Coenzyme Q₁₀ (CoQ₁₀) is a poorly-water soluble compound that is being investigated for the treatment of carcinomas. The aim of this study was to investigate the feasibility of preparing phospholipid-stabilized dispersions of the anticancer agent for continuous pulmonary delivery using a vibrating-mesh nebulizer. We determined the physicochemical properties (drug particle size distribution in dispersion, zeta potential, surface tension, and rheology) and compared the aerosolization profiles (nebulization performance, aerodynamic drug deposition and total emitted dose) of dispersions of CoQ₁₀ prepared with different phospholipids. The hydrodynamic sizes of the drug particles in dispersion were primarily in the submicron range, but formulations with drug particle sizes greater than the aperture size of the nebulizer presented superior aerosolization profiles. At high shear rates, certain formulations presented increased shear-thickening behavior, which was connected to a decrease in mass and drug output over time, and with decreased aerodynamic and geometric sizes. Other formulations presented shear-thinning behavior and showed similarly high drug depositions. In this investigation, we found that dispersed formulations of CoQ₁₀ presented different in vitro performance for pulmonary delivery based on their rheological behavior. In conclusion, this characterization methodology provides an innovative approach to screen formulations of poorly-water soluble compounds for continuous (no clogging) active vibrating-mesh nebulization.

15 Apr 2013·Cancer Research

Abstract 5542: Identification of Enolase1 as key drug target in breast cancer by the Berg Interrogative Biology™ platform.

Author: Vishnudas, Vivek K. ; Sarangarajan, Rangaprasad ; Hazarika, Suwagmani ; Narain, Niven R. ; Sundararajan, Pragalath ; Fowler, Samantha ; Jing, Enxuan

AbstractBreast cancer is characterized by subtypes of various expression of her2/neu, estrogen receptor (ER), and progesterone receptor (PR) markers. The metabolic drivers and correlation to glycolytic intermediates that contribute to breast tumor phenotypes are not fully understood. The Berg Interrogative Biology™ discovery platform was used to delineate distinct molecular signatures which reflect the oncogenic mechanistic chain of events. Human Enolase-I was identified as a critical node (hub) in the Berg cancer systems network output. Causal association of ENO1 and other proteins with diverse biological roles such as regulation of gene expression, genomic integrity, mRNA/microRNA processing, protein and ECM stability, and cytoskeleton organization provides a powerful snap shot of molecular events modulated by ENO1. As a key glycolytic enzyme, Enolase I has been implicated in tumor growth, cell death, and drug resistance. To further unravel the metabolic regulation by Eno1 in breast cancer, we generated lentivirus mediated stable human enolase I knockdown and overexpression breast cancer cell lines which were subjected to functional assays. The metabolic assessments using Seahorse XF96 analyzer revealed decreased glycolysis in Eno1 knockdown cells and increased glycolysis in Eno1 overexpressing breast cancer cells, confirming the functional perturbation induced by Eno1 protein levels. Bioenergetic profiling showed corresponding changes in OCR in both basal and uncoupled states in Eno1 overexpressing breast cancer cells, suggesting these cells underwent metabolic remodeling in response to Eno1 activity/levels. Proliferation assays using DNA quantification and cell viability revealed significant inhibition of cancer cell growth with Enolase I knockdown, while the Eno1 overexpression promoted proliferation of breast cancer cells normoxic/hypoxic conditions, congruent to expression changes of proliferation markers. Moreover, in response to perturbation of Eno1 mediated energy metabolism, various breast cancer cells displayed altered phosphorylation/activation of AMP-activated protein kinase, which mediated Eno1 induced proliferation change in different breast cancer cells. The approach in conjunction with multi-omic technologies and data integration using the Berg BNI (BayesianNetwork Inference) leverages the robust biological output of the networks with an agnostic, unbiased analytical tool to truly gain insight into the underlying pathophysiology of breast cancers. Ongoing efforts to overlay this novel insight into combination strategies with chemotherapy will advance the understanding to realize data-driven biomarker and therapeutic end-points. Taken together, the data suggest that Enolase1 is a novel disease target breast cancer and serves as a fulcrum point in the metabolic governance of breast tumor development and progression.Citation Format: Enxuan Jing, Suwagmani Hazarika, Pragalath Sundararajan, Samantha Fowler, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Niven R. Narain. Identification of Enolase1 as key drug target in breast cancer by the Berg Interrogative Biology™ platform. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5542. doi:10.1158/1538-7445.AM2013-5542

15 Apr 2013·Cancer Research

Abstract 5230: Berg Interrogative Biology™ Informatics Suite: data driven integration of multi-omic technologies using Bayesian AI.

Author: Sarangarajan, Rangaprasad ; Du, Min ; Narain, Niven R. ; Jimenez, Joaquin J. ; Vemulapalli, Vijetha ; Rodrigues, Leonardo ; Vishnudas, Vivek K. ; Keibish, Michael ; Akmaev, Viatcheslav R. ; Walshe, Anthony

AbstractThe Berg Interrogative Biology™ Informatics Suite is an automated data-processing instrument for generation of actionable hypotheses using data generated exclusively via the Berg Interrogative Biology™ approach. The Berg Interrogative Biology™ is a platform that systematically interrogates the biological environment in proprietary in-vitro and in-vivo biological model systems. The biologically relevant data output include molecular data from multi-omics technologies such as proteomics, lipidomics, metabolomics and genomics generated within the context of Berg Interrogative Biology™ is subsequently processed by a set of mathematical algorithms within Informatics Suite. The steps include filtering of datasets with methods that allows for missing data without compromising data quality, normalization of data using technology specific methods, imputation of missing data by rigorous statistical approaches, and generation of a molecular interactome model by integrating data across experiments and technologies. Consequently, the multi-omics data is subjected to analysis using a Bayesian Network inference approach and a multi-omic cause-and-effect relationships are inferred for each analyzed condition de novo. In addition to inferring cross-molecular species interaction networks, in-silico perturbation experiments may be performed to predict cascades of molecular and phenotypic responses to a gene or protein knock-down or over-expression model. Model response is analyzed by statistical techniques and submitted to a Rich Internet Application (RIA) that allows a dynamic and interactive meta-analysis of integrated molecular interaction networks. The Informatics Suite pipeline was applied to multi-omic data set generated via the use of the Berg Interrogative Biology™ process in an in-vitro model of angiogenesis. Comprehensive implementation of the platform technology with the informatics workflow not only identified new and physiologically relevant molecular interactions, but also confirmed previously known canonical interaction pathways described in the literature. Thus, the Informatics Suite within the Berg Interrogative Biology™ platform represents a novel computational component for integrative analysis of multi-omics molecular data that is fast, accurate and leads to a rank-ordered number of actionable hypotheses positioning Berg Interrogative Biology™ as one of the most innovative and efficient approaches in drug and biomarker discovery.Citation Format: Leonardo Rodrigues, Vijetha Vemulapalli, Anthony Walshe, Min Du, Michael Keibish, Joaquin J. Jimenez, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Viatcheslav R. Akmaev, Niven R. Narain. Berg Interrogative Biology™ Informatics Suite: data driven integration of multi-omic technologies using Bayesian AI. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5230. doi:10.1158/1538-7445.AM2013-5230

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