Perfluoroaryl Bicyclic Cell-Penetrating Peptides for Delivery of Antisense Oligonucleotides

Exon-skipping antisense oligonucleotides are effective treatments for genetic diseases, yet exon-skipping activity requires that these macromolecules reach the nucleus. While cell-penetrating peptides can improve delivery, proteolytic instability often limits efficacy. It is hypothesized that the bicyclization of arginine-rich peptides would improve their stability and their ability to deliver oligonucleotides into the nucleus. Two methods were introduced for the synthesis of arginine-rich bicyclic peptides using cysteine perfluoroarylation chemistry. Then, the bicyclic peptides were covalently linked to a phosphorodiamidate morpholino oligonucleotide (PMO) and assayed for exon skipping activity. The perfluoroaryl cyclic and bicyclic peptides improved PMO activity roughly 14-fold over the unconjugated PMO. The bicyclic peptides exhibited increased proteolytic stability relative to the monocycle, demonstrating that perfluoroaryl bicyclic peptides are potent and stable delivery agents.National Science Foundation (U.S.) (Grant 1122374)National Institute of Child Health and Human Development (U.S.) (Grant F30HD093358

Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

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.National Science Foundation (U.S.) (Grant 1122374

Chimeras of Cell-Penetrating Peptides Demonstrate Synergistic Improvement in Antisense Efficacy

Phosphorodiamidate morpholino oligonucleotides (PMOs) make up a promising class of therapeutics for genetic disease. PMOs designed for "exon skipping" must be internalized into cells, reach the nucleus, and act on pre-mRNA to mediate their effects. One tactic for improving PMO delivery and exon skipping is to covalently conjugate PMOs to cell-penetrating peptides (CPPs). Here, we report the synthesis of PMOs conjugated to CPP chimeras, constructed by combining multiple CPPs into one sequence. The chimeric CPPs synergistically improve PMO activity up to 70-fold compared to that of the PMO alone and beyond the expected effects of each component peptide. By investigating the design space of CPP chimeras, we demonstrate that all components must be covalently attached, that the order of the two sequences matters, and that peptide identity can tune activity. We identified one chimera (pVEC-Bpep) to investigate in more detail and found that it engages mechanisms of endocytosis different from those of its parent peptides. We also examined the extent to which the beneficial effect comes from improved cellular uptake as opposed to the downstream steps required for exon skipping. Given the complexity of intracellular delivery, we anticipate this work will lead researchers to consider combining molecules with different physicochemical properties to aid in the delivery of biologic cargoes.National Institutes of Health (Award F30HD093358

Perfluoroarene–Based Peptide Macrocycles to Enhance Penetration Across the Blood–Brain Barrier

Here we describe the utility of peptide macrocyclization through perfluoroaryl-cysteine S_NAr 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 <i>ex vivo</i> 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

Blood–brain-barrier organoids for investigating the permeability of CNS therapeutics

In vitro models of the blood–brain barrier (BBB) are critical tools for the study of BBB transport and the 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 coculture 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 maintain 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 d. An additional day is required to analyze 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 neuropathologies. Keywords: biological models; blood–brain barrier; cytological techniques; drug screenin

Blood-brain-barrier spheroids as an in vitro screening platform for brain-penetrating agents

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

Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

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

Deep learning to design nuclear-targeting abiotic miniproteins

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 the 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 an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic 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

A Tumor-Homing Peptide Platform Enhances Drug Solubility, Improves Blood–Brain Barrier Permeability and Targets Glioblastoma

Background: Glioblastoma (GBM) is the most common and deadliest malignant primary brain tumor, contributing significant morbidity and mortality among patients. As current standard-of-care demonstrates limited success, the development of new efficacious GBM therapeutics is urgently needed. Major challenges in advancing GBM chemotherapy include poor bioavailability, lack of tumor selectivity leading to undesired side effects, poor permeability across the blood–brain barrier (BBB), and extensive intratumoral heterogeneity. Methods: We have previously identified a small, soluble peptide (BTP-7) that is able to cross the BBB and target the human GBM extracellular matrix (ECM). Here, we covalently attached BTP-7 to an insoluble anti-cancer drug, camptothecin (CPT). Results: We demonstrate that conjugation of BTP-7 to CPT improves drug solubility in aqueous solution, retains drug efficacy against patient-derived GBM stem cells (GSC), enhances BBB permeability, and enables therapeutic targeting to intracranial GBM, leading to higher toxicity in GBM cells compared to normal brain tissues, and ultimately prolongs survival in mice bearing intracranial patient-derived GBM xenograft. Conclusion: BTP-7 is a new modality that opens the door to possibilities for GBM-targeted therapeutic approaches. Keywords: glioblastoma; peptide; brevican; drug targeting; precision medicine; chemotherapy; blood–brain barrie

A Tumor-Homing Peptide Platform Enhances Drug Solubility, Improves Blood&ndash;Brain Barrier Permeability and Targets Glioblastoma

Background: Glioblastoma (GBM) is the most common and deadliest malignant primary brain tumor, contributing significant morbidity and mortality among patients. As current standard-of-care demonstrates limited success, the development of new efficacious GBM therapeutics is urgently needed. Major challenges in advancing GBM chemotherapy include poor bioavailability, lack of tumor selectivity leading to undesired side effects, poor permeability across the blood&ndash;brain barrier (BBB), and extensive intratumoral heterogeneity. Methods: We have previously identified a small, soluble peptide (BTP-7) that is able to cross the BBB and target the human GBM extracellular matrix (ECM). Here, we covalently attached BTP-7 to an insoluble anti-cancer drug, camptothecin (CPT). Results: We demonstrate that conjugation of BTP-7 to CPT improves drug solubility in aqueous solution, retains drug efficacy against patient-derived GBM stem cells (GSC), enhances BBB permeability, and enables therapeutic targeting to intracranial GBM, leading to higher toxicity in GBM cells compared to normal brain tissues, and ultimately prolongs survival in mice bearing intracranial patient-derived GBM xenograft. Conclusion: BTP-7 is a new modality that opens the door to possibilities for GBM-targeted therapeutic approaches