Integrated computer-aided formulation design:A case study of andrographolide/cyclodextrin ternary formulation

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.

Investigation of molecular aggregation mechanism of glipizide/cyclodextrin complexation by combined experimental and molecular modeling approaches

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.

Size-dependent macrophage-targeting of mannose-modified rosiglitazone liposomes to alleviate inflammatory bowel disease

Inflammatory bowel disease (IBD) is a refractory chronic intestinal inflammatory disease caused by a malfunction of immune system. As the key immune cells in the intestine, macrophages play an important role in maintaining intestinal homeostasis and tissue repair of the IBD. Pharmacological modulation of macrophage function exhibits the promising therapeutic effect for IBD. In this study, mannose-modified liposomes (MAN-LPs) are prepared for macrophage targeting to improve therapeutic efficiency. Rosiglitazone (ROSI) as an agonist of peroxisome proliferators-activated receptor γ (PPAR-γ) is used as the model drug to fabricate different sized liposomes. The impacts of mannose modification and particle size for macrophage targeting are investigated in cells, zebrafish, and mouse models and the therapeutic effects of the MAN-LPs are evaluated on dextran sulfate sodium (DSS)-induced IBD mouse. Compared to unmodified liposome, MAN-LPs display higher uptake by RAW 264.7 cells and better co-localization with macrophage in zebrafish model. Furthermore, MAN-LPs could effectively accumulate in the inflammatory intestinal sites in IBD mouse model. Most importantly, the targeting ability of MAN-LPs is obviously enhanced with the increasing of particle size, whereas the largest MAN-LPs particles achieve the best anti-inflammatory effect in cells, and a higher therapeutic efficiency in IBD mouse model. Therefore, mannose-modified liposome is a promising strategy for macrophage-targeting in IBD treatment. Particle size of MAN-LPs will affect macrophage targeting ability, as well as the therapeutic effect in-vivo.

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

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.

Progress in oncolytic viruses modified with nanomaterials for intravenous application

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.

Predicting oral disintegrating tablet formulations by neural network techniques

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.

Near-infrared fluorophores with absolute aggregation-caused quenching and negligible fluorescence re-illumination for in vivo bioimaging of nanocarriers

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.

Interviews on the Cooperation between Hainan and Macao region in the TCM Industry to Promote Hainan’s TCM Industry

Objective To develop the internationalization and modernization of Hainan’s TCM industry,explore the development direction and fill in the research gap.Methods Design qualitative approaches,including fieldwork,semi-structured interviews,and policy analysis,were employed to identify development challenges for the cooperation between Hainan and Macao region in TCM industry.Ten interviews with key TCM industry stakeholders in Hainan and Macao region were conducted in 2020.These stakeholders are from schools,hospitals,companies,and governments.Hainan Province is the only free trade port with Chinese characteristics.Macao region is an international free trade port that has special economic relations with European and Portuguese-speaking countries.Therefore,this study focuses on the cooperation in the traditional Chinese medicine(TCM)industry between Hainan and Macao region.Besides,it investigates the traditional Chinese medicine industry in Hainan and Macao region according to the SWOT model.Results and Conclusion The study revealed strengths,weaknesses,opportunities,and threats for cooperation between Hainan and Macao region in TCM industry.Cooperation between Hainan and Macao region can be taken as a priority outline and a standard for the internationalization and modernization of Hainan’s TCM industry.

Engineering hyaluronic acid-based nanoassemblies for monoclonal antibody delivery-design,characterization,and biological insights

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.

Deep learning for in vitro prediction of pharmaceutical formulations

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.

Integrated in silico formulation design of selfemulsifying drug delivery systems

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.

Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques

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.