A Computational Model for Predicting Nanoparticle Accumulation in Tumor Vasculature

Vascular targeting of malignant tissues with systemically injected nanoparticles (NPs) holds promise in molecular imaging and anti-angiogenic therapies. Here, a computational model is presented to predict the development of tumor neovasculature over time and the specific, vascular accumulation of blood-borne NPs. A multidimensional tumor-growth model is integrated with a mesoscale formulation for the NP adhesion to blood vessel walls. The fraction of injected NPs depositing within the diseased vasculature and their spatial distribution is computed as a function of tumor stage, from 0 to day 24 post-tumor inception. As the malignant mass grows in size, average blood flow and shear rates increase within the tumor neovasculature, reaching values comparable with those measured in healthy, pre-existing vessels already at 10 days. The NP vascular affinity, interpreted as the likelihood for a blood-borne NP to firmly adhere to the vessel walls, is a fundamental parameter in this analysis and depends on NP size and ligand density, and vascular receptor expression. For high vascular affinities, NPs tend to accumulate mostly at the inlet tumor vessels leaving the inner and outer vasculature depleted of NPs. For low vascular affinities, NPs distribute quite uniformly intra-tumorally but exhibit low accumulation doses. It is shown that an optimal vascular affinity can be identified providing the proper balance between accumulation dose and uniform spatial distribution of the NPs. This balance depends on the stage of tumor development (vascularity and endothelial receptor expression) and the NP properties (size, ligand density and ligand-receptor molecular affinity). Also, it is demonstrated that for insufficiently developed vascular networks, NPs are transported preferentially through the healthy, pre-existing vessels, thus bypassing the tumor mass. The computational tool described here can effectively select an optimal NP formulation presenting high accumulation doses and uniform spatial intra-tumor distributions as a function of the development stage of the malignancy.     

Large, rapidly evolving intergenic spacers in the mitochondrial DNA of the salamander family Ambystomatidae (Amphibia: Caudata)

We report the presence, in the mitochondrial DNA (mtDNA) of all of the sexual species of the salamander family Ambystomatidae, of a shared 240- bp intergenic spacer between tRNAThr and tRNAPro. We place the intergenic spacer in context by presenting the sequence of 1,746 bp of mtDNA from Ambystoma tigrinum tigrinum, describe the nucleotide composition of the intergenic spacer in all of the species of Ambystomatidae, and compare it to other coding and noncoding regions of Ambystoma and several other vertebrate mtDNAs. The nucleotide substitution rate of the intergenic spacer is approximately three times faster than the substitution rate of the control region, as shown by comparisons among six Ambystoma macrodactylum sequences and eight members of the Ambystoma tigrinum complex. We also found additional inserts within the intergenic spacers of five species that varied from 87-444 bp in length. The presence of the intergenic spacer in all sexual species of Ambystomatidae suggests that it arose at least 20 MYA and has been a stable component of the ambystomatid mtDNA ever since. As such, it represents one of the few examples of a large and persistent intergenic spacer in the mtDNA of any vertebrate clade.      

Computational Modeling of Tumor Response to Drug Release from Vasculature-Bound Nanoparticles

Systemically injected nanoparticle (NPs) targeting tumor vasculature offer a venue for anti-angiogenic therapies as well as cancer detection and imaging. Clinical application has been limited, however, due to the challenge of elucidating the complex interplay of nanotechnology, drug, and tumor parameters. A critical factor representing the likelihood of endothelial adhesion is the NP vascular affinity, a function of vascular receptor expression and NP size and surface-bound ligand density. We propose a theoretical framework to simulate the tumor response to vasculature-bound drug-loaded NPs and examine the interplay between NP distribution and accumulation as a function of NP vascular affinity, size, and drug loading and release characteristics. The results show that uniform spatial distribution coupled with high vascular affinity is achievable for smaller NPs but not for larger sizes. Consequently, small (100 nm) NPs with high vascular affinity are predicted to be more effective than larger (1000 nm) NPs with similar affinity, even though small NPs have lower drug loading and local drug release compared to the larger NPs. Medium vascular affinity coupled with medium or larger sized NPs is also effective due to a more uniform distribution with higher drug loading and release. Low vascular affinity hampered treatment efficacy regardless of NP size, with larger NPs additionally impeded by heterogeneous distribution and drug release. The results further show that increased drug diffusivity mainly benefits heterogeneously distributed NPs, and would negatively affect efficacy otherwise due to increased wash-out. This model system enables evaluation of efficacy for vascular-targeted drug-loaded NPs as a function of critical NP, drug, and tumor parameters.     

