Correlative Biomarker Analysis of Intrinsic Subtypes and Efficacy Across the MONALEESA Phase III Studies.

PURPOSE: The prognostic and predictive value of intrinsic subtypes in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer treated with endocrine therapy and ribociclib (RIB) is currently unknown. We evaluated the association of intrinsic subtypes with progression-free survival (PFS) in the MONALEESA trials. METHODS: A retrospective and exploratory PAM50-based analysis of tumor samples from the phase III MONALEESA-2, MONALEESA-3, and MONALEESA-7 trials was undertaken. The prognostic relationship of PAM50-based subtypes with PFS and risk of disease progression by subtype and treatment were evaluated using a multivariable Cox proportional hazards model, adjusting for age, prior chemotherapy, performance status, visceral disease, bone-only metastases, histological grade, number of metastatic sites, prior endocrine therapy, and de novo metastatic disease. RESULTS: Overall, 1,160 tumors from the RIB (n = 672) and placebo (n = 488) cohorts were robustly profiled. Subtype distribution was luminal A (LumA), 46.7%; luminal B (LumB), 24.0%; normal-like, 14.0%; HER2-enriched (HER2E), 12.7%; and basal-like, 2.6% and was generally consistent across treatment arms and trials. The associations between subtypes and PFS were statistically significant in both arms (P < .001). The risks of disease progression for LumB, HER2E, and basal-like subtypes were 1.44, 2.31, and 3.96 times higher compared with those for LumA, respectively. All subtypes except basal-like demonstrated significant PFS benefit with RIB. HER2E (hazard ratio [HR], 0.39; P < .0001), LumB (HR, 0.52; P < .0001), LumA (HR, 0.63; P = .0007), and normal-like (HR, 0.47; P = .0005) subtypes derived benefit from RIB. Patients with basal-like subtype (n = 30) did not derive benefit from RIB (HR, 1.15; P = .77). CONCLUSION: In this retrospective exploratory analysis of hormone receptor-positive and human epidermal growth factor receptor 2-negative advanced breast cancer, each intrinsic subtype exhibited a consistent PFS benefit with RIB, except for basal-like.

Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches.

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 CAR-T cell therapies, and mathematical modeling of CAR-T 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 CAR-T 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.

Single-cell modeling of routine clinical blood tests reveals transient dynamics of human response to blood loss.

Low blood count is a fundamental disease state and is often an early sign of illnesses including infection, cancer, and malnutrition, but our understanding of the homeostatic response to blood loss is limited, in part by coarse interpretation of blood measurements. Many common clinical blood tests actually include thousands of single-cell measurements. We present an approach for modeling the unsteady-state population dynamics of the human response to controlled blood loss using these clinical measurements of single-red blood cell (RBC) volume and hemoglobin. We find that the response entails (1) increased production of new RBCs earlier than is currently detectable clinically and (2) a previously unrecognized decreased RBC turnover. Both component responses offset the loss of blood. The model provides a personalized dimensionless ratio that quantifies the balance between increased production and delayed clearance for each individual and may enable earlier detection of both blood loss and the response it elicits.

Oxygen-dependent flow of sickle trait blood as an in vitro therapeutic benchmark for sickle cell disease treatments.

Although homozygous sickle cell disease is often clinically severe, the corresponding heterozygous state, sickle cell trait, is almost completely benign despite the fact that there is only a modest difference in sickle hemoglobin levels between the two conditions. In both conditions, hypoxia can lead to polymerization of sickle hemoglobin, changes in red cell mechanical properties, and impaired blood flow. Here, we test the hypothesis that differences in the oxygen-dependent rheological properties in the two conditions might help explain the difference in clinical phenotypes. We use a microfluidic platform that permits quantification of blood rheology under defined oxygen conditions in physiologically sized microchannels and under physiologic shear rates. We find that, even with its lower sickle hemoglobin concentration, sickle trait blood apparent viscosity increases with decreasing oxygen tension and may stop flowing under completely anoxic conditions, though far less readily than the homozygous condition. Sickle cell trait blood flow becomes impaired at significantly lower oxygen tension than sickle cell disease. We also demonstrate how sickle cell trait can serve as a benchmark for sickle cell disease therapies. We characterize the rheological effects of exchange transfusion therapy by mixing sickle blood with nonsickle blood and quantifying the transfusion targets for sickle hemoglobin composition below which the rheological response resembles sickle trait. These studies quantify the differences in blood flow phenotypes of sickle cell disease and sickle cell trait, and they provide a potentially powerful new benchmark for evaluating putative therapies in vitro.

