Artificial neural networks in the modeling of drugs release profiles from hydrodynamically balanced systems (original) (raw)
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Application of artificial neural networks in the design of controlled release drug delivery systems
Advanced Drug Delivery Reviews, 2003
Controlled release drug delivery systems offer great advantages over the conventional dosage forms. However, there are great challenges to efficiently develop controlled release drug delivery systems due to the complexity of these delivery systems. Traditional statistic response surface methodology (RSM) is one of the techniques that has been employed to develop and formulate controlled release dosage forms. However, there are
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 NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY Review Article
International Journal of Applied Pharmaceutics, 2018
Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also.
Journal of pharmaceutical and …, 2000
Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.
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.
A Novel Artificial Intelligence System in Formulation Dissolution Prediction
Computational Intelligence and Neuroscience
Artificial neural network (ANN) techniques are widely used to screen the data and predict the experimental result in pharmaceutical studies. In this study, a novel dissolution result prediction and screen system with a backpropagation network and regression methods was modeled. For this purpose, 21 groups of dissolution data were used to train and verify the ANN model. Based on the design of input data, the related data were still available to train the ANN model when the formulation composition was changed. Two regression methods, the effective data regression method (EDRM) and the reference line regression method (RLRM), make this system predict dissolution results with a high accuracy rate but use less database than the orthogonal experiment. Based on the decision tree, a data screen function is also realized in this system. This ANN model provides a novel drug prediction system with a decrease in time and cost and also easily facilitates the design of new formulation.
Brazilian Journal of Pharmaceutical Sciences, 2020
Dexketoprofen trometamol (DT) is an active S (+) enantiomer of ketoprofen, and a non-steroidal antiinflammatory agent. DT has a short biological half-life and the dosing interval is quite short when there is a need to maintain the desirable effect for longer time periods. Consequently, a controlled release DT tablet was designed for oral administration aiming to minimize the number of doses and the possible side effects. Calculations of the parameters for controlled release DT tablets were shown clearly. Controlled release matrix-type tablet formulations were prepared using hydroxypropyl methylcellulose (HPMC) (low and high viscosity), Eudragit RS and Carbopol, and the effects of different polymers on DT release from the tablet formulations were investigated. The dissolution rate profiles were compared and analyzed kinetically. An Artificial Neural Network (ANN) model was developed to predict drug release and a successful model was obtained. Subsequently, an optimum formulation was selected and evaluated in terms of its analgesic and anti-inflammatory activity. Although the developed controlled release tablets did not have an initial dose, they were found to be as effective as commercially available tablets on the market. Dissolution and in vivo studies have shown that the prepared tablets were able to release DT for longer time periods, making the tablets more effective, convenient and more tolerable.
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.
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.