In the United States, lung cancer causes more deaths per year (135,720 estimated for 2020) than does any other type of cancer, and 84% of lung cancer deaths are caused by non-small cell lung cancer (NSCLC) (Siegel et al., 2020). Treatment for NSCLC has expanded from a limited set of chemotherapeutics and surgery to include immunotherapy, radiotherapy, targeted adjunct therapy, and immune checkpoint inhibitors (Jászai & Schmidt, 2019; Liu et al., 2017). Between 1996 and 2010, it’s estimated that the percentage of NSCLC patients receiving some combination of targeted therapy and chemotherapy has increased by 20% for stage I/II patients, 28% for stage IIIA patients, and at least 15% for stage IIIB/IV patients (Kaniski et al., 2017). Parallel with the advancements in treatments, the prognosis for these patients has also been improving. The 5-year survival rate in NSCLC has advanced from 10.7% in 1973 to slightly less than 21% as of 2019 (Lu et al., 2019). Today, a combination of chemotherapy (including platinum-based doublets), immune checkpoint inhibitors (e.g. PD-1/PD-L1), and antiangiogenics (e.g. bevacizumab) is recommended as first-line therapy for the management of metastatic or recurrent NSCLC (Lung Cancer – Non-Small Cell – Types of Treatment, 2012).
Chemotherapy resistance (acquired or intrinsic) is a regular occurrence in NSCLC. In a 2006 study of resected NSCLC, Thomas A. d’Amato et al. found extreme to intermediate resistance to carboplatin in 68% of samples, to cisplatin in 63% of samples, and to paclitaxel in 40% of samples (d’Amato et al., 2006). The KEYNOTE-001 trial for pembrolizumab in NSCLC had an objective response rate of 19.4%, indicating that most patients did not significantly respond to treatment (Garon et al., 2015). Over a long enough period of treatment, almost all NSCLC becomes treatment resistant (Chang, 2011).
Theoretically, dosages could simply be increased to improve efficacy in cases of treatment resistant NSCLC. However, chemotherapeutics, antiproliferatives, and immune checkpoint inbhibitors have narrow therapeutic windows. In cases of acquired or intrinsic resistance to chemotherapy, dosages cannot be easily increased without producing serious adverse effects, including gastrointestinal disturbances, immunosuppression, and anemia (Ahmad & Gadgeel, 2016). Due to both the high side-effect burden and rate of resistance, patients are typically moved to second-line or experimental therapies. The majority of NSCLC clinical patients are using drugs still in clinical trial (Non-Small Cell Lung Cancer Treatment (PDQ®)–Patient Version – National Cancer Institute, 2020). There is, therefore, a critical need to improve efficacy of both first-line therapeutics and experimental therapeutics to improve therapeutic outcomes, patient survival, and ultimately patient quality of life.
Mathematical modeling of drug pharmacokinetics and pharmacodynamics is an extremely efficient method for optimization of therapeutic dosing schedules, without the considerable time and resource investment required to conduct a suite of in vivo clinical trials. One can leverage data from multiple studies, involving diverse patient populations, and varying drug combinations and administration schedules, to build a complete mathematical description of the disease and therapeutics. After model building, the model can be used as a computational platform to simulate a series of “what if?” scenarios, and to derive the best scheduling and dosing of therapeutic drug intervention in a given set of patients.
We have previously published a mathematical model of NSCLC growth dynamics to demonstrate that administering bevacizumab (BEV) and pemetrexed/cisplatin (PEM/CIS) sequentially with a gap of 1 day, rather than concurrently, would improve the efficacy of this combination (quantified as final tumor volume) by more than 50% without the need for increasing therapeutic doses. However, optimal scheduling of this combination in humans has yet to be verified with large sets of clinical data, and the model needs to be generalized to other combination therapies, including immune checkpoint inhibitors.
To address this critical need, we have collated, individual tumor progression data from 11 different clinical trials (> 8000 stage II through stage IV, metastatic, and non-metastatic patients) involving bevacizumab and multiple chemotherapies such as pemetrexed, cisplatin, apomab, paclitaxel, carboplatin, gemcitabine, and erlotinib. We are currently adding to this dataset 5 clinical trials involving immune checkpoint inhibitors (e.g. atezolizumab) alone or in combination with docetaxel in (~3,000 patients).
Our overall objectives in this application are to (1) generalize our model of NSCLC growth and response to BEV-PEM/CIS to the greater set of combination therapies and modes of action (including immune checkpoint inhibitors) represented in the requested data, and (2) to individualize our model’s predictive ability for precision medicine applications – taking into account significant population characteristics that could influence treatment response. Ultimately this tool will be refined into a mobile device application to guide the scheduling and dosing of therapeutic interventions in NSCLC patients. The rationale for this project is that optimization of therapeutic drug dose and scheduling will ultimately improve clinical outcome in patients with NSCLC.
Upon successful completion of the proposed research, we expect to have established a mechanistic and translational platform for modeling the dynamics of tumor growth in response to various drug combinations currently in use for NSCLC (Aim 1). In addition, we expect to determine individual patient characteristics that are associated with treatment response. This in silico tool will be used to determine the optimal dosing schedule of therapeutic interventions in individual patients with NSCLC (Aim 2). This contribution will be significant as it will positively impact healthcare and clinical outcome in a highly prevalent and intractable disease.
References
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