Drug combination nanoparticles for advanced treatment of metastatic TNBC
用于晚期治疗转移性 TNBC 的药物组合纳米颗粒
基本信息
- 批准号:10648912
- 负责人:
- 金额:$ 19.98万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-03-10 至 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:Animal ModelAntibody-drug conjugatesAntimetabolitesBehaviorBindingBiological MarkersBreast Cancer CellBreast Cancer ModelBreast Cancer TreatmentCause of DeathCell Surface ReceptorsCellsClinicClinicalCombination Drug TherapyCombined Modality TherapyDevelopmentDiseaseDisseminated Malignant NeoplasmDistantDoseDrug CombinationsDrug CompoundingDrug Delivery SystemsDrug KineticsDrug SynergismDrug TargetingENG geneEligibility DeterminationEndoglinFormulationFutureGoalsGuidelinesHalf-LifeHumanImageImmune checkpoint inhibitorImmunocompetentImmunotherapeutic agentImmunotherapyIn VitroIntercellular adhesion molecule 1Life ExtensionLigandsLipidsLungMembrane GlycoproteinsMessenger RNAMetastatic Neoplasm to the LungMetastatic breast cancerMicrometastasisModelingMonitorMusMyeloid-derived suppressor cellsNeoplasm MetastasisNew AgentsOutcomePD-1/PD-L1PDL1 inhibitorsPaclitaxelPatientsPeptidesPharmaceutical PreparationsPlasmaPopulationPrimary NeoplasmPrognosisPropertyPublishingRegimenReportingSiteSpleenSurvival RateTechnologyTestingTherapeuticTimeTissuesToxic effectTreatment EfficacyTreatment outcomeUp-RegulationWomanWorkanti-PD-L1 therapyanti-PD1 antibodiescancer cellcancer immunotherapycellular targetingcheckpoint therapychemotherapyclinical translationdosagegemcitabineimmunoregulationimprovedimproved outcomein vivoindexinginnovationinsightlipid nanoparticlemalignant breast neoplasmmortalitynanoformulationnanoparticlenovel drug combinationnovel therapeuticspembrolizumabpeptide drugprogrammed cell death ligand 1receptorreceptor expressionresponsesynergismsystemic toxicitytargeted treatmenttaxanetherapeutic targettherapy outcometreatment effecttriple-negative invasive breast carcinomatumortumor growthuptake
项目摘要
Project Description
Triple negative breast cancer (TNBC) is the most challenging subtype to treat among all breast cancer cases
due to lack of cell surface receptors. Patients with metastatic TNBC have median overall survival of only 13.3
months with treatment. Newly approved immune checkpoint inhibitors such as PD-1 antibody only extend the
survival by 2-4 months when used with chemotherapy, and the efficacy highly relies on expression of targets
such as PD-L1 in tumors. Despite new treatment options, metastatic TNBC is predominantly treated by highly
potent chemotherapy, with combination therapy being advantageous over single agents regarding response rate
and overall survival. However, the drugs in combination chemotherapy have distinct physicochemical properties
that hinders formulation into single dosage; their distinct pharmacokinetic behavior hinders the in vivo synergism.
