Abstract 3884: Advancing Glioblastoma drug regimen development to support combination therapy through integrated PKPD modeling and simulation-based predictions

Abstract

Abstract Despite advancements in therapies, such as surgery, irradiation (IR) and chemotherapy, outcome for patients suffering from glioblastoma remains fatal; the median survival rate is only about 15 months. Even with novel therapeutic targets, networks and signaling pathways being discovered, monotherapy with such agents targeting such pathways has been disappointing in clinical trials. Development of tumor resistance, particularly to temozolomide (TMZ), creates a substantial clinical challenge. The primary focus of our work is to rationally develop novel combination therapies and dose regimens that mitigate resistance development. Specifically, our aim is to combine TMZ with small molecule inhibitors that are either currently in clinical trials or are approved drugs for other cancer types, and which target the disease at various resistance signaling pathways that are induced in response to TMZ monotherapy. To accomplish this objective, an integrated PKPD modeling approach is used. The approach is largely based on the work of Cardilin, et al, 2018. A PK model for each drug is first defined. This is subsequently linked to a PD model description of tumor growth dynamics in the presence of a single drug or combinations of drugs. A key outcome of these combined PKPD models are tumor static concentration (TSC) curves of dual or triple combination drug regimens that identify combination drug exposures predicted to arrest tumor growth. This approach has been applied to TMZ in combination with abemaciclib (a dual CDK4/6 small molecule inhibitor) based on data from a published study evaluating abemaciclib efficacy in combination with TMZ in a glioblastoma xenograft model (Raub, et al, 2015). A PKPD model was developed to predict tumor growth kinetics for TMZ and abemaciclib monotherapy, as well as combination therapy. Population PK models in immune deficient NSG mice for temozolomide and abemaciclib were developed based on data obtained from original and published studies. Subsequently, the PK model was linked to tumor volume data obtained from U87-MG GBM subcutaneous xenografts, again using both original data as well as data from the Raub, et al, 2015 study. Model parameters quantifying tumor volume dynamics were precisely estimated (coefficient of variation < 30%). The developed PKPD model was used to calculate plasma concentrations of TMZ and abemaciclib that would arrest tumor growth, as well as combinations of concentrations of the two drugs that would accomplish the same endpoint. This so-called TSC curve for the TMZ and abemaciclib combination pair evidenced an additive effect of the two agents when administered together. These results will be presented. In addition, results from on-going PKPD studies of TMZ in combination with two other small molecule inhibitors, RG7388, an MDM2 inhibitor, and GDC0068, an AKT inhibitor, will also be presented. Citation Format: Saugat Adhikari, Harlan E. Shannon, Karen E. Pollok, Robert E. Stratford. Advancing Glioblastoma drug regimen development to support combination therapy through integrated PKPD modeling and simulation-based predictions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3884.