Optimisation of high performance liquid chromatography separation of neuroprotective peptides

Experimental design in HPLC separation of pharmaceuticals

Design of Experiments (DoE) is an indispensable tool in contemporary drug analysis as it simultaneously balances a number of chromatographic parameters to ensure optimal separation in High Pressure Liquid Chromatography (HPLC). This manuscript briefly outlines the theoretical background of the DOE and provides step-by-step instruction for its implementation in HPLC pharmaceutical practice. It particularly discusses the classification of various design types and their possibilities to rationalize the different stages of HPLC method development workflow, such as the selection of the most influential factors, factors optimization and assessment of the method robustness. Additionally, the application of the DOE-based Analytical Quality by Design (AQbD) concept in the LC method development has been summarized. Recent achievements in the use of DOE in the development of stability-indicating LC and hyphenated LC-MS methods have also been briefly reported. Performing of Quantitative structure retention relationship (QSRR) study enhanced with DOE-based data collection was recomended as a future perspective in description of retention in HPLC system.

Recent applications of retention modelling in liquid chromatography

Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography

A peak-tracking algorithm for chromatograms recorded using liquid chromatography and mass spectrometry was developed. Peaks are tracked across chromatograms using the spectrometric information, the statistical moments of the chromatographic peaks, and the relative retention. The algorithm can be applied to pair chromatographic peaks in two very different chromatograms, obtained for different samples using different methods. A fast version of the algorithm was specifically tailored to process chromatograms obtained during method development or optimization, where a few similar mobile-phase-composition gradients (same eluent components, but different ranges and programming rates) are applied to the same sample for the purpose of obtaining model parameters to describe the retention of sample components. Due to the relative similarity between chromatograms, time-saving preselection protocols can be used to locate a candidate peak in another chromatogram. The algorithm was applied to two different samples featuring isomers. The automatically tracked peaks and the resulting retention parameters generally yielded prediction errors of less than 1%.

UHPLC Analysis of Saffron (Crocus sativus L.): Optimization of Separation Using Chemometrics and Detection of Minor Crocetin Esters

Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components.

Sequence-dependent separation of trinucleotides by ion-interaction reversed-phase liquid chromatography-A structure-retention study assisted by soft-modelling and molecular dynamics

We studied sequence-dependent retention properties of synthetic 5'-terminal phosphate absent trinucleotides containing adenine, guanine and thymine through reversed-phase liquid chromatography (RPLC) and QSRR modelling. We investigated the influence of separation conditions, namely mobile phase composition (ion interaction agent content, pH and organic constituent content), on sequence-dependent separation by means of ion-interaction RPLC (II-RPLC) using two types of models: experimental design-artificial neural networks (ED-ANN), and linear regression based on molecular dynamics data. The aim was to determine those properties of the above-mentioned analytes responsible for the retention dependence of the sequence. Our results show that there is a deterministic relation between sequence and II-RPLC retention properties of the studied trinucleotides. Further, we can conclude that the higher the content of ion-interaction agent in the mobile phase, the more prominent these properties are. We also show that if we approximate the polar component of solvation energy in QSRR by the electrostatic work in transferring molecules from vacuum to water, and the non-polar component by the solvent accessible surface area, these parameters best describe the retention properties of trinucleotides. There are some exceptions to this finding, namely sequences 5'-NAN-3', 5'-ANN-3', 5'-TGN-3', 5'-NTA-3'and 5'-NGA-3' (N stands for generic nucleotide). Their role is still unknown, but since linear regression including these specific constellations showed a higher observable variance coverage than the model with only the basic descriptors, we may assume that solvent-analyte interactions are responsible for the exceptional behaviour of 5'-NAN-3' & 5'-ANN-3' trinucleotides and some intramolecular interactions of neighbouring nucleobases for 5'-TGN-3', 5'-NTA-3'and 5'-NGA-3' trinucleotides.

The proton permeability of self-assembled polymersomes and their neuroprotection by enhancing a neuroprotective peptide across the blood-brain barrier after modification with lactoferrin

Biotherapeutics such as peptides possess strong potential for the treatment of intractable neurological disorders. However, because of their low stability and the impermeability of the blood-brain barrier (BBB), biotherapeutics are difficult to transport into brain parenchyma via intravenous injection. Herein, we present a novel poly(ethylene glycol)-poly(d,l-lactic-co-glycolic acid) polymersome-based nanomedicine with self-assembled bilayers, which was functionalized with lactoferrin (Lf-POS) to facilitate the transport of a neuroprotective peptide into the brain. The apparent diffusion coefficient (D*) of H(+) through the polymersome membrane was 5.659 × 10(-26) cm(2) s(-1), while that of liposomes was 1.017 × 10(-24) cm(2) s(-1). The stability of the polymersome membrane was much higher than that of liposomes. The uptake of polymersomes by mouse brain capillary endothelial cells proved that the optimal density of lactoferrin was 101 molecules per polymersome. Fluorescence imaging indicated that Lf101-POS was effectively transferred into the brain. In pharmacokinetics, compared with transferrin-modified polymersomes and cationic bovine serum albumin-modified polymersomes, Lf-POS obtained the greatest BBB permeability surface area and percentage of injected dose per gram (%ID per g). Furthermore, Lf-POS holding S14G-humanin protected against learning and memory impairment induced by amyloid-β25-35 in rats. Western blotting revealed that the nanomedicine provided neuroprotection against over-expression of apoptotic proteins exhibiting neurofibrillary tangle pathology in neurons. The results indicated that polymersomes can be exploited as a promising non-invasive nanomedicine capable of mediating peptide therapeutic delivery and controlling the release of drugs to the central nervous system.

