Comparison of Full Factorial Design,Central Composite Design,and Box-Behnken Design in Chromatographic Method Development for the Determination of Fluconazole and Its Impurities

This paper presents the development and optimization of a liquid chromatographic method for the determination of fluconazole and its impurities by experimental design methodology. Four experimental design types were applied: two-level full factorial design, central composite design, Box-Behnken design, and three-level full factorial design. The advantages and drawbacks of each design are described and detailed statistical evaluation of mathematical models was performed. The central composite design and three-level full factorial design created significantly better models comparing to the other methods. As the central composite design requires a smaller number of experiments, its models were used for theoretical examination of experimental space. Multiobjective optimization aiming to achieve maximal separation of all investigated substances and minimal analysis duration was performed by a grid point search. The defined optimal separation was achieved on a C₁₈ (125 mm × 4 mm, 5 µm particle size) column with a mobile phase consisting of acetonitrile and water (5 mM ammonium formate) (15:85, v/v); a column temperature of 25°C; a flow rate of 1.2 mL min^?1; and a detection wavelength of 260 nm.     

Application of a column selection system and DryLab software for high-performance liquid chromatography method development

This paper describes a strategy for the development of chromatographic methods for drug candidates based upon the use of simple MS compatible mobile phases and optimization of the chromatographic selectivity through variations of the stationary phase and mobile phase pH. The strategy employs an automated column selection system and a series of HPLC columns, varying in hydrophobicity and silanol activity, in combination with DryLab software to develop chromatographic methods for the separation of mixtures of bupivacaine and its metabolites; acidic, basic, and neutral compounds; and atenolol, nitrendipine, and their degradation products.     

A new chromatographic response function for use in size-exclusion chromatography optimization strategies: Application to complex organic mixtures

A new chromatographic response function (CRF) is presented aiming at designing an optimal chromatographic separation protocol for assessing the molecular size distribution of complex organic mixtures, such as those of natural organic matter from different sources (atmospheric, aqueous, and terrestrial). This CRF can be applied to mixtures of unknown solutes, being well suited for describing separation processes of pair of peaks of highly unequal area, and also for overlapping and asymmetric peaks. The performance of the developed CRF was compared to that of an existing response function, using simulated chromatograms. The capability of the new function to qualify the resolution degree that it is attained under different chromatographic conditions was further assessed through a size-exclusion chromatography study of a variety of different organic compounds, via a two-level full factorial design. It was proved that this function is a reliable alternative to optimize simultaneously the composition of the mobile phase (pH, ionic strength, and organic modifier concentration) and the instrumental variables (flow rate).     

Gold-silver-graphene hybrid nanosheets-based sensors for sensitive amperometric immunoassay of alpha-fetoprotein using nanogold-enclosed titania nanoparticles as labels

A stepwise method development strategy has been employed to develop a robust HPLC method to resolve several closely eluting structurally related impurities in an active pharmaceutical ingredient (API). This strategy consisted of automated column screening, optimization of the most critical chromatographic parameters, DryLab(?) modeling, and experimental verification of optimized separation conditions. DryLab(?) was used to predict an optimized gradient profile and separation temperature and these predictions were verified experimentally. A discussion of the accuracy of these predictions is presented. The robustness of the method was verified and the ability of DryLab(?) to predict, with reasonable accuracy, the outcome of such robustness studies was also examined. Once the robustness was established by the DryLab(?) predictions the remainder of the subsequent verification by experiment becomes a simple reiterative exercise. This study also demonstrates that factors such as column chemistry and critical chromatographic parameters can have a profound and oftentimes interrelated effect on the chromatographic separation of isomers, bromo analogs and other structurally very similar impurities. Therefore, it is critical to adopt a rational strategy, as demonstrated here, to evaluate the interplay of these factors, thereby greatly enhancing method development efficiency.     

