4.1 Article

Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin

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SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10928-012-9276-y

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Antibody drug conjugate; Clinical translation; Mechanistic mathematical model; Pharmacokinetics-pharmacodynamics; Brentuximab-vedotin; SGN-35

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To build a multiscale mechanism based pharmacokinetic-pharmacodynamic (PK/PD) model for antibody drug conjugates (ADCs), using brentuximab-vedotin as an example, for preclinical to clinical translation of ADC efficacy. Brentuximab-vedotin experimental data, collected from diverse publications, were employed in the following steps to build and validate the model: (1) characterization of ADC and payload PK at the cellular level, (2) characterization of payload PK in plasma and tumor tissue of xenograft mouse, (3) characterization of ADC PK in mouse plasma, (4) prediction of the tumor payload concentrations in xenograft mouse by integrating parameters obtained from steps 1-3 with the novel tumor disposition model for ADC, (5) characterization of preclinical brentuximab-vedotin tumor growth inhibition data using the novel PK/PD model, (6) characterization of ADC and payload PK in cancer patients, and (7) prediction of clinical responses of brentuximab-vedotin using the PK/PD model, by integrating PK parameters obtained from step 6, and translated mouse parameters from step 5; and comparing them with clinical trial results. The tumor disposition model was able to accurately predict xenograft tumor and plasma payload concentrations. PK/PD model predicted progression free survival rates and complete response rates for brentuximab-vedotin in patients were comparable to the observed clinical results. It is essential to understand and characterize the disposition of ADC and payload, at the cellular and physiological level, to predict the clinical outcome of ADC. A first of its kind mechanistic model has been developed for ADCs, which can integrate preclinical biomeasures and PK/PD data, to predict clinical response.

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