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.

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Development and validation of a new PCR optimization method by combining experimental design and artificial neural network

Polymerase chain reaction (PCR) is one of the most powerful techniques in a variety of clinical and biological research fields. In this paper, a chemometrics approach, combining experimental design (ED) and artificial neural network (ANN), was proposed for optimization of PCR amplification of lycopene cyclase gene carRA in Blakeslea Trispora. Five-level star design was carried out to obtain experimental information and provide data source for ANN modeling. Nine variables were used as inputs in ANN, including the added amount of template, primer, dNTP, polymerase and magnesium ion, the temperature of denaturating, annealing and extension, and the number of cycles. The output variable was the efficiency (yield) of the PCR. Based on the developed model, the effects of each parameter on PCR efficiency were predicted and the most suitable operation condition for present system was determined. At last, the validation experiment was performed under the optimized condition, and the expectant results were produced. The results obtained in this paper showed that the combination of ANN and ED provided a satisfactory optimization model with good descriptive and predictive abilities, indicating that the method of combining ANN and ED can be a useful tool in PCR optimization and other biological applications.

Statistical designs and response surface techniques for the optimization of chromatographic systems

This paper describes fundamentals and applications of multivariate statistical techniques for the optimization of chromatographic systems. The surface response methodologies: central composite design, Doehlert matrix and Box-Behnken design are discussed and applications of these techniques for optimization of sample preparation steps (extractions) and determination of experimental conditions for chromatographic separations are presented. The use of mixture design for optimization of mobile phases is also related. An optimization example involving a real separation process is exhaustively described. A discussion about model validation is presented. Some applications of other multivariate techniques for optimization of chromatographic methods are also summarized.

Simultaneous optimization by neuro-genetic approach of a multiresidue method for determination of pesticides in Passiflora alata infuses using headspace solid phase microextraction and gas chromatography

A simultaneous optimization strategy based on neuro-genetic approach has been applied to a HS-SPME-GC-ECD (Headspace Solid Phase Microextraction coupled to Gas Chromatography with Electron Capture Detection) method for simultaneous determination of the pesticides chlorotalonil, methyl parathion, malathion, alpha-endosulfan and beta-endosulfan in herbal infusions of Passiflora alata (Dryander). Two types of extractive fibers were used: a home-made device coated by sol-gel process with polydimethylsiloxane-poly(vinyl alcohol) (PDMS/PVA) and a commercial PDMS. The effects of extraction parameters such as dilution of the infusion, extraction temperature and time, as well as sample ionic strength were evaluated through the Doehlert design. To find a model that could relate these extraction parameters with the extraction efficiency of all pesticide simultaneously, a Bayesian Regularized Artificial Neural Network (BRANN) approach was employed. Subsequently, Genetic Algorithm (GA) was applied to attain the optimum values from the model developed by the neural network. The use of the proposed approach allowed the determination of a single extraction condition that maximized the peak areas of all pesticides simultaneously, showing a promising and a suitable new procedure to the optimization process of complex analytical problems.

Neuro-genetic multioptimization of the determination of polychlorinated biphenyl congeners in human milk by headspace solid phase microextraction coupled to gas chromatography with electron capture detection

Polychlorinated biphenyls (PCB) can eventually contaminate breast milk, which is a serious issue to the newborn due to their high vulnerability. Solid phase microextraction (SPME) can be a very convenient technique for their isolation and pre-concentration prior chromatographic analysis. Here, a simultaneous multioptimization strategy based on a neuro-genetic approach was applied to a headspace SPME method for determination of 12 PCB in human milk. Gas chromatography with electron capture detection (ECD) was adopted for the separation and detection of the analytes. Experiments according to a Doehlert design were carried out with varied extraction time and temperature, media ionic strength and concentration of the methanol (co-solvent). To find the best model that simultaneously correlate all PCB peak areas and SPME extraction conditions, a multivariate calibration method based on a Bayesian Neural Network (BNN) was applied. The net output from the neural network was used as input in a genetic algorithm (GA) optimization operation (neuro-genetic approach). The GA pointed out that the best values of the overall SPME operational conditions were the saturation of the media with NaCl, extraction temperature of 95 degrees C, extraction time of 60 min and addition of 5% (v/v) methanol to the media. These optimized parameters resulted in the decrease of the detection limits and increase on the sensitivity for all tested analytes, showing that the use of neuro-genetic approach can be a promising way for optimization of SPME methods.

Recent developments in methods and technology for analysis of biological samples by MALDI-TOF-MS

Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS) is widely used in a variety of fields because it has the characteristics of speed, ease of use, high sensitivity, and wide detectable mass range for obtaining molecular weights and for structural characterization of macromolecules. In this article we summarize recent developments in matrix additives, new matrices, and sample-pretreatment methods using off-probe or on-probe techniques or nanomaterials for MALDI-TOF-MS analysis of biological samples.