An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design

Gao, Hanlu; Wang, Wei; Dong, Jie; Ye, Zhuyifan; Ouyang, Defang

doi:10.1016/j.ejpb.2020.12.001

Cited by 47 publications

(28 citation statements)

References 66 publications

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“…This study substantially advances the field of cancer nanomedicine by integrating ML and AI technologies with PBPK modeling to study cancer nanomedicine. ML and AI models are useful to predict PBPK-related parameters, such as in vitro dissolution rate, hepatic clearance, and membrane permeability of small molecular drugs; 54 , 55 and these parameters are in turn helpful to support PBPK model development. While ML and AI methods have been applied to support PBPK model development of small molecular drugs, 54 , 55 they have not been applied to develop PBPK models of nanomedicines.…”

Section: Discussionmentioning

confidence: 99%

“…ML and AI models are useful to predict PBPK-related parameters, such as in vitro dissolution rate, hepatic clearance, and membrane permeability of small molecular drugs; 54 , 55 and these parameters are in turn helpful to support PBPK model development. While ML and AI methods have been applied to support PBPK model development of small molecular drugs, 54 , 55 they have not been applied to develop PBPK models of nanomedicines. ML and AI models are known to be relatively complex and require a large amount of data to train and evaluate.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Lin¹,

Chou²,

Cheng³

et al. 2022

IJN

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Background Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. Methods This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. Results The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R 2 ) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DE max ), delivery efficiency at 24 h (DE 24 ), at 168 h (DE 168 ), and at the last sampling time (DE Tlast ). The corresponding R 2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19–29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy. Conclusion This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Lin¹,

Chou²,

Cheng³

et al. 2022

IJN

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“…In pharmaceutical science, the MD simulation has gradually become an increasingly vital tool to help scientists understand the drug delivery mechanism of dissolution, solubility, controlled release, and targeted delivery 21 . In the past ten years, our group had investigated numerous dosage forms, including the preparation and dissolution behavior of solid dispersion 22 , 23 , the interaction between drug and cyclodextrin 24 , liposome 25 , drug–phospholipid complex 19 , self-assembly platinum prodrug 26 .…”

Section: Introductionmentioning

confidence: 99%

Integrated in silico formulation design of self-emulsifying drug delivery systems

Gao¹,

Jia

Dong³

et al. 2021

Acta Pharmaceutica Sinica B

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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.

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“…Over the last decade, interest regarding the use of ML algorithms across diverse disciplines in pharmaceutical design and development has grown [ 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. While ML models have been produced to optimise lipid-based formulation (LBF) development [ 3 , 22 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the application of more novel ML approaches for bio-enabling formulations currently focuses on solid dispersions (SDs) [ 21 , 34 , 35 ]. However, their application to LBFs, particularly supersaturated LBFs (sLBFs), remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

et al. 2021

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In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

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An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design

Cited by 47 publications

References 66 publications

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Integrated in silico formulation design of self-emulsifying drug delivery systems

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

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