Biology subjects

Ramon, A. E.

Publications and source records attributed to Ramon, A. E..

3 recordsLinked to original sources

bioRxiv2025

NewroBus for the brain: humanized TfR1-targeting nanobodies with high BBB permeability and cargo transport capacity

Effective delivery of therapeutics to the brain is restricted by the blood-brain barrier (BBB). A strategy to overcome this limitation involves taking advantage of receptor-mediated transcytosis pathways, such as those mediated by transferrin receptor 1 (TfR1), which is highly expressed on brain endothelial cells and naturally transports iron-bound transferrin across the BBB. To exploit this mechanism, we immunized camelids with human TfR1 and cloned 470 VHH nanobody sequences from their B cells. From this repertoire, 24 nanobodies (TfR1b-Nbs) were identified that bind human TfR1 on the cell membrane. These nanobodies were screened for binding to human TfR1, lack of interference with transferrin binding and TfR1-mediated iron uptake, and the ability to cross the BBB via human TfR1-mediated transcytosis in newly generated humanized Tfr1h knock-in rats. To improve developability and reduce potential immunogenicity, selected TfR1b-Nbs were humanized and optimized with computational and artificial intelligence (AI) algorithms, enhancing humanness, solubility, and VHH-nativeness. Eight optimized TfR1b-Nbs retained BBB permeability and were fused to humanized anti-TNF nanobody inhibitors (TNFI- or TNFI-{beta}), generating 16 heterodimers. Fusion to these TNFIs served as a functional readout, confirming that TfR1b-Nbs can shuttle biologically active, BBB-impermeable payloads into the central nervous system (CNS). All heterodimers demonstrated CNS delivery after intravenous administration, and selected constructs also reached the brain via subcutaneous injection, maintaining high serum and cerebrospinal fluid (CSF) levels for up to 72 hours. A pilot study with one heterodimer showed that chronic administration in rats humanized for both transferrin and TfR1 caused no hematological toxicity or signs of anemia - a key safety concern when targeting TfR1. These results establish humanized TfR1b-Nbs - designated NewroBus - as promising BBB shuttles for the safe and effective therapeutic delivery of biologics to the brain.

bioengineering↗

bioRxiv2025

Development of potent humanized TNFα inhibitory nanobodies for therapeutic applications in TNFα-mediated diseases

Tumor necrosis factor-alpha (TNF) is a key pro-inflammatory cytokine implicated in the pathogenesis of numerous inflammatory and autoimmune diseases, including rheumatoid arthritis, inflammatory bowel disease, and neurodegenerative disorders such as Alzheimers Disease. Effective inhibition of TNF is essential for mitigating disease progression and improving patient outcomes. In this study, we present the development and comprehensive characterization of potent humanized TNF inhibitory nanobodies (TNFINbs) derived from camelid single-domain antibodies. In silico analysis of the original camelid nanobodies revealed low immunogenicity, which was further reduced through machine-learning-guided humanization and developability optimization. The two humanized TNFI-Nb variants we developed demonstrated exceptional anti-TNF activity, achieving IC50 values in the picomolar range. Binding assays confirmed their high affinity for TNF, underscoring robust neutralization capabilities. These TNFI-Nbs present valid alternatives to conventional monoclonal antibodies currently used in human therapy, offering potential advantages in potency, specificity, and reduced immunogenicity. Our findings establish a solid foundation for further preclinical development and clinical translation of TNF-targeted nanobody therapies in TNF-mediated diseases.

biochemistry↗

bioRxiv2024

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

1In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalisability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelts potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver. 2 Significance StatementRapidly predicting protein biophysical traits with accuracy is a key goal in protein engineering, yet efforts to develop reliable predictors are often hindered by limited and disparate experimental measurements. We introduce a framework to predict biophysical traits using few training data, leveraging diverse machine learning approaches via a semi-supervised framework combined with ensemble learning. We applied this framework to develop NanoMelt, a tool to predict nanobody thermostability trained on a new dataset of apparent melting temperatures. Nanobodies are increasingly important in research and therapeutics due to their ease of production and small size, which allows deeper tissue penetration and seamless combination into multi-specific compounds. NanoMelt outperforms available methods for protein thermostability prediction and can streamline nanobody development by guiding the design and selection of highly stable nanobodies during discovery and optimization campaigns.

bioengineering↗