EGFr, FGFr and PDGFr: Emerging Targets for Anticancer Drug Design
Bibliographic record
Abstract
Number of cancer affected individuals are increasing day by each year, 11 million people are diagnosed with cancer out of which 7.6 million people die of this deadly disease which is a very significant figure in worldwide mortality. It has been estimated that there will be 16 million new cancer cases every year by 2020. Despite tremendous chemotherapeutics are given to treat cancer toxicity appears to be the most seminal point which can kill normal body cells along with abnormal cancerous cells. Therefore, researchers have been devoted to discover less toxic new chemotherapeutics which can prevent damage to the normal tissues. Recent advancements in molecular biology of cancer and different pathways involved in malignant transformation of cells clearly demonstrate that one of the important mechanisms for progression of cancer is abnormal signal transduction via tyrosine protein kinase. Tyrosine kinase catalyzes phosphorylation of tyrosine residues in proteins. The phosphorylation of protein residue results into the functions of protein. Tyrosine kinase function in many signal transduction cascades wherein extracellular signal is transmitted through the cell membrane receptors (EGFr/FGFr/PDGFr/C-src) to the nucleus where gene encoding this receptor protein maybe modified by this signal. Mutation of gene may causes abnormal signal transduction and leads to the progression of cancer. Therefore EGFr, FGFr and PDGFr have become the emerging targets for development of promising anticancer leads having lower toxicity. The present review is an attempt in this direction dealing with various aspects of cancer, molecular pharmacology of EGFr, FGFr and PDGFr tyrosine protein kinases which has a direct bearing on the design and development of newer chemotherapeutics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".