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Time-dependent population PK models of single-agent atezolizumab in patients with cancer

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Abstract

Purpose

The time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies.

Methods

The first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function.

Results

The final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient’s status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model.

Conclusion

The overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients’ disease status, as shown with other checkpoint inhibitors.

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Authors and Affiliations

  1. Certara Strategic Consulting, Certara, 54 Rue de Londres, 75009, Paris, France

    Mathilde Marchand

  2. Clinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Rong Zhang, Phyllis Chan, Benjamin Wu & Jin Y. Jin

  3. BioAnalytical Sciences, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Valerie Quarmby

  4. Product Development, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Marcus Ballinger & Nitzan Sternheim

  5. Clinical Pharmacology, Genentech-Roche, 84 Chemin des Grives, 13013, Marseille, France

    René Bruno

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  1. Mathilde Marchand
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Contributions

MM, PC, VQ, MB, NS, JYJ and RB wrote the article and designed the research. RZ prepared the datasets. MM performed the analyses.

Corresponding author

Correspondence to René Bruno.

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Conflict of interest

M. Marchand is employed by Certara Strategic Consulting. R. Zhang, B. Wu, P. Chan, V. Quarmby, M. Ballinger, N. Sternheim, J.Y. Jin, and R. Bruno are employed by Genentech, Inc.

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Marchand, M., Zhang, R., Chan, P. et al. Time-dependent population PK models of single-agent atezolizumab in patients with cancer. Cancer Chemother Pharmacol 88, 211–221 (2021). https://doi.org/10.1007/s00280-021-04276-4

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  • DOI: https://doi.org/10.1007/s00280-021-04276-4

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