Towards the Development of Redox-Responsive Eu(III) Complexes for Cancer Imaging

Cancer cells display redox dysregulation and lowered cytosolic pH as a means to maintain their over-proliferation. The development of an approach to probe tissue redox state could potentially allow discrimination between healthy and cancerous tissues. Magnetic Resonance Imaging (MRI) is a non-invasive technique used in the diagnosis of a variety of pathologies. The high spatial resolution and its use of non-ionizing radiation make MRI an attractive technique for cancer screening and detection. The quality of an MR image can be further enhanced with the aid of contrast agents, the majority of which are Gd(III) complexes. Gd(III) complexes provide positive contrast via a T1 mechanism. Recent advancements in the development of new MRI contrast agents have drawn attention to the Eu(II)/Eu(III) redox couple. The Eu(II) ion, which is isoelectronic with Gd(III), exhibits similar T1- MRI properties, while the Eu(III) ion displays PARACEST MRI properties. These differences in MRI properties can be exploited to design a redox-responsive MRI contrast agent. Such a probe could potentially allow the non-invasive detection of redox dysregulation in vivo. The goal of this project is to develop a series of Eu(III) imaging agents and investigate the effect of ligand identity on their redox properties. So far, four Eu(III) complexes of glycine-, lysine-, aspartate- and tyrosine-based ligands have been synthesized and characterized by 1H NMR. Cyclic voltammetry was used to evaluate the redox properties of these metal complexes between pH 5.5 – 8.5. Preliminary results show that of the four Eu(III) complexes investigated, the aspartate-based complex displays the most significant pH-dependent redox properties, with a 250 mV change in redox potential over the pH range studied. Future work will involve further investigation of the redox potentials of these complexes over a broader pH range

Differential effects of angiotensin II type 2 receptor antagonism in mice models of obesity

Angiotensin II ( ang II) is a vasoactive hormone derived from the renin angiotensin system (RAS), which regulates blood pressure and fluid balance in the body. Ang II effects are mediated via two major receptors: type 1 (AT 1) and type 2 (AT 2). Adipocytes contain a local RAS in which ang II upregulates adipogenesis, fatty acid and triglyceride synthesis primarily mediated via the AT 2 receptor in cultured adipocytes. Preliminary studies from our lab tested the importance of AT 2 receptors in vivo and reported a decrease in adiposity by AT 2 antagonism in the lean, but not the genetically obese db/db mouse. To further explore these effects, we used another genetic model of obesity (ob/ob) and diet-induced obese (DIO) mice and treated them for 2-3 weeks with the AT 2 receptor antagonist, PD 123,319. Body weight, fat pad weight and plasma glucose, leptin and insulin levels and fatty acid synthase (FAS) and glycerol-3-phosphate dehydrogenase (GPDH) activity were measured. Consistent with previous findings in lean mice, the AT 2 antagonist decreased abdominal fat pad weight in ob/ob mice and accelerated weight loss in D10 mice. Also, correlated with these effects, AT2 blockade decreased FAS activity in ob/ob mice and lowered blood glucose levels in DIO mice. No significant changes were seen in the other parameters that were measured. In combination with recently published data, this research further supports the role of the AT 2 receptor in modulating ang II effects on adipocyte metabolism. Defining this role is crucial in determining and preventing the contribution of adipocyte-derived RAS to systemic disorders such as obesity-related hypertension

Utilization of the Scheduling Software Platform, YouCanBookMe

Media/Software Revie

Managing transfer and scale-up of a process with atypical impact of dissolved oxygen concentration on productivity and product quality

Dissolved oxygen (DO) is a routinely measured and controlled process parameter in mammalian cell cultures for monoclonal antibody production in stirred tank bioreactors. For typical Chinese hamster ovary (CHO) cell lines, DO is controlled around a specific set-point, but growth, productivity, and product quality are relatively independent of DO over a wide range relative to controller capability. Thus DO control is primarily used to ensure sufficient oxygen is provided to the cells to support their metabolism during growth and antibody production. Such processes can be transferred from one facility or scale to another with limited concern for detailed analysis of potential DO gradients within the bioreactor or differences in probe handling and pressure compensation methods. This paper describes challenges associated with DO impact to productivity and product quality in a low-density CHO fed-batch process executed at 15 mL, 2 L, 12 kL, and 20 kL bioreactor scales. The work was initially motivated by unexpectedly low productivity at the 20 kL scale. Due to gradients within the 20 kL bioreactor and differences in pressure compensation strategies, the actual DO concentration during the run was up to 175% of the concentration at the 2 L process development scale. Subsequent experiments at the 15 mL and 2 L scales showed an inverse correlation between titer and DO set-point over the range of 10% to 60% air saturation. For the 2nd run at the 20 kL scale, the set-point was lowered and pressure compensation methods were adjusted, resulting in a significantly higher titer. The lower effective DO concentration was also applied at a second manufacturing facility, where a higher titer was again achieved. While product quality was acceptable for the large scale runs with lower DO, process characterization studies demonstrated that DO set-point was correlated with the charge heterogeneity profile (Figure 1). The ideal DO range for higher productivity was correlated with higher likelihood of a charge heterogeneity profile outside of the target performance range. This presentation describes how statistical models generated from process characterization data, along with considerations of bioreactor configuration, mixing, and gassing strategies can be applied to develop a manufacturing process to simultaneously deliver acceptable product quality and meet productivity requirements. Please click Additional Files below to see the full abstract

