Quantitative systems pharmacology modeling provides insight into inter-mouse variability of Anti-CTLA4 response

Abstract

Clinical responses of immuno-oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti-CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter-animal variability of tumor growth profiles in response to anti-CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti-CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.

FIGURE 1

Structure of anti‐CTLA4 quantitative system pharmacology (QSP) model developed to capture the response variability in CT26 tumor volume profiles. (a) Schematics of the QSP model. ^ActCD8, activated CD8+ T cell; ^InactCD8, inactive CD8+ T cell; ^ProlifCD8, proliferating CD8+ T cell; CTL, cytotoxic T lymphocyte; TC, tumor cell; TCd, damaged tumor cell; Treg, regulatory T cell. (b) Individual tumor volume profiles in CT26 syngeneic mice in response to anti‐CTLA4 antibody 9H10 treatment given introvenously Q3dx3. (c) Number of mice exhibiting progressive disease (PD), partial response (PR), and complete response (CR) per dose group

FIGURE 2

An NLME model captures inter‐animal variability in tumor volume profiles. (a) Model fit of tumor volume profiles of 9H10 treated CT26 tumors. Black dots represent observed data. Red lines represent predicted median tumor volume profiles. Blue shaded areas represent predicted 90% confidence intervals. (b) Goodness‐of‐fit plot showing observed versus model predicted individual data points. (c) Distribution of the standardized random effects of model estimates. NLME, nonlinear mixed effect; PBS, phosphate–buffered saline

FIGURE 3

Validation of the NLME model using dataset from an independent experiment with CT26 syngeneic tumors. Validation dataset consists of a PBS (control) group (left) and a 9H10 treatment group at 10 mg/kg (right). Blue: Simulations with the model fitted from the training dataset. Red: Simulations with the updated tumor proliferation rate fitted from the validation dataset while keeping the other parameter values the same as the model fitted from the training dataset. CI, confidence interval; NLME, nonlinear mixed effect; PBS, phosphate–buffered saline

FIGURE 4

Single or combination of two parameters cannot distinguish tumors with progressive disease (PD), partial response (PR), and complete response (CR). (a) Box plots summarizing single parameters by response category. *Denotes level of significance; *p value < 0.05; **p value < 0.005; ***p value < 0.0001 from Wilcoxon test. (b) Paired plots for combination of any two parameters and distribution histogram for any single parameter. Colors indicate response groups

FIGURE 5

Baseline level of CD8+ T cells in the tumor microenvironment can alter treatment response. (a) Simulated dose response of a representative non‐responding mouse from 10 mg/kg treatment group of the training dataset. (b) Simulated dose response using the same parameter values as (a) except doubling the CD8+ T cell concentration at the time of treatment start