Novel Approaches for Efficient Delivery of Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Epidermal growth factor receptors (EGFRs) have potential to be considered as therapeutic target for cancer treatment especially in cancer patients with overexpression of EGFR. Cetuximab as a first monoclonal antibody and Imatinib as the first small molecule tyrosine kinase inhibitor (SMTKI) were approved by FDA in 1998 and 2001. About 28 SMTKIs have been approved until 2015 and a large number of compound with kinase inhibitory activity are at the different phases of clinical trials. Although Kinase inhibitors target specific intracellular pathways, their tissue or cellular distribution are not specific. So treatment with these drugs causes serious dose dependent side effects. Targeted delivery of kinase inhibitors via dendrimers, polymeric nanoparticles, magnetic nanoparticles and lipid based delivery systems such as liposomes, solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC) can lead to reduction of side effects and improving therapeutic efficacy of the drugs in the target organs. Furthermore formulation of these drugs is challenged by their physicochemical properties such as solubility and dissolution rate. The main approaches in order to increase dissolution rate, are particle size reduction, self-emulsification, cyclodextrin complexation, crystal modification and amorphous solid dispersion. Synergistic therapeutic effect, decreased side effects and drug resistant, reduced cost and increased patient compliance are the advantages associated with using combination therapy especially in the treatment of cancer. Combination of TKIs with chemotherapeutic agents or biopharmaceuticals such as monoclonal antibodies and oligonucleotides and also combination of two TKIs within one formulation is possible by new targeting delivery systems. This article reviews the recent advances in the design and development of delivery systems for TKIs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".