Furthermore, these drugs are non-specific to cancer cells and often show systemic toxicity. Our long-term goal
is to develop advanced and well-characterized drug delivery approaches to improve treatment outcomes of drugs
and drug combinations. In this project, we propose a dual-drug loaded dual-peptide ligand incorporated drug
combination nanoparticle (DCNP) approach for targeting and inhibition of metastatic TNBC. The lipid-based
DCNP leverage on effective gemcitabine and paclitaxel combination in clinic to deliver both drugs in prolonged
and synchronized manner. Through incorporation of two peptide ligands that targets TNBC cells (through ICAM-
1) and tumor vasculature (through endoglin), the DCNP could target both primary tumor and metastasis for
enhanced chemotherapy. Furthermore, both drugs are reported to upregulate PD-L1 and reduce population of
myeloid derived suppressor cells (MDSCs) in the tumor. These effects can sensitize TNBC cells for PD-1/PD-L1
therapy. The DCNPs that synchronize and co-deliver both drugs may further augment such effects thus generate
improved chemoimmuno therapeutic outcomes. The objectives of this project are to validate the concepts of
improved targeting and chemotherapy of metastatic TNBC with a dual-targeting DCNP approach and enhanced
combination therapy with immune checkpoint inhibitors. In Aim 1, we will prepare ligands-incorporated DCNPs
with high drug loading, proper size, and good stability, and test their cellular binding and efficacy, and effects on
cancer cell expression of PD-L1. In Aim 2, we will validate enhanced in vivo targeting efficiency with the dual-
ligand strategy and correlate it with expression levels of receptors and evaluate inhibition of primary tumor and
metastasis with monitoring of survival. In Aim 3, we will assess the upregulation of PD-L1 and reduction of
MDSCs by the DCNPs and carry out a pilot survival study with a chemoimmuno combination regimen. If
successful, this targeted drug combination strategy could substantially improve the treatment of metastatic TNBC.
Through tuning composition and targeting ligands, this highly translatable nanoformulation technology can be
potentially used for existing and future drug combinations to treat various metastatic cancers.
项目描述
三阴性乳腺癌(TNBC)是所有乳腺癌病例中治疗最具挑战性的亚型
因为缺乏细胞表面受体。转移性TNBC患者的中位总生存期仅为13.3
数月治疗。新批准的免疫检查点抑制剂,如PD-1抗体,仅延长了
当与化疗一起使用时,存活2-4个月,并且功效高度依赖于靶点的表达
例如肿瘤中的PD-L1。尽管有新的治疗选择,但转移性TNBC主要通过高度免疫抑制剂治疗。
强效化疗,联合治疗在缓解率方面优于单药治疗
和总生存期。然而,联合化疗中的药物具有独特的理化性质
这阻碍了配制成单剂量;它们独特的药代动力学行为阻碍了体内协同作用。
此外,这些药物对癌细胞是非特异性的,并且通常表现出全身毒性。我们的长期目标
是开发先进的和特征良好的药物输送方法,以改善药物的治疗效果,
和药物组合。在本项目中,我们提出了一种双载药的双肽配体掺入药物
本发明提供了用于靶向和抑制转移性TNBC的组合纳米颗粒(DCNP)方法。基于脂质的
DCNP在临床中利用有效的吉西他滨和紫杉醇组合,以延长两种药物的给药时间
同步的方式。通过掺入靶向TNBC细胞的两种肽配体(通过ICAM-1),
1)和肿瘤血管(通过内皮糖蛋白),DCNP可以靶向原发肿瘤和转移瘤,
加强化疗此外,据报道,这两种药物都能上调PD-L1并减少
骨髓来源的抑制细胞(MDSC)在肿瘤中的作用。这些作用可使TNBC细胞对PD-1/PD-L1敏感
疗法同步和共同递送两种药物的DCNP可以进一步增强这种作用,从而产生
改善化学免疫治疗结果。本项目的目标是验证以下概念:
用双靶向DCNP方法改善转移性TNBC的靶向和化疗,
与免疫检查点抑制剂的联合治疗。在目标1中,我们将制备配体掺入的DCNP
具有载药量高、大小合适、稳定性好等特点,并测试了它们的细胞结合力和药效,
PD-L1的癌细胞表达。在目标2中,我们将验证用双靶向药物增强的体内靶向效率。
配体策略,并将其与受体的表达水平相关联,并评估原发性肿瘤的抑制,
转移并监测存活率。在目标3中,我们将评估PD-L1的上调和PD-L2的减少。
通过DCNPs的MDSC,并进行了一项初步的生存研究与化学免疫组合方案。如果
如果成功的话,这种靶向药物组合策略可以显著改善转移性TNBC的治疗。
通过调整组合物和靶向配体,这种高度可转化的纳米制剂技术可以被应用于临床。
潜在地用于现有和未来的药物组合以治疗各种转移性癌症。
项目成果
期刊论文数量(0)
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