Three-dimensional multispecies nonlinear tumor growth—II: Tumor invasion and angiogenesis

We extend the diffuse interface model developed in Wise et al. (2008) to study nonlinear tumor growth in 3-D. Extensions include the tracking of multiple viable cell species populations through a continuum diffuse-interface method, onset and aging of discrete tumor vessels through angiogenesis, and incorporation of individual cell movement using a hybrid continuum-discrete approach. We investigate disease progression as a function of cellular-scale parameters such as proliferation and oxygen/nutrient uptake rates. We find that heterogeneity in the physiologically complex tumor microenvironment, caused by non-uniform distribution of oxygen, cell nutrients, and metabolites, as well as phenotypic changes affecting cellular-scale parameters, can be quantitatively linked to the tumor macro-scale as a mechanism that promotes morphological instability. This instability leads to invasion through tumor infiltration of surrounding healthy tissue. Models that employ a biologically founded, multiscale approach, as illustrated in this work, could help to quantitatively link the critical effect of heterogeneity in the tumor microenvironment with clinically observed tumor growth and invasion. Using patient tumor-specific parameter values, this may provide a predictive tool to characterize the complex in vivo tumor physiological characteristics and clinical response, and thus lead to improved treatment modalities and prognosis.     

An Integrated Computational/Experimental Model of Lymphoma Growth

Non-Hodgkin''s lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical element of molecular resistance has been traced to the loss of functionality in proteins such as the tumor suppressor p53. We investigate the tissue-scale physiologic effects of this loss by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. We compare between drug-sensitive Eμ-myc Arf-/- and drug-resistant Eμ-myc p53-/- lymphoma cell tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-tumor histological staining of the tumor-infiltrated inguinal lymph node in vivo. We compare model-predicted tumor growth with that observed from intravital microscopy and macroscopic imaging in vivo, finding that the model is able to accurately predict lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the Eμ-myc p53-/- cells seem to pack more closely within the tumor than the Eμ-myc Arf-/- cells, thus possibly exacerbating diffusion gradients of oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between Eμ-myc Arf-/- and Eμ-myc p53-/- tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin''s lymphoma and provides a platform to generate confirmable predictions of tumor growth.     

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

Introduction

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

Objectives

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

Method

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

Result

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.

Conclusion

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

     

Pituitary adenylate cyclase-activating peptide affects homeostatic sleep regulation in healthy young men

Pituitary adenylate cyclase-activating peptide (PACAP) is involved in autonomous regulation, including timekeeping, by its action on the suprachiasmatic nucleus and on neuroendocrine secretion, energy metabolism, and transmitter release. In particular, the interactions between PACAP and the glutamatergic system are well recognized. We compared the effect of intravenously administered PACAP to that of placebo in eight healthy male subjects. PACAP in a concentration of 4x12.5 microg was administered in a pulsatile fashion hourly between 2200 and 0100. Sleep EEG was recorded from 2300 to 1000, which was also the time when subjects were allowed to sleep. Blood samples were taken every 20 min between 2200 and 0700 for the determination of cortisol, GH, and prolactin. PACAP administration led to no changes in the macro-sleep structure as assessed according to standard criteria. Spectral analysis revealed a significant reduction in the theta-frequency range in the first 4-h interval and of the spindle frequency range in the second 4-h interval of the registration period. This was accompanied by an increase in the time constant tau of the physiological delta-power decline in the course of the night, i.e., a less pronounced dynamic of the reduction of delta-power with time. This was accompanied by a trend (P<0.1) toward decreased prolactin secretion in the first 4-h period of the night. No other changes in endocrine secretion were observed. We concluded that PACAP leads to a reduction of the dynamics of homeostatic sleep regulation and prolactin secretion. Both effects are the opposite of those seen after sleep deprivation but similar to the changes after napping, i.e., a reduced sleep propensity. This implies that PACAP might be involved in homeostatic sleep regulation.