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Model-Based Patient Selection and Dosing Strategies for HRAS and PIK3CA Dysregulated HNSCC: A QSP Model for Alpelisib
Jaehee Shim1, Douglas Chung1, Alison Smith2
1Certara Applied BioSimulation, Sheffield, UK.
Abstract:
Identifying ideal candidates for cancer therapies is challenging, especially with multiple oncogenic variants involved. In head and neck squamous cell carcinoma, the PI3Kα-AKT-mTOR and HRAS-MAPK pathways are frequently dysregulated. This study demonstrates how a quantitative systems pharmacology (QSP) model can help optimize clinical trial design by guiding patient selection and dosing strategies based on oncogenic genotypes. A QSP model was developed to capture the experimentally observed dynamics of the HRAS and PI3K pathways across five molecularly defined patient cohorts in the KURRENT-HN Phase I/II trial (NCT04997902). The model assessed which genotypes would benefit the most from combination therapy with tipifarnib (farnesyl transferase inhibitor) and alpelisib (PIK3CA inhibitor). Simulation results identified the PIK3CA gain of function (GOF) as the genotype most likely to benefit. Virtual population analysis of PIK3CA GOF with dose escalation to 600 mg b.i.d. tipifarnib and 250 mg q.d. alpelisib suggested that a higher tipifarnib dose could enhance tumor response, potentially due to significant dependency of the PIK3CA-mutant cells on mTORC1 signaling. These simulations were consistent with the clinical data. This key dependency can be targeted by tipifarnib by blocking farnsylation of RHEB, an essential activator of mTORC1. Vpop responders showed that reduced intracellular mTOR activity in simulations increased the likelihood of tumor volume reduction. Global sensitivity analysis identified compensatory feedback, tumor proliferation rate, and PI3K-mTOR crosstalk as key determinants of tumor response. This novel QSP application exemplifies an innovative bottom-up modeling approach to support patient selection and dosing strategies for future clinical studies.
Insights
Quantitative systems pharmacology modeling optimized cancer therapy selection. The study identified PIK3CA gain-of-function genotypes as most responsive to combination therapy, guiding future clinical trial designs.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Head and neck squamous cell carcinoma frequently involves dysregulation of the PI3Kα-AKT-mTOR and HRAS-MAPK pathways.
- Identifying optimal patient candidates for targeted cancer therapies, especially with multiple oncogenic variants, remains a challenge.
- Quantitative systems pharmacology (QSP) offers a promising approach to model complex biological systems and guide therapeutic strategies.
Purpose of the Study:
- To develop and apply a QSP model to optimize clinical trial design for head and neck squamous cell carcinoma.
- To guide patient selection and dosing strategies based on oncogenic genotypes for combination therapy.
- To assess the impact of specific genotypes on response to tipifarnib and alpelisib combination therapy.
Main Methods:
- Development of a QSP model capturing HRAS and PI3K pathway dynamics in five molecularly defined patient cohorts.
- Simulation of combination therapy with tipifarnib (farnesyl transferase inhibitor) and alpelisib (PIK3CA inhibitor).
- Virtual population analysis and global sensitivity analysis to identify key determinants of tumor response.
Main Results:
- The PIK3CA gain-of-function (GOF) genotype was identified as most likely to benefit from the combination therapy.
- Higher tipifarnib doses (600 mg b.i.d.) with alpelisib (250 mg q.d.) showed enhanced tumor response in PIK3CA GOF simulations.
- Reduced mTOR activity correlated with increased tumor volume reduction, highlighting a key dependency targeted by tipifarnib.
Conclusions:
- QSP modeling can effectively guide patient selection and optimize dosing strategies for targeted cancer therapies.
- PIK3CA GOF mutations represent a key genotype dependency that can be therapeutically targeted in head and neck squamous cell carcinoma.
- This study exemplifies a novel bottom-up modeling approach for enhancing future clinical trial design and drug development.
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