A novel semi-mechanistic tumor growth fraction model for translation of preclinical efficacy of anti-glypican 3 antibody drug conjugate to human

Abstract

The growing fraction (GF) of tumor has been reported as one of the predictive markers of the efficacy of chemotherapeutics. Therefore, a semi-mechanistic model has been developed that describes tumor growth on the basis of cell cycle, allowing the incorporation of the GF of a tumor in pharmacokinetic/pharmacodynamic (PK/PD) modeling. Efficacy data of anti-glypican 3 (GPC3) antibody drug conjugate (ADC) in a hepatocellular carcinoma (HCC) patient derived xenograft (PDX) model was used for evaluation of this proposed model. Our model was able to describe the kinetics of growth inhibition of HCC PDX models following treatment with anti-GPC3 ADC remarkably well. The estimated tumurostatic concentrations were used in tandem with human PKs translated from cynomolgus monkey for prediction of the efficacious dose. The projected efficacious human dose of anti-GPC3 ADC was in the range 0.20-0.63 mg/kg for the Q3W dosing regimen, with a median dose of 0.50 mg/kg. This publication is the first step in evaluating the applicability of GF in PK/PD modeling of ADCs. The authors are hopeful that incorporation of GF will result in an improved translation of the preclinical efficacy of ADCs to clinical settings and thereby better prediction of the efficacious human dose.

Keywords: PK/PD; antibody drug conjugate; growth fraction; population modeling; translation; tumor growth.