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 p...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.展开更多
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 vario...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.展开更多
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...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.展开更多
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 t...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.展开更多
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 mobil...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.展开更多
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 ...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.展开更多
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, mo...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.展开更多
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 mos...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.展开更多
Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds.Quantum mechanics(QM)–based crystal structure predictions(CSPs)have somewhat alleviated...Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds.Quantum mechanics(QM)–based crystal structure predictions(CSPs)have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies,but the high computing cost poses a challenge to its widespread application.The present study aims to construct DeepCSP,a feasible pure machine learning framework for minute-scale rapid organic CSP.Initially,based on 177,746 data entries from the Cambridge Crystal Structure Database,a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule.Simultaneously,a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule.Subsequently,the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis,and finally,the density-based crystal structure ranking would be output.Two such distinct algorithms,performing the generation and ranking functionalities,respectively,collectively constitute the DeepCSP,which has demonstrated compelling performance in marketed drug validations,achieving an accuracy rate exceeding 80%and a hit rate surpassing 85%.Inspiringly,the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.展开更多
The lipid nanoparticle(LNP)has been so far proven as a strongly effective delivery system for mRNA and siRNA.However,the mechanisms of LNP's distribution,metabolism,and elimination are complicated,while the transp...The lipid nanoparticle(LNP)has been so far proven as a strongly effective delivery system for mRNA and siRNA.However,the mechanisms of LNP's distribution,metabolism,and elimination are complicated,while the transportation and pharmacokinetics(PK)of LNP are just sparsely investigated and simply described.This study aimed to build a model for the transportation of RNA-LNP in Hela cells,rats,mice,and humans by physiologically based pharmacokinetic(PBPK)and quantum mechanics(QM)models with integrated multi-source data.LNPs with different ionizable lipids,particle sizes,and doses were modeled and compared by recognizing their critical parameters dominating PK.Some interesting results were found by the models.For example,the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids;the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release.Moreover,the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result.In summary,the transportation model of RNA LNP among various species for the first time was successfully constructed.Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling.The resulting new understandings are related to biodegradability,safety,and RNA release ability which are highly concerned issues of the formulation.This would benefit the design and research of RNA-LNP in the future.展开更多
The current spotlight of cancer therapeutics is shifting towards personalized medicine with the widespread use of monoclonal antibodies(mAbs).Despite their increasing potential,mAbs have an intrinsic limitation relate...The current spotlight of cancer therapeutics is shifting towards personalized medicine with the widespread use of monoclonal antibodies(mAbs).Despite their increasing potential,mAbs have an intrinsic limitation related to their inability to cross cell membranes and reach intracellular targets.Nanotechnology offers promising solutions to overcome this limitation,however,formulation challenges remain.These challenges are the limited loading capacity(often insufficient to achieve clinical dosing),the complex formulation methods,and the insufficient characterization of mAb-loaded nanocarriers.Here,we present a new nanocarrier consisting of hyaluronic acid-based nanoassemblies(HANAs)specifically designed to entrap mAbs with a high efficiency and an outstanding loading capacity(50%,w/w).HANAs composed by an mAb,modified HA and phosphatidylcholine(PC)resulted in sizes of~100 nm and neutral surface charge.Computational modeling identified the principal factors governing the high affinity of mAbs with the amphiphilic HA and PC.HANAs composition and structural configuration were analyzed using the orthogonal techniques cryogenic transmission electron microscopy(cryo-TEM),asymmetrical flow field-flow fractionation(AF4),and small-angle X-ray scattering(SAXS).These techniques provided evidence of the formation of core-shell nanostructures comprising an aqueous core surrounded by a bilayer consisting of phospholipids and amphiphilic HA.In vitro experiments in cancer cell lines and macrophages confirmed HANAs’low toxicity and ability to transport mAbs to the intracellular space.The reproducibility of this assembling process at industrial-scale batch sizes and the long-term stability was assessed.In conclusion,these results underscore the suitability of HANAs technology to load and deliver biologicals,which holds promise for future clinical translation.展开更多
Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs.Tissue engineering has developed rapidly in recen...Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs.Tissue engineering has developed rapidly in recent years,while scaffolds,growth factors,and stem cells have been successfully used for the reconstruction of various tissues and organs.However,time-consuming production,high cost,and unpredictable tissue growth still need to be addressed.Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets,with great potential to accelerate scientific discovery and enhance clinical practice.The convergence of machine learning and tissue engineering,while in its infancy,promises transformative progress.This paper will review the latest progress in the application of machine learning to tissue engineering,summarize the latest applications in biomaterials design,scaffold fabrication,tissue regeneration,and organ transplantation,and discuss the challenges and future prospects of interdisciplinary collaboration,with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.展开更多
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 learnin...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.展开更多
Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has ...Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins(CDs)systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.展开更多
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 ...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.展开更多
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 stu...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.展开更多
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 flu...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.展开更多
基金financially supported by the Universityof Macao Research Grant (MYRG2016-00038-ICMS-QRCM &MYRG2016-00040-ICMS-QRCM)Macao Science and Technology Development Fund (FDCT) (Grant No. 103/2015/A3)the National Natural Science Foundation of China (Grant No. 61562011 )
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.
