Culture-based blood–brain barrier (BBB) models are crucial tools to enable rapid screening of brain-penetrating drugs. However, reproducibility of in vitro barrier properties and permeability remain as major challenges. Here, we report that self-assembling multicellular BBB spheroids display reproducible BBB features and functions. The spheroid core is comprised mainly of astrocytes, while brain endothelial cells and pericytes encase the surface, acting as a barrier that regulates transport of molecules. The spheroid surface exhibits high expression of tight junction proteins, VEGF-dependent permeability, efflux pump activity and receptor-mediated transcytosis of angiopep-2. In contrast, the transwell co-culture system displays comparatively low levels of BBB regulatory proteins, and is unable to discriminate between the transport of angiopep-2 and a control peptide. Finally, we have utilized the BBB spheroids to screen and identify BBB-penetrant cell-penetrating peptides (CPPs). This robust in vitro BBB model could serve as a valuable next-generation platform for expediting the development of CNS therapeutics.
In vitro models of the blood brain barrier (BBB) are crucial tools for the study of BBB transport and development of drugs that can reach the CNS. Brain endothelial cells grown in culture are often used to model the BBB however it is challenging to maintain reproducible BBB properties and function. “BBB organoids” are obtained following co-culture of endothelial cells, pericytes and astrocytes under low adhesion conditions. These organoids reproduce many features of the BBB, including the expression of tight junctions, molecular transporters and drug efflux pumps and hence can be used to model drug transport across the BBB. This protocol provides a comprehensive description of the techniques required to culture and maintaina BBB organoids. We also describe two separate detection approaches that can be used to analyze drug penetration into the organoids: confocal fluorescence microscopy and mass spectrometry imaging. Using our protocol, BBB organoids can be established within 2–3 days. An additional day is required to analyse drug permeability. The BBB organoid platform represents an accurate, versatile and cost-effective in vitro tool. It can easily be scaled to a high-throughput format, offering a tool for BBB modeling that could accelerate therapeutic discovery for the treatment of various neuro-pathologies.
Here we describe the utility of peptide macrocyclization through perfluoroaryl-cysteine SNAr chemistry to improve the ability of peptides to cross the blood–brain barrier. Multiple macrocyclic analogues of the peptide transportan-10 were investigated that displayed increased uptake in two different cell lines and improved proteolytic stability. One of these analogues (M13) exhibited substantially increased delivery across a cellular spheroid model of the blood–brain barrier. Through ex vivo imaging of mouse brains, we demonstrated that this perfluoroarene-based macrocycle of TP10 exhibits increased penetration of the brain parenchyma following intravenous administration in mice. Finally, we evaluated macrocyclic analogues of the BH3 domain of the BIM protein to assess if our approach would be applicable to a peptide of therapeutic interest. We identified a BIM BH3 analogue that showed increased penetration of the brain tissue in mice.
There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed "Mach", reach 10 kDa average mass, are more effective than any previously known variant in cells, and can also deliver proteins into the cytosol. The Mach miniproteins are nontoxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.
Cell-penetrating peptides (CPPs) can facilitate the intracellular delivery of large therapeutically relevant molecules, including proteins and oligonucleotides. Although hundreds of CPP sequences are described in the literature, predicting efficacious sequences remains difficult. Here, we focus specifically on predicting CPPs for the delivery of phosphorodiamidate morpholino oligonucleotides (PMOs), a compelling type of antisense therapeutic that has recently been FDA approved for the treatment of Duchenne muscular dystrophy. Using literature CPP sequences, 64 covalent PMO–CPP conjugates were synthesized and evaluated in a fluorescence-based reporter assay for PMO activity. Significant discrepancies were observed between the sequences that performed well in this assay and the sequences that performed well when conjugated to only a small-molecule fluorophore. As a result, we envisioned that our PMO–CPP library would be a useful training set for a computational model to predict CPPs for PMO delivery. We used the PMO activity data to fit a random decision forest classifier to predict whether or not covalent attachment of a given peptide would enhance PMO activity at least 3-fold. To validate the model experimentally, seven novel sequences were generated, synthesized, and tested in the fluorescence reporter assay. All computationally predicted positive sequences were positive in the assay, and one sequence performed better than 80% of the tested literature CPPs. These results demonstrate the power of machine learning algorithms to identify peptide sequences with particular functions and illustrate the importance of tailoring a CPP sequence to the cargo of interest.
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