Biology subjects

Onuoha, S.

Publications and source records attributed to Onuoha, S..

5 recordsLinked to original sources

bioRxiv2026

Engineering armoured in vivo CAR T cells through targeted delivery and transient mRNA gating

In vivo generation of chimeric antigen receptor (CAR) T cells offers an off-the-shelf, scalable alternative to ex-vivo therapies, but is constrained by inefficiency, toxicity and durability. We report the first armoured in-vivo CAR T platform with tunable, transient activity, that enhances safety and efficacy. Our platform utilises CD8 targeted lipid nanoparticles (LNP) to transiently deliver CAR and armouring membrane-bound IL12 mRNAs. Payloads utilize a T-cell-restricted (T-trex) strategy to constrain CAR and IL-12 expression to T cells, enabling potent antigen-dependent activity with spatio-temporal control. Selective targeting of T-cells with a T-trex CAR showed enhanced expression. The addition of T-trex IL12 mRNA provides a tunable, localised amplification of CAR function, enhancing antigen-dependent cytotoxicity and cytokine production with robust tumour control. These findings establish a novel dose-efficient armoured in-vivo CAR T approach and general framework for programmable immune cell engineering.

immunology↗

bioRxiv2025

Deep learning assessment of nativeness and pairing likelihood for antibody and nanobody design with AbNatiV2

1Immune systems create antibodies that balance good binding and stability with low toxicity and self- reactivity. Quantifying the nativeness of a candidate sequence - its likelihood of belonging to natural immune repertoires - has thus emerged as a valuable strategy for hit selection from synthetic libraries, optimisation and humanisation, and for guiding de novo design towards developable candidates. We previously introduced AbNatiV, a transformer-based VQ-VAE for nativeness assessment, which proved effective across multiple nanobody engineering tasks. However, AbNatiV1 operated on unpaired sequences, limiting applicability to conventional VH-VL antibodies. Moreover, its performance on nanobody nativeness was constrained by the limited number and diversity of nanobody repertoires available at the time. Here, we sequenced new camelid repertoires, curated additional recent datasets, and present AbNatiV2: an enhanced architecture comprising various models each trained on [≥] 20 million sequences. AbNatiV2 improves nanobody nativeness classification across held-out and diverse test sets, and more robustly detects nativeness changes upon CDR grafting. We also introduce p-AbNatiV2, a cross-attention model fine-tuned on 3.7 million paired human sequences. p-AbNatiV2 provides residue- and sequence-level humanness for VH/VL pairs and learns pairing-likelihood via noise-contrastive training. On held-out tests, it assigns the native pair a higher score in 74% of cases, substantially outperforming recent pairing models. Together, AbNatiV2 and p-AbNatiV2 extend nativeness assessment and engineering to both nanobodies and conventional antibodies, supporting design decisions at single-residue, Fv-sequence, and paired-domain levels. We make AbNatiV2 available as downloadable software and webserver.

bioengineering↗

bioRxiv2025

Highly potent novel multi-armoured IL13Rα2 CAR-T subverts the immunosuppressive microenvironment of Glioblastoma

Glioblastoma remains one of the most challenging and lethal brain cancers, with limited treatment options. While CAR-T cells have shown promise in some patients, sustaining T-cell activity and overcoming the immunosuppressive tumour microenvironment remain significant hurdles. Here, we present an armoured CAR-T cell design to address these challenges and enhance persistence in GBM tumours. We developed a highly specific humanised single-domain antibody targeting IL13R2 and included it alongside four additional modular elements in a single retroviral vector for CAR-T generation. Our results demonstrate that this single-cassette CAR-T cell design possesses high resilience against TGF-{beta}-mediated immunosuppression, enhanced tumour-killing capacity through IL-12 secretion while maintaining a favourable safety profile, extended persistence in the host, and an additional layer of safety control through the incorporation of a suicide switch. Importantly, despite its complexity, the construct can still be manufactured efficiently. These advancements represent a significant step forward in addressing key challenges associated with CAR-T cell therapy in solid tumours.

cancer biology↗

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↗

bioRxiv2021

Characterisation of a novel ACE2-based therapeutic with enhanced rather than reduced activity against SARS-CoV2 variants

The human angiotensin-converting enzyme 2 acts as the host cell receptor for SARS-CoV-2 and the other members of the Coronaviridae family SARS-CoV-1 and HCoV-NL63. Here we report the biophysical properties of the SARS-CoV-2 spike variants D614G, B.1.1.7 and B.1.351 with affinities to the ACE2 receptor and infectivity capacity, revealing weaknesses in the developed neutralising antibody approaches. Furthermore, we report a pre-clinical characterisation package for a soluble receptor decoy engineered to be catalytically inactive and immunologically inert, with broad neutralisation capacity, that represents an attractive therapeutic alternative in light of the mutational landscape of COVID-19. This construct efficiently neutralised four SARS-CoV-2 variants of concern. The decoy also displays antibody-like biophysical properties and manufacturability, strengthening its suitability as a first-line treatment option in prophylaxis or therapeutic regimens for COVID-19 and related viral infections.

immunology↗