|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Predicting oral disintegrating tablet formulations by neural network techniques显示文摘Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product. | Run Han Yilong Yang Xiaoshan Li Defang Ouyang | 2018 | Asian Journal of Pharmaceutical Sciences2018，13，4： | 6 |
| 2 | Deep learning for in vitro prediction of pharmaceutical formulations显示文摘Current pharmaceutical formulation development still strongly relies on the traditional trialand-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly.Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies. | Yilong Yang Zhuyifan Ye Yan Su Qianqian Zhao Xiaoshan Li Defang Ouyang | 2019 | Acta Pharmaceutica Sinica B2019，9，1： | 3 |
| 3 | Preparation and characterization of pelletized solid dispersion of resveratrol with mesoporous silica microparticles to improve dissolution by fluid-bed coating techniques显示文摘With hydrophilic surface and high surface area, porous silica has been applied to load insoluble drugs. Compared to solvent equilibrium method, resveratrol(RES)–mesoporous silica microparticles(MSM) solid dispersion prepared by fluid bed demonstrated higher drug loading and more complete dissolution. Pore volume and diameter have more remarkable effects than surface area to the drug loading and in vitro dissolution profiles. RES–polyethylene glycol solid dispersion with high drug loading showed fast but incomplete dissolution due to the recrystallization. The combination of fluid bed and MSM was an effective strategy to improve drug loading as well as dissolution for poorly water-soluble drugs. | Jian Li Xiaoqing Miao Tongkai Chen Defang Ouyang Ying Zheng | 2016 | Asian Journal of Pharmaceutical Sciences2016，11，4： | 3 |
| 4 | Investigation of molecular aggregation mechanism of glipizide/cyclodextrin complexation by combined experimental and molecular modeling approaches显示文摘Cyclodextrin complexation is a wise strategy to enhance aqueous solubility of waterinsoluble drugs.However,the aggregation mechanism of drug-cyclodextrin complexes is still unclear.This research aimed to investigate the molecular aggregation mechanism of glipizide/cyclodextrin complexation by the combination of experimental and modeling methods.Binding free energies between glipizide and cyclodextrins from modeling calculations were higher than those by the phase solubility diagram method.Both experimental and modeling results showed that methylated-β-cyclodextrin exhibited the best solubilizing capability to glipizide.Size-measurement results confirmed the aggregation between glipizide and all four cyclodextrins in high concentrations.Glipizide/γ-cyclodextrin and glipizide/β-cyclodextrin complexes showed stronger aggregation trend than HP-β-cyclodextrin and methylated-β-cyclodextrin.The substituted groups in the rim of HP-β-cyclodextrin and methylated-β-cyclodextrin lead to weak aggregation.This research provided us a clear molecular mechanism of glipizide/cyclodextrin complexation and aggregation.This research will also benefit the formulation development of cyclodextrin solubilization. | Tianhe Huang Qianqian Zhao Yan Su Defang Ouyang | 2019 | Asian Journal of Pharmaceutical Sciences2019，14，6： | 2 |
| 5 | Pathological Changes in Andrias davidianus Infected with Chinese Giant Salamander Ranavirus显示文摘Chinese giant salamander ranavirus(CGSRV) is an emerging pathogen in captive populations of the Chinese giant salamander(Andrias davidianus). We processed 140 morbid Chinese giant salamanders from seven captive breeding populations over five years, and describe the disease associated with CGSRV infection. The most common gross signs were significant swelling of the legs and coelomic cavity, erythema of the legs and ventrum in juveniles; cutaneous erosions and ulcerations in adults, particularly the limbs and the head; and marked petechial or ecchymotic hemorrhages of the internal organs, particularly the liver, spleen and kidney. Histological examination showed degeneration, necrosis, and inflammation in many organs, particularly in the organs where hemorrhage was observed. There was evidence of eosinophilic inclusion bodies in degenerated and necrotic cells. We identified virus particles and empty capsids without viral nucleoid in the inclusion bodies using electron microscopy. Virus particles were hexagonal or round shape, and appeared in paracrystalline arrays, aggregates, or singly. All enveloped viral particles were 140–160 nm. Polymerase chain reaction followed by sequencing verified that the virus particles were CGSRV. These results collectively support that CGSRV was the etiologic agent responsible for these mass die-offs of the Chinese giant salamander. The pathology described herein will be useful in diagnosing cases of ranaviral disease caused by CGSRV, and provide evidence that this pathogen is a significant threat to the Chinese giant salamander. | Yi GENG Matthew James GRAY Kaiyu WANG Defang CHEN Ping OUYANG Xiaoli HUANG Changliang HE Zhijun ZHONG Zexiao YANG | 2016 | Asian Herpetological Research2016，7，4： | 2 |
