A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases

Abstract

Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.

Comparison of the immunomodulatory potential of platinum-based anti-cancer drugs and anthracyclins on human monocyte-derived cells

“…To optimize the result, the treatment should be tailored and the most effective concentrations of drugs acting on both tumor cells and immune cells should be calculated [ 3 ]. Based on our results, different aspects of the differentiation of monocytes into macrophages or DCs also appeared to be drug- and dose-dependent, affecting their functional characteristics.…”

“…The importance of optimizing the concentration, timing, and combination of chemotherapeutic agents has become apparent in improving therapeutic responses [ 3 ]. Current clinical practice in chemotherapy aims to achieve a systemic drug concentration, based on patient-specific body measurements (e.g., body surface area (BSA) [ 3 ].…”

“…The importance of optimizing the concentration, timing, and combination of chemotherapeutic agents has become apparent in improving therapeutic responses [ 3 ]. Current clinical practice in chemotherapy aims to achieve a systemic drug concentration, based on patient-specific body measurements (e.g., body surface area (BSA) [ 3 ]. However, not only cancer cells but also macrophages and DCs, as well as their precursors, are exposed to the agents used, which can significantly affect the success of a therapeutic intervention.…”

Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand?

“…Advances in computational biology tools and integrating information obtained from different omics methods could develop accurate and rapid models to predict tumor behavior and response to treatment (124)(125)(126). Examples of attempts to create virtual tumor models include virtual patient-ModCell and genome-scale metabolic models used in various cancers, including CRC (127)(128)(129).…”

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

“…Our model parameters, which can be determined in multiple ways (e.g., from early time point imaging and/or histopathology data), present a clear advantage over the standard measures currently used in the clinic for predicting treatment outcome and patient survival. The mathematical model presented in this report is based on an extensive series of prior methodological reports ( Pascal et al, 2013a ; Hosoya et al, 2016 ; Dogra et al, 2018 ; Goel et al, 2019 ; Dogra et al, 2020b ; Das et al, 2013 ; Pascal et al, 2013b ; Wang et al, 2016 ; Koay et al, 2014 ; Frieboes et al, 2015 ; Wang et al, 2015 ; Brocato et al, 2018 ; Cristini et al, 2017 ; Dogra et al, 2019 ; Brocato et al, 2019 ; Dogra et al, 2020a ; Goel et al, 2020 ; Anaya et al, 2021 ), but with rigorous emphasis on parameters related to the cancer-immune response to the treatment with checkpoint inhibitors in the setting of patients with solid tumors. In particular, we have developed a model that contains key mechanistic biological and physical processes involved in checkpoint inhibitor therapy, which has been objectively validated against imaging data of patient tumor burden and measured response, defined by both change in total tumor burden and patient survival ( Butner et al, 2020 ).…”