@article{Jarrett2016,

title = {"Sensitivity analysis of a pharmacokinetic model of vaginal anti-HIV microbicide drug delivery"},

author = {Angela M Jarrett and Yajing Gao and M Yousuff Hussaini and Nicholas G Cogan and David F Katz},

url = {http://www.sciencedirect.com/science/article/pii/S0022354916003786},

doi = {https://doi.org/10.1016/j.xphs.2016.02.015},

year  = {2016},

date = {2016-05-31},

journal = {Journal of pharmaceutical sciences},

volume = {105},

number = {5},

pages = {1772-1778},

publisher = {Elsevier},

abstract = {Uncertainties in parameter values in microbicide pharmacokinetics (PK) models confound the models' use in understanding the determinants of drug delivery and in designing and interpreting dosing and sampling in PK studies. A global sensitivity analysis (Sobol' indices) was performed for a compartmental model of the pharmacokinetics of gel delivery of tenofovir to the vaginal mucosa. The model’s parameter space was explored to quantify model output sensitivities to parameters characterizing properties for the gel–drug product (volume, drug transport, initial loading) and host environment (thicknesses of the mucosal epithelium and stroma and the role of ambient vaginal fluid in diluting gel). Greatest sensitivities overall were to the initial drug concentration in gel, gel–epithelium partition coefficient for drug, and rate constant for gel dilution by vaginal fluid. Sensitivities for 3 PK measures of drug concentration values were somewhat different than those for the kinetic PK measure. Sensitivities in the stromal compartment (where tenofovir acts against host cells) and a simulated biopsy also depended on thicknesses of epithelium and stroma. This methodology and results here contribute an approach to help interpret uncertainties in measures of vaginal microbicide gel properties and their host environment. In turn, this will inform rational gel design and optimization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahmoradi2016,

title = {"Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer"},

author = {Rachel Walker and Pedro E Navas and Samuel H Friedman and Simona Galliani and Aleksandra Karolak and Fiona MacFarlane and Robert Noble and Jan Poleszczuk and Shonagh Russell and Katarzyna A Rejniak and Amir Shahmoradi and Frederik Ziebell and Jason Brayer and Daniel Abate-Daga and Heiko Enderling},

url = {http://www.shahmoradi.org/pubs/Shahmoradi_2016d.pdf},

doi = { http://dx.doi.org/10.1101/055988},

year  = {2016},

date = {2016-05-30},

journal = {bioRxiv},

pages = {055988},

publisher = {Cold Spring Harbor Labs Journals},

abstract = {The poor immunogenicity of pancreatic tumors makes them particularly difficult to treat. Standard chemotherapies and single agent immunotherapies have had notoriously little success in this arena. Oncolytic virus therapy has the potential to enhance the penetration of

immunotherapeutically-delivered CAR T cells into the tumor and improve treatment outcomes. We evaluate this potential by combining two different mathematical approaches: an ordinary differential equation model to simulate population level tumor response to cytotoxic activity of T cells, coupled with an agentbased model to simulate the enhancement of CAR T cell penetration by oncolytic virus therapy},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahmoradi2016b,

title = {"Dissecting the roles of local packing density and longer‐range effects in protein sequence evolution"},

author = {Amir Shahmoradi, Claus O Wilke},

url = {http://onlinelibrary.wiley.com/doi/10.1002/prot.25034/full},

doi = {10.1002/prot.25034},

year  = {2016},

date = {2016-04-09},

journal = {Proteins: Structure, Function, and Bioinformatics},

volume = {84},

number = {6},

pages = {841-854},

abstract = {What are the structural determinants of protein sequence evolution? A number of site-specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site-specific sequence variability in proteins. We show that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as contact number and the weighted contact number, represent the combined effects of local packing density and longer-range effects. As an alternative, we propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino-acid side chains, behaves nearly identically to the relative solvent accessibility, and each individually can explain, on average, approximately 34% of the site-specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10% of variation can be explained by nonlocal effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined effects of the immediate amino-acid neighbors of that site and effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize on the relative importance of immediate contacts and longer-range effects on evolutionary variation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahmoradi2016b,

