Subjects: Medicine, Pharmacy >> Pharmacology submitted time 2022-12-26
Chen-yu Wang
Di-hong Yang
Xiao-min Niu
Lu Han
Wen Wang
Roman Filimonov
Sajid Ali Shah
Xin-yang Weng
Zheng Jiao
Abstract：
Introduction: Combining immune checkpoint inhibitor and chemotherapies provides more benefits than traditional treatment options in patients with NSCLC. However, some patients still have no clinical benefits. Clinical accessible biomarkers are necessary to predict clinical outcomes and optimize dose strategies. The study aimed to investigate accessible biomarkers that can predict clinical outcomes and optimize dosing strategies of atezolizumab and nab-paclitaxel combination therapy in patients with NSCLC by quantitative systems pharmacology (QSP).
Methods: The model was developed based on a published QSP model of triple-negative breast cancer using the SimBiology toolbox in MATLAB. The model included four compartments. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and used available data from real clinical trials (IMpower131) for model calibration and validation.
Results: The final QSP model predictions are consistent with clinically reported efficacy endpoints. CD8+ and CD4+ T cell densities in tumor are significantly affected by the response status. Roc analysis further implicating their potential to be predictive biomarkers for this double combination regimen. Virtual clinical trial simulation shows reduced nab-paclitaxel doses from 100 mg/m2 to 75 mg/m2 would leads to lower ORR but was higher than atezolizumab monotherapy. Three atezolizumab dosing strategies combined with nab-paclitaxel showed comparable efficacy. ?to compare different schedules of the two drugs for simulated therapeutic optimization.
Conclusion: This study provides a QSP model, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.
Peer Review Status:
Awaiting Review
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