Monoclonal and bispecific antibodies as novel therapeutics
Bibliographic record
Abstract
Gene amplification, over-expression, and mutation of growth factors, or the receptors themselves, causes increased signaling through receptor kinases, which has been implicated in many human cancers and is associated with poor prognosis. Tumor growth has been shown to be decreased by interrupting this process of extensive growth factor-mediated signaling by directly targeting either the surface receptor or the ligand and thereby preventing cell survival and promoting apoptosis. Monoclonal antibodies have long been eyed as a potential new class of therapeutics targeting cancer and other diseases. Antibody-based therapy initially entered clinical practice when trastuzumab/Herceptin became the first clinically approved drug against an oncogene product as a well-established blocking reagent for tumors with hyperactivity of epidermal growth factor signaling pathways. In the first part of this review we explain basic terms related to the development of antibody-based drugs, give a brief historic perspective of the field, and also touch on topics such as the "humanization of antibodie" or creation of hybrid antibodies. The second part of the review gives an overview of the clinical usage of bispecific antibodies and antibodies "armed" with cytotoxic agents or enzymes. Further within this section, cancer-specific, site-specific, or signaling pathway-specific therapies are discussed in detail. Among other antibody-based therapeutic products, we discuss: Avastin (bevacizumab), CG76030, Theragyn (pemtumomab), daclizumab (Zenapax), TriAb, MDX-210, Herceptin (trastuzumab), panitumumab (ABX-EGF), mastuzimab (EMD-72000), Erbitux (certuximab, IMC225), Panorex (edrecolomab), STI571, CeaVac, Campath (alemtuizumab), Mylotarg (gemtuzumab, ozogamicin), and many others. The end of the review deliberates upon potential problems associated with cancer immunotherapy.
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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) | 0.001 | 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) | 0.004 | 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".