Treatment with monoclonal antibodies in cancer - efficacy and prospects
Bibliographic record
Abstract
Objective: This review paper examines the effectiveness and prospects of monoclonal antibody therapies in oncology. The development of these therapies has revolutionized targeted cancer treatment due to the specificity and molecular precision of the antibodies. The article discusses their mechanisms of action, clinical applications and new trends, with a special focus on the role of personalized therapies. Materials and Methods: The review covers key studies on monoclonal antibody therapies, analyzing their mechanisms of action, such as antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). The paper also considers the integration of these therapies with other treatments, such as chemotherapy and immunotherapy. Main results: Monoclonal antibodies have shown high efficacy in the treatment of various cancers, including breast, ovarian and lung cancers, by targeting specific antigens such as HER2 and PD-1/PD-L1. Advances in bispecific antibodies, drug-antibody conjugates and personalized biomarkers are further improving treatment outcomes. Challenges such as resistance and side effects are being addressed through genetic engineering and innovative drug delivery systems. Conclusions: Monoclonal antibody therapies have revolutionized cancer treatment, offering precise and personalized therapeutic approaches. Further research into combination therapies and new antibody technologies promises to overcome current limitations and expand their therapeutic potential.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".