Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Abstract

BackgroundImmune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune–cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.MethodsA QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.ResultsWith the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.ConclusionsThis study provides a QSP platform, 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.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

“…It classifies as highly invasive with limited treatment and poor outcomes [ 67 ]. The authors developed a virtual patient cohort of atezolizumab (anti-PD-L1) and nab-paclitaxel treatments for this cancer to identify immune biomarkers and optimal treatment for clinical trials [ 34 ].…”

“…Previous studies on bispecific TCE cited above led to the construction of a QSP model, combining TCE and immune checkpoint inhibitor, anti-PD-L1, with similar modules described in the Breast neoplasm section [ 34 , 44 ]. They predict that the efficacy of treatment is dictated by the patient’s variability and unique characteristics.…”

“…Recently, a diversity of software platforms have been employed to assist in developing QSP models [ 23 , 26 ], such as Simbiology and toolboxes in MATLAB [ 27 ]; The R-based packages nlmixr [ 23 ], mrgsolve [ 28 ], RxODE [ 29 ], nlme [ 30 ], and Cell Collective platform [ 31 – 33 ]; Based on our review across 51 models, we note that the MATLAB environment and tools are more popular among QSP modelers [ 34 – 47 ].…”

Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors

“…As has been noted, a deep understanding of immune response dynamics and the interplay with tumor-infiltrating processes and tumor cell growth can help to overcome the current challenges in IO and, in this sense, the mathematical model plays an important role. Several QSP models have been published for single agent or combination therapies and applied to different types of tumors, including melanoma, breast cancer, and NSCLC [21,23,25,40]. However, the development of these types of models is challenging due to the large number of parameters, many of them taken from different sources, and the relatively small amount of observed data usually available.…”

“…Presence of tumor resistance mechanisms used to evade CD8 T cells-mediated death such as the recruitment of immune suppressor cells (e.g., Treg) and expression of the PD-L1 ligand leading to CD8 T cell exhaustion, αPD1 inhibits the tumor resistance mechanisms, which can be at least partly blocked by immune checkpoint inhibitors (Equations ( 8) and ( 9)) [21,23,39,40].…”