2023 - A Coruña - Spain

PAGE 2023: Drug/Disease Modelling - Oncology
Ida Neldemo

Application of tumor size modelling and simulations to support the dose selection of BI 907828 for a Phase II study

Ida Neldemo (1), Céline Sarr (1), Lena E. Friberg (1,2), Reinhard Sailer (3), Mehdi Lamar (3), Girish Jayadeva (3), Alejandro Pérez-Pitarch (3), David Busse (3)

(1) Pharmetheus AB, Uppsala, Sweden, (2) Department of Pharmacy, Uppsala University, Uppsala, Sweden, (3) Boehringer Ingelheim Pharma GmbH & Co.KG, Ingelheim, Germany

Introduction: Inactivation of tumor protein 53 (p53) is a key mechanism which promotes tumor survival and proliferation. p53 inactivation can be due to mutations in TP53 or downregulation of wild-type p53 by the key negative regulator mouse double-minute 2 (MDM2). BI 907828 is an MDM2- p53 antagonist that is being developed for the treatment of advanced solid tumors. Preliminary data have been encouraging in a variety of tumor types, e.g., biliary tract cancer (BTC), in the monotherapy Phase I clinical trial (NCT03449381) [1].

Objectives: To support the selection of BI 907828 dose for the Brightline-2 study (NCT05512377) in patients with locally advanced or metastatic, MDM2 amplified, TP53 wild type BTC, pancreatic cancer or other selected solid tumors [2] by describing the exposure-tumor size dynamics, as well as dropout from tumor assessments, based on patients with advanced or metastatic solid tumors enrolled in the Phase I clinical trial who received at least one dose of BI 907828.

Methods: Longitudinal tumor size data were available in 81 patients with a variety of advanced or metastatic solid tumors (with/without/unknown MDM2 amplification status: N=54/21/6 patients). Patients received BI 907828 orally on day 1 every third week (q3w) (5-80 mg) or on day 1 and day 8 every fourth week (5-60 mg). Tumor assessment was performed every 6, 8 or 12 weeks until progressive disease (PD) according RECIST 1.1 [3].

Population modeling was applied to analyze the longitudinal tumor size (sum of longest diameters, SLD) data, as well as the dropout from tumor assessments, using NONMEM 7.4 [4]. The longitudinal SLD data was described by a tumor growth inhibition (TGI) model [5]. Different plasma exposure metrics derived from the available BI 907828 PK model were explored in combination with and without wash-out of the drug effect. Additional patient specific covariate-parameter relationships were evaluated using the stepwise covariate model building procedure (SCM) with adaptive scope reduction (ASR) [6,7]. Dropout from tumor assessments was described by a logistic regression model, where predictors of dropout from tumor size assessments were investigated by using the SCM with ASR by 1) exploring SLD-based predictors (e.g., PD and change from baseline tumor size) and time, and 2) patient specific factors conditioned on the SLD based predictors. Model diagnostics (including goodness of fit plots and visual predictive checks) were applied to evaluate the predictivity of the developed modeling framework.

Longitudinal SLD data was simulated, using the R based package mrgsolve version 0.10.0, every 6 weeks for one year, from the developed TGI and dropout models, in 10000 virtual patients with advanced or metastatic solid tumors and a 70 kg body weight. Virtual patients received 20, 30, 45 and 60 mg BI 907828 orally q3w.

Results: The TGI model adequately predicted the SLD data. The tumor growth was described by a first-order growth rate constant (kG) and the tumor shrinkage component by a drug-induced cell kill rate constant (kD). kD was related linearly to the average concentration during a dosing interval (Cav,ss). The drug-induced tumor shrinkage diminished with a half-life of 4.7 weeks. kG, kD and SLD at baseline were associated with inter-individual variability. Probability of dropout from tumor assessments increased at the occurrence of PD and decreased exponentially over time. Model diagnostics indicated that the final models were qualified to simulate SLD (and dropout from tumor assessments) up to one year after start of BI 907828 treatment.

Simulations illustrated that the median relative decrease from SLD at baseline, in a 70 kg population with advanced or metastatic solid tumor after one year of treatment with BI 907828 q3w (assuming no dose reductions or dose delays), was larger the higher the dose levels, i.e., 3.85%, 8.04%, 14.2% and 19.7% for the 20, 30, 45 and 60 mg dose levels, respectively.

Conclusions: The developed models predicted that the tumor shrinkage was higher in patients receiving higher doses (i.e., high Cav,ss) in patients with advanced or metastatic solid tumors treated with BI 907828 monotherapy. Among assessment of other endpoints, these results contributed to selecting the 45 mg dose level as the initial dose in the Brightline-2 Phase II clinical trial. Further dose optimization for BI 907828 is ongoing in solid tumor patient populations. 



References:
[1] P. Schoeffski, N. Yamamoto, T. Bauer, M. Patel, M.M. Gounder, J. Geng, R. Sailer, G. Jayadeva, P. Lorusso. A phase I dose-escalation and expansion study evaluating the safety and efficacy of the MDM2–p53 antagonist BI 907828 in patients (pts) with solid tumours [abstract]. In: European Society for Medical Oncology Congress; 2022 Sep 9-13. Paris, France. Abstract nr 4520.
[2] Brightline-2: A Study to Test Whether BI 907828 Helps People With Cancer in the Biliary Tract, Pancreas, Lung or Bladder. Identifier NCT05512377. https://clinicaltrials.gov/ct2/show/NCT05512377
[3] Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009 Jan;45(2):228-47.
[4] Beal SL, Sheiner LB, Boeckmann AJ, Bauer RJ, editors. NONMEM 7.4 Users Guides. (1989–2019). https://nonmem.iconplc.com/nonmem744. Gaithersburg, MD: ICON plc; 2019.
[5] Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, et al. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. J Clin Oncol. 2009 Sep;27(25):4103-8
[6] Jonsson EN, Karlsson MO. Automated Covariate Model Building within NONMEM. Pharm Res. 1998 Sep;15(9):1463-8.
[7] Jonsson, E. N., Harling, K, PAGE 27 (2018) Abstr 8429 [www.page-meeting.org/?abstract=8429]
The authors met criteria for authorship as recommended by the International Committee of Medical Journal Editors (ICMJE). The authors did not receive payment related to the development of the abstract. Boehringer Ingelheim was given the opportunity to review the abstract for medical and scientific accuracy, as well as intellectual property considerations. The study was supported and funded by Boehringer Ingelheim



Reference: PAGE 31 (2023) Abstr 10465 [www.page-meeting.org/?abstract=10465]
Poster: Drug/Disease Modelling - Oncology
Click to open PDF poster/presentation (click to open)
Top