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Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis

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Abstract

The mathematical modeling of tumor xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic–pharmacodynamic (PK–PD) models encompassing drug, biomarker and tumor-size data. This paper investigates the reciprocal consistency of three types of models: drug-to-tumor, such as established drug-driven tumor growth inhibition (TGI) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to-tumor, e.g. the more recent biomarker-driven TGI models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to-tumor models with a drug-to-tumor TGI model. Using the Simeoni TGI model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the drug-to-tumor potency of a new candidate drug based only on biomarker response is discussed.

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Acknowledgements

The research leading to these results has received support (for MLS, GDN) from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115156, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies in kind contribution. The DDMoRe project is also supported by financial contribution from Academic and SME partners. This work does not necessarily represent the view of all DDMoRe partners.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Industriale e dell’Informazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy

    Maria Luisa Sardu & Giuseppe De Nicolao

  2. Clinical Pharmacology & Pharmacometrics, Janssen Research & Development, 2340, Beerse, Belgium

    Italo Poggesi

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  1. Maria Luisa Sardu
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  2. Italo Poggesi
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  3. Giuseppe De Nicolao
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Correspondence to Maria Luisa Sardu.

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Sardu, M.L., Poggesi, I. & De Nicolao, G. Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis. J Pharmacokinet Pharmacodyn 42, 611–626 (2015). https://doi.org/10.1007/s10928-015-9427-z

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  • DOI: https://doi.org/10.1007/s10928-015-9427-z

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