Abstract 4524: A preclinical PKPD modeling & simulation strategy: building a predictive model of dose, schedule and therapeutic index for small molecule targeted anticancer agents
Authors: Jones Rhys DO , Odedra Rajesh , Yates James WT , Morentin Gutierrez Pablo , Davies Barry , Hudson Kevin , Barry Simon T. 
Published: August 2015

All anti-cancer medicines must balance the benefits vs. the side effects in choosing the right dose and schedule to deliver to patients. Exploring dose and schedule options following an empirical approach in the clinic can be a costly, time-consuming process with high probability that cohorts of patients do not receive sufficient therapeutic benefit. Pre-clinical in vivo models and PKPD modelling can be used to explore therapeutic index (TI) and refine dose schedule options to prioritise in clinical testing. Experimental strategies to articulate the framework for a combination strategy are required to build a quantitative understanding of the extent and duration of compound-target suppression and the relationship to TI.

These principals have been tailored to the small molecule Oncology drug discovery setting with a focus on maximising the knowledge and value derived from existing data packages typically delivered for drug projects during the lead identification, optimisation and candidate selection period. Datasets typically available include in vivo PK, biomarker (PD) and mouse/rat xenograft tumour growth inhibition (efficacy). Broad project analysis revealed a need to ensure that study designs are built to explore both dose-response and exposure-time (scheduling) relationships. This is because projects commonly need to answer the following three fundamental questions. (1) What is the extent and duration of target suppression required to deliver a biological effect? (2) How do effects on the pathway correlate with efficacy and effects on normal tissues and do these effects change with time? (3) How does therapeutic margin change with alternative target suppression profiles (scheduling)? Building this framework pre-clinically to answer these key questions is critical to defining the clinical dose and schedule in an effective way as differentiated dose and schedules are difficult to explore broadly in the clinic.

This framework is exemplified with the following. (1) AKT inhibition has multiple MOA in both the tumour cells and normal tissues. Efficacy can be achieved by delivering the compound with different target inhibition profiles, which may maximise benefit for patients, and pre-clinical modelling can be used to prioritise the clinical intermittent schedules to be tested. (2) Targeting PI3Kalpha signalling can have a narrow TI, due to effects on glucose homeostasis, which can impair the combination potential. Pre-clinical modelling has shown that this can be improved by using an intermittent schedule for combination options. (3) DNA repair pathways are critical for many tumour cells and normal cells. While these inhibitors have significant impact on tumour cells they also impact normal cells, particularly bone marrow. It has been possible to build a scheduling model that retains efficacy but prevents neutropenia therefore increasing TI.

Citation Format: Rhys DO Jones, Rajesh Odedra, James WT Yates, Pablo Morentin Gutierrez, Barry Davies, Kevin Hudson, Simon T. Barry. A preclinical PKPD modeling & simulation strategy: building a predictive model of dose, schedule and therapeutic index for small molecule targeted anticancer agents. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4524. doi:10.1158/1538-7445.AM2015-4524