(W-105) Informing Early Trial Design in Oncology Through Clinical Trial Simulations from Population Pharmacokinetic-Tumor Growth Inhibition Models

Perrine Courlet, PhD – Associate Director, EMD Serono Research and Development Institute; Andrew Santulli, Masters degree – Assistant Director, Enhanced Pharmacodynamics; Scott Van Wart, PhD – VP, Enhanced Pharmacodynamics; Karthik Venkatakrishnan, PhD – Global Head of Quantitative Pharmacology, EMD Serono Research and Development Institute; Wei Gao, PhD – Global Head of Pharmacometrics, EMD Serono Research and Development Institute

Objectives: Dose optimization is a critical objective of early clinical development of oncology drugs. The value of longitudinal tumor size in Phase 1 dose escalation studies can be maximized using modeling and simulation to inform dose selection¹. To this end, designing Phase 1 clinical trials (CT) requires careful consideration of various factors such as dose and sample size.

We present a workflow using pharmacokinetic (PK) data, tumor growth inhibition (TGI) modeling, and clinical trial simulations (CTS) to aid in the identification of the most informative Phase 1 CT design for efficient dose selection for later phases of development.

The results in this abstract have been previously presented in part at PAGE, Rome 07/27/2024.

Methods: A virtual population database was first generated using a QSP model (modified from published model for antitumor activity^2,3). Virtual PK and longitudinal tumor size data were generated for five dose levels (DL) of interest at predefined PK and tumor assessment timepoints in a colorectal carcinoma (CRC) population.

As the data is limited in a phase 1 CT, more conventional and identifiable PK-TGI models were fit to the data (step 1), with the best fit being selected.

In step 2, various Phase 1 CT designs, including diverse DLs and sample sizes were generated by sampling from the virtual population. PK-TGI model parameters were estimated for each CT. These steps were repeated to generate 500 datasets per simulation scenario, which were subsequently fit using the step 1 PK-TGI model.

Step 3 sampled from each estimated parameter distribution, which generated 1000 simulated CT datasets with 100 subjects per DL. This enabled the evaluation of tumor shrinkage at various timepoints for each scenario and DL.

For each CTS, a target dose was identified, defined as the lowest dose producing a median tumor shrinkage ≥20% relative to baseline at week 8. This dose was compared to the “true” reference dose based upon the QSP simulated dataset using the aforementioned definition (step 4).

Results: In step 1, a Claret model adequately captured the virtual population tumor size time-course data in participants with CRC.

Step 2 provided summary statistics of the final parameter estimates across the 500 CT simulation-estimation runs.

Steps 3 and 4 demonstrated that a design with balanced sample sizes better informed the model fits, thereby increasing the probability of selecting an active dose. Unbalanced designs tended to increase the probability of selecting a non-optimal dose. Thus, the balanced CT design better informed the dose selection in this case study, lending support to the incorporation of dose-ranging backfill cohorts in Phase 1 studies to inform dose selection strategies in subsequent clinical development.

Conclusion: Our workflow provides valuable proof-of-principle for leveraging longitudinal tumor size data to enhance dose selection decision-making in oncology drug development, aiding in early CT design optimization.

Citations: [1] Bruno R, Bottino D, De Alwis DP, Fojo AT, Guedj J, Liu C, Swanson KR, Zheng J, Zheng Y, Jin JY. Progress and opportunities to advance clinical cancer therapeutics using tumor dynamic models. Clinical Cancer Research. 2020 Apr 15;26(8):1787-95.
[2] Liao MZ, Lu D, Kågedal M, Miles D, Samineni D, Liu SN, Li C. Model‐Informed Therapeutic Dose Optimization Strategies for Antibody–Drug Conjugates in Oncology: What Can We Learn From US Food and Drug Administration–Approved Antibody–Drug Conjugates?. Clinical Pharmacology & Therapeutics. 2021 Nov;110(5):1216-30.
[3] Singh AP, Guo L, Verma A, Wong GG, Shah DK. A cell-level systems PK-PD model to characterize in vivo efficacy of ADCs. Pharmaceutics. 2019 Feb 25;11(2):98.