Exposure‐tumour growth inhibition modelling of brigimadlin using phase I solid tumour data to support phase II dose selection

This research focuses on leveraging quantitative modeling to support dose selection in oncology drug development, presenting development of an exposure-tumour growth inhibition (E-TGI) model to investigate the relationship between brigimadlin exposure and tumour shrinkage using data from a phase I clinical trial. The aim was to support the selection of the recommended phase II dose (RP2D) for brigimadlin, a potent MDM2-p53 antagonist. 

• Exposure-response modeling: The E-TGI model characterized the relationship between brigimadlin exposure and tumour size dynamics
• Dose-response predictions: Simulations quantified tumour shrinkage associated with each proposed dose of brigimadlin.
• Dose selection: Based on these findings, 45 mg every 3 weeks was selected as the RP2D for phase II development

By improving understanding of dose-response relationships, these findings contribute to evidence-based decision-making in oncology drug development.

The landscape of oncology drug development is undergoing a significant shift, driven by the FDA’s Project Optimus challenging the traditional “Maximum Tolerated Dose” (MTD) paradigm. Advocating for a more nuanced approach that prioritizes optimizing both efficacy and safety, Project Optimus offers an alternative to traditional MTD, which often leads to administering doses that, while maximizing tumor response, also result in significant toxicity, impacting patient quality of life. 

In this context, this publication offers valuable insights into how advanced modeling techniques can support this evolution.

The publication highlights the power of integrated modeling, a technique that leverages mathematical models to connect and analyze various data streams from clinical trials. These include:

• Biomarkers: These biological indicators provide insights into drug response at the molecular level.
• Survival data: This reflects the long-term impact of the drug on patient outcomes.
• Safety profiles: This encompasses the adverse events associated with drug administration.

By integrating these data points, researchers can develop comprehensive models that:

• Uncover complex relationships: Understanding how drug dosage influences biomarker responses, survival rates, and safety profiles is crucial for optimizing treatment.
• Simulate and predict outcomes: These models enable the simulation of different dosing strategies, allowing researchers to predict their effects on patient outcomes.
• Move beyond MTD: Integrated modeling provides a framework for identifying doses that maximize therapeutic benefit while minimizing toxicity, aligning perfectly with the goals of Project Optimus.

Project Optimus emphasizes the importance of utilizing pharmacokinetic (PK) and pharmacodynamic (PD) data, as well as exposure-response analyses, to inform dose selection. Integrated modeling takes this a step further by incorporating biomarkers and survival data, providing a holistic view of the drug’s impact.

As we continue to make progress toward more personalized care and improved patient well-being, integrated modeling represents a cornerstone of our and our industry’s approach. By embracing these advanced techniques, we actively contribute to oncology treatments that are both effective and tolerable, ultimately improving the lives of patients.

Key takeaways:

• The traditional MTD approach is being challenged by the need for more patient-centric dosing strategies.
• Integrated modeling provides a powerful tool for analyzing complex data from oncology trials.
• This approach aligns with the goals of Project Optimus, facilitating the identification of optimal doses that balance efficacy and safety.
• The use of mathematical models, as described in the publication, is crucial for the future of oncology drug development.