Quantum dots to monitor RNAi delivery and improve gene silencing

A critical issue in using RNA interference for identifying genotype/phenotype correlations is the uniformity of gene silencing within a cell population. Variations in transfection efficiency, delivery-induced cytotoxicity and ‘off target’ effects at high siRNA concentrations can confound the interpretation of functional studies. To address this problem, we have developed a novel method of monitoring siRNA delivery that combines unmodified siRNA with seminconductor quantum dots (QDs) as multi color biological probes. We co-transfected siRNA with QDs using standard transfection techniques, thereby leveraging the photostable fluorescent nanoparticles to track delivery of nucleic acid, sort cells by degree of transfection and purify homogenously-silenced subpopulations. Compared to alternative RNAi tracking methods (co-delivery of reporter plasmids and end-labeling the siRNA), QDs exhibit superior photostability and tunable optical properties for an extensive selection of non-overlapping colors. Thus this simple, modular system can be extended toward multiplexed gene knockdown studies, as demonstrated in a two color proof-of-principle study with two biological targets. When the method was applied to investigate the functional role of T-cadherin (T-cad) in cell–cell communication, a subpopulation of highly silenced cells obtained by QD labeling was required to observe significant downstream effects of gene knockdown

Small-scale land-use variability affects Anopheles spp. distribution and concomitant Plasmodium infection in humans and mosquito vectors in southeastern Madagascar

Additional file 1. Individual health assessment form

Reflexive and Selective Competitive Behaviors—Inertia, Imitation, and Interfirm Rivalry

Competitive dynamics research has established the important impact that the level of firm competitive activity has on rival response and firm performance. Less understood, however, are inputs that influence firm activity, specifically, the extent to which firms reflexively repeat prior activity versus selectively taking actions. Drawing from the awareness–motivation–capability framework, we develop and test theory that firm decision makers are not only predisposed to behave reflexively, but are also influenced by contextual factors, suggesting cognitive selection. Utilizing a longitudinal sample of marketing activity of 58 firms and 2,164 firm–rival dyads in 11 industries, we find that firms undertake both reflexive and selective competitive processes. Positive effects of prior levels of activity are moderated by the firm’s own prior performance, as well as the rivals’ similarity and industry standing

More accurate process understanding from process characterization studies using Monte Carlo simulation, regularized regression, and classification models

Establishment of an appropriate control strategy with defined operating ranges (OR) predicted to meet a target product profile is a critical component of commercializing new biologics under the Quality by Design (QbD) approach. Process characterization (PC) studies are performed to expand process understanding by achieving two main goals: 1) determining which process parameters have significant effects on quality attributes and 2) establishing models describing the relationships between these critical process parameters (CPP) and critical quality attributes (CQA). Risk assessment and design of experiments (DOE) techniques are effectively deployed in the industry to identify parameters to study and build process understanding. However, the true value of the data produced by these studies can be compromised by the inherent flaws with traditional data analysis techniques. In particular, p-value based methods such as stepwise regression are prone to generate false positives and overestimated parameter coefficients. Many of the deficiencies of traditional stepwise regression can be alleviated by applying Monte Carlo cross validation (MCCV) and simulations to stepwise algorithms. These methods can greatly enhance process understanding and assist in the selection of CPPs. Regularized regression methods such as LASSO, ridge, and elastic net are also designed to overcome many of the issues inherent in techniques based on ordinary least squares. However, a superior strategy is to build multiple models using a variety of techniques and use the insights gained from each to establish the relationships between CPPs and CQAs. Use of complementary methods during data analysis allows more informed decisions to be made during model construction. Please click Additional Files below to see the full abstract

Identification of copper as a cell culture media component causing metabolite depletion and product sequence variants

The level of peptide sequence variants in a biologic drug substance batch is a critical product quality attribute that should be monitored and controlled. These sequence variants are typically caused by DNA single nucleotide variants that arise in cloning and amplification, mistranscription due to unstable vector DNA or cell age/production stresses, or mistranslation via tRNA wobble or mischarging. In this work, a low frequency of monoclonal antibody sequence variants was detected by mass spectrometry in a drug substance batch. The variants were distributed throughout the heavy and light chains at average levels of under 1% per site with no apparent codon bias. No product-coding DNA mutations were detected via deep sequencing data. This pattern of low level, widely-distributed variation strongly suggested a misincorporation mechanism via mischarging of aminoacyl-tRNA, presumably due to amino acid depletion during the process. Copper is a critical cell culture media component that can be modulated in fed-batch processes to induce lactate consumption via its role as a cofactor for mitochondrial function and respiration. However, complete consumption of lactate can also trigger reduced levels of other metabolites required for recombinant protein assembly, which can lead to product sequence variants. To investigate the potential relationship between media copper supplementation and sequence variants, various levels of copper were supplemented into the basal media for fed-batch cultures at the 250 mL bioreactor scale. Mass spectrometry analysis of the partially purified antibody indicated a positive correlation between the amount of copper supplemented and the level of detected sequence variants as well as a mechanism for sequence variant reduction via targeted nutrient feeding. This work has identified a potential mechanism of sequence variant generation related to cell culture media copper levels as well as process alterations to prevent such variation in future batches, highlighting the importance of carefully controlling trace metal levels. Additional studies may be required to validate the potential mechanism