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Adaptive Neuro-Fuzzy Modeling of Poorly Soluble Drug Formulations

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Purpose

The purpose of this study was to evaluate the efficiency of a neuro-fuzzy logic-based methodology to model poorly soluble drug formulations and predict the development of the particle size that has been proven to be an important factor for long-term stability.

Methods

An adaptive neuro-fuzzy inference system was used to model the natural structures within the data and construct a set of fuzzy rules that can subsequently used as a predictive tool. The model was implemented in Matlab 6.5 and trained using 75% of an experimental data set. Subsequently, the model was evaluated and tested using the remaining 25%, and the predicted values of the particle size were compared to the ones from the experimental data. The produced adaptive neuro-fuzzy inference system-based model consisted of four inputs, i.e., acetone, propylene glycol, POE-5 phytosterol (BPS-5), and hydroxypropylmethylcellulose 90SH-50, with four membership functions each. Moreover, 256 fuzzy rules were employed in the model structure.

Results

Model training resulted in a root mean square error of 4.5 × 10−3, whereas model testing proved its highly predictive efficiency, achieving a correlation coefficient of 0.99 between the actual and the predicted values of the particle size (mean diameter).

Conclusions

Neuro-fuzzy modeling has been proven to be a realistic and promising tool for predicting the particle size of drug formulations with an easy and fast way, after proper training and testing.

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Abbreviations

ANFIS:

adaptive neuro-fuzzy inference system

ANN:

artificial neural network

BPS-5:

PEG-5 soy sterol

EDM:

empirical-data-based model

FD:

factorial design

FL:

fuzzy logic

HPMC:

hydroxypropylmethylcellulose

\(In^{i}_{j} {\left( k \right)}\) :

j = 1,2,...,N i

\(k = 1,...,M_{i} ,i = 1,2i\) :

denotes the experimental phase, i.e., FD (i = 1) or RSM (i = 2), M i is the number of available data per causal factor, N i is the number of causal factors per experimental phase i, \(In^{i}_{j} {\left( k \right)}\) denotes the value of the causal factor j for the ith experimental phase and the kth experiment

IT:

infusion technique

M 25% :

the 25% of the available samples per causal factor denoting the size of the testing vector

M 75% :

the 75% of the available samples per causal factor denoting the size of the training vector

OXC:

oxcarbazepine

PIDS:

polarization intensity differential scattering

PSi(k), k = 1, ..., M i :

the particle size estimated at the ith experimental phase and the kth experiment

PSANFIS(k), k = 1, ..., M25%:

the particle size estimated by ANFIS for the kth experiment that belongs to the testing vector

PSANN(k), k = 1, ..., M25%:

the particle size estimated by ANN for the kth experiment that belongs to the testing vector

RMSE:

root mean-square error

RSM:

response surface methodology

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Authors and Affiliations

  1. Phoqus Pharmaceuticals Limited, 10 Kings Hill Avenue, Kings Hill, West Malling, Kent, ME19 4PQ, UK

    Dionysios Douroumis

  2. Department of Pharmaceutical Technology, University of Jena, Lessing Strasse 8, D-07743, Jena, Germany

    Dionysios Douroumis & Alfred Fahr

  3. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Leontios J. Hadjileontiadis

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  1. Dionysios Douroumis
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  3. Alfred Fahr
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Correspondence to Dionysios Douroumis.

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Douroumis, D., Hadjileontiadis, L.J. & Fahr, A. Adaptive Neuro-Fuzzy Modeling of Poorly Soluble Drug Formulations. Pharm Res 23, 1157–1164 (2006). https://doi.org/10.1007/s11095-006-0021-3

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