Scaling behavior of drug transport and absorption in in silico cerebral capillary networks

Drug delivery to the brain is challenging due to the presence of the blood-brain barrier. Mathematical modeling and simulation are essential tools for the deeper understanding of transport processes in the blood, across the blood-brain barrier and within the tissue. Here we present a mathematical model for drug delivery through capillary networks with increasingly complex topologies with the goal to understand the scaling behavior of model predictions on a coarse-to-fine sequence of grids. We apply our model to the delivery of L-Dopa, the primary drug used in the therapy of Parkinson\u27s Disease. Our model replicates observed blood flow rates and ratios between plasma and tissue concentrations. We propose an optimal network grain size for the simulation of tissue volumes of 1 cm3 that allows to make reliable predictions with reasonable computational costs

Boost-phase discrimination research

The final report describes the combined work of the Computational Chemistry and Aerothermodynamics branches within the Thermosciences Division at NASA Ames Research Center directed at understanding the signatures of shock-heated air. Considerable progress was made in determining accurate transition probabilities for the important band systems of NO that account for much of the emission in the ultraviolet region. Research carried out under this project showed that in order to reproduce the observed radiation from the bow shock region of missiles in their boost phase it is necessary to include the Burnett terms in the constituent equation, account for the non-Boltzmann energy distribution, correctly model the NO formation and rotational excitation process, and use accurate transition probabilities for the NO band systems. This work resulted in significant improvements in the computer code NEQAIR that models both the radiation and fluid dynamics in the shock region

Cathepsin D Expression and Gemcitabine Resistance in Pancreatic Cancer.

BackgroundCathepsin-D (CatD), owing to its dual role as a proteolytic enzyme and as a ligand, has been implicated in cancer progression. The role of CatD in pancreatic ductal adenocarcinoma is unknown.MethodsCatD expression quantified by immunohistochemistry of tumor-tissue microarrays of 403 resected pancreatic cancer patients from the ESPAC-Tplus trial, a translational study within the ESPAC (European Study Group for Pancreatic Cancer) trials, was dichotomously distributed to low and high H scores (cut off 22.35) for survival and multivariable analysis. The validation cohort (n = 69) was recruited based on the hazard ratio of CatD from ESPAC-Tplus. 5-fluorouracil-, and gemcitabine-resistant pancreatic cancer cell lines were employed for mechanistic experiments. All statistical tests were two-sided.ResultsMedian overall survival was 23.75 months and median overall survival for patients with high CatD expression was 21.09 (95% confidence interval [CI] = 17.31 to 24.80) months vs 27.20 (95% CI = 23.75 to 31.90) months for low CatD expression (χ2 LR, 1DF = 4.00; P = .04). Multivariable analysis revealed CatD expression as a predictive marker in gemcitabine-treated (z stat = 2.33; P = .02) but not in 5-fluorouracil-treated (z stat = 0.21; P = .82) patients. An independent validation cohort confirmed CatD as a negative predictive marker for survival (χ2 LR, 1DF = 6.80; P = .009) and as an independent predictive marker in gemcitabine-treated patients with a hazard ratio of 3.38 (95% CI = 1.36 to 8.38, P = .008). Overexpression of CatD was associated with a concomitant suppression of the acid sphingomyelinase, and silencing of CatD resulted in upregulation of acid sphingomyelinase with rescue of gemcitabine resistance.ConclusionsAdjuvant gemcitabine is less effective in pancreatic ductal adenocarcinoma with high CatD expression, and thus CatD could serve as a marker for biomarker-driven therapy

Immune Cell and Stromal Signature Associated with Progression-free Survival of Patients with Resected Pancreatic Ductal Adenocarcinoma

Background & Aims: Changes to the microenvironment of pancreatic ductal adenocarcinomas (PDACs) have been associated with poor outcomes of patients. We studied the associations between composition of the pancreatic stroma (fibrogenic, inert, dormant, or fibrolytic stroma) and infiltration by inflammatory cells and times of progression-free survival (PFS) of patients with PDACs after resection. Methods: We obtained 1824 tissue microarray specimens from 385 patients included in the European Study Group for Pancreatic Cancer trial 1 and 3 and performed immunohistochemistry to detect alpha smooth muscle actin, type 1 collagen, CD3, CD4, CD8, CD68, CD206, and neutrophils. Tumors that expressed high and low levels of these markers were compared with patient outcomes using Kaplan-Meier curves and multivariable recursive partitioning for discrete-time survival tree analysis. Prognostic index was delineated by a multivariable Cox proportional hazards model of immune cell and stromal markers and PFS. Findings were validated using 279 tissue microarray specimens from 93 patients in a separate cohort. Results: Levels of CD3, CD4, CD8, CD68, and CD206 were independently associated with tumor recurrence. Recursive partitioning for discrete-time survival tree analysis identified a high level of CD3 as the strongest independent predictor for longer PFS. Tumors with levels of CD3 and high levels of CD206 associated with a median PFS time of 16.6 months and a median prognostic index of –0.32 (95% confidence interval [CI] –0.35 to –0.31), whereas tumors with low level of CD3 cell and low level of CD8 and high level of CD68 associated with a median PFS time of 7.9 months and a prognostic index of 0.32 (95% CI 0.050–0.32); we called these patterns histologic signatures. Stroma composition, when unassociated with inflammatory cell markers, did not associate significantly with PFS. In the validation cohort, the histologic signature resulted in an error matrix accuracy of predicted response of 0.75 (95% CI 0.64–0.83; accuracy P < .001). Conclusions: In an analysis of PDAC tissue microarray specimens, we identified and validated a histologic signature, based on leukocyte and stromal factors, that associates with PFS times of patients with resected PDACs. Immune cells might affect the composition of the pancreatic stroma to affect progression of PDAC. These findings provide new insights into the immune response to PDAC