| 6 | Integrated in silico formulation design of selfemulsifying drug delivery systems显示文摘The drug formulation design of self-emulsifying drug delivery systems(SEDDS)often requires numerous experiments,which are time-and money-consuming.This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches.4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods.Random forest(RF)showed the best prediction performance with 91.3% for accuracy,92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation.The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy(89.51%).The central composite design(CCD)was used to screen the best ratio of oil-surfactant-cosurfactant.Finally,molecular dynamic(MD)simulation was used to investigate the molecular interaction between excipients and drugs,which revealed the diffusion behavior in water and the role of cosurfactants.In conclusion,this research combined machine learning,central composite design,molecular modeling and experimental approaches for rational SEDDS formulation design.The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design. | Haoshi Gao Haoyue Jia Jie Dong Xinggang Yang Haifeng Li Defang Ouyang | 2021 | Acta Pharmaceutica Sinica B2021，11，11： | 1 |
| 7 | Investigating the molecular dissolution process of binary solid dispersions by molecular dynamics simulations显示文摘Dissolution molecular mechanism of solid dispersions still remains unclear despite thousands of reports about this technique. The aim of current research was to investigate the molecular dissolution mechanism of solid dispersions by molecular dynamics simulations. The formation of ibuprofen/polymer solid dispersions was modeled by the simulated annealing method. After that, the models of solid dispersions were immersed into the water box with 25–30 ? thicknesses and 50–100 ns MD simulations were performed to all systems.Simulation results showed various dissolution behaviors in different particle sizes and various polymers of solid dispersions. Small-sized particles of solid dispersions dissolved quickly in the water, while the large particles of PEG or PVP-containing solid dispersions gradually swelled in the dissolution process and drug molecules may aggregate together. In the dissolution process, the carboxylic groups of ibuprofen molecules turned its direction from polymer molecules to external water box and then the drug molecules left the polymer coils.At the same time, polymer coils gradually relaxed and became free polymer chains in the solution. In addition, solid dispersion with poloxamer could prevent the precipitate of drug molecules in the dissolution process, which is different from those of PEG or PVPcontaining systems. This research provided us clear images of dissolution process of solid dispersions at the molecular level. | TengIan Chan Defang Ouyang | 2018 | Asian Journal of Pharmaceutical Sciences2018，13，3： | 1 |
| 8 | Integrated computer-aided formulation design:A case study of andrographolide/cyclodextrin ternary formulation显示文摘Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists,which is time-consuming,high cost and waste materials.This research aims to integrate various computational tools,including machine learning,molecular dynamic simulation and physiologically based absorption modeling(PBAM),to enhance andrographolide(AG)/cyclodextrins(CDs)formulation design.The light GBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy.AG/γ-CD inclusion complexes showed the strongest binding affinity,which was experimentally validated by the phase solubility study.The molecular dynamic simulation was used to investigate the inclusion mechanism between AG andγ-CD,which was experimentally characterized by DSC,FTIR and NMR techniques.PBAM was applied to simulate the in vivo behavior of the formulations,which were validated by cell and animal experiments.Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate(TPGS)significantly increased the intracellular uptake of AG in MDCKMDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers.The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills,respectively.In conclusion,this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility,dissolution rate and bioavailability.The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design. | Haoshi Gao Yan Su Wei Wang Wei Xiong Xiyang Sun Yuanhui Ji Hua Yu Haifeng Li Defang Ouyang | 2021 | Asian Journal of Pharmaceutical Sciences2021，16，4： | 1 |
| 9 | Artificial intelligence-aided discovery of prolyl hydroxylase 2 inhibitors to stabilize hypoxia inducible factor-1α and promote angiogenesis显示文摘From ZINC database with a total of 1.8 million small molecules, four compounds are identified as prolyl hydroxylase 2 inhibitors through a virtual screening workflow that sequentially incorporates machine learning, molecular docking, and molecular dynamics. Among them, compound 103,(E)-5-(5-((2-(1Htetrazol-5-yl)hydrazineylidene)methyl)furan-2-yl)isoindoline-1,3-dione, promotes the migration and capillary tube formation capacity of human umbilical vein endothelial cells through enhancing the stability of hypoxia inducible factor-1α and increasing the level of vascular endothelial growth factor. | Jianzhong Zhu Cheng Chen Jie Dong Shasha Cheng Guodong Li Chunming Wang Defang Ouyang Chung-Hang Leung Ligen Lin | 2023 | Chinese Chemical Letters2023，34，2： | 0 |