基金financially supported by the FDCT Project 0029/2018/A1the University of Macao Research Grants(MYRG2019-00041-ICMS)performed in part at the High-Performance Computing Cluster(HPCC)which is supported by Information and Communication Technology Office(ICTO)of the University of Macao。
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.
基金University of Macao research grants (MYRG2016-00038-ICMSQRCM and MYRG2016-00040-ICMS-QRCM) are gratefully acknowledged for providing financial support
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.
基金supported by the University of Macao Research Grants(MYRG2016-00038-ICMS-QRCM and MYRG2016-00040-ICMS-QRCM)in part at the High-Performance Computing Cluster(HPCC)which is supported by Information and Communication Technology Office(ICTO)of the University of Macao
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.
基金supported by grants from the National Key R&D Program of China(Grant Nos.2021YFA0909900,X.Z.2022YFC2403401,F.L.)+3 种基金the National Natural Science Foundation of China(Grant Nos.32222045 and 32171384,X.Z.82073368,F.L.)the Liaoning Revitalization Talents Program(Grant No.XLYC2007071,F.L.)the Top-notch Talents Project of 2022“Kunlun Yingcai Advanced Innovation and Entrepreneurship”in Qinghai Province(Y.X.)。
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.
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.
基金financially supported by the National Natural Science Foundation of China (Nos. 82073715, 81872754)the Science and Technology Development Fund, Macao SAR (No. FDCT 0001/2021/AKP)the Research Fund of University of Macao (No. MYRG2020-00091-ICMS)。
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.
基金supported by the Multi-Year Research Grants from the University of Macao(MYRG2019-00032-ICMS and MYRG2020-00113-ICMS)the Macao FDCT research grant(0108/2021/A)Molecular modeling was performed at the High-Performance Computing Cluster(HPCC),which is supported by the Information and Communication Technology Office(ICTO)of the University of Macao.
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.
基金We are thankful for the funding provided by University of Macao Research Grant(MYRGCRG2022-00008-ICMS)Shenzhen-Hong Kong-Macao Science and Technology Program(Category C)of Shenzhen Science and Technology Innovation Commission(SGDX20210823103802016)+1 种基金industry-university-research cooperation project and Zhuhai-Hong Kong-Macao cooperation project from Zhuhai Science and Technology Innovation Bureau(ZH22017002210010PWC)This study was partially performed at Super Intelligent Computing Center,which is supported by Internet of Things for Smart City of the University of Macao.We thank the Macao Polytechnic University for the financial support of the CSD database.
文摘Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds.Quantum mechanics(QM)–based crystal structure predictions(CSPs)have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies,but the high computing cost poses a challenge to its widespread application.The present study aims to construct DeepCSP,a feasible pure machine learning framework for minute-scale rapid organic CSP.Initially,based on 177,746 data entries from the Cambridge Crystal Structure Database,a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule.Simultaneously,a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule.Subsequently,the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis,and finally,the density-based crystal structure ranking would be output.Two such distinct algorithms,performing the generation and ranking functionalities,respectively,collectively constitute the DeepCSP,which has demonstrated compelling performance in marketed drug validations,achieving an accuracy rate exceeding 80%and a hit rate surpassing 85%.Inspiringly,the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.
基金supported by the UM Macao PhD Scholarship(China),UM Postdoctoral Fellow of UM Talent Programme(China),the University of Macao Multi-Year Research Grant e Collaborative Research Grant(MYRG-CRG2022-00008-ICMS,China)the Shenzhen-Hong Kong-Macao Science and Technology Program(Category C)of Shenzhen Science and Technology Innovation Commission(SGDX20210823103802016,China)Industry-university-research cooperation project and Zhuhai-Hong Kong-Macao cooperation project from Zhuhai Science and Technology Innovation Bureau(ZH22017002210010PWC,China).
文摘The lipid nanoparticle(LNP)has been so far proven as a strongly effective delivery system for mRNA and siRNA.However,the mechanisms of LNP's distribution,metabolism,and elimination are complicated,while the transportation and pharmacokinetics(PK)of LNP are just sparsely investigated and simply described.This study aimed to build a model for the transportation of RNA-LNP in Hela cells,rats,mice,and humans by physiologically based pharmacokinetic(PBPK)and quantum mechanics(QM)models with integrated multi-source data.LNPs with different ionizable lipids,particle sizes,and doses were modeled and compared by recognizing their critical parameters dominating PK.Some interesting results were found by the models.For example,the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids;the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release.Moreover,the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result.In summary,the transportation model of RNA LNP among various species for the first time was successfully constructed.Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling.The resulting new understandings are related to biodegradability,safety,and RNA release ability which are highly concerned issues of the formulation.This would benefit the design and research of RNA-LNP in the future.
