Effects of Anti-VEGF on Predicted Antibody Biodistribution: Roles of Vascular Volume, Interstitial Volume, and Blood Flow

BACKGROUND: The identification of clinically meaningful and predictive models of disposition kinetics for cancer therapeutics is an ongoing pursuit in drug development. In particular, the growing interest in preclinical evaluation of anti-angiogenic agents alone or in combination with other drugs requires a complete understanding of the associated physiological consequences. METHODOLOGY/PRINCIPAL FINDINGS: Technescan™ PYP™, a clinically utilized radiopharmaceutical, was used to measure tissue vascular volumes in beige nude mice that were naïve or administered a single intravenous bolus dose of a murine anti-vascular endothelial growth factor (anti-VEGF) antibody (10 mg/kg) 24 h prior to assay. Anti-VEGF had no significant effect (p>0.05) on the fractional vascular volumes of any tissues studied; these findings were further supported by single photon emission computed tomographic imaging. In addition, apart from a borderline significant increase (p = 0.048) in mean hepatic blood flow, no significant anti-VEGF-induced differences were observed (p>0.05) in two additional physiological parameters, interstitial fluid volume and the organ blood flow rate, measured using indium-111-pentetate and rubidium-86 chloride, respectively. Areas under the concentration-time curves generated by a physiologically-based pharmacokinetic model changed substantially (>25%) in several tissues when model parameters describing compartmental volumes and blood flow rates were switched from literature to our experimentally derived values. However, negligible changes in predicted tissue exposure were observed when comparing simulations based on parameters measured in naïve versus anti-VEGF-administered mice. CONCLUSIONS/SIGNIFICANCE: These observations may foster an enhanced understanding of anti-VEGF effects in murine tissues and, in particular, may be useful in modeling antibody uptake alone or in combination with anti-VEGF

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Purpose To investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (<0.1%). In analysing the reliability of Ktrans, when considering regions with a CV<20%, ≈25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole-tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data

The Biomphalaria glabrata DNA methylation machinery displays spatial tissue expression, is differentially active in distinct snail populations and is modulated by interactions with Schistosoma mansoni

BBSRC Grant (BB/K005448/1)Background The debilitating human disease schistosomiasis is caused by infection with schistosome parasites that maintain a complex lifecycle alternating between definitive (human) and intermediate (snail) hosts. While much is known about how the definitive host responds to schistosome infection, there is comparably less information available describing the snail?s response to infection. Methodology/Principle findings Here, using information recently revealed by sequencing of the Biomphalaria glabrata intermediate host genome, we provide evidence that the predicted core snail DNA methylation machinery components are associated with both intra-species reproduction processes and inter-species interactions. Firstly, methyl-CpG binding domain protein (Bgmbd2/3) and DNA methyltransferase 1 (Bgdnmt1) genes are transcriptionally enriched in gonadal compared to somatic tissues with 5-azacytidine (5-AzaC) treatment significantly inhibiting oviposition. Secondly, elevated levels of 5-methyl cytosine (5mC), DNA methyltransferase activity and 5mC binding in pigmented hybrid- compared to inbred (NMRI)- B. glabrata populations indicate a role for the snail?s DNA methylation machinery in maintaining hybrid vigour or heterosis. Thirdly, locus-specific detection of 5mC by bisulfite (BS)-PCR revealed 5mC within an exonic region of a housekeeping protein-coding gene (Bg14-3-3), supporting previous in silico predictions and whole genome BS-Seq analysis of this species? genome. Finally, we provide preliminary evidence for parasite-mediated host epigenetic reprogramming in the schistosome/snail system, as demonstrated by the increase in Bgdnmt1 and Bgmbd2/3 transcript abundance following Bge (B. glabrata embryonic cell line) exposure to parasite larval transformation products (LTP). Conclusions/Significance The presence of a functional DNA methylation machinery in B. glabrata as well as the modulation of these gene products in response to schistosome products, suggests a vital role for DNA methylation during snail development/oviposition and parasite interactions. Further deciphering the role of this epigenetic process during Biomphalaria/Schistosoma co-evolutionary biology may reveal key factors associated with disease transmission and, moreover, enable the discovery of novel lifecycle intervention strategiespublishersversionPeer reviewe

Measurement of technetium-99m incorporation in fractionated red blood cells.

<p>(A) Technetium-99m radioactivity, expressed as percentage of injected ^99mTc dose per gram (%ID/g), in fractionated blood for mice (n = 5) whose red blood cells were labeled by the direct method. Mice were either naïve or administered a single intravenous bolus dose (10 mg/kg) of the cross-species anti-VEGF antibody, B20-4.1, 24 h prior to assay. (B) Technetium-99m radioactivity, expressed as %ID/g, in fractionated blood from mice (n = 5) whose red blood cells were labeled by the indirect method. All donor mice were naïve; recipient mice were naïve or received a single intravenous bolus dose (10 mg/kg) of the cross-species anti-VEGF antibody, B20-4.1, 24 h prior to assay.</p

Diagram of physiologically-based pharmacokinetic (PBPK) model to predict antibody uptake in tissues.

<p>Shown is a typical tissue sub-model component of the PBPK model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017874#pone.0017874-Ferl1" target="_blank">[13]</a> used to assess the influence of parameter variability among literature and measured <i>V_v</i>, <i>V_i</i> and <i>Q</i> values on tissue uptake of an IgG (expressed as AUC_0–7). Antibody enters tissue from the central plasma compartment via arterial blood flow where it continues to the lungs via venous blood flow or returns directly to the central plasma compartment through the lymphatic system subsequent to extravasation into interstitial space. The AUC_0–7 values listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017874#pone-0017874-t004" target="_blank"><b>Table 4</b></a> are the sum of AUCs of absolute antibody amount vs. time in the two tissue compartments (x₂ and x₃) multiplied by 100% and divided by the product of the total injected dose and mass of tissue, yielding AUC in units of %ID/g × time. Note that the muscle sub-model includes extra compartments, included in the AUC_0–7 calculation, that describe FcRn mediated recycling and degradation of antibody.</p