Isolation of human monoclonal antibodies by mammalian cell display
Bibliographic record
Abstract
Due to their low immunogenicity in patients, humanized or fully human mAbs are becoming increasingly important for the treatment of a growing number of diseases, including cancer, infections, and immune disorders. Here, we describe a technology allowing for the rapid isolation of fully human mAbs. In contrast to previously described methods, B cells specific for an antigen of interest are directly isolated from peripheral blood mononuclear cells (PBMC) of human donors. Recombinant, antigen-specific single-chain Fv (scFv) libraries are generated from this pool of B cells and screened by mammalian cell surface display by using a Sindbis virus expression system. This method allows isolating antigen-specific antibodies by a single round of FACS. The variable regions (VRs) of the heavy chains (HCs) and light chains (LCs) are isolated from positive clones and recombinant fully human antibodies produced as whole IgG or Fab fragments. In this manner, several hypermutated high-affinity antibodies binding the Qbeta virus like particle (VLP), a model viral antigen, as well as antibodies specific for nicotine were isolated. All antibodies showed high expression levels in cell culture. The human nicotine-specific mAbs were validated preclinically in a mouse model. Thus, the technology presented here allows for rapid isolation of high-affinity, fully human antibodies with therapeutic potential from human volunteers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".