Dataset Information

BODIPY-doped silica nanoparticles with reduced dye leakage and enhanced singlet oxygen generation.

ABSTRACT: Photodynamic therapy (PDT) is a promising modality for cancer treatment. The essential element in PDT is the photosensitizer, which can be excited by light of a specific wavelength to generate cytotoxic oxygen species (ROS) capable of killing tumor cells. The effectiveness of PDT is limited in part by the low yield of ROS from existing photosensitizers and the unwanted side effects induced by the photosensitizers toward normal cells. Thus the design of nanoplatforms with enhanced PDT is highly desirable but remains challenging. Here, we developed a heavy atom (I) containing dipyrromethene boron difluoride (BODIPY) dye with a silylated functional group, which can be covalently incorporated into a silica matrix to form dye-doped nanoparticles. The incorporated heavy atoms can enhance the generation efficiency of ROS. Meanwhile, the covalently dye-encapsulated nanoparticles can significantly reduce dye leakage and subsequently reduce unwanted side effects. The nanoparticles were successfully taken up by various tumor cells and showed salient phototoxicity against these cells upon light irradiation, demonstrating promising applications in PDT. Moreover, the incorporated iodine atom can be replaced by a radiolabeled iodine atom (e.g., I-124, I-125). The resulting nanoparticles will be good contrast agents for positron emission tomography (PET) imaging with their PDT functionality retained.

SUBMITTER: Wang Z

PROVIDER: S-EPMC4515827 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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BODIPY-doped silica nanoparticles with reduced dye leakage and enhanced singlet oxygen generation.

Wang Zhuyuan Z Hong Xuehua X Zong Shenfei S Tang Changquan C Cui Yiping Y Zheng Qingdong Q

Scientific reports 20150727

Photodynamic therapy (PDT) is a promising modality for cancer treatment. The essential element in PDT is the photosensitizer, which can be excited by light of a specific wavelength to generate cytotoxic oxygen species (ROS) capable of killing tumor cells. The effectiveness of PDT is limited in part by the low yield of ROS from existing photosensitizers and the unwanted side effects induced by the photosensitizers toward normal cells. Thus the design of nanoplatforms with enhanced PDT is highly d ...[more]

PMID: 26211417

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Project description:Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (ΦΔ ) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting ΦΔ values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q2 =0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for ΦΔ values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting ΦΔ values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.

Dataset Information

BODIPY-doped silica nanoparticles with reduced dye leakage and enhanced singlet oxygen generation.

Publications

BODIPY-doped silica nanoparticles with reduced dye leakage and enhanced singlet oxygen generation.

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