Development of an HPLC Assay Methodology for a Desonide Cream with Chemometrics Assisted Optimization

A new reversed-phase liquid chromatographic method for the separation and simultaneous quantification of desonide, sorbic acid, methylparaben, propyl gallate, and the major degradation product of desonide in a hydrophilic cream was developed with the aid of experimental design, resolution loss functions, and chemometrics methods. A strategy involving a screening phase and a fractional factorial design revealed the most influent chromatographic variables (pH and organic solvent content). The arc tangent resolution function was adopted as the optimization loss function. These variables were further optimized using a central composite design. Multivariate curve resolution and partial least squares regression were tested to optimize the chromatographic run time. The latter revealed to be superior in terms of precision and allowed the validation of a method with a total run time 3 times lower (approximately 8 min). The experimental design and chemometrics models enabled an efficient use of time and resources in predicting the optimum separation conditions for the desonide formulation. The validation of the resulting method according to the current ICH guidelines confirmed its selectivity, linearity, accuracy, and precision.     

Combined column–mobile phase mixture statistical design optimization of high-performance liquid chromatographic analysis of multicomponent systems

A statistical approach for the simultaneous optimization of the mobile and stationary phases used in reversed-phase liquid chromatography is presented. Mixture designs using aqueous mixtures of acetonitrile (ACN), methanol (MeOH) and tetrahydrofuran (THF) organic modifiers were performed simultaneously with column type optimization, according to a split-plot design, to achieve the best separation of compounds in two sample sets: one containing 10 neutral compounds with similar retention factors and another containing 11 pesticides. Combined models were obtained by multiplying a linear model for column type, C8 or C18, by quadratic or special cubic mixture models. Instead of using an objective response function, combined models were built for elementary chromatographic criteria (retention factors, resolution and relative retention) of each solute or pair of solutes and, after their validation, the global separation was accomplished by means of Derringer's desirability functions. For neutral compounds a 37:12:8:43 (v/v/v/v) percentage mixture of ACN:MeOH:THF:H₂O with the C18 column and for pesticides a 15:15:70 (v/v/v) ACN:THF:H₂O mixture with the C8 column provide excellent resolution of all peaks.     

Computer simulation for the prediction of separation as a function of pH for reversed-phase high-performance liquid chromatography. I. Accuracy of a theory-based model.

Computer simulation software (DryLab I/mp) is described for predicting high-performance liquid chromatographic separation as a function of changes in mobile phase pH. Three experimental runs with pH (only) varied are used to derive values of pKa plus capacity factors (k') for the ionized and non-ionized form of each ionizable solute. Various tests of the experimental data then allow classification of each solute as acidic, basic, neutral (including strong or weak acids or bases) and amphoteric. Experimental data are reported for the separation of several substituted anilines as a function of pH and solvent composition (%B). Experimental requirements for the accurate prediction of separation (ca. +/- 2-4% in alpha) as a function of pH are discussed. The reliability of the software is demonstrated for three different samples: mixtures of (a) substituted benzoic acids, (b) substituted anilines and (c) catecholamine-related compounds.     

Computer-assisted method development and optimization in high-performance liquid chromatography

This paper reports the use of DryLab, a computer simulation software package, to assist in the development and optimization of a reversed-phase high-performance liquid chromatographic (HPLC) method for the separation of a model drug candidate and its degradation products. Prior to the optimization process, columns with various bonded phases are evaluated for their chromatographic performance using the sample of interest. Simultaneous optimization of two separation variables and the use of resolution maps to predict the optimal conditions are illustrated. Options to optimize column conditions (column length and flow-rate) to further reduce run time are briefly discussed. The accuracy of DryLab-predicted retention times and resolution is compared with experimental values. The DryLab software used in this study provided satisfactory predictions for the selected model, with average errors of less than 3.5 and 11.8% for retention time and resolution, respectively.     

Gas chromatography–mass spectroscopy optimization by computer simulation,application to the analysis of 93 volatile organic compounds in workplace ambient air

GC–MS optimization method including both advantages from chromatographic separation and mass spectrometric detection was designed for a set of 93 volatile organic compounds. Only a few experiments were necessary to determine the thermodynamic retention parameters for all compounds on a RTX-VMS column. From these data, computer simulation was used in order to predict the retention times of the compounds in temperature programmed gas chromatography. Then, an automatic selection of ions from the NIST database was performed and compared to the optimum conditions (full separation of VOC). This simulation-selection procedure was used to screen a numerous set of GC and MS conditions in order to quickly design a GC–MS method whatever the set of compounds considered.     

