Novel anti-CD30/CD3 bispecific antibodies activate human T cells and mediate potent anti-tumor activity
Bibliographic record
Abstract
CD30 is expressed on Hodgkin lymphomas (HL), many non-Hodgkin lymphomas (NHLs), and non-lymphoid malignancies in children and adults. Tumor expression, combined with restricted expression in healthy tissues, identifies CD30 as a promising immunotherapy target. An anti-CD30 antibody-drug conjugate (ADC) has been approved by the FDA for HL. While anti-CD30 ADCs and chimeric antigen receptors (CARs) have shown promise, their shortcomings and toxicities suggest that alternative treatments are needed. We developed novel anti-CD30 x anti-CD3 bispecific antibodies (biAbs) to coat activated patient T cells (ATCs) ex vivo prior to autologous re-infusions. Our goal is to harness the dual specificity of the biAb, the power of cellular therapy, and the safety of non-genetically modified autologous T cell infusions. We present a comprehensive characterization of the CD30 binding and tumor cell killing properties of these biAbs. Five unique murine monoclonal antibodies (mAbs) were generated against the extracellular domain of human CD30. Resultant anti-CD30 mAbs were purified and screened for binding specificity, affinity, and epitope recognition. Two lead mAb candidates with unique sequences and CD30 binding clusters that differ from the ADC in clinical use were identified. These mAbs were chemically conjugated with OKT3 (an anti-CD3 mAb). ATCs were armed and evaluated in vitro for binding, cytokine production, and cytotoxicity against tumor lines and then in vivo for tumor cell killing. Our lead mAb was subcloned to make a Master Cell Bank (MCB) and screened for binding against a library of human cell surface proteins. Only huCD30 was bound. These studies support a clinical trial in development employing ex vivo-loading of autologous T cells with this novel biAb.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".