基金supported by the government of Xunta de Galicia(Competitive Reference Groups,Consellería de Educación e Ordenación Universitaria,Xunta de Galicia,No.ED431C 2021/17)by the ISCⅢ thorough AES 2020,Award No.AC20/00028 and within the framework of EuroNanoMed Ⅲ+3 种基金part of the project Proof of Concept(No.PDC2021-120929-I00)financed by the Spanish Ministry of Science and Innovation-AEI/10.13039/501100011033the European Union NextGenerationEU/PRTRthe Spanish Ministry of Science,Innovation and Universities(No.FPU18/00095).
文摘The current spotlight of cancer therapeutics is shifting towards personalized medicine with the widespread use of monoclonal antibodies(mAbs).Despite their increasing potential,mAbs have an intrinsic limitation related to their inability to cross cell membranes and reach intracellular targets.Nanotechnology offers promising solutions to overcome this limitation,however,formulation challenges remain.These challenges are the limited loading capacity(often insufficient to achieve clinical dosing),the complex formulation methods,and the insufficient characterization of mAb-loaded nanocarriers.Here,we present a new nanocarrier consisting of hyaluronic acid-based nanoassemblies(HANAs)specifically designed to entrap mAbs with a high efficiency and an outstanding loading capacity(50%,w/w).HANAs composed by an mAb,modified HA and phosphatidylcholine(PC)resulted in sizes of~100 nm and neutral surface charge.Computational modeling identified the principal factors governing the high affinity of mAbs with the amphiphilic HA and PC.HANAs composition and structural configuration were analyzed using the orthogonal techniques cryogenic transmission electron microscopy(cryo-TEM),asymmetrical flow field-flow fractionation(AF4),and small-angle X-ray scattering(SAXS).These techniques provided evidence of the formation of core-shell nanostructures comprising an aqueous core surrounded by a bilayer consisting of phospholipids and amphiphilic HA.In vitro experiments in cancer cell lines and macrophages confirmed HANAs’low toxicity and ability to transport mAbs to the intracellular space.The reproducibility of this assembling process at industrial-scale batch sizes and the long-term stability was assessed.In conclusion,these results underscore the suitability of HANAs technology to load and deliver biologicals,which holds promise for future clinical translation.
基金supported by the Macao Science and Technology Development Fund(0071/2024/RIA1)University of Macao Multi-Year Research Grant(MYRG-GRG2023-00077-ICMS-UMDF).
文摘Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs.Tissue engineering has developed rapidly in recent years,while scaffolds,growth factors,and stem cells have been successfully used for the reconstruction of various tissues and organs.However,time-consuming production,high cost,and unpredictable tissue growth still need to be addressed.Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets,with great potential to accelerate scientific discovery and enhance clinical practice.The convergence of machine learning and tissue engineering,while in its infancy,promises transformative progress.This paper will review the latest progress in the application of machine learning to tissue engineering,summarize the latest applications in biomaterials design,scaffold fabrication,tissue regeneration,and organ transplantation,and discuss the challenges and future prospects of interdisciplinary collaboration,with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.
基金financially supported by the University of Macao Research Grant(MYRG2016-00038-ICMS-QRCM,MYRG2016-00040-ICMS-QRCM and MYRG2017-00141-FST,China)Macao Science and Technology Development Fund(FDCT,Grant no.103/2015/A3,China)the National Natural Science Foundation of China(Grant no.61562011)
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.
基金supported by the University of Macao Research Grants(MYRG2016-00038ICMS-QRCM and MYRG2016-00040-ICMS-QRCM,Macao,China).
文摘Most pharmaceutical formulation developments are complex and ideal formulations are generally obtained after extensive experimentation.Machine learning is increasingly advancing many aspects in modern society and has achieved significant success in multiple subjects.Current research demonstrated that machine learning can be adopted to build up high-accurate predictive models in drugs/cyclodextrins(CDs)systems.Molecular descriptors of compounds and experimental conditions were employed as inputs,while complexation free energy as outputs.Results showed that the light gradient boosting machine provided significantly improved predictive performance over random forest and deep learning.The mean absolute error was 1.38 kJ/mol and squared correlation coefficient was0.86.The evaluation of relative importance of molecular descriptors further demonstrated the key factors affecting molecular interactions in drugs/CD systems.In the specific ketoprofen-CD systems,machine learning model showed better predictive performance than molecular modeling calculation,while molecular simulation could provide structural,dynamic and energetic information.The integration of machine learning and molecular simulation could produce synergistic effect for interpreting and predicting pharmaceutical formulations.In conclusion,the developed predictive models were able to quickly and accurately predict the solubilizing capacity of CD systems.Current research has taken an important step toward the application of machine learning in pharmaceutical formulation design.
基金financially supported by the Science and Technology Development Fund(FDCT)of Macao(0029/2018/A1,China)the University of Macao Research Grants(MYRG2019-00041-ICMS,China)performed in part at the High-Performance Computing Cluster(HPCC)which is supported by Information and Communication Technology Office(ICTO)of the University of Macao,China。
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.
基金financially supported by the University of Macao Research Grants (MYRG2020-00113-ICMS,China)。
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.
基金Shanghai Municipal Commission of Science and Technology,Grant/Award Numbers:21430760800,19XD1400300National Natural Science Foundation of China,Grant/Award Numbers:81872826,81872815,81973247,82030107。
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.
