the HR. Only patients in the control arm that are similar or matched in baseline risk to patients in the treatment arm are included in the analysis.…”

Section: Cph Modeling and Case Matchingmentioning

confidence: 99%

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Confounding Factors in Exposure-Response Analyses and Mitigation Strategies for Monoclonal Antibodies in Oncology

Kawakatsu

Bruno

Kågedal

et al. 2020

Preprint

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Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on three different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (1) coxproportional hazards modeling and case-matching, (2) tumor growth inhibition-overall survival (TGI-OS) modeling, and (3) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programs. Therefore, the strengths and weaknesses of the other two approaches are considered, and the favorable utility of TGI-OS modeling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials, and proposes that development programs be designed to study more dose levels in earlier trials.

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Section: Discussionmentioning

confidence: 99%

Section: Cph Modeling and Case Matchingmentioning

confidence: 95%

Section: Pharmacokinetic Parametersmentioning

confidence: 99%

Section: Cph Modeling and Case Matchingmentioning

confidence: 99%

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Confounding Factors in Exposure-Response Analyses and Mitigation Strategies for Monoclonal Antibodies in Oncology

Kawakatsu

Bruno

Kågedal

et al. 2020

Preprint

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“…Thus, the key analyte for exposure-response analysis is usually the conjugate. 31,32 A recent publication showed that for multiple vc-MMAE antibody-drug conjugates, efficacy (objective response rate [ORR]), and safety (grade 2+ peripheral neuropathy [PN]) end points appeared to correlate well with conjugate (acMMAE) exposure, but not with unconjugated MMAE over the doses tested in the phase 1 studies. 12 Similar exposure-response findings were also observed for brentuximab vedotin.…”

Section: Impact On Decision-making In Drug Developmentmentioning

confidence: 99%

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody‐Drug Conjugates

Li¹,

Chen²,

Chen³

et al. 2020

The Journal of Clinical Pharma

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Antibody-drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody-drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early-and late-stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small-molecule therapeutics. The complex structure of antibody-drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody-drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure-response modeling, platform modeling by pooling data across multiple antibody-drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

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Characterizing Exposure–Response Relationship for Therapeutic Monoclonal Antibodies in Immuno‐Oncology and Beyond: Challenges, Perspectives, and Prospects

Dai

Vugmeyster

Mangal

2020

Clin Pharma and Therapeutics

111

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Recent data from immuno-oncology clinical studies have shown the exposure-response (E-R) relationship for therapeutic monoclonal antibodies (mAbs) was often confounded by various factors due to the complex interplay of patient characteristics, disease, drug exposure, clearance, and treatment response and presented challenges in characterization and interpretation of E-R analysis. To tackle the challenges, exposure relationships for therapeutic mAbs in immuno-oncology and oncology are reviewed, and a general framework for an integrative understanding of E-R relationship is proposed. In this framework, baseline factors, drug exposure, and treatment response are envisioned to form an interconnected triangle, driving the E-R relationship and underlying three components that compose the apparent relationship: exposure-driven E-R, baseline-driven E-R, and response-driven E-R. Various strategies in data analysis and study design to decouple those components and mitigate the confounding effect are reviewed for their merits and limitations, and a potential roadmap for selection of these strategies is proposed. Specifically, exposure metrics based on a single-dose pharmacokinetic model can be used to mitigate responsedriven E-R, while multivariable analysis and/or case control analysis of data obtained from multiple dose levels in a randomized study may be used to account for the baseline-driven E-R. In this context, the importance of collecting data from multiple dose levels, the role of prognostic factors and predictive factors, the potential utility of clearance at baseline and its change over time, and future directions are discussed.

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Exposure–response analyses of trastuzumab emtansine in patients with HER2-positive advanced breast cancer previously treated with trastuzumab and a taxane

Cited by 24 publications

References 17 publications

Confounding Factors in Exposure-Response Analyses and Mitigation Strategies for Monoclonal Antibodies in Oncology

Confounding Factors in Exposure-Response Analyses and Mitigation Strategies for Monoclonal Antibodies in Oncology

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody‐Drug Conjugates

Characterizing Exposure–Response Relationship for Therapeutic Monoclonal Antibodies in Immuno‐Oncology and Beyond: Challenges, Perspectives, and Prospects

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