52


May / June 2013


shaped. Thus at low temperatures, the protein is folded; as temperature increases, a protein unfolds exposing different functionalities to the separation phases and retention increases. At even higher temperature, the protein completely unfolds, and retention decreases.


To establish the impact of the method, they run 2, 6 or 9 experiments to establish patterns of typical peak characteristics, for example: retention time tR, peak width, peak area, asymmetry factors.


To trouble shoot problematic separations, they used ACDLabs LC Simulator. For an example protein of mass 100 kDa, the separation was very sensitive to temperature changes. This software mapped how the relationship between temperature and retention factor. As per pH selection in the Pfizer talk, flatter sections of the k v T plot gave more robust methods, since here small changes in temperature would have only minor effect on retention.


The accuracy of the method to identify a given peak was defined as a retention time within 2% and peak width within 20% of target values. Peak tracking was achieved by recognising peak area and a change in retention linear with respect to the percentage of strong eluting solvent (%B) in the mobile phase, in addition to mass spectrometry when available.


Their strategy was to model the peak of the API and other compounds of interest in a sample and ignore others.


Petersson concluded that there is no expectation of regulatory relief at this stage.


Talk 4: Automated chiral and achiral selectivity tuning through combined stationary and mobile phase optimization approaches in LC


Prof Frederick Lynen from the Pfizer analytical research centre (PARC) at the University of Ghent presented progress in stationary phase optimised liquid chromatography (SOSLC), a variant on commercially available phase optimised liquid chromatography (POPLC). They combine columns in series, often 5 cm in length with specialised connections. They found that retention in these setups is additive with connected phases, and independent of column length. Preliminary analysis of the pure stationary phase determines retention of analytes of interest, which is followed by optimisation of the column segments which combine. SOSLC was not practicable in isocratic mode, due to wide variation of hydrophilicity / hydrophobicity of analytes. They found better results with linear gradients, comprised of isocratic steps. Lynen claimed they can predict the retention and gradient at specific sections of the combined column.


To model retention, they used classical retention maps, gathering 5-


point plots of k v eluting solvent content (Ψ). They used critical peak pairs to rank column combinations, using the difference in retention time between these compounds as a criterion for how effective a combination was at separation. Top–ranked was a combination of a 6 cm C18 phase and 12 cm C18 with embedded polar groups. For fixed gradients, increasing the number of stationary phases understandably increased the combinations available in terms of where each component phase fits into the combined stationary phase. Unfortunately, crunching these numbers is time-consuming, and a lab computer can take in excess of half an hour to do that. In a worst-case scenario, it took ca. 160 minutes by this ‘brute force’ approach.


To streamline this optimisation, they used a genetic algorithm approach. As per the earlier Pfizer talk, this is an application of an evolutionary algorithm and similar steps are applied. Here, ‘mutations’ as random changes also include the number of component phases, say increasing or decreasing by one phase. There was a pleasant gain from this: computing time was only ca. 6 minutes.


A disadvantage of the POPLC approach is the need for specialised nut linkages, though these do provide low void volumes. They tried linear connection of ‘normal’ stationary phases, which increases the void volume in between the phases but should find use in preparative chromatography, where efficiency and peak width is less important than for complex mixtures in biological matrices. They focused on the ‘API window’ of a chromatogram, with the target of separating the analyte of interest and its purity. Lynen argued that prediction of k worked well for this.


The remainder of the talk focused on the application to chiral analytes, where attaching different phases gave different retention order. Again, this should be most useful to preparative work. There the not great peak shapes he presented would have little impact on resolution, since the selectivity factors they focused on appear to provide useful separation power. It was noted from the audience that the SOSLC technique could be at the whim of stationary phase batch-to-batch variations.


He concluded with a plug for the hyphenated techniques in chromatography conference in Bruges, January 2014 (htc- conference.org), and hinted at their groups’ promising supercritical fluid chromatography (SFC) work to follow.


Talk 5: Semi-automated processing of LC/GC-MS data with open source software


Mark Earll from the agro-chemical giant Syngenta presented their efforts toward metabolomics, using extracts from tomato plants as a case study.


These analyses produce swathes of data, partly because of 3D datasets from MS data, plotting time vs. mass/charge (m/z) vs. signal intensity. Thus experiments separate polar and non-polar analytes, and use positive or negative ionisation mode in MS. A typical experiment would produce 1000 samples x 1000 peaks.


The software MZ mine provides solutions to level this mountain of data. Before that, traditional semi-manual Excel spreadsheet work would take ca. 2 weeks, followed by use of SIMCA to plot PCA and look for patterns. Earll gave a brief history of MZ mine: it started in Finland in 2004, and since 2010 Syngenta sponsored its development. Now his organisation have semi-automated operations.


Earll outlined the peak identification process: mass detection, extracting single ions, peak detection, aligning across samples then merge scans and fill any gaps retrospectively. They also use a ‘batch mode’: first a representative scan, then adjust parameters. It then took 5-6 hours to analyze data for a batch of 50 samples.


They use the popular soft ionisation technique of electrospray ionisation (ESI) LC-MS, which generates adducts with a low amount of fragmentation. Here peak recognition was achieved using retention time, accurate mass of parent ion/adducts. They also match results to their compound library, which searches public databases such as chemspider.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60