Artificial neural networks in analysis of indinavir and its degradation products retention

Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2⁴ was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.     

Optimization of the separation of a group of triazine herbicides by micellar capillary electrophoresis using experimental design and artificial neural networks

The micellar electrokinetic chromatography separation of a group of triazine compounds was optimized using a combination of experimental design (ED) and artificial neural network (ANN). Different variables affecting separation were selected and used as input in the ANN. A chromatographic exponential function (CEF) combining resolution and separation time was used as output to obtain optimal separation conditions. An optimized buffer (19.3 mM sodium borate, 15.4 mM disodium hydrogen phosphate, 28.4 mM SDS, pH 9.45, and 7.5% 1-propanol) provides the best separation with regard to resolution and separation time. Besides, an analysis of variance (ANOVA) approach of the MEKC separation, using the same variables, was developed, and the best capability of the combination of ED-ANN for the optimization of the analytical methodology was demonstrated by comparing the results obtained from both approaches. In order to validate the proposed method, the different analytical parameters as repeatability and day-to-day precision were calculated. Finally, the optimized method was applied to the determination of these compounds in spiked and nonspiked ground water samples.     

Evaluation of chemical equilibria with the use of artificial neural networks

Multivariate calibration with experimental design (ED) and artificial neural networks (ANN) modeling can be used to estimate equilibria constants from any kind of protonation or metal–ligand equilibrium data like potentiometry, polarography, spectrophotometry, extraction, etc. The method was tested on evenly or randomly distributed experimental error-free data and data with random noise and the results show that even rather higher experimental errors do not influence significantly the prediction power and correctness of ANN prediction. ANN with appropriate ED can provide accurate prediction of stability constants with the relative errors in the range of ±4% or smaller while the approach is very robust. Comparison with a hard model evaluation based on non-linear regression techniques shows excellent agreement. Proposed ANN method is of a general nature and, in principal, can be adopted to any analytical technique used in equilibria studies.     

Combining artificial neural networks and experimental design to prediction of kinetic rate constants

A “soft-modelling” computational approach of artificial neural networks (ANNs) combined with experimental design (ED) has been applied successfully in Chemical Kinetics for the prediction of kinetic rate constants. The system studied comprises two consecutive first-order reactions and the kinetic data were computed determining the values of both rate constants. The kinetic curves were distributed according to an ED, and the central star composite experimental design (CSCED) was chosen as the most appropriate. Computational treatments were performed on synthetic data endowed with noise, after which they were applied to the data measured in an experimental reaction between carbonyl cyanide 3-clorophenylhydrazone with 2-mercaptoethanol, computing the experimental kinetic data of absorbance acquired at 3 wavelengths. The combined ANN and ED approach applied in chemical kinetics proved to be robust and of general applicability and has the advantage of being a “soft-modelling” method such that it was not necessary to solve the system of ordinary differential equations to determine the explicit mathematical function between the data and the kinetic rate constants. Additionally, upon using the CSCED experimental design, it was possible to substantially reduce the number of experiments.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

Application of Artificial Neural Networks in Multifactor Optimization of Selectivity in Capillary Electrophoresis

ABSTRACT

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) was proposed in the optimization of selectivity in capillary electrophoresis. The effect of the buffer composition, concentration, SDS concentration, ethanol percentage and the applied voltage on the separation of six choice solutes was examined by using orthogonal design. Feedforward-type neural networks with faster back propagation (BP) algorithm were applied to model the separation process, and then optimization of the experimental conditions was carried out in the modeled neural network with 5-7-1 structure, which had been confirmed to be able to provide the maximum performance. It was demonstrated that by combining ANN modeling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. Because of its general validity, the new proposed approach can also be applied in other separation conditions.     

Mathematical modeling and simulation of inulinase adsorption in expanded bed column

A mathematical model for an expanded bed column was developed to predict breakthrough curves for inulinase adsorption on Streamline SP ion-exchange adsorbent, using a crude fermentative broth with cells as the feedstock. The kinetics and mass transfer parameters were estimated using the PSO (particle swarm optimization) heuristic algorithm. The parameters were estimated for each expansion degree (ED) using three breakthrough curves at initial inulinase concentrations of 65.6 U mL⁻¹. In sequence, the model parameters for an ED of 2.5 were validated using the breakthrough curve at an initial concentration of 114.4 U mL⁻¹. The applicability of the validated model in process optimization was investigated, using the model as a process simulator and experimental design methodology to optimize the column and process efficiencies. The results demonstrated the usefulness of this methodology for expanded bed adsorption processes.     

Optimization of Spore and Antifungal Lipopeptide Production During the Solid-state Fermentation of <Emphasis Type="Italic">Bacillus subtilis</Emphasis>

Bacillus subtilis strain TrigoCor 1448 was grown on wheat middlings in 0.5-l solid-state fermentation (SSF) bioreactors for the production of an antifungal biological control agent. Total antifungal activity was quantified using a 96-well microplate bioassay against the plant pathogen Fusarium oxysporum f. sp. melonis. The experimental design for process optimization consisted of a 2⁶⁻¹ fractional factorial design followed by a central composite face-centered design. Initial SSF parameters included in the optimization were aeration, fermentation length, pH buffering, peptone addition, nitrate addition, and incubator temperature. Central composite face-centered design parameters included incubator temperature, aeration rate, and initial moisture content (MC). Optimized fermentation conditions were determined with response surface models fitted for both spore concentration and activity of biological control product extracts. Models showed that activity measurements and spore production were most sensitive to substrate MC with highest levels of each response variable occurring at maximum moisture levels. Whereas maximum antifungal activity was seen in a limited area of the design space, spore production was fairly robust with near maximum levels occurring over a wider range of fermentation conditions. Optimization resulted in a 55% increase in inhibition and a 40% increase in spore production over nonoptimized conditions.     

Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks

Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

Optimization of a solid-phase microextraction procedure for the determination of herbicides by micellar electrokinetic chromatography

The use of a different optimization procedure that involves Experimental Design (ED) and Artificial Neural Networks (ANN) for the off-line coupling solid-phase microextraction-micellar electokinetic chomatography (SPME-MEKC) is presented. This combination of ED and ANN, mathematical tools not previously used in SPME-MEKC optimization, allowed us to obtain good extraction efficiencies in the SPME procedure for the determination of a group of eleven triazine herbicides in groundwater samples. Both extraction and desorption steps were carried out by solution stirring at 900 rpm. Optimal conditions for the off-line SPME procedure were: extraction with a poly(dimethylsiloxane)/divinylbenzene SPME fiber for 120 min, 10% (w/v) NaCl, desorption time 40 min, and 70% (v/v) of methanol/buffer as desorption mixture. Detection limits lay between 0.80 microg L(-1) and 4.89 microg L(-1). Finally, the optimized method was applied to the determination of these compounds in spiked and non-spiked groundwater samples using a previously optimized MEKC separation.     

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.     

Kinetic Modeling and Parameter Estimation in a Tower Bioreactor for Bioethanol Production

In this work, a systematic method to support the building of bioprocess models through the use of different optimization techniques is presented. The method was applied to a tower bioreactor for bioethanol production with immobilized cells of Saccharomyces cerevisiae. Specifically, a step-by-step procedure to the estimation problem is proposed. As the first step, the potential of global searching of real-coded genetic algorithm (RGA) was applied for simultaneous estimation of the parameters. Subsequently, the most significant parameters were identified using the Placket–Burman (PB) design. Finally, the quasi-Newton algorithm (QN) was used for optimization of the most significant parameters, near the global optimum region, as the initial values were already determined by the RGA global-searching algorithm. The results have shown that the performance of the estimation procedure applied in a deterministic detailed model to describe the experimental data is improved using the proposed method (RGA–PB–QN) in comparison with a model whose parameters were only optimized by RGA.     

Monitoring of fosinopril sodium impurities by liquid chromatography-mass spectrometry including the neural networks in method evaluation

In this paper, the mass spectrometry (MS) detection has been applied for screening of fosinopril sodium impurities which arise during forced stress study. Before MS analysis, liquid chromatographic method with suitable mobile phase composition was developed. The separation was done on SunFire 100 mm x 4.6 mm 3.5 microm particle size column. The mobile phases which consisted of methanol-ammonium acetate buffer-acetic acid, in different ratios, were used in a preliminary study. Flow rate was 0.3 mL min(-1). Under these conditions, percent of methanol, concentration of ammonium acetate buffer and acetic acid content were tested simultaneously applying central composite design (CCD) and artificial neural network (ANN). The combinations of experimental design (ED) and ANN present powerful technique in method optimization. Input and output variables from CCD were used for network training, verification and testing. Multiple layer perceptron (MLP) with back propagation (BP) algorithm was chosen for network training. When the optimal neural topology was selected, network was trained by adjusting strength of connections between neurons in order to adapt the outputs of whole network to be closer to the desired outputs, or to minimize the sum of the squared errors. From the method optimization the following mobile phase composition was selected as appropriate: methanol-10 mM ammonium acetate buffer-acidic acid (80:19.5:0.5 v/v/v). This mobile phase was used as inlet for MS. According to molecular structure and literature data, electrospray positive ion mode was applied for analysis of fosinopril sodium and its impurities. The proposed method could be used for screening of fosinopril sodium impurities in bulk and pharmaceuticals, as well as for tracking the degradation under stress conditions.     

Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography

The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.     

Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.     

Methylmercury determination in biological samples by derivatization,solid-phase microextraction and gas chromatography with microwave-induced plasma atomic emission spectrometry

A method for the extraction and gas chromatographic determination of methylmercury in biological matrices is presented. By combining the advantages of two extraction techniques-microwave-assisted extraction (MAE) and solid-phase microextraction (SPME)--the separation of methylmercury from biological samples is possible. Specifically, the procedure involves microwave extraction with 3 M hydrochloric acid, followed by aqueous-phase derivatization with sodium tetraphenylborate and headspace SPME with a silica fibre coated with polydimethylsiloxane (PDMS). For optimization of the derivatization-SPME procedure, a central composite experimental design with alpha = 1.682 and two central points was used to model gas-chromatographic peak areas as functions of pH, extraction temperature and sorption time. A desirability function was then used for the simultaneous optimization for methylmercury and Hg(II). The optimal derivatization-SPME conditions identified were close to pH 5, temperature 100 degrees C, and sorption time 15 min. The identification and quantification of the extracted methylmercury is carried out by gas chromatography with microwave-induced plasma atomic emission spectrometry detection. The validity of the new procedure is shown by the results of analyses of certified reference materials.