Strategies for Generating Diverse Antibody Repertoires Using Transgenic Animals Expressing Human Antibodies
Bibliographic record
Abstract
Therapeutic molecules derived from antibodies have become a dominant class of drugs used to treat human disease. Increasingly, therapeutic antibodies are discovered using transgenic animal systems that have been engineered to express human antibodies. While the engineering details differ, these platforms share the ability to raise an immune response that is comprised of antibodies with fully human idiotypes. Although the predominant transgenic host species has been mouse, the genomes of rats, rabbits, chickens and cows have also been modified to express human antibodies. The creation of transgenic animal platforms expressing human antibody repertoires has revolutionized therapeutic antibody drug discovery. The observation that the immune systems of these animals are able to recognize and respond to a wide range of therapeutically relevant human targets has led to a surge in antibody-derived drugs in current development. While the clinical success of fully human monoclonal antibodies derived from transgenic animals is well established, recent trends have seen increasingly stringent functional design goals and a shift in difficulty as the industry attempts to tackle the next generation of disease-associated targets. These challenges have been met with a number of novel approaches focused on the generation of large, high quality and diverse antibody repertoires. In this Perspective, we describe some of the strategies and considerations we use for manipulating the immune systems of transgenic animal platforms (such as XenoMouse®) with a focus on maximizing the diversity of the primary response and steering the ensuing antibody repertoire towards a desired outcome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".