Correlation between nivolumab exposure and treatment outcomes in non–small-cell lung cancer

Basak, Edwin A.; Koolen, Stijn L.W.; Hurkmans, Daan P.; Schreurs, Marco W. J.; Bins, Sander; Hoop, Esther Oomen De; Wijkhuijs, Annemarie J.M.; Besten, Ilse den; Sleijfer, Stefan; Debets, Reno; Veldt, Astrid A.M. van der; Aerts, Joachim; Mathijssen, Ron H.J.

doi:10.1016/j.ejca.2018.12.008

Cited by 62 publications

(61 citation statements)

References 27 publications

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“…Importantly, according to phase I trials, nivolumab dosing as low as 0.1 mg/kg, leading to simulated trough concentrations of 2.5 µg/mL, was sufficient to ensure optimal PD-1 inhibition [ 1 , 2 ]. However, in a pilot study in NSCLC patients, it was shown that a target trough level of > 34 µg/mL was associated with a higher response rate [ 66 ]. This discrepancy could come from some flaws in the very way target engagement was initially measured in circulating T lymphocytes, and not at the tumor level, during early phase I studies (see the Exposure–Efficacy Relationships paragraph below).…”

Section: Discussionmentioning

confidence: 99%

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

et al. 2019

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Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20-40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure-response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure-response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

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Section: Discussionmentioning

confidence: 99%

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

et al. 2019

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“…21 There was also confusion in the literature on whether a higher dose of nivolumab should be considered due to an apparent E-R relationship. [22][23][24] Thus, the apparent E-R relationship for these therapeutic mAbs is not always informative and sometimes can be misleading. The phenomenon is not well understood and has created challenges regarding the dose selection and optimization of these agents.…”

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confidence: 99%

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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“…For this, it might be helpful to consider the concept of early tumor shrinkage [48]. There are discussions whether higher doses of Nivolumab in shorter application intervals could be applied in the initial therapy stage to achieve this early tumor shrinkage by faster reaching the drug serum steady state and thus providing better responses [63,64]. The tumor size distributions shown in Figure 3 for patient KE-02 confirm this approach quantitatively.…”

Section: Discussionmentioning

confidence: 91%

How Mathematical Modeling Could Contribute to the Quantification of Metastatic Tumor Burden Under Therapy: Insights in Immunotherapeutic Treatment of Non-Small Cell Lung Cancer

Schlicke

Kuttler

Schümann

2020

Preprint

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Abstract Background: Cancer is one of the leading death causes globally with about 8.2 million deaths per year and an increase in numbers in recent years. About 90% of cancer deaths do not occur due to primary tumors but due to metastases, of which most are not clinically identifiable because of their relatively small size at primary diagnosis and limited technical possibilities. However, therapeutic decisions are formed depending on the existence of metastases and their properties. Therefore non-identified metastases might have huge influence in the treatment outcome. The quantification of clinically visible and invisible metastases is important for the choice of an optimal treatment of the individual patient as it could clarify the burden of non-identifiable tumors as well as the future behavior of the cancerous disease. Results: The mathematical model presented in this study gives insights in how this could be achieved, taking into account different treatment possibilities and therefore being able to compare therapy schedules for individual patients with different clinical parameters. The framework was tested on three patients with NSCLC, one of the deadliest types of cancer worldwide, and clinical history including platinum-based chemotherapy and PD-L1-targeted immunotherapy. Results yield promising insights into the framework to establish methods to quantify effects of different therapy methods and prognostic features for individual patients already at stage of primary diagnosis.

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Correlation between nivolumab exposure and treatment outcomes in non–small-cell lung cancer

Cited by 62 publications

References 27 publications

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

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

How Mathematical Modeling Could Contribute to the Quantification of Metastatic Tumor Burden Under Therapy: Insights in Immunotherapeutic Treatment of Non-Small Cell Lung Cancer

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