Tailor‐Made Core‐Multishell Nanocarriers for the Delivery of Cationic Analgesics to Inflamed Tissue

Unbehauen, Michael; Łabuz, Dominika; Schwarzl, Richard; Moré, Sam Dylan; Netz, Roland R.; Stein, Christoph; Machelska, Halina; Haag, Rainer

doi:10.1002/adtp.201900007

Cited by 2 publications

(2 citation statements)

References 47 publications

(123 reference statements)

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“…In the past 50 years, clinical practice has been going through a transition fueled by technological developments. Biomed ical engineering, [1][2][3][4][5][6][7][8][9] material science, [10][11][12][13][14][15][16][17][18] and artificial intel ligence (AI) [19][20][21][22][23][24] have been transforming the entire medical landscape: diagnostics, [25][26][27][28][29][30][31][32][33] drug development, 24,[34][35][36] drug delivery, [37][38][39][40][41][42][43][44][45][46] and data analytics. [47][48][49]…”

Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

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Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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“…[14,15] Nanovehicles, such as nanoparticles and polymers, could simultaneously deliver two different drug payloads in tumors, but very limited biomaterial carriers to date have been approved clinically and using minimum amount of excipient as possible is encouraged for the large-scale industry. [16][17][18][19] In addition, the efficacy of the combinatorial drug treatment largely depends on a certain molar ratio of the coadministered drugs. For instance, combination of irinotecan and floxuridine can be synergistic at 1:1 molar ratio, but turns into antagonism at 1:10 molar ratio.…”

Section: Introductionmentioning

confidence: 99%

Active Targeting Nanoparticle Self‐Assembled from Cisplatin‐Palbociclib Amphiphiles Ensures Optimal Drug Ratio for Combinatorial Chemotherapy

Xiang

Liu

Chen

et al. 2021

Advanced Therapeutics

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Triple negative breast cancer (TNBC) not only exhibits aggressive progression and metastasis, but also responds to neither hormone therapy nor checkpoint blockade therapy. Thus, combinatorial chemotherapy is indispensable for TNBC treatment. However, due to the different pharmacokinetics of individual drugs and weak synergistic effects caused by random drug molar ratio in tumor, direct coadministration is far from satisfactory. Constructing a nanoscale drug delivery system with fixed drug molar ratios and maximum drug content is an urgent problem. Herein, an active targeting nanoparticle self‐assembled from cisplatin‐palbociclib amphiphiles with optimal drug ratio is reported. The combination of cisplatin and palbociclib at a molar ratio of 1:2 is found to have the best synergistic effect and is selected for follow‐up studies. After conjugating two palbociclib molecules to one cisplatin molecule, a fixed cisplatin/palbociclib (1:2) molar ratio is enabled and this amphiphilic conjugate can self‐assemble into nanoparticles. A small amount of iRGD coupled 1,2‐distearoyl‐sn‐glycero‐3‐phosphoethanolamine‐N‐polyethylene glycol‐2000 (DSPE‐PEG‐iRGD) that further stabilizes the nanoparticle is introduced to fabricate the desired nanoparticle (iRGD‐PCN) and endow active tumor‐targeting ability. On an orthotopic TNBC mouse model, iRGD‐PCN efficiently accumulates in tumors and inhibits tumor growth. Additionally, the formation of lung metastasis nodes is also significantly suppressed. In general, the active targeting nanoparticles self‐assembled from cisplatin‐palbociclib amphiphiles provide options for combinatorial chemotherapy.

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Tailor‐Made Core‐Multishell Nanocarriers for the Delivery of Cationic Analgesics to Inflamed Tissue

Cited by 2 publications

References 47 publications

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Active Targeting Nanoparticle Self‐Assembled from Cisplatin‐Palbociclib Amphiphiles Ensures Optimal Drug Ratio for Combinatorial Chemotherapy

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