title = {"Intermediate divergence levels maximize the strength of structure–sequence correlations in enzymes and viral proteins"},

author = {Eleisha L Jackson and Amir Shahmoradi and Stephanie J Spielman and Benjamin R Jack and Claus O Wilke},

url = {http://onlinelibrary.wiley.com/doi/10.1002/pro.2920/full},

doi = {10.1002/pro.2920},

year  = {2016},

date = {2016-03-24},

journal = {Protein Science},

volume = {25},

number = {7},

pages = {1341-1353},

abstract = {Structural properties such as solvent accessibility and contact number predict site-specific sequence variability in many proteins. However, the strength and significance of these structure–sequence relationships vary widely among different proteins, with absolute correlation strengths ranging from 0 to 0.8. In particular, two recent works have made contradictory observations. Yeh et al. (Mol. Biol. Evol. 31:135–139, 2014) found that both relative solvent accessibility (RSA) and weighted contact number (WCN) are good predictors of sitewise evolutionary rate in enzymes, with WCN clearly out-performing RSA. Shahmoradi et al. (J. Mol. Evol. 79:130–142, 2014) considered these same predictors (as well as others) in viral proteins and found much weaker correlations and no clear advantage of WCN over RSA. Because these two studies had substantial methodological differences, however, a direct comparison of their results is not possible. Here, we reanalyze the datasets of the two studies with one uniform analysis pipeline, and we find that many apparent discrepancies between the two analyses can be attributed to the extent of sequence divergence in individual alignments. Specifically, the alignments of the enzyme dataset are much more diverged than those of the virus dataset, and proteins with higher divergence exhibit, on average, stronger structure–sequence correlations. However, the highest structure–sequence correlations are observed at intermediate divergence levels, where both highly conserved and highly variable sites are present in the same alignment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Barnes2016,

title = {"In-flight ultrasound identification of pneumothorax"},

author = {Quick JA and Uhlich RM and Ahmad S and Barnes SL and Coughenour JP},

url = {https://www.ncbi.nlm.nih.gov/pubmed/26407979},

doi = {10.1007/s10140-015-1348-z},

year  = {2016},

date = {2016-02-23},

journal = {Emerg Radiol},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahmoradi2016b,

title = {"Numerical simulation of the impact of polymer rheology on polymer injectivity using a multilevel local grid refinement method"},

author = {Hai-Shan Luo and Mojdeh Delshad and Zhi-Tao Li and Amir Shahmoradi},

url = {https://link.springer.com/article/10.1007/s12182-015-0066-1},

doi = {10.1007/s12182-015-0066-1},

year  = {2016},

date = {2016-02-01},

journal = {Petroleum Science},

volume = {13},

number = {1},

pages = {110-125},

publisher = {China University of Petroleum (Beijing)},

abstract = {Polymer injectivity is an important factor for evaluating the project economics of chemical flood, which is highly related to the polymer viscosity. Because the flow rate varies rapidly near injectors and significantly changes the polymer viscosity due to the non-Newtonian rheological behavior, the polymer viscosity near the wellbore is difficult to estimate accurately with the practical gridblock size in reservoir simulation. To reduce the impact of polymer rheology upon chemical EOR simulations, we used an efficient multilevel local grid refinement (LGR) method that provides a higher resolution of the flows in the near-wellbore region. An efficient numerical scheme was proposed to accurately solve the pressure equation and concentration equations on the multilevel grid for both homogeneous and heterogeneous reservoir cases. The block list and connections of the multilevel grid are generated via an efficient and extensible algorithm. Field case simulation results indicate that the proposed LGR is consistent with the analytical injectivity model and achieves the closest results to the full grid refinement, which considerably improves the accuracy of solutions compared with the original grid. In addition, the method was validated by comparing it with the LGR module of CMG_STARS. Besides polymer injectivity calculations, the LGR method is applicable for other problems in need of near-wellbore treatment, such as fractures near wells.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sorace2016b,

title = {"Abstract P6-01-03: Trastuzumab-induced hypoxia changes in a HER2+ murine model of breast cancer"},

author = {AG Sorace and CC Quarles and TE Yankeelov},

url = {http://cancerres.aacrjournals.org/content/76/4_Supplement/P6-01-03.short},

