Progettazione e sintesi di nuovi inibitori selettivi di ALDH1A3 come potenziali agenti anticancro
Bibliographic record
Abstract
Ad oggi, il cancro è la seconda causa di morte al mondo dopo le malattie cardiovascolari, anche a causa della scarsa efficacia delle terapie convenzionali, spesso poco selettive e limitate dall’insorgenza di farmaco-resistenza e dalla persistenza delle cellule staminali tumorali. La necessità di nuove opzioni terapeutiche con meccanismi d’azione innovativi ha portato all’attenzione la famiglia delle Aldeide Deidrogenasi (ALDH, EC: 1.2.1.3) ed, in particolare, dell’isoforma 1A3, sovraespressa in diversi tipi di tumori, come il glioblastoma multiforme (GBM), il tumore cerebrale primario aggressivo più comune, ed il cancro al seno triplo negativo (TNBC), un sottotipo di tumore che colpisce 8000 donne ogni anno in Italia, e riconosciuto biomarcatore delle cellule staminali tumorali. Nell’ultima decade, il gruppo di ricerca della Prof.ssa La Motta ha identificato una nuova classe di composti capaci di inibire selettivamente ALDH1A3. Recentemente, una III generazione, brevettata per il trattamento del tumore al seno, è stata sviluppata e l’hit compound ha mostrato risultati molto promettenti. In questo lavoro di tesi, una nuova serie di 9 derivati di III generazione è stata sintetizzata al fine di migliorare l’affinità e la selettività nei confronti dell’enzima bersaglio, esplorare lo spazio chimico del nucleo e migliorare le proprietà farmacocinetiche, in particolare la solubilità. I derivati sono stati inviati ai collaboratori presso l’Università di Dalhousie (Nova Scotia, Canada) per la valutazione dell’attività funzionale. Tali risultati saranno fondamentali per generare delle nuove SAR per indirizzare i successivi sforzi sintetici. To date, cancer is the second leading cause of death worldwide after cardiovascular disease. Poor outcomes are often associated with ineffective conventional therapies, the rising phenomenon of drug resistance, and the presence of cancer stem cells (CSCs). In need of new therapeutic approaches with innovative mechanisms of action, the Aldehyde Dehydrogenase (ALDH, EC: 1.2.1.3) family recently garnered significant attention from the scientific community. In particular, the 1A3 isoform, being identified as a CSC marker, is overexpressed in several types of tumors, such as glioblastoma multiforme (GBM), the most common aggressive primary brain tumor, and triple-negative breast cancer (TNBC), a tumor subtype that affects nearly 8000 women every year just in Italy. Over the past decade, Prof. La Motta’s research group has identified a new class of compounds capable of selectively inhibiting ALDH1A3. Recently, a library of third-generation inhibitors was developed, with one compound (VE3) emerging for its promising results in an in vivo breast cancer model, and an Italian provisional patent was applied. In this thesis work, a new series of 9 third-generation derivatives was designed and developed to explore the chemical space of the scaffold in the attempt to improve affinity and selectivity towards the targeted enzyme, generate robust structure-activity relationships, and enhance the pharmacokinetic properties, e.g. water solubility. The compounds are currently under functional activity evaluation at Dalhousie University (Nova Scotia, Canada).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".