Tumor-Targeted Nanoparticles Deliver a Vitamin D-Based Drug Payload for the Treatment of EGFR Tyrosine Kinase Inhibitor-Resistant Lung Cancer
Bibliographic record
Abstract
Mutation in the tyrosine kinase (TK) domain of the epidermal growth factor receptor ( EGFR) gene drives the development of lung cancer. EGFR tyrosine kinase inhibitors (EGFR TKIs), including erlotinib and afatinib, are initially effective in treating EGFR mutant nonsmall cell lung cancer (NSCLC). However, drug resistance quickly develops due to several mechanisms, including induction of the epithelial-mesenchymal transition (EMT). No effective therapies are currently available for patients who develop EMT-associated EGFR TKI resistance. 1,25-Dihydroxyvitamin D3 (1,25D3) promotes epithelial differentiation and inhibits growth of NSCLC cells. 1,25D3 thus represents a promising agent for the treatment of EMT-associated EGFR TKI resistance. However, 1,25D3 induces the expression of 24-hydroxylase (24OHase), which decreases 1,25D3 activity. CTA091, a potent and selective 24OHase inhibitor, has been developed to attenuate this adverse effect. CTA091 also suppresses renal 24OHase activity and so may promote hypercalcemia. To exploit favorable effects of 1,25D3 plus CTA091 in tumor cells while avoiding problematic systemic effects of 24OHase inhibition, we developed EGFR-targeted, liposomal nanoparticles (EGFR-LP) to offer tumor-targeted co-delivery of 1,25D3 and CTA091. We then established an EMT-associated model of EGFR TKI resistance, and showed that such nanoparticles improved cellular uptake of 1,25D3 and CTA091, drove pro-epithelial signaling by upregulating E-cadherin ( CDH1), and significantly inhibited the growth of EGFR TKI resistant cells. Our results demonstrated that the delivery of vitamin D-based drug payloads via tumor-targeted EGFR-LP has promise as a new therapy for EFGR TKI resistant lung cancer. Future studies will focus on in vivo evaluation of biological activity, therapeutic benefits, and systemic toxicity prior to clinical translation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".