Development, optimization and validation of gas chromatographic fingerprinting of Brazilian commercial gasoline for quality control

Three-step development, optimization and validation strategy for GC fingerprints of Brazilian commercial gasoline is described. A suitable chromatographic system was selected first. The following step was to improve acceptable chromatographic resolution with reduced analysis time, which is recommended for routine applications. Optimization was carried out using full three-level factorial designs. Optimal conditions were obtained for flow rate, oven ramps, injection volume and split ratio. Finally, several validation parameters were performed. Therefore, a feasible and reliable fingerprint was established to identify Brazilian commercial gasoline quality. This strategy can also be applied to develop fingerprints for quality control of other fuels.     

Development and optimization of a method for analyzing biodiesel mixtures with non-aqueous reversed phase liquid chromatography

Biodiesel (a mixture of fatty acid esters) is normally analyzed using gas chromatography/flame ionization detection, as specified by the ASTM D6584 and EN14105 standards. This paper proposes a binary gradient method for analyzing biodiesel mixtures using non-aqueous reverse phase HPLC with a UV detector capable of overcoming the drawbacks of the gas chromatographic technique normally used. The new analytical method was developed by means of a statistical sensitivity analysis applied to the main parameters influencing the recording, using the full factorial design method combined with the Yates algorithm and the steepest ascent optimization procedure. The present study shows the influence of the main biodiesel mixture separation analysis parameters. The resulting tool proved valid for analyzing not only biodiesel but also any traces of unreacted oil.     

Experimental designs for modeling retention patterns and separation efficiency in analysis of fatty acid methyl esters by gas chromatography-mass spectrometry

The retention behavior of components analyzed by chromatography varies with instrumental settings. Being able to predict how changes in these settings alter the elution pattern is useful, both with regards to component identification, as well as with regards to optimization of the chromatographic system. In this work, it is shown how experimental designs can be used for this purpose. Different experimental designs for response surface modeling of the separation of fatty acid methyl esters (FAME) as function of chromatographic conditions in GC have been evaluated. Full factorial, central composite, Doehlert and Box-Behnken designs were applied. A mixture of 38 FAMEs was separated on a polar cyanopropyl substituted polysilphenylene-siloxane phase capillary column. The temperature gradient, the start temperature of the gradient, and the carrier gas velocity were varied in the experiments. The modeled responses, as functions of chromatographic conditions, were retention time, retention indices, peak widths, separation efficiency and resolution between selected peak pairs. The designs that allowed inclusion of quadratic terms among the predictors performed significantly better than factorial design. Box-Behnken design provided the best results for prediction of retention, but the differences between the central composite, Doehlert and Box-Behnken designs were small. Retention indices could be modeled with much better accuracy than retention times. However, because the errors of predicted tR of closely eluting peaks were highly correlated, models of resolution (Rs) that were based on retention time had errors in the same range as corresponding models based on ECL.     

正交设计法用于取代茂铁硼烷化合物的TLC溶剂系统的选择

采用薄层色谱技术分离一组取代茂铁硼烷化合物。以含有烷基卤化铵离子对试剂的丙酮－环己烷流动相,用正交设计法选择色谱分离条件。把离子对试剂种类、浓度和丙酮、环己烷含量作为影响分离效果的因素。实验条件按〔Ｌ９（３４）〕正交设计表安排。应用统计力学方差分析法对反映分离效果的薄层色谱指标——色谱总分离度（ＤＳ）进行分析,确定了影响化合物分离的主要因素和最佳展开剂体系。在最佳条件下的结果令人满意。     

Off-line optimization of the separation of 2,4-dinitrophenylhydrazones by gas chromatography using chemometric techniques

An off-line optimization technique based on soft modeling methods has been used to determine the optimum conditions for the separation of eight carbonyl-2,4-dinitrophenylhydrazones by gas chromatography equipped with a nitrogen-phosphorous detector. In this method theoretical simulations of peak retention time and peak width at base-line are coupled to a factorial design to map the chromatographic response surface. The calculated retention times from isothermal data have been used to calculate the chromatographic parameters. A full 3(3) factorial design was used to locate the optimum conditions for the temperature program, utilizing initial oven temperature, T(i), the programming rate for the first ramp, PR(1), and the programming rate for the second ramp, PR(2) as independent variables. The optimum conditions obtained for these parameters were: T(i), 140 degrees C; PR(1), 12 degrees Cmin(-1); PR(2), 8 degrees Cmin(-1). The maximum analysis times achieved by this optimum was 10.6 min.     

Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC

Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network–genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer’s desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples.     

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.