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Imaging-guided delivery of RNAi for anticancer treatment.

ABSTRACT: The RNA interference (RNAi) technique is a new modality for cancer therapy, and several candidates are being tested clinically. In the development of RNAi-based therapeutics, imaging methods can provide a visible and quantitative way to investigate the therapeutic effect at anatomical, cellular, and molecular level; to noninvasively trace the distribution; to and study the biological processes in preclinical and clinical stages. Their abilities are important not only for therapeutic optimization and evaluation but also for shortening of the time of drug development to market. Typically, imaging-functionalized RNAi therapeutics delivery that combines nanovehicles and imaging techniques to study and improve their biodistribution and accumulation in tumor site has been progressively integrated into anticancer drug discovery and development processes. This review presents an overview of the current status of translating the RNAi cancer therapeutics in the clinic, a brief description of the biological barriers in drug delivery, and the roles of imaging in aspects of administration route, systemic circulation, and cellular barriers for the clinical translation of RNAi cancer therapeutics, and with partial content for discussing the safety concerns. Finally, we focus on imaging-guided delivery of RNAi therapeutics in preclinical development, including the basic principles of different imaging modalities, and their advantages and limitations for biological imaging. With growing number of RNAi therapeutics entering the clinic, various imaging methods will play an important role in facilitating the translation of RNAi cancer therapeutics from bench to bedside.

SUBMITTER: Wang J

PROVIDER: S-EPMC5226392 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

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Project description:The full complement of molecular pathways contributing to Parkinson’s disease (PD) pathogenesis remains unknown. Here, to address this issue, we began by using a high-resolution variant of functional magnetic resonance imaging (fMRI) to pinpoint brainstem regions differentially affected by, and resistant to, the disease. Then, relying on the imaging information as a guide, we profiled gene expression levels of postmortem brain samples and used a factorial statistical model to identify a disease related decrease in the expression of the polyamine enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1). Next, a series of studies were performed to confirm the pathogenic relevance of this finding. First, to test for a causal link between polyamines and α-synuclein toxicity, we investigated a yeast model expressing α-synuclein. Polyamines were found to enhance the toxicity of α-synuclein, and an unbiased genome-wide screen for modifiers of α-synuclein toxicity identified Tpo4, a member of a family of proteins responsible for polyamine transport. Second, to test for a causal link between SAT1 activity and PD histopathology we investigated a mouse model expressing α-synuclein. DENSPM (N1, N11-diethylnorspermine), a polyamine analog that increases SAT1 activity, was found to reduce PD histopathology, while Berenil (diminazene aceturate), a pharmacological agent that reduces SAT1 activity, worsened the histopathology. Third, we genotyped PD patients and controls and isolated a rare but novel variant in the SAT1 gene, although the functional significance of this genetic variant was not identified. Taken together, the results suggest that the polyamine pathway contributes to PD pathogenesis. Imaging-guided microarray In principle, gene expression profiling techniques like microarray are well suited to identify molecular pathways contributing to the pathogenesis of complex diseases. In practice, however, microarray applied to diseases of the brain present a number of analytic challenges. By identifying regions within the same brain structure that are differentially targeted by and resistant to a disease, imaging-guided microarray is an approach designed to address these limitations. Specifically, guided by the spatial information generated from high resolution functional imaging, a 2x2 factorial analysis-of-variance can be designed, including both within and between group factors, and this “double subtraction” model is effective in improving signal-to-noise in a microarray experiment. Relying on imaging findings, we harvested the DMNV from 6 postmortem brains with evidence of PD and from 5 control brains. The postmortem PD cases were evaluated for pathological changes (Lewy body-containing neurons and Lewy neurites evidenced with antibodies directed against α-synuclein aggregates) that matched the pattern proposed by Braak. We relied on the imaging results to identify a neighboring medullary region relatively unaffected by the disease to be used as a within-brain control. We decided on the inferior olivary nucleus (ION), because it is histologically identifiable, and harvested the ION from each of the 6 PD cases and 5 controls. Microarray techniques were used to generate gene expression profiles for each of the 22 tissue samples. A repeated-measures 2x2 factorial ANOVA model constructed for the imaging study was applied to the expression dataset, in which expression levels from two regions of the medulla (DMNV vs. ION) were included as the first within group factor, diagnosis (PD vs. controls) was the between group factor, and age and sex were included as covariates. Based on current literature, one of the top hits (SAT1) was investigated further to determine if it played a role in PD pathogenesis.

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Imaging-guided delivery of RNAi for anticancer treatment.

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