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The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

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Abstract

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

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Abbreviations

PLC:

Prednisolone-loaded chitosan nanoparticles

PRED:

Prednisolone

HMW:

High molecular weight

MMW:

Medium molecular weight

ED:

Effective diameter

LC:

Loading capacity

EE:

Encapsulation efficiency

ZP:

Zeta potential

PD:

Polydispersity

ATR–FTIR:

Attenuated total reflectance–Fourier transform infrared spectroscopy1

DLS:

Dynamic light scattering

PCA:

Principal component analysis

PLS:

Partial least squares regression

R2:

Coefficient of determination

RMSE:

Residual mean square error

PAT:

Process analytical technologies

NIR:

Near-infrared

MW:

Molecular weight

MWCO:

Molecular weight cutoff

LDA:

Linear discriminate analysis

LOO:

Leave one out

AI:

Artificial intelligence

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Funding

This project was graciously funded by the Zarqa University Deanship of Scientific Research (#15, Damascus Highway, Zarqa, 13110, Jordan).

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan

    Jehad Nasereddin, Reem Al Wadi & Ahlam Zaid Al-Kilani

  2. Department of Pharmaceutics and Pharmaceutical Technology, The Faculty of Pharmacy and Medical Sciences, University of Petra, Amman, 11196, Jordan

    Asad Abu Khalil & Mohammad Al Natour

  3. Faculty of Pharmacy, Mutah University, Al Karak, 61710, Jordan

    Wael Abu Dayyih

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  1. Jehad Nasereddin
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Contributions

JN: primary supervision, experimental procedures, writing of the manuscript. RAW: experimental procedure. AZK: supervision, manuscript review. AAK: NANOPARTICLE Characterization, manuscript review. MN: nanoparticle characterization, manuscript review. WAD: analytical method validation.

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Correspondence to Jehad Nasereddin.

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Nasereddin, J., Al Wadi, R., Zaid Al-Kilani, A. et al. The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles. AAPS PharmSciTech 25, 38 (2024). https://doi.org/10.1208/s12249-024-02756-3

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