Fc Engineering for Developing Therapeutic Bispecific Antibodies and Novel Scaffolds
Bibliographic record
Abstract
Therapeutic monoclonal antibodies [mAbs] have become molecules of choice to treat autoimmune disorders, inflammatory diseases and cancer. Moreover, bispecific/multispecific antibodies that target more than one antigen or epitope on a target cell or recruit effector cells [T cell, natural killer (NK) cell or Macrophage cell] towards target cells have shown great potential to maximize the benefits of antibody therapy. In the past decade, many novel concepts to generate bispecific and multispecific antibodies have evolved successfully into a range of formats from full bispecific immunoglobulin gammas [IgGs] to antibody fragments. Impressively, antibody fragments such as bispecific T-cell engager [BiTE], bispecific killer cell engager [BiKE], trispecific killer cell engager [TriKE], tandem diabody [Tandab] and dual-affinity-retargeting [DART] are showing exciting results in terms of recruiting and activating self-immune effector cells to target and lyse tumor cells. Promisingly, Fc antigen binding fragment [Fcab] and monomeric antibody or half antibody may be particularly advantageous to target solid tumours owing to their small size and thus good tissue penetration potential while, on the other hand, keeping crystallizable fragment [Fc] related effector functions such as antibody-dependent cellular cytotoxicity [ADCC], complement-dependent cytotoxicity [CDC], antibody dependent cell-mediated phagocytosis [ADCP] and extended serum half-life via interaction with neonatal Fc receptor [FcRn]. This review, therefore, focuses on the progress of Fc engineering in generating bispecific molecules and on the use of small antibody fragment as scaffolds for therapeutic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".