A general approach to reduce off-target radioactivity in vivo via Tetrazine-Knock-Out (TKO)

Sarkar, S.; Pham, J. M.; Edwards, K. J.; Sharma, N.; Xu, K.; King, A. P.; Del Castillo, A. F.; Farwell, M. D.; Pryma, D. A.; Schuster, S. J.; Sellmyer, M. A.

doi:10.1101/2024.05.30.596510

bioRxiv · 10.1101/2024.05.30.596510

A general approach to reduce off-target radioactivity in vivo via Tetrazine-Knock-Out (TKO)

Sarkar, S.·Pham, J. M.·Edwards, K. J.·Sharma, N.·Xu, K.·King, A. P.·Del Castillo, A. F.·Farwell, M. D.·Pryma, D. A.·Schuster, S. J.·Sellmyer, M. A.

View original source ↗Export metadata ↓

Abstract

Monoclonal antibodies have had a remarkable impact on cancer therapy due to their high target specificity. However, their large molecular weight results in slow blood clearance, which can take weeks to clear from circulation. As companion nuclear imaging and diagnostic tools, these characteristics force delayed imaging and the use of isotopes with long half-lives such as 89Zr. For optimal clinical application, it is desirable that radioimmunoconjugates remain in the blood for just enough time to accumulate adequately in target tissues, while non-targeted or circulating radioactivity is ideally rapidly excreted from the body to maximize imaging contrast and minimize radiation dose to healthy tissues. We addressed the current challenges of antibody-based imaging by developing rituximab radioimmunoconjugates that accumulate sufficient activity for tumor imaging within 24 h of administration, while clearing circulating radioactivity via administration of a small molecule clearing agent. Rituximab, an anti-CD20 monoclonal antibody, is used as standard first-line therapy for diffuse large B-cell lymphoma. CD20 is expressed by 95% of B-lymphocytes and their malignant counterparts, making it a therapeutic target for B-cell malignancies. We attached 125I, 68Ga, and 89Zr to rituximab using a "clickable" linker containing trans-cyclooctene and tested the ability of tetrazines to induce the inverse electron demand Diels-Alder reaction (iEDDA) after antibody administration. This "tetrazine-knock-out" (TKO) approach liberates the radioactivity from rituximab in the bloodstream, resulting in its rapid renal excretion which enhances target-to-background ratios, and minimizes off-target radiation exposure. Due to the internalization of the radioimmunoconjugate in CD20+ tumor cells, no substantial clearance was observed from Raji xenografts. We characterized different leaving groups, several cellular models and antibodies with distinct internalizaing properties. The TKO approach opens opportunities to use radiolabeled antibodies for low-abundance or heterogeneously expressed biologic targets and may allow radioimmunotherapy (RIT) for targets traditionally untenable due to dose-limiting toxicities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/596510v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11bc2f0org.highwire.dtl.DTLVardef@19b4f72org.highwire.dtl.DTLVardef@17cee32org.highwire.dtl.DTLVardef@fa4491_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

bioengineering Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Sarkar, S., Pham, J. M., Edwards, K. J., Sharma, N., Xu, K., King, A. P., Del Castillo, A. F., Farwell, M. D., Pryma, D. A., Schuster, S. J., Sellmyer, M. A.. 2024-06-02. A general approach to reduce off-target radioactivity in vivo via Tetrazine-Knock-Out (TKO). https://doi.org/10.1101/2024.05.30.596510

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

AtomWeaver: Multi-Component Flow Matching with a Structured Geometric Prior Facilitates Non-Canonical Peptide Design

Fixed-backbone sequence discovery, or inverse folding, is a critical recurring task in the development of new polypeptide therapeutics. Once promising backbones are established for a target pocket, computational inverse folding methods greatly help accelerate generation of candidate sequences. Such methods are mature for the traditional case of limiting to the fixed twenty-letter canonical vocabulary; however, they cannot access the broader space of non-canonical amino acids (NCAAs). This design constraint is exacerbated for peptide binders, a fast-growing modality that readily incorporates NCAAs, though in practice non-canonical design frequently depends on laborious medicinal-chemistry campaigns. An extension of inverse folding to NCAAs is thus critical to accelerating design of novel therapeutic peptides. AtomWeaver uses a joint all-site, atom-level generative scheme that does not restrict side-chain categorical assignment by either predetermined or co-resolving residue identity. Conditioned only on a fixed peptide backbone and its target protein, its multi-component flow guides side-chain atoms as unlabeled points in R3 from a nested shell prior to a variable-count final atom cloud. Identity is then read by matching each predicted cloud against a reference library of canonical and non-canonical templates. Since identity is decided only at decode time, the addressable vocabulary is a property of the library rather than of the trained weights: a new NCAA costs one reference structure and no retraining, and the model can select residues it was never prompted for and never saw in training. On a deep mutational scan of two peptide-target systems, AtomWeaver's canonical readout shows high observed mean agreement with experimental values among the compared inverse-folding methods. In the mixed canonical-noncanonical setting that canonical-only baselines cannot support at all, it likewise retains ranking signal across both systems. AtomWeaver also displayed self-consistent designs on de novo binder backbones, with the highest interface confidence among compared methods. Notably, it reached these metrics while achieving broad empirical coverage of our 300-residue vocabulary, including four non-canonical types never visible in training. AtomWeaver thus serves canonical and non-canonical peptide design alike, while transforming residue vocabulary to an expandable inference-time choice.

bioengineering↗

bioRxiv2026

The Influence of Obesity and Body Shape on Sagittal Plane Knee Kinematics and Kinetics during Obstacle Crossing

Altered walking mechanics in individuals with obesity can contribute to knee osteoarthritis. The gait deviations may become more pronounced during obstacle crossing. In women, body fat distribution may further influence knee load, especially when excess fat accumulates in the thighs and hips. However, relatively little is known about how regional fat distribution affects gait in women with obesity. This study investigated how obesity and, among women, different fat distributions (Apple: more abdominal fat; Pear: more lower-limb fat) influence knee biomechanics during walking with and without obstacle crossing. Participants were 15 controls without obesity (NB) and 27 with obesity (OB). Within female participants, 10 without obesity (fNB) were compared with 20 with obesity, stratified by waist-hip ratio (Apple:10, Pear:10). Speed-adjusted statistical parametric mapping applied a general linear model (NB vs. OB) and an analysis of covariance (fNB vs. Apple vs. Pear). OB exhibited a significantly greater late-stance knee extension moment than NB across all tasks, and this difference persisted among fNB, Apple, and Pear in obstacle tasks (p<0.05). OB walked with reduced knee flexion during the early-stance leading limb after crossing a medium-height obstacle (p=0.048) and a high-height obstacle (p=0.008) compared to NB. There were significant body-shape effects (p<0.05), and post-hoc comparisons confirmed that Pear had lower knee angles than fNB in both leading-limb conditions after crossing medium- and high-height obstacles (p=0.008 and p=0.001, respectively). These findings suggest that obstacle crossing helps illuminate how excess weight influences knee biomechanics, and how regional fat distribution modulates the degree of this alteration.

bioengineering↗

bioRxiv2026

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