| 10 | Near-infrared fluorophores with absolute aggregation-caused quenching and negligible fluorescence re-illumination for in vivo bioimaging of nanocarriers显示文摘Environment-responsive fluorophores with aggregation-caused quenching(ACQ)properties have been applied to track nanocarriers with reduced artefacts caused by unbound or free fluorophores but suffer from incomplete fluorescence quenching and significant re-illumination,which undermine bioimaging accuracy.Herein,through structural modifications to reinforce the hydrophobicity,planarity and rigidity of fluorophores with an aza-BODIPY framework,probes featuring absolute ACQ(aACQ)and negligible re-illumination are developed and evaluated in various nanocarriers.aACQ probes,FD-B21 and FD-C7,exhibit near-infrared emission,high quantum yield,photostability,water sensitivity,and negligible re-illumination in blood,plasma and 1%Tween-80 in contrast to ACQ probe P2 and conventional probe DiR.All nanocarriers can be labeled efficiently by the tested fluorophores.Polymeric micelles(PMs)labeled by different aACQ probes manifest similar biodistribution patterns,which however differ from that of DiR-labeled PMs and could be ascribed to the appreciable re-illumination of DiR.Significantly lower re-illumination is also found in aACQ probes(2%-3%)than DiR(20%-40%)in Caco-2,Hela,and Raw264.7 cells.Molecular dynamics simulations unravel the molecular mechanisms behind aggregation and re-illumination,supporting the hypothesis of planarity dependency.It is concluded that aACQ fluorophores demonstrate excellent water sensitivity and negligible fluorescence re-illumination,making themselves useful tools for more accurate bioimaging of nanocarriers. | Yifan Cai Xin Ji Yunsen Zhang Chang Liu Zichen Zhang Yongjiu Lv Xiaochun Dong Haisheng He Jianping Qi Yi Lu Defang Ouyang Weili Zhao Wei Wu | 2023 | Aggregate2023，4，2： | 0 |
| 11 | Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm显示文摘Lipid nanoparticle(LNP) is commonly used to deliver mRNA vaccines.Currently,LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time.Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines.Firstly,325 data samples of mRNA vaccine LNP formulations with IgG titer were collected.The machine learning algorithm,lightGBM,was used to build a prediction model with good performance(R^(2)>0.87).More importantly,the critical substructures of ionizable lipids in LNPs were identified by the algorithm,which well agreed with published results.The animal experimental results showed that LNP using DLin-MC3-DMA(MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102,which was consistent with the model prediction.Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment.The result showed that the lipid molecules aggregated to form LNPs,and mRNA molecules twined around the LNPs.In summary,the machine learning predictive model for LNP-based mRNA vaccines was first developed,validated by experiments,and further integrated with molecular modeling.The prediction model can be used for virtual screening of LNP formulations in the future. | Wei Wang Shuo Feng Zhuyifan Ye Hanlu Gao Jinzhong Lin Defang Ouyang | 2022 | Acta Pharmaceutica Sinica B2022，12，6： | 0 |
| 12 | Progress in oncolytic viruses modified with nanomaterials for intravenous application显示文摘In oncolytic virus(OV)therapy,a critical component of tumor immunotherapy,viruses selectively infect,replicate within,and eventually destroy tumor cells.Simultaneously,this therapy activates immune responses and mobilizes immune cells,thereby eliminating residual or distant cancer cells.However,because of OVs’high immunogenicity and immune clearance during circulation,their clinical applications are currently limited to intratumoral injections,and their use is severely restricted.In recent years,numerous studies have used nanomaterials to modify OVs to decrease virulence and increase safety for intravenous injection.The most commonly used nanomaterials for modifying OVs are liposomes,polymers,and albumin,because of their biosafety,practicability,and effectiveness.The aim of this review is to summarize progress in the use of these nanomaterials in preclinical experiments to modify OVs and to discuss the challenges encountered from basic research to clinical application. | Liting Chen Zhijun Ma Chen Xu Youbang Xie Defang Ouyang Shuhui Song Xiao Zhao Funan Liu | 2023 | Cancer Biology & Medicine2023，20，11： | 0 |
| 13 | Predicting liposome formulations by the integrated machine learning and molecular modeling approaches显示文摘Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future. | Run Han Zhuyifan Ye Yunsen Zhang Yaxin Cheng Ying Zheng Defang Ouyang | 2023 | Asian Journal of Pharmaceutical Sciences2023，18，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员