Application of artificial neural networks in the design of controlled release drug delivery systems (original) (raw)
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Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms
Pharmaceutics, 2012
Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization ...
Artificial Neural Network in Drug Delivery and Pharmaceutical Research
The Open Bioinformatics Journal, 2013
Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage...
Acta poloniae pharmaceutica
Artificial neural networks (ANNs) were used as modeling tools for prediction of various drugs release patterns from hydrodynamically balanced systems (HBS) composed with Metholose 90SH (hydroxypropylmethylcellulose--HPMC). The objective was to provide predictive and data-mining models of analyzed problem. It was found that ANNs are capable to accurately predict release patterns of different drugs from HBS based on the description of the formulation as well as chemical structure of the drug. Overall generalization error RMSE was 8.7 and after inclusion of pilot study in learning dataset it decreased to ca. 4.5. Sensitivity analysis of ANNs was applied to reduce native input vector from 77 to 7 inputs in order to improve the performance of predictive models. Simultaneously, it revealed crucial variables governing release of drugs from HBS.
Journal of Pharmaceutical Sciences, 1999
Artificial neural networks applied to in vitro−in vivo correlations (ANN−IVIVC) have the potential to be a reliable predictive tool that overcomes some of the difficulties associated with classical regression methods, principally, that of providing an a priori specification of the regression equation structure. A number of unique ANN configurations are presented, that have been evaluated for their ability to determine an IVIVC from different formulations of the same product. Configuration variables included a combination of architectural structures, learning algorithms, and input−output association structures. The initial training set consisted of two formulations and included the dissolution from each of the six cells in the dissolution bath as inputs, with associated outputs consisting of 1512 pharmacokinetic time points from nine patients enrolled in a crossover study. A third formulation IVIVC data set was used for predictive validation. Using these data, a total of 29 ANN configurations were evaluated. The ANN structures included the traditional feed forward, recurrent, jump connections, and general regression neural networks, with input−output association types consisting of the direct mapping of the dissolution profiles to the pharmacokinetic observations, mapping the individual dissolution points to the individual observations, and using a "memorative" input−output association. The ANNs were evaluated on the basis of their predictive performance, which was excellent for some of these ANN models. This work provides a basic foundation for ANN−IVIVC modeling and is the basis for continued modeling with other desirable inputs, such as formulation variables and subject demographics.
Performance comparison of neural network training algorithms in modeling of bimodal drug delivery
International Journal of Pharmaceutics, 2006
The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal drug delivery. Artificial neural network (ANN) as a multilayer perceptron feedforward network was incorporated for developing a predictive model of the formulations. Five different training algorithms belonging to three classes: gradient descent, quasi-Newton (Levenberg-Marquardt, LM) and genetic algorithm (GA) were used to train ANN containing a single hidden layer of four nodes. The next objective of the current study was to compare the performance of aforementioned algorithms with regard to predicting ability. The ANNs were trained with those algorithms using the available experimental data as the training set. The divergence of the RMSE between the output and target values of test set was monitored and used as a criterion to stop training. Two versions of gradient descent backpropagation algorithms, i.e. incremental backpropagation (IBP) and batch backpropagation (BBP) outperformed the others. No significant differences were found between the predictive abilities of IBP and BBP, although, the convergence speed of BBP is three-to four-fold higher than IBP. Although, both gradient descent backpropagation and LM methodologies gave comparable results for the data modeling, training of ANNs with genetic algorithm was erratic. The precision of predictive ability was measured for each training algorithm and their performances were in the order of: IBP, BBP > LM > QP (quick propagation) > GA. According to BBP-ANN implementation, an increase in coating levels and a decrease in the amount of pectin-chitosan generally retarded the drug release. Moreover, the latter causal factor namely the amount of pectin-chitosan played slightly more dominant role in determination of the dissolution profiles.
Journal of the Iranian Chemical Society, 2018
Drug delivery systems are potential systems with ability to release drugs with a variety of mechanisms. Some mechanisms may consist of multiple steps, where the release rate of each step can vary from the others. Moreover, a drug release is a kinetic process intrinsically and the initial amount of the loaded drug may influence the amount of the release. Therefore, to achieve the desirable release, the loading and release processes should be investigated simultaneously, while researchers have considered these processes independently so far. Considering the fact that functional dependence between loading and release is not obvious, we proposed the combination of experimental design and powerful non-linear generalized artificial neural network (G-ANN) methods for the simultaneous optimization of these processes. Here, the functionalized PEGylated KIT-6 ([β-CD@PEGylated KIT-6]) NPs and curcumin were selected as a nano-carrier and drug, respectively. The curcumin release was optimized by G-ANN by considering the the feedback from the loading step. The obtained optimal parameters were as follows: (in the release process) 1.80 of the weight ratio of drug to nano-carrier, 5.70 of pH and 120 h of release time; and (in the loading process) 43 h of loading time and 2.2 of weight ratio of drug/nano-carrier.
Neural network based optimization of drug formulations
Advanced drug delivery …, 2003
A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in an RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The purpose of this review is to describe the basic concept of the multi-objective simultaneous optimization technique, in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Superior function of the ANN approach was demonstrated by the optimization for typical numerical examples.
CHEMICAL & PHARMACEUTICAL BULLETIN, 2008
The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (؊1, 0, ؉1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2 3 factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference (f 1 2.19) and similarity (f 2 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms.