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RNA interference (RNAi) holds unique potential as a clinically viable modality to pharmacologically regulate oncogenes in sequence-specific manner. However, systemic delivery of RNAi to tumors encounters myriad obstructions, and strategies to overcome such barriers have largely consisted of academic demonstrations with few approaches reaching patients. Here we report the development of a self-agglomerating nanohydrogel (SANGs) platform that selectively localizes to tumor tissue, is efficiently internalized by cancer cells, is agnostic to RNAi payload, and achieves functional suppression of multiple oncogene targets. After intravenous injection, SANGs preferentially accumulate and are retained in primary and metastatic loci in four aggressive cancer models in rodents. SANGs deliver multiple RNAi payloads that significantly suppress oncogene expression and sensitize previously resistant tumors while being safe and well tolerated in simulated clinical applications across three species. We propose, and provide the first direct evidence in support of, a mechanism featuring emergent material properties by which SANGs achieve durable solid-tumor delivery without attachment of cell- or tumor-targeting ligands. Overall, the SANGs platform is an enabling technology for RNAi-based cancer therapeutics and is poised for advanced pharmaceutical development with multiple solid-tumor indications. RNAi therapy has huge potential but effective delivery to target location is a major issue. Here, the authors report on the delivery of RNAi to tumors using self-agglomerating nanohydrogels that can overcome the different delivery barriers and supply multiple RNAi payloads.

Muscle spindles encode mechanosensory information by mechanisms that remain only partially understood. Their complexity is expressed in mounting evidence of various molecular mechanisms that play essential roles in muscle mechanics, mechanotransduction and intrinsic modulation of muscle spindle firing behaviour. Biophysical modelling provides a tractable approach to achieve more comprehensive mechanistic understanding of such complex systems that would be difficult/impossible by more traditional, reductionist means. Our objective here was to construct the first integrative biophysical model of muscle spindle firing. We leveraged current knowledge of muscle spindle neuroanatomy and in vivo electrophysiology to develop and validate a biophysical model that reproduces key in vivo muscle spindle encoding characteristics. Crucially, to our knowledge, this is the first computational model of mammalian muscle spindle that integrates the asymmetric distribution of known voltage‐gated ion channels (VGCs) with neuronal architecture to generate realistic firing profiles, both of which seem likely to be of great biophysical importance. Results predict that particular features of neuronal architecture regulate specific characteristics of Ia encoding. Computational simulations also predict that the asymmetric distribution and ratios of VGCs is a complementary and, in some instances, orthogonal means to regulate Ia encoding. These results generate testable hypotheses and highlight the integral role of peripheral neuronal structure and ion channel composition and distribution in somatosensory signalling. What is the central question of the study? How does the neuronal architecture and asymmetric distribution of voltage‐gated channels influence mechanosensory encoding by muscle spindle afferents? What is the main finding and its importance? The results predict that neuronal architecture and the distribution and ratios of voltage‐gated ion channels are a complementary and, in some instances, orthogonal means to regulate Ia encoding. The importance of these findings highlights the integral role of peripheral neuronal structure and ion channel expression in mechanosensory signalling. Generally, our computational approach offers an integrative means to generate testable hypotheses and prioritize targets for future mechanistic studies.