HPLC

Retention modelling has successfully been used for the optimisation of analytical scale separations of small molecules for approximately 30 years, and several commercial software are available, i.e., DryLab, ACD/LC Simulator, ChromSword and Osiris.

When defining a method development strategy for peptides and proteins involving retention modelling it was, however, realised these software typically not are capable of accurately modelling the retention of proteins.

As described by Snyder and Dolan [1] the following isocratic relationships are required when numerically determining the retention of proteins in gradient elution:

lnk = a + b x + c x2 (1)

where k is the isocratic retention factor, a, b and c are system and analyte specific constants and x the fraction of the strong solvent. Eqn. 1 is valid for reversed phase (RPC) and hydrophobic interaction chromatography (HIC).

In order to account for ion exchange chromatography (IEC) and hydrophilic interaction chromatography (HILIC) the following equation is needed.

lnk = d + e ln(x) (2)

For small molecules simultaneously modelling gradient shape and temperature has been found to be very effective. The relationship which is normally used (eqn. 3) was, however, found to be insufficient for proteins.

lnk = f + g / T (3)

In order to accurately account for the retention of proteins it was necessary to add a 2nd order term.

lnk = f + g / T + h / T2 (4)

This poster describes the adaption and validation of the retention models necessary in order to accurately model and optimise RPC and IEC separations of peptides and proteins. Analytes with an increasing importance for the pharmaceutical industry.

[1] L.R. Snyder, J.W. Dolan, High-Performance Gradient Elution: The Practical Application of the Linear-Solvent-Strength Model, Wiley, Hoboken, NJ, 2007.

A new automated method development strategy has been developed at Janssen Pharmaceutical companies of J&J in collaboration with ACD/Labs.

The automated procedure employs 2 x Waters UPLC-SQD instruments working in tandem with 6 columns, 4 pH values (2.5, 4.8, 6.9 and 9.2), various solvent strengths, temperatures and gradients. 4 solvent channels, A-D, are used on each instrument. Channels A and B are connected to a fixed solvent composition, ammonium acetate 10 mM in water, and acetonitrile, respectively. Channel C has a 6-position switch valve allowing access to various combinations of acetonitrile—methanol and 2-propanol to reach the software predicted solvent ratio required for the next experiment in order to improve the separation. Channel D will use 3 positions: two at fixed pH values and a third reserved for the predicted pH required for the next experiment.

The efficient Design of Experiment (DoE) described in 3 consecutive Waves, follows rigorous rules of finding the optimum separation in each Wave and taking advantage of this information in the next step or for the next Wave. Once the project is defined, the instruments are working independently with help from ACD/AutoChrom to select the best experimental parameters for the next steps. Automated peak tracking and data interpretation is used for each step. All data can also be reviewed manually at the end of the experimental session.

The poster presents results for column, pH and organic solvent screening and optimisation, as well as a scientific discussion supporting the rationale of this innovative approach to method development.

A major problem in the discovery of new biologically active compounds from natural product sources is the re-isolation of previously known compounds. Such re-isolations waste time and resources, distracting chemists from focusing on more promising leads. To address this problem, dereplication strategies are needed that enable crude extracts to be screened for the presence of known compounds before isolation efforts are initiated. Our laboratory is engaged in ongoing investigations to identify anticancer drug leads from filamentous fungi. This project represents a significant dereplication challenge, as the taxonomy of the source materials are rarely known, and, thus, the literature cannot be interrogated to identify likely known compounds. To address this problem, an Ultra Performance Liquid Chromatography-Photodiode Array-High Resolution Tandem Mass Spectrometry (UPLC-PDA-HRMS-MS/MS) method was developed for dereplication of fungal secondary metabolites in crude culture extracts. A database was constructed by recording HRMS and MS/MS spectra of fungal metabolites, utilizing both positive and negative ionization modes. Additional information, such as UV-absorption maxima and retention times, were also recorded. Small-scale cultures that showed cytotoxic activities were dereplicated before engaging in the scale-up or purification processes. Using these methods, approximately 50% of the cytotoxic extracts could be eliminated from further study by the confident identification of known compounds. More importantly, both human and financial resources could be devoted to samples most likely to yield new chemistries. Examples are discussed that demonstrate the feasibility of this approach. The unique attributes of this dereplication methodology include the focus on bioactive secondary metabolites from fungi, the use of a 10 min chromatographic method, and the inclusion of both HRMS and MS/MS data. The method is applicable not only for drug discovery programs from fungi, but may also be useful for the detection and identification of mycotoxins in other areas, such as food commodities.