A Perspective on EGFR and Proteasome-based Targeted Therapy forCancer
Bibliographic record
Abstract
BACKGROUND: Cancer is known to be the most leading cause of death worldwide. It is understood that the sources causing cancer mainly include the activity of endogenous oncogenes, nonviral compounds and the fundamental portion of these oncogenes; the tyrosine kinase activity and proteasome activity are the main biomarkers responsible for cell proliferation. These biomarkers can be used as main targets and are believed to be the 'prime switches' for the signal communication activity to regulate cell death and cell cycle. Thus, signal transduction inhibitors (ligandreceptor tyrosine kinase inhibitors) and proteasome inhibitors can be used as a therapeutic modality to block the action of signaling between the cells as well as protein breakdown in order to induce cell apoptosis. AIMS: This article highlights the key points and provides an overview of the recent patents on EGFR and proteosome-based inhibitors having therapeutic efficacy. This review focuses on the patents related to therapeutic agents, their preparation process and the final outcome. OBJECTIVE: The main objective of this study is to facilitate the advancement and current perspectives in the treatment of cancer. CONCLUSION: There are numerous strategies discussed in these patents to improve the pharmacokinetics and pharmacodynamics of EGFR and proteasome inhibitors. Further, the resistance to targeted therapy after long-term treatment can be overcome by using various excipients that can be used as a strategy to carry the drug. However, there is a need and scope for improving targeted therapeutics for cancer treatment with better fundamentals and characteristics. The widespread research on cancer therapy can create the path for future advancements in therapy with more prominent outcomes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".