February 1, 2011 (Vol. 31, No. 3)

Use of a Knowledgebase Facilitates Efficient Development of Chromatographic Methods

Early in the drug discovery process, it is necessary to begin studies on enantiomerically pure compounds due to the potential for unique efficacy and adverse effects associated with one enantiomer versus the other. In general, it is impractical to synthesize these pure samples prior to confirmation of the viability of the candidate compound. Rather, it is necessary to purify the compounds, typically using chromatographic techniques such as preparative-scale high-performance liquid chromatography and supercritical fluid chromatography.

Identifying candidate methods for scale-up can be an expensive process. It typically is time-consuming, but also costly in terms of solvent consumption and instrument time. Perhaps most troubling is the potential for delays in returning samples to the originating chemist for subsequent tests. Recently systems have become available that address these concerns.

An automated chromatographic application databasing and evaluation system (ACADES) can leverage organizational knowledge to reduce the time and number of injections required to develop a purification method, while reducing the manual effort required in the process. Advanced Chemistry Development’s ACADES incorporates database and automation components to compile, evaluate, and leverage a chiral knowledgebase (Figure 1).

Figure 1. The ACADES system for coordination and facilitiation of chiral method development

Building a Knowledgebase

The key to building a chiral knowledgebase lies in automation. Since most organizations are limited by instrument availability and manpower, it is critical to set up a system to automatically collect the knowledge during day-to-day operations. Fortunately, the experiment-intensive nature of the task faced by the purification lab provides a wealth of experimental data to form the basis of the knowledgebase. The challenge is efficient capture of the information.

The minimum information necessary to form a unit of knowledge should be obvious—the structure, the method, and the effectiveness of that method (often measured in terms of enantiomeric peak resolution). Typically, chromatographic information, including chemical structures, chromatograms, and peak resolutions, are scattered across CDSs (chromatographic data systems), registries, electronic notebooks, and paper or PDF reports. This information must be automatically compiled to form the knowledgebase.

Scriptable automation components designed to interface with various analytical and structure-based systems can import chromatograms, and where possible, instrument parameters from native CDSs such as ChemStation. Queries on ELNs using metadata, such as sample names, can retrieve chemical structures.

Configured to collect, compile, and evaluate data as it is generated, the system can monitor methods rated by effectiveness. With little to no additional manual effort from users, information is stored in an analytical database supporting chromatographic, structural, and metadata content.

Fast user review of the chromatographic ratings is streamlined through the use of a customizable table view (Figure 2). Visually, the chromatographer can confirm the accuracy of the rating, revising as necessary, and flag the most viable method for scale-up. Thus in a few seconds, a complete knowledgebase can be compiled for a given project compound.

To effectively predict chiral selectivities for a given method, it is necessary to accumulate a complete variety of selectivities on the compounds of interest. The temptation may be to retain only the effective method used during scale-up, providing some insight into the effectiveness of a given method for a given compound. However, to maximize the value of the knowledgebase, at least semi-quantitative values are required—good, fair, and bad. These can form the basis of a system designed to indicate the probability of success of a method for a new structure.

Figure 2. ACADES table-based overview of separation methods for the test sample

Mining the Knowledgebase

Accurate prediction of chiral selectivities based on chemical structures is challenging, as small changes in a single functional group can result in dramatic changes in the effectiveness of a method. However, it has been demonstrated that structure similarity will correlate to a large degree with selectivity. Screens targeted according to structure can thus reduce the number of injections that are required, provided that an effective ranking system can be designed.

Structure Similarity Search. A number of methods have been designed to quantitatively rank chemical structures in terms of similarity, including Tanimoto, Dice, Cosine, and others. While the precise value for the similarity is dependent on the algorithm used, the ranking order is generally the same. This implies that any algorithm incorporating structure similarity terms can be used, but that for consistent evaluation of similarity from day-to-day, the given algorithm should be standardized.

Physicochemical Parameters. Incorporating predicted physicochemical parameters such as polar surface area can, for given methods, provide additional clues as to the chances of success of the method. To develop a more targeted “chiral similarity search”, it is necessary to check the correlation of an array of parameters to chiral selectivity rather than individual parameters that may change with the given method. Obviously, then, it is necessary to compile a knowledgebase for each candidate method before implementing a search algorithm.

For this reason, the most common approach is to specifically target structure similarity search. However, as a knowledgebase grows, the method-ranking system can be enhanced by incorporating physicochemical parameters.

Figure 3 gives a general approach to the creation of a screening strategy based on the results of a combined structure similarity and predicted physicochemical ranking of chromatographic methods. The organization can create customized strategies like these, or use generic approaches based simply on structure similarity. Early work by one laboratory indicated that prioritized screening based on a top six structure similarity approach can closely match the success rate of a full 24-method screen.

In more than 90% of cases, a viable scale-up system was identified using six injections. More than half of the remaining compounds were found to be non-viable using any of the 18 additional methods.

In theory, this kind of system can be used to quantitatively model chiral selectivities that can be expected for each method for a given set of enantiomers. However, this level of rigor is unnecessary. A simple ranking of methods based on the approach, and connection of the results to the automated screening system, is extremely effective.

It is typically reported that as many as 95% of compounds generated by medicinal chemists can be handled with a set of screens designed to address a large variety of compounds—usually less than 50 standard methods. The remaining compounds can provide purification labs with considerable headaches. Applying a structure-searchable knowledgebase, such as ChirBase for ChromManager, can provide over 150,000 candidate methods for the most challenging of compounds.

Modern drug discovery organizations typically generate sufficient data to enable dramatically improved chiral screening throughput. Automatic mining of the various software systems that contain this data can create an appropriate knowledgebase. Structure-based queries can then use this knowledgebase to prioritize screening experiments, reducing the number of injections required to identify viable methods for scale-up. The result is improved sample turnaround and reduced solvent and other instrumentation costs.

Figure 3. Schematic for prioritization of chiral screens

Micheal McBrien ([email protected]) is chromatography product manager, and Susan Ling is marketing communications specialist at Advanced Chemistry Development.

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