ArticleInternational journal of nanomedicine2013
Heuristic modeling of macromolecule release from PLGA microspheres.
Article in International journal of nanomedicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.The AAPS journal · 2025Article
- Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Machine learning models to accelerate the design of polymeric long-acting injectables.Nature communications · 2023Article
- Article
- State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation.Pharmaceutics · 2022Review
- Development of composite PLGA microspheres containing exenatide-encapsulated lecithin nanoparticles for sustained drug release.Asian journal of pharmaceutical sciences · 2020Article
- Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.AAPS PharmSciTech · 2020Article
- SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models.BMC bioinformatics · 2019Article
- Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools.Computational and mathematical methods in medicine · 2018Article
- Computational intelligence models to predict porosity of tablets using minimum features.Drug design, development and therapy · 2017Article
- A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.Computer methods and programs in biomedicine · 2016Review
- Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.PloS one · 2016Article
- Degradability, bioactivity, and osteogenesis of biocomposite scaffolds of lithium-containing mesoporous bioglass and mPEG-PLGA-b-PLL copolymer.International journal of nanomedicine · 2015Article
- Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate.International journal of nanomedicine · 2015Article
- Empirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulations.International journal of nanomedicine · 2015Article
- Modeling Polymeric Drug Release: The Emerging Role of Machine Learning.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.
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