Model‐based prediction of effective target exposure for MEN1611 in combination with trastuzumab in HER2‐positive advanced or metastatic breast cancer patients

Abstract

MEN1611 is a novel orally bioavailable PI3K inhibitor currently in clinical development for patients with HER2‐positive (HER2+) PI3KCA mutated advanced/metastatic breast cancer (BC) in combination with trastuzumab (TZB). In this work, a translational model‐based approach to determine the minimum target exposure of MEN1611 in combination with TZB was applied. First, pharmacokinetic (PK) models for MEN1611 and TZB in mice were developed. Then, in vivo tumor growth inhibition (TGI) data from seven combination studies in mice xenograft models representative of the human HER2+ BC non‐responsive to TZB (alterations of the PI3K/AkT/mTOR pathway) were analyzed using a PK‐pharmacodynamic (PD) TGI model for co‐administration of MEN1611 and TZB. The established PK‐PD relationship was used to quantify the minimum effective MEN1611 concentration, as a function of TZB concentration, needed for tumor eradication in xenograft mice. Finally, a range of minimum effective exposures for MEN1611 were extrapolated to patients with BC, considering the typical steady‐state TZB plasma levels in patients with BC following three alternative regimens (i.v. 4 mg/kg loading dose +2 mg/kg q1w, i.v. 8 mg/kg loading dose +6 mg/kg q3w or s.c. 600 mg q3w). A threshold of about 2000 ng·h/ml for MEN1611 exposure associated with a high likelihood of effective antitumor activity in a large majority of patients was identified for the 3‐weekly and the weekly i.v. schedule for TZB. A slightly lower exposure (i.e., 25% lower) was found for the 3‐weekly s.c. schedule. This important outcome confirmed the adequacy of the therapeutic dose administered in the ongoing phase 1b B‐PRECISE‐01 study in patients with HER2+ PI3KCA mutated advanced/metastatic BC.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

“…Among them, the Simeoni TGI model [ 223 ] has been applied by several international research groups on a huge panel of xenograft studies as well as in vitro data [ 228 ] involving a multitude of different cancer cell lines and anticancer agents, becoming a reference in the field. Several features determined the popularity of this model ( Figure 6 ): (1) it provides quantitative measurements of the anticancer drug efficacy that, differently from simple efficacy metrics directly computed on experimental data (i.e., in vivo TGI percentage), are tumor/compound-specific and independent of experimental conditions (i.e., dose, time and dosing regimen) [ 229 ], allowing a drug ranking; (2) it is able to predict outcomes of administration schedules not experimentally tested, reducing in vivo studies; (3) it provides estimates of the minimal drug concentration level needed to guarantee tumor eradication in xenograft mice [ 230 ] that strongly correlated to doses administered in patients [ 231 ], anticipating the minimal efficacious exposure to be targeted in clinics and supporting the study design of early clinical studies during the drug development process [ 232 , 233 ].…”

Role of pharmacometrics and systems pharmacology in facilitating efficient dose optimization in oncology

“…Tosca et al 2 illustrate a translational model-based approach integrating PK and tumor growth inhibition (TGI) data in mice to extrapolate a range of minimum effective concentrations for MEN1611, a compound in clinical development in combination with trastuzumab for patients with breast cancer. Using the PK/PD-TGI model built to characterize the PK/PD relationship for the combination therapy, the minimum target exposure for tumor eradication was proposed and confirmed in an ongoing phase Ib study illustrating an effective use of translational PK/PD models to predict target exposures from preclinical data.…”

“…Tosca et al 2 . illustrate a translational model‐based approach integrating PK and tumor growth inhibition (TGI) data in mice to extrapolate a range of minimum effective concentrations for MEN1611, a compound in clinical development in combination with trastuzumab for patients with breast cancer.…”

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

“…Modeling and simulation has gained increasing relevance in anticancer drug development as it provides the opportunity to guide dose selection in early clinical phases, 1–3 inform clinical trial designs, 4 optimize administration protocols, anticipate clinical outcomes, 5,6 and support evidence of effectiveness and/or safety 7 . For these reasons, adopting in silico approaches to clinical trials meets pharmaceutical industry needs of a more efficient development of anticancer agents and is strongly encouraged by regulatory agencies 8–11 …”