White blood cell population dynamics for risk stratification of acute coronary syndrome.

The complete blood count (CBC) provides a high-level assessment of a patient's immunologic state and guides the diagnosis and treatment of almost all diseases. Hematology analyzers evaluate CBCs by making high-dimensional single-cell measurements of size and cytoplasmic and nuclear morphology in high throughput, but only the final cell counts are commonly used for clinical decisions. Here, we utilize the underlying single-cell measurements from conventional clinical instruments to develop a mathematical model guided by cellular mechanisms that quantifies the population dynamics of neutrophil, lymphocyte, and monocyte characteristics. The dynamic model tracks the evolution of the morphology of WBC subpopulations as a patient transitions from a healthy to a diseased state. We show how healthy individuals and hospitalized patients with similar WBC counts can be robustly classified based on their WBC population dynamics. We combine the model with supervised learning techniques to risk-stratify patients under evaluation for acute coronary syndrome. In particular, the model can identify more than 70% of patients in our study population with initially negative screening tests who will be diagnosed with acute coronary syndrome in the subsequent 48 hours. More generally, our study shows how mechanistic modeling of existing clinical data can help realize the vision of precision medicine.

Multi-scale flowsheet simulation of an integrated continuous purification-downstream pharmaceutical manufacturing process.

Properties of active pharmaceutical ingredients influence the critical quality attributes (CQAs) of final solid dosage forms (e.g. tablets). In the last decade, continuous manufacturing has been shown to be a promising alternative to batch processing in the pharmaceutical industry. Therefore, a quantitative model-based analysis of the influence of upstream API properties on downstream processing quality metrics will lead to enhanced QbD in pharmaceutical drug product manufacturing (Benyahia et al., 2012). In this study, a dynamic flowsheet simulation of an integrated API purification step (crystallization), followed by filtration and drying, with a downstream process (powder mixing) is presented. Results show that the temperature profile of a cooling crystallization process influences the crystal size distribution which in turn impacts the RSD and API concentration of the powder mixing process, which in turn has a direct effect on tablet properties (Boukouvala et al., 2012). A hybrid PBM-DEM model is also presented to demonstrate the coupling of particle-scale information with process-scale information leading to enhanced elucidation of the dynamics of the overall flowsheet simulation.

A combined experimental and modeling approach to study the effects of high-shear wet granulation process parameters on granule characteristics.

The purpose of the current work is to study the effects of high-shear wet granulation process parameters on granule characteristics using both experimental and modeling techniques. A full factorial design of experiments was conducted on three process parameters: water amount, impeller speed and wet massing time. Statistical analysis showed that the water amount has the largest impact on the granule characteristics, and that the effect of other process variables was more pronounced at higher water amount. At high water amounts, an increase in impeller speed and/or wet massing time showed a decrease in granule porosity and compactability. A strong correlation between granule porosity and compactability was observed. A three-dimensional population balance model which considers agglomeration and consolidation was employed to model the granulation process. The model was calibrated using the particle size distribution from an experimental batch to ensure a good match between the simulated and experimental particle size distribution. The particle size distribution of three other batches were predicted, each of which was manufactured under different process parameters (water amount, impeller speed and wet massing time). The model was able to capture and predict successfully the shifts in granule particle size distribution with changes in these process parameters.