Programmable Delivery of Synergistic Cancer Drug Combinations Using Bicompartmental Nanoparticles

被引:13
作者
Gregory, Jason V. [1 ,2 ]
Vogus, Douglas R. [3 ]
Barajas, Alexandra [4 ]
Cadena, Melissa A. [5 ]
Mitragotri, Samir [3 ]
Lahann, Joerg [1 ,2 ,5 ]
机构
[1] Univ Michigan, Biointerfaces Inst, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Chem Engn, Ann Arbor, MI 48109 USA
[3] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[4] Univ Calif Santa Barbara, Dept Chem Engn, Santa Barbara, CA 93106 USA
[5] Univ Michigan, Dept Biomed Engn, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
cancer; combination therapy; drug delivery; nanomedicine; nanoparticles; CO-DELIVERY; MULTIDRUG-RESISTANCE; ACETALATED DEXTRAN; 1ST-LINE TREATMENT; IN-VITRO; LAPATINIB; PACLITAXEL; SYSTEM; PARTICLES; RELEASE;
D O I
10.1002/adhm.202000564
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Delivery of multiple therapeutics has become a preferred method of treating cancer, albeit differences in the biodistribution and pharmacokinetic profiles of individual drugs pose challenges in effectively delivering synergistic drug combinations to and at the tumor site. Here, bicompartmental Janus nanoparticles comprised of domains are reported with distinct bulk properties that allow for independent drug loading and release. Programmable drug release can be triggered by a change in the pH value and depends upon the bulk properties of the polymers used in the respective compartments, rather than the molecular structures of the active agents. Bicompartmental nanoparticles delivering a synergistic combination of lapatinib and paclitaxel result in increased activity against HER2+ breast cancer cells. Surprisingly, the dual drug loaded particles also show significant efficacy toward triple negative breast cancer, even though this cancer model is unresponsive to lapatinib alone. The broad versatility of the nanoparticle platform allows for rapid exploration of a wide range of drug combinations where both their relative drug ratios and temporal release profiles can be optimized.
引用
收藏
页数:11
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