On the equations describing chromatographic peaks and the problem of the deconvolution of overlapped peaks

Nikitas, P.; Pappa-Louisi, A.; Papageorgiou, A.

doi:10.1016/s0021-9673(01)00524-6

Cited by 95 publications

(48 citation statements)

References 19 publications

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“…The best fit with the least number of peaks and fit parameters was achieved with 'exponential modified Gaussian functions'. 21 The relative contribution of each peak to the total spectrum was calculated from the peak areas and each peak was assumed to represent a population of NR molecules in a given environment. The goodness of fits was judged by the residuals and reduced x 2 .…”

Section: Kinetics Of Surfactant Induced Virus-splitting With Fluorescmentioning

confidence: 99%

Quantitative determination of the surfactant-induced split ratio of influenza virus by fluorescence spectroscopy

Lee

Sahin

Neeleman

et al. 2016

Human Vaccines & Immunotherapeutics

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The majority of marketed seasonal influenza vaccines are prepared using viruses that are chemically inactivated and treated with a surfactant. Treating with surfactants has important consequences: it produces 'split viruses' by solubilizing viral membranes, stabilizes free membrane proteins and ensures a low level of reactogenicity while retaining high vaccine potency. The formulation stability and potency of split influenza vaccines are largely determined by the specifics of this 'splitting' process; namely, the consequent conformational changes of proteins and interactions of solubilized particles, which may form aggregates. Robust methods to quantitatively determine the split ratio need to be developed before optimal splitting conditions can be investigated to streamline production of superior influenza vaccines.Here, we present a quantitative method, based on both steady-state and time-resolved fluorescence spectroscopy, to calculate the split ratio of the virus after surfactant treatment. We use the lipophilic dye Nile Red (NR) as a probe to elucidate molecular interactions and track changes in molecular environments. Inactivated whole influenza viruses obtained from a sucrose gradient were incubated with NR and subsequently treated with increasing concentrations of the surfactant Triton X-100 (TX-100) to induce virus splitting. NR's emission spectra showed that the addition of TX-100 caused~27 nm red-shifts in the emission peak, indicative of increasingly hydrophilic environments surrounding NR. The emission spectra of NR at different surfactant concentrations were analyzed with multi-peak fitting to ascertain the number of different micro-environments surrounding NR and track its population change in these different environments. Results from both the emission spectra and fluorescence lifetime spectroscopy revealed that NR showed presence in 3 distinct molecular environments. The split ratio of the virus was then calculated from the percentages of NR in these environments using both fluorescence emission and lifetime data. This study can pave the way for the development of robust methods to rapidly quantify splitting extent during vaccine manufacturing. KEYWORDS fluorescence emission; fluorescence lifetime; influenza split virus vaccine; influenza virus; membranesurfactant interactions; Nile Red; partitioning of dye in membranes; protein aggregation; split ratio quantification; surfactant; Triton X-100; vaccine formulation; vaccine manufacturing; virus splitting; virus-surfactant interactions

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Section: Kinetics Of Surfactant Induced Virus-splitting With Fluorescmentioning

confidence: 99%

Quantitative determination of the surfactant-induced split ratio of influenza virus by fluorescence spectroscopy

Lee

Sahin

Neeleman

et al. 2016

Human Vaccines & Immunotherapeutics

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“…37 Thus, there are still arguments about the capability of GA to deconvolute overlapping peaks. 38,39 In order to overcome the shortcomings and to improve the performance of GA approaches, hybrid models of GA and others, such as neural networks, have been created. In addition, one must rely on the knowledge of a given field in order to build reliable structures of GAs and adjust the model and the corresponding parameters to represent the real problem.…”

Section: Comparisons Among Different Fitting Approachesmentioning

confidence: 99%

“…In order to deconvolute accurately overlapping peaks using the technique, the actual number of components of the unresolved peaks must be given. 39 This represents a great difficulty in real samples.…”

Section: Comparisons Among Different Fitting Approachesmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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“…For this purpose, each of the pure protein peaks is described by the same mathematical function with varying shape parameters. An overview of potential peak functions can be found in [12]. From these, an asymmetric Gaussian distribution is chosen: Chem.…”

mentioning

confidence: 99%

High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems

et al. 2008

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Chromatographic processes can be optimized in various ways. However, the two most prominent approaches are either based on statistical data analysis or on experimentally validated simulation models. Both approaches heavily rely on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. Hence, here a closed-loop optimization strategy is proposed in that an automated high throughput liquid handling platform is combined with a genetic algorithm. This setup enables process optimization on the mini-scale and thus saves time as well as material costs. The practicability and robustness of the proposed high throughput method is demonstrated with two exemplary optimization tasks: first, optimization of the buffer composition in the capture step for a binary protein mixture (lysozyme and cytochrome), and second, optimization of multilinear gradient elution for the separation of a ternary mixture (ribonuclease and cytochrome, and lysozyme). IntroductionChromatography is widely used as a separation technique in the biotechnological industry. High selectivity and gentle conditions have made it an essential step in current purification processes for biological macromolecules, for instance, proteins. However, due to complex and dynamic interactions between protein molecules and adsorbent materials, the design of optimal separation processes is very difficult and time consuming. Heuristic design methods that are based on previous experiences with similar separation problems require a great amount of expert knowledge and usually do not lead to the global process optimum. Furthermore, process optimization is often restricted by time-to-market requirements and must, hence, be performed as fast as possible.Most methods for process optimization that are found in the literature today divide into two classes: model-based optimization and direct process optimization. In model-based optimization, mathematical models are utilized to mimic the studied processes. Optimization is performed in-silico and thus has the clear advantage of not restricting the optimization by lab schedules. Limiting factors are only computational effort and reliability or validity of the applied simulation models. The development of mechanistic models requires good process understanding, initial experiments for parameter estimation, and independent experiments for model validation. The latter is also true for black box models (for example [1,2]). The determination of mechanistic model parameters, such as effective mass transfer coefficients and isotherm coefficients, is generally very complex and requires large amounts of material and time, especially when the interactions of realistic multi-component mixtures are considered without significant model simplifications.An alternative to the model-based approach is to directly identify process optima based on the results of experiments that are iteratively planned by an optimization algorithm, such as repeated design of experiments (DoE) or an evolutionary strat...

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On the equations describing chromatographic peaks and the problem of the deconvolution of overlapped peaks

Cited by 95 publications

References 19 publications

Quantitative determination of the surfactant-induced split ratio of influenza virus by fluorescence spectroscopy

Quantitative determination of the surfactant-induced split ratio of influenza virus by fluorescence spectroscopy

Chemometrics and its applications to x‐ray spectrometry

High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems

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