year  = {2016},

date = {2016-02-01},

journal = {Cancer Research},

volume = {76},

number = {4 supplement},

pages = {P6-01-03-P6-01-03},

publisher = {American Association for Cancer Research},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Syed2016,

title = {"Dimerization of glycoprotein Ibα is not sufficient to induce platelet clearance"},

author = {Xin Liang and Anum K Syed and Susan R Russell and Jerry Ware and Renhao Li},

url = {http://onlinelibrary.wiley.com/doi/10.1111/jth.13221/abstract},

doi = {10.1111/jth.13221},

year  = {2016},

date = {2016-02-01},

journal = {Journal of Thrombosis and Haemostasis},

volume = {14},

number = {2},

pages = {381-386},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sorace2016b,

title = {"Trastuzumab improves tumor perfusion and vascular delivery of cytotoxic therapy in a murine model of HER2+ breast cancer: preliminary results"},

author = {Anna G. Sorace and C. Chad Quarles and Jennifer G. Whisenant and Ariella B. Hanker and J. Oliver McIntyre and Violeta M. Sanchez and Thomas E. Yankeelov},

url = {https://link.springer.com/article/10.1007/s10549-016-3680-8},

doi = {10.1007/s10549-016-3680-8},

year  = {2016},

date = {2016-01-20},

journal = {Breast Cancer Research and Treatment},

volume = {155},

number = {2},

pages = {273–284},

abstract = {To employ in vivo imaging and histological techniques to identify and quantify vascular changes early in the course of treatment with trastuzumab in a murine model of HER2+ breast cancer. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was used to quantitatively characterize vessel perfusion/permeability (via the parameter Ktrans) and the extravascular extracellular volume fraction (ve) in the BT474 mouse model of HER2+ breast cancer (N = 20) at baseline, day one, and day four following trastuzumab treatment (10 mg/kg). Additional cohorts of mice were used to quantify proliferation (Ki67), microvessel density (CD31), pericyte coverage (α-SMA) by immunohistochemistry (N = 44), and to quantify human VEGF-A expression (N = 29) throughout the course of therapy. Longitudinal assessment of combination doxorubicin ± trastuzumab (N = 42) tested the hypothesis that prior treatment with trastuzumab will increase the efficacy of subsequent doxorubicin therapy. Compared to control tumors, trastuzumab-treated tumors exhibited a significant increase in Ktrans (P = 0.035) on day four, indicating increased perfusion and/or vessel permeability and a simultaneous significant increase in ve (P = 0.01), indicating increased cell death. Immunohistochemical and ELISA analyses revealed that by day four the trastuzumab-treated tumors had a significant increase in vessel maturation index (i.e., the ratio of α-SMA to CD31 staining) compared to controls (P < 0.001) and a significant decrease in VEGF-A (P = 0.03). Additionally, trastuzumab dosing prior to doxorubicin improved the overall effectiveness of the therapies (P < 0.001). This study identifies and validates improved perfusion characteristics following trastuzumab therapy, resulting in an improvement in trastuzumab-doxorubicin combination therapy in a murine model of HER2+ breast cancer. This data suggests properties of vessel maturation. In particular, the use of DCE-MRI, a clinically available imaging method, following treatment with trastuzumab may provide an opportunity to optimize the scheduling and improve delivery of subsequent cytotoxic therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lima2016b,

title = {"Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement"},

author = {Shalla Hanson and David Robert Grimes and Jake P Taylor-King and Benedikt Bauer and Pravnam I Warman and Ziv Frankenstein and Artem Kaznatcheev and Michael J Bonassar and Vincent L Cannataro and Zeinab Y Motawe and Ernesto ABF Lima and Sungjune Kim and Marco L Davila and Arturo Araujo},

url = {http://www.biorxiv.org/content/early/2016/04/22/049908},

doi = {https://doi.org/10.1101/049908},

year  = {2016},

date = {2016-01-01},

journal = {bioRxiv},

pages = {049908},

publisher = {Cold Spring Harbor Labs Journals},

abstract = {Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic. To inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients' tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients. We outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}