Chetan Rathi (1), Helai Mohammad (2), Caretha Creasy (3), Andrew Fedoriw (4), Ridhi Parasrampuria (5), Geraldine Ferron-Brady (5), Sandra Visser (5)
(1) Clinical Pharmacology Modeling & Simulation, GSK, Waltham, MA, US, (2) SK Life Science Lab, US, (3) UroGen Pharma, US, (4) Anagenex, US, (5) Clinical Pharmacology Modeling & Simulation, GSK, Upper Providence, PA, US
Objectives: GSK3368715 and GSK3326595 are novel type 1 PRotein MethylTransferase (PRMT) and PRMT5 inhibitors respectively which have been investigated as monotherapy in adults with advanced-stage solid tumors [1,2]. Non-clinical studies have demonstrated promising synergistic efficacy in multiple xenograft models when administered in combination [3]. The objective of this analysis was to develop a translational pharmacokinetic-tumor growth inhibition (PK-TGI) model to predict the potential of synergistic clinical efficacy for this novel-novel combination in Hormone Receptor positive (HR+) breast cancer population.
Methods: PK model was fitted to PK data after single and multiple dose administration of both individual compounds in mice. In vivo tumor growth data in the MDA-MB-468 xenograft mouse model [3] with both compounds alone or in combination was characterized using a TGI model to estimate growth rate, monotherapy efficacy, and interaction parameters. For translation to humans, a human PK-TGI model was developed using PK parameters obtained from the preliminary clinical population PK model for these agents, tumor size growth and baseline tumor size [4] parameters derived from clinical data in HR+ breast cancer whereas the drug effect parameters and interaction term were assumed to be the same between species. Clinical trial simulations were performed to predict the probability of response (PoR) for scenarios with varying doses of the combination partners using the human PK-TGI model and assuming between subject variability on PK, tumor growth rate and baseline tumor size parameters. All studies were conducted in accordance with the GSK Policy on the Care, Welfare and Treatment of Laboratory Animals and were reviewed by the Institutional Animal Care and Use Committee at GSK.
Results: PK for both agents was characterized in mice using a two-compartment model. Non-clinical TGI model included a first order growth rate to describe the tumor kinetics in absence of any treatment in MDA-MB-468 xenograft; monotherapy efficacy for each agent was described using a linear drug effect and the synergy between them was captured using an interaction term. The human PK-TGI model included a two-compartment PK model; tumor growth rate was back calculated from the median progression free survival of approximately 6 months in heavily pre-treated HR+ breast cancer population. Clinical trial simulations predicted a 73.4% PoR for the combination therapy (50 mg Type 1 PRMT and 200 mg PRMT5 inhibitors) compared to predicted PoR of 1.3% (150 mg Type 1 PRMT) and 40.1% (400 mg PRMT5) for each monotherapy.
Conclusions: The translational PK-TGI model-based clinical trial simulations predicted a higher PoR for the combination compared to either agent as a monotherapy. This potential synergistic efficacy could be achieved at clinically relevant doses of individual agents. This modeling and simulation framework could be applied for other compounds and assist in making go-no-go decisions.
References:
[1] El-Khoueiry, Anthony B et al. “Phase 1 study of GSK3368715, a type I PRMT inhibitor, in patients with advanced solid tumors.” British journal of cancer vol. 129,2 (2023): 309-317. doi:10.1038/s41416-023-02276-0
[2] Siu, L.L. et al. “438O – METEOR-1: A phase I study of GSK3326595, a first-in-class protein arginine methyltransferase 5 (PRMT5) inhibitor, in advanced solid tumours.” Annals of oncology vol. 30,5 (2019): v159. doi:10.1093/annonc/mdz244.
[3] Fedoriw, Andrew et al. “Anti-tumor Activity of the Type I PRMT Inhibitor, GSK3368715, Synergizes with PRMT5 Inhibition through MTAP Loss.” Cancer cell vol. 36,1 (2019): 100-114.e25. doi:10.1016/j.ccell.2019.05.014
[4] https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/208716Orig1s000MultidisciplineR.pdf
Reference: PAGE 32 (2024) Abstr 11131 [www.page-meeting.org/?abstract=11131]
Poster: Drug/Disease Modelling - Oncology