A next generation mathematical model for the in vitro to clinical translation of T-cell engagers.

T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC50 from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.

From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy.

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.

Hydrostatic mechanical stress regulates growth and maturation of the atrioventricular valve.

During valvulogenesis, cytoskeletal, secretory and transcriptional events drive endocardial cushion growth and remodeling into thin fibrous leaflets. Genetic disorders play an important role in understanding valve malformations but only account for a minority of clinical cases. Mechanical forces are ever present, but how they coordinate molecular and cellular decisions remains unclear. In this study, we used osmotic pressure to interrogate how compressive and tensile stresses influence valve growth and shape maturation. We found that compressive stress drives a growth phenotype, whereas tensile stress increases compaction. We identified a mechanically activated switch between valve growth and maturation, by which compression induces cushion growth via BMP-pSMAD1/5, while tension induces maturation via pSer-19-mediated MLC2 contractility. The compressive stress acts through BMP signaling to increase cell proliferation and decrease cell contractility, and MEK-ERK is essential for both compressive stress and BMP mediation of compaction. We further showed that the effects of osmotic stress are conserved through the condensation and elongation stages of development. Together, our results demonstrate that compressive/tensile stress regulation of BMP-pSMAD1/5 and MLC2 contractility orchestrates valve growth and remodeling.

Award Winner in the Young Investigator Category, 2017 Society for Biomaterials Annual Meeting and Exposition, Minneapolis, MN, April 05-08, 2017: Lymph node stiffness-mimicking hydrogels regulate human B-cell lymphoma growth and cell surface receptor expression in a molecular subtype-specific manner.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma, with multiple molecular subtypes. The activated B-cell-like DLBCL subtype accounts for roughly one-third of all the cases and has an inferior prognosis. There is a need to develop better class of therapeutics that could target molecular pathways in resistant DLBCLs; however, this requires DLBCLs to be studied in representative tumor microenvironments. The pathogenesis and progression of lymphoma has been mostly studied from the point of view of genetic alterations and intracellular pathway dysregulation. By comparison, the importance of lymphoma microenvironment in which these malignant cells arise and reside has not been studied in as much detail. We have recently elucidated the role of integrin signaling in lymphomas and demonstrated that inhibition of integrin-ligand interactions abrogated the proliferation of malignant cells in vitro and in patient-derived xenograft. Here we demonstrate the role of lymph node tissue stiffness on DLBCL in a B-cell molecular subtype specific manner. We engineered tunable bioartificial hydrogels that mimicked the stiffness of healthy and neoplastic lymph nodes of a transgenic mouse model and primary human lymphoma tumors. Our results demonstrate that molecularly diverse DLBCLs grow differentially in soft and high stiffness microenvironments, which further modulates the integrin and B-cell receptor expression level as well as response to therapeutics. We anticipate that our findings will be broadly useful to study lymphoma biology and discover new class of therapeutics that target B-cell tumors in physical environments. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1833-1844, 2017.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

BACKGROUND: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. RESULTS: In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. CONCLUSIONS: JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1.

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor ß (TGF-ß) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-ß induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-ß exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-ß and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-ß and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-ß treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.

Maintenance of electrostatic stabilization in altered tubulin lateral contacts may facilitate formation of helical filaments in foraminifera.

Microtubules in foraminiferan protists (forams) can convert into helical filament structures, in which longitudinal intraprotofilament interactions between tubulin heterodimers are thought to be lost, while lateral contacts across protofilaments are still maintained. The coarse geometric features of helical filaments are known through low-resolution negative stain electron microscopy (EM). In this study, geometric restraints derived from these experimental data were used to generate an average atomic-scale helical filament model, which anticipated a modest reorientation in the lateral tubulin heterodimer interface. Restrained molecular dynamics (MD) simulations of the nearest neighbor interactions combined with a Genalized Born implicit solvent model were used to assess the lateral, longitudinal, and seam contacts in 13-3 microtubules and the reoriented lateral contacts in the helical filament model. This electrostatic analysis suggests that the change in the lateral interface in the helical filament does not greatly diminish the lateral electrostatic interaction. After longitudinal dissociation, the 13-3 seam interaction is much weaker than the reoriented lateral interface in the helical filament model, providing a plausible atomic-detail explanation for seam-to-lateral contact transition that enables the transition to a helical filament structure.

Molecular evidence for ß-tubulin neofunctionalization in Retaria (Foraminifera and radiolarians).

Foraminifera and radiolarians are closely related amoeboid protists (i.e., retarians) often characterized by their shells and pseudopodia. Previous studies hypothesized that the unusual "Type 2" ß-tubulin (ß2) is critically involved in forming helical filaments (HFs), a unique microtubule (MT) assembly/disassembly intermediate found in foraminiferan reticulopodia. Such noncanonical ß-tubulin sequences have also been found in two radiolarian species and appear to be closely related to the foraminiferan ß2. In this study, we report 119 new ß-tubulin transcript sequences from six foraminiferans, four radiolarians, and a related non-retarian species. We found that foraminiferan and radiolarian ß2-tubulins share some of the unusual substitutions in the structurally essential and usually conserved domains. In the ß-tubulin phylogeny, retarian ß2-tubulin forms a monophyletic clade, well separated from the canonical ß-tubulin (ß1) ubiquitous in eukaryotes. Furthermore, we found that foraminiferan and radiolarian ß2-tubulin lineages were under positive selection, and used homology models for foraminiferan α- and ß-tubulin hexamers to understand the structural effect of the positively selected substitutions. We suggest that the positively selected substitutions play key roles in the transition of MT to HF by altering the lateral and longitudinal interactions between α- and ß-tubulin heterodimers. Our results indicate that the unusual ß2-tubulin is a molecular synapomorphy of retarians, and the ß-tubulin gene duplication occurred before the divergence of Foraminifera and radiolarians. The duplicates have likely been subjected to neofunctionalization responsible for the unique MT to HF assembly/disassembly dynamics, and/or other unknown physiological processes in retarian protists.