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Updated: Nov 4, 2025
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer
Hiroyuki Sayama1, Diana Marcantonio2, Takeyuki Nagashima1
1Astellas Pharma Inc, Ibaraki, Japan.
Abstract:
KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRASG12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRASG12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.
Insights
A new quantitative systems pharmacology model predicts that ASP2453, a KRAS G12C inhibitor, may offer superior clinical response compared to AMG 510 in non-small cell lung cancer patients.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- KRAS G12C mutations drive cancer proliferation by disrupting normal cell signaling.
- Targeting KRAS G12C is a key strategy in cancer therapy.
- Existing KRAS G12C inhibitors like AMG 510 show clinical activity, but further advancements are sought.
Purpose of the Study:
- To develop a quantitative systems pharmacology (QSP) model to predict the clinical efficacy of ASP2453, a novel KRAS G12C inhibitor.
- To compare the predicted clinical response of ASP2453 against a competitor, AMG 510, in non-small cell lung cancer (NSCLC).
- To support the clinical trial design and development of ASP2453.
Main Methods:
- Developed a QSP model integrating KRAS signaling pathways with tumor growth dynamics.
- Parameterized the model using in vitro (ERK1/2 phosphorylation) and in vivo (xenograft) data for ASP2453.
- Generated a virtual patient population using publicly available clinical data for AMG 510 to simulate treatment responses.
Main Results:
- The QSP model predicted a greater clinical response for ASP2453 compared to AMG 510 in NSCLC.
- Simulations indicated potential for improved tumor size reduction with ASP2453 treatment.
- The model provides a framework for evaluating and differentiating KRAS G12C inhibitors.
Conclusions:
- ASP2453 demonstrates predicted superior clinical efficacy over AMG 510 in NSCLC based on QSP modeling.
- These findings support the further clinical investigation of ASP2453.
- The QSP model serves as a valuable tool for predicting drug response and guiding clinical trial strategies for KRAS G12C inhibitors.
