PD-L1 as a Potential Target in Cancer Therapy (Review)
Bibliographic record
Abstract
Introduction. Cancer is one of the most serious and common diseases with a high level of mortality. Due to this reason the searching of new directions and methods of cancer treatment is becoming more and more important with each passing year. Significant advances in cancer immunotherapy have been reached over the past few decades. Moreover, an inhibition of the interaction between the programmed cell death receptor (PD-1) and its ligand (PD-L1), is sure to be perspective direction of the immuno-oncological therapy development. Text. PD-1/PD-L1 interaction plays a pivotal role in negative regulation of immune system, that protects host’s cells and tissues from the excessive immune response. However, it is also used by tumor cells to avoid the host's immune system. The discovery of this mechanism led to the development of inhibiting PD-1 or PD-L1 agents that enhance anti-tumor immunity. Meanwhile, anti-PD-L1 agents provide less toxicity in comparison with anti-PD-1 agents. FDA currently approved Atesolizumab, Durvalumab, and Avelumab PD-L1 inhibitors for cancer treatment. These agents demonstrated effective response during the clinical trials, however, they are used for a limited number of oncological diseases. In addition, BMS-936559 is a promising agent that had passed the first stage of the clinical trials. Nevertheless, immunotherapy involving PD-L1 inhibitors is closely related to a vast number of severe side effects including immune-mediated effects caused by the inhibition of PD-L1 ligands located on healthy cells. In these terms, the development of new agents deprived of these disadvantages is the reason for further studies. Conclusion. Immunotherapy in cancer uncovers new perspectives in treatment of refractory to standard therapies forms of cancer. And the development of new and improvement of existing PD-L1 blocking agents are of great importance in fighting against tumoral diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".