BackgroundNumerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.MethodsA quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.ResultsThe model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.ConclusionsThis study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0327-9) contains supplementary material, which is available to authorized users.

The mechanisms of cardiovascular and renal protection observed in clinical trials of SGLT2 inhibitors (SGLT2i) are incompletely understood and likely multifactorial, including natriuretic, diuretic, and antihypertensive effects, glomerular pressure reduction, and lowering of plasma and interstitial fluid volume. To quantitatively evaluate the contribution of proposed SGLT2i mechanisms of action on changes in renal hemodynamics and volume status, we coupled a mathematical model of renal function and volume homeostasis with clinical data in healthy subjects administered 10 mg dapagliflozin once-daily. The minimum set of mechanisms necessary to reproduce observed clinical responses (urinary sodium and water excretion, serum creatinine and sodium) was determined, and important unobserved physiologic variables (glomerular pressure, blood and interstitial fluid volume) were then simulated. We further simulated the response to SGLT2i in diabetic virtual patients with and without renal impairment. Multiple mechanisms were required to explain the observed response: 1) direct inhibition of sodium and glucose reabsorption through SGLT2, 2) SGLT2-driven inhibition of NHE3 sodium reabsorption, and 3) osmotic diuresis coupled with peripheral sodium storage. The model also showed that the consequences of these mechanisms include lowering of glomerular pressure, reduction of blood and interstitial fluid volume, and mild blood pressure reduction, in agreement with clinical observations. The simulations suggest that these effects are more significant in diabetic patients than healthy subjects, and that while glucose excretion may diminish with renal impairment, improvements in glomerular pressure and blood volume are not diminished at lower GFR, suggesting cardiorenal benefits of SGLT2i may be sustained in renally impaired patients.

Aim To develop a quantitative drug‐disease systems model to investigate the paradox that sodium‐glucose co‐transporter (SGLT)2 is responsible for >80% of proximal tubule glucose reabsorption, yet SGLT2 inhibitor treatment results in only 30% to 50% less reabsorption in patients with type 2 diabetes mellitus (T2DM). Materials and methods A physiologically based four‐compartment model of renal glucose filtration, reabsorption and excretion via SGLT1 and SGLT2 was developed as a system of ordinary differential equations using R/IQRtools. SGLT2 inhibitor pharmacokinetics and pharmacodynamics were estimated from published concentration‐time profiles in plasma and urine and from urinary glucose excretion (UGE) in healthy people and people with T2DM. Results The final model showed that higher renal glucose reabsorption in people with T2DM versus healthy people was associated with 54% and 28% greater transporter capacity for SGLT1 and SGLT2, respectively. Additionally, the analysis showed that UGE is highly dependent on mean plasma glucose and estimated glomerular filtration rate (eGFR) and that their consideration is critical for interpreting clinical UGE findings. Conclusions Quantitative drug‐disease system modelling revealed mechanistic differences in renal glucose reabsorption and UGE between healthy people and those with T2DM, and clearly showed that SGLT2 inhibition significantly increased glucose available to SGLT1 downstream in the tubule. Importantly, we found that the findings of lower than expected UGE with SGLT2 inhibition are explained by the shift to SGLT1, which recovered additional glucose (~30% of total).

Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CART cell therapies, and mathematical modeling of CART cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CART cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity. Keywords cellular kinetic-pharmacodynamic model, chimeric antigen receptor-T cell (CAR-T cell) therapy, model-informed drug development (MIDD), quantitative systems pharmacology (QSP)

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