Effective Treatment of Human CML By RNAi In Vivo in a Xenotransplantation Mouse Model
Notice bibliographique
Résumé
Abstract Background: Considering the heterogeneity of leukemic cells in patients, current treatment regimens of chemotherapy and bone marrow transplantation lack specificity and are associated with frequent relapses and severe adverse effects. Hence, there is a need to develop novel therapeutics that can target the disease by its molecular fingerprint with minimal side effects.Despite the wide potential of RNA interference (RNAi) for translational therapeutics, systemic application of siRNA is hampered by rapid renal clearance, degradation by serum nucleases or associated immune responses. Lipid nanoparticles (LNPs) containing ionizable cationic lipids, when mixed with siRNA, embody the most advanced delivery platform for systemic administration of siRNA based therapeutics. Here, we established and employed the BCR-ABL dependent K562-CML xenotransplantation model as a proof of principle to validate LNP mediated siRNA functional delivery in vivo. Methods and Results: A microfluidic mixing technology was used to obtain reproducible ionizable cationic LNPs loaded with anti-BCR-ABL or CTRL siRNA. To determine the delivery efficiency of LNP-siRNA formulations, human leukemic K562 cells were incubated with siRNA-containing LNPs at various concentrations. Almost 100% of cells had taken up siRNA containing LNPs even at the lowest concentration of 0.0625µg/ml with stable uptake kinetics. We also observed near 100% uptake of LNP-siRNA in hard to transfect primary patient cells (CML, AML, ALL and MDS). Looking at the on-target functional efficacy of LNP-siRNA formulations, we observed a time and dose dependent increase in apoptosis (annexin V assay) and decrease in cell viability (alamar blue assay) of K562 cells treated with anti-BCR-ABL siRNA but not CTRL siRNA. A robust knockdown in BCR-ABL mRNA levels (65-90%) at 72 hours and protein at 96 hours was observed which confirmed that cell death was an on-target effect. Colony-forming potential of primary human CD34+ CML cells was significantly reduced when treated with anti-BCR-ABL siRNA compared to CTRL siRNA and to CD34+ cells from healthy donors. To translate our findings in vivo, we evaluated the safety profile, delivery potential and functional efficacy of LNP-siRNA in mice. A total dose of 15mg/kg (3 injections of 5mg/kg at day 0, 1 and 2) in healthy NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) mice resulted in 100% LNP positive cells in peripheral blood up to day 10. The formulations were highly tolerable in vivo with no significant differences in body weight and complete blood counts between treated and control mice. Moreover, serum analysis showed no significant toxicity in mice following LNP-siRNA treatment. With a focus on hematopoietic tissues following systemic administration, NSG mice received transplants of human K562 cells (stably expressing GFP and luciferase) intrafemorally and were injected intravenously for 3 consecutive injections of LNP-siRNA (1 or 5mg/kg body weight) at 8 hours interval. Interestingly, almost 100% LNP uptake was observed in xenograft leukemic cells in bone marrow at 48 hours at both doses. The leukemic burden of luciferase expressing K562 cells in mice was quantified using in vivo imaging before and during treatment. Treatment with anti-BCR-ABL siRNA for 10 days (n=7) resulted in a 0.5 fold decrease, whereas CTRL siRNA (n=7) resulted in a net 5-fold increase of luciferase signal, thus proving the efficacy of our approach in vivo. Conclusion: We show a highly efficient and non-toxic delivery in vitro and in vivo with nearly 100% uptake of LNP-siRNA formulations in bone marrow of leukemic mice. By inhibiting BCR-ABL we show a reduction of leukemic burden in our xenotransplant model, while leukemic cells expanded in CTRL siRNA treated mice. Our study provides a proof-of-principle that the combined use of lipid nanoparticles and RNAi technology can be used to target leukemia cells in vivo with promising therapeutic implications. Disclosures Walsh: Precision Nanosystems Inc.: Employment. Thomas:Precision Nanosystems Inc.: Employment. Ramsay:Precision Nanosystems Inc.: Employment. Heuser:Karyopharm: Research Funding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».