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Research Article

Rationalized design to explore the full potential of PLGA microspheres as drug delivery systems

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Article: 2219864 | Received 19 Jan 2023, Accepted 02 May 2023, Published online: 05 Jun 2023

References

  • Colbourn EA, Roskilly SJ, Rowe RC, York P. (2011). Modelling formulations using gene expression programming – a comparative analysis with artificial neural networks. Eur J Pharm Sci 44:1–9.
  • Colbourn EA, Rowe RC. (2009). Novel approaches to neural and evolutionary computing in pharmaceutical formulation: challenges and new possibilities. Future Med Chem 1:713–26.
  • Damiati SA, Rossi D, Joensson HN, Damiati S. (2020). Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics. Sci Rep 10:19517.
  • Echezarreta-López MM, Landin M. (2013). Using machine learning for improving knowledge on antibacterial effect of bioactive glass. Int J Pharm 453:641–7.
  • Gago J, Pérez-Tornero O, Landín M, et al. (2011). Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases. J Plant Physiol 168:1858–65.
  • Garcia-del Rio L, Diaz-Rodriguez P, Landin M. (2021). Design of novel orotransmucosal vaccine-delivery platforms using artificial intelligence. Eur J Pharm Biopharm 159:36–43.
  • Gentile P, Nandagiri VK, Daly J, et al. (2016). Localised controlled release of simvastatin from porous chitosan–gelatin scaffolds engrafted with simvastatin loaded PLGA-microparticles for bone tissue engineering application. Mater Sci Eng C Mater Biol Appl 59:249–57. Engineering: C
  • Han FY, Thurecht KJ, Whittaker AK, Smith MT. (2016). Bioerodable PLGA-based microparticles for producing sustained-release drug formulations and strategies for improving drug loading. Front Pharmacol 7:1–11
  • Henshaw CA, Dundas AA, Cuzzucoli Crucitti V, et al. (2021). Droplet microfluidic optimisation using micropipette characterisation of bio-instructive polymeric surfactants. Molecules 26:3302.
  • Hu H, Liao Z, Xu M, et al. (2023). Fabrication, optimization, and evaluation of paclitaxel and curcumin coloaded PLGA nanoparticles for improved antitumor activity. ACS Omega 8:976–86.
  • Lamparelli EP, Lovecchio J, Ciardulli MC, et al. (2021). Chondrogenic commitment of human bone marrow mesenchymal stem cells in a perfused collagen hydrogel functionalized with HTGF-B1-releasing PLGA microcarrier. Pharmaceutics 13:399.
  • Landín M, Rowe RC, York P. (2009). Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations. Eur J Pharm Sci 38:325–31.
  • Lefnaoui S, Rebouh S, Bouhedda M, Yahoum MM. (2020). Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium. Drug Deliv Transl Res 10:168–84.
  • Li G, Yao L, Li J, et al. (2018). Preparation of poly(lactide-co-glycolide) microspheres and evaluation of pharmacokinetics and tissue distribution of BDMC-PLGA-MS in rats. Asian J Pharm Sci 13:82–90.
  • Li H, Liao Z, Yang Z, et al. (2021). 3D printed poly(ε-caprolactone)/meniscus extracellular matrix composite scaffold functionalized with Kartogenin-releasing PLGA microspheres for meniscus tissue engineering. Front Bioeng Biotechnol 9:662381.
  • Li L, Li Z, Guo Y, et al. (2022). Preparation of uniform-sized GeXIVA[1,2]-loaded PLGA microspheres as long-effective release system with high encapsulation efficiency. Drug Deliv 29:2283–95.
  • Makadia HK, Siegel SJ. (2011). Poly lactic-co-glycolic acid (PLGA) as biodegradable controlled drug delivery carrier. Polymers (Basel) 3:1377–97.
  • Mensah RA, Kirton SB, Cook MT, et al. (2019). Optimising poly(lactic-co-glycolic acid) microparticle fabrication using a taguchi orthogonal array design-of-experiment approach. PLoS ONE 14:e0222858.
  • Ogay V, Mun EA, Kudaibergen G, et al. (2020). Progress and prospects of polymer-based drug delivery systems for bone tissue regeneration. 25.
  • Oliveira MB, Mano JF. (2011). Polymer-based microparticles in tissue engineering and regenerative medicine. Biotechnol Prog 27:897–912.
  • Operti MC, Bernhardt A, Grimm S, et al. (2021). PLGA-based nanomedicines manufacturing: technologies overview and challenges in industrial scale-up. Int J Pharm 605:120807.
  • Operti MC, Dölen Y, Keulen J, et al. (2019). Microfluidics-assisted size tuning and biological evaluation of PLGA particles. Pharmaceutics 11:590.
  • Ortega-Oller I, Padial-Molina M, Galindo-Moreno P, et al. (2015). Bone regeneration from PLGA micro-nanoparticles. BioMed Res Int 2015:1–18.
  • Park K, Skidmore S, Hadar J, et al. (2019). Injectable, long-acting PLGA formulations: analyzing plga and understanding microparticle formation. J Control Release 304:125–34.
  • Rouco H, Diaz-Rodriguez P, Rama-Molinos S, et al. (2018). Delimiting the knowledge space and the design space of nanostructured lipid carriers through artificial intelligence tools. Int J Pharm 553:522–30.
  • Rowe RC, Colbourn EA. (2003). Neural computing in product formulation. Chem Educ 9:1–8.
  • Santoyo S, de Jalón EG, Ygartua P, et al. (2002). Optimization of topical cidofovir penetration using microparticles. Int J Pharm 242:107–13.
  • Shao Q, Rowe RC, York P. (2006). Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur J Pharm Sci 28:394–404.
  • Simões MF, Silva G, Pinto AC, et al. (2020). Artificial neural networks applied to quality-by-design: from formulation development to clinical outcome. Eur J Pharm Biopharm 152:282–95.
  • Szlęk J, Pacławski A, Lau R, et al. (2016). Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs. Comput Methods Programs Biomed 134:137–47.
  • Tran V-T, Benoît J-P, Venier-Julienne M-C. (2011). Why and how to prepare biodegradable, monodispersed, polymeric microparticles in the field of pharmacy? Int J Pharm 407:1–11.
  • Varde NK, Pack DW. (2004). Microspheres for controlled release drug delivery. Expert Opin. Biol. Ther 4:35–51.
  • Varela-Fernández R, Bendicho-Lavilla C, Martin-Pastor M, et al. (2022). Design, optimization, and in vitro characterization of idebenone-loaded PLGA microspheres for LHON treatment. Int J Pharm 616:121504.
  • Vasileiou K, Vysloužil J, Pavelková M, et al. (2018). The size-reduced Eudragit® RS microparticles prepared by solvent evaporation method – monitoring the effect of selected variables on tested parameters, 66:274–280.
  • Vysloužil J, Doležel P, Kejdušová M, et al. (2014). Influence of different formulations and process parameters during the preparation of drug-loaded PLGA microspheres evaluated by multivariate data analysis. Acta Pharm 64:403–17.
  • Wang L-Y, Gu Y-H, Su Z-G, Ma G-H. (2006). Preparation and improvement of release behavior of chitosan microspheres containing insulin. Int J Pharm 311:187–95.
  • Yasin H, Al-Taani B, Salem M. (2021). Preparation and characterization of ethylcellulose microspheres for sustained-release of pregabalin. Res Pharm Sci 16:1–15.
  • Yeo Y, Park K. (2004). Control of encapsulation efficiency and initial burst in polymeric microparticle systems. Arch Pharm Res 27:1–12.