Drug Transport and Absorption on a Capillary Network

Drug delivery to the brain is more difficult than other organs due to the presence of the blood-brain-barrier. One potential method to mitigate this difficulty is to load drug molecules into artificial carriers called liposomes, and apply focused ultrasound to the target area. The ultrasound waves cause the liposomes to release their contents, and may also increase permeability of the blood-brain-barrier. We present a compartmental model of capillary networks using a system of ordinary differential equations. Applying this model to the delivery of L-dopa (used to treat Parkinson\u27s disease) and Doxorubicin (used in cancer chemotherapy), we search for an ultrasound schedule which optimally delivers medicine to a specific target area while minimizing potential side-effects. By delivering medication to only where it is needed, overall health can be significantly improved. Additionally, this type of model could be adapted to individual patients\u27 unique anatomies, further improving quality of care

Infection of Accessory Dendritic Cells by Human Immunodeficiency Virus Type 1

Many details of the pathogenesis of the human immunodeficiency virus type 1 remain to be elucidated. Details of how the virus gains entry via the mucosal surface upon sexual contact or during breast feeding remain obscure. The means by which the infection travels throughout the body as well as the nature of the major reservoirs of virus infection remains, for the most part, unknown. Recent studies raise the possibility that cells of the Langerhans/dendritic lineage play a central role in human immunodeficiency virus (HIV-1) infection and pathogenesis. It has been known for several years that veiled dendritic cells in the circulation as well as skin Langerhans are infected in people with prolonged HIV-1 infections. More recently it has been found that a large burden of viral DNA sequences is found, not only in the circulating T-cell population, but also in a population that is defined as a non-T, non-B, nonrnonocyte/macrophage population rich in T-helper dendritic cells. Detailed analysis of infection of primary blood-derived T-helper dendritic cells by HIV-1 shows that such cells are the most susceptible cells in the blood to infection by this virus. The cells also produce much more virus per cell than do purified populations of other blood mononuclear cells. Moreover, primary blood- derived T-helper dendritic cells are not killed by infection by HIV-1. These cells are susceptible to lymphotropic, monocyte tropic, and primary isolates of HIV-1. The sensitivity of primary blood-derived T-helper dendritic cells to infection by HIV-1 has been shown to be attributable to rapid uptake of virus particles as well as rapid synthesis of viral DNA. Subsequent steps of virus replication also occur more rapidly and more efficiently in populations of primary blood- derived T-helper dendritic cells than they do in purified preparations of blood-derived T cells and monocyte/macrophages. Studies with primates using the simian immunodeficiency virus (SIV) show that dendritic cells at the surface of sexual mucosa are rapidly infected upon exposure to high concentrations of the virus. SIV is also produced in abundance in Langerhans cells located at the surface of the sexual mucosa in animals infected for prolonged periods of time

Scaling behavior of drug transport and absorption in in silico cerebral capillary networks.

Drug delivery to the brain is challenging due to the presence of the blood-brain barrier. Mathematical modeling and simulation are essential tools for the deeper understanding of transport processes in the blood, across the blood-brain barrier and within the tissue. Here we present a mathematical model for drug delivery through capillary networks with increasingly complex topologies with the goal to understand the scaling behavior of model predictions on a coarse-to-fine sequence of grids. We apply our model to the delivery of L-Dopa, the primary drug used in the therapy of Parkinson's Disease. Our model replicates observed blood flow rates and ratios between plasma and tissue concentrations. We propose an optimal network grain size for the simulation of tissue volumes of 1 cm3 that allows to make reliable predictions with reasonable computational costs

Parameter ranges governing the overall blood flow, the plasma concentration and the active transport of L-Dopa.

<p>The precise values used in each simulation are listed in the figure captions.</p

A schematic depiction of the lattice refinement process.

<p>The coarse blue network <i>G</i>₁ is replaced by the finer red network <i>G</i>₂. Both networks fill out the same computational volume. The arrows indicate the direction of the flow through the network.</p