Compound acquisition

The majority of the companies listed in Tables 1 and 2 store their available compounds in the form of dry powders (or whatever the natural state is), although samples dissolved in DMSO are becoming more available. For dry, solvent-free compounds selected from a supplier database, the samples will be weighed, usually manually, into cluster tubes or vials before shipping to the end user. This mode of operation may seem tedious, however, it does keep costs down for the preparation of samples for which automated weighing, e.g. of resinous compounds, is profoundly difficult. 

Handling samples obtained in solution is more straightforward. The solutions are usually in DMSO, stored frozen between +4 and -20 °C. Automated plate duplication and cherry-picking of individual wells are now commonplace procedures. Thus, end users are not forced to accept all compounds from a specific plate, since the ability to generate daughter plates from parents is a simple matter. Samples can be dispensed into any compatible well-based system, such as monoblocks or cluster tubes, or 96-well plates can be combined to create higher density plates. The use of solution-phase samples also allows a significant reduction in deliverable sample sizes, often 

Plate formats are flexible, with more library snppliers accepting reqnests for the higher density screening plates, such as 384- and 1536-well formats, with a concomitant rednction in sample size delivered. Of conrse, the reduction in sample size however does not necessarily result in a pro-rata rednction in cost  

The vast majority of compounds are provided with no intellectual property rights retained by the snpplier. As a consequence, the end nser is able to captnre IP for use of the compounds immediately on making a discovery. Composition of matter rights is another issue, however, since in almost aU cases, the compounds are already in the public domain. Thus, in these situations, the end user only creates novel compositions of matter through chemical modifications to the initial hits as part of a lead-optimisation 

In summary, there is a wealth of molecular information and stractural diversity available from a wide variety of suppliers of screening libraries. These resources are becoming more and more a key part of the pharmaceutical research and development process and are no longer simply regarded as a source of cheap or poor quality structures. Those providers who recognise the need for better quality molecules that fit descriptors for lead- or drug-likeness will be the ones who are likely to benefit the most from this recognition. In turn, those benefits will be passed back to the research and development community as a further improvement in supply and collaboration. 

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Many of these unwanted functionalities have been collected based on chemists feedback from hit identification and lead optimization projects, and by looking at compounds not considered good starting points for optimization by medicinal chemistry or difficult to synthesize [35]. However, one could say that beauty is in the eye of the beholder and selecting attractive chemical starting points depends upon the experience and prejudice of individual chemists. An interesting study at Pharmacia in which 13 chemists reviewed about 22000 compounds in a compound acquisition program showed that medicinal chemists were inconsistent in the compounds they reject [36]. Furthermore, it was found that individual medicinal chemists do not consistently reject the same compound. 

These unwanted functional groups can be easily encoded as SMARTS [37] to be used as structural alerts for HTS compound prioritization or compound acquisition. OpenEye s Filter [38] is an excellent example of such a filtering tool. 

If, after applying lead-Hke criteria (property and chemical filters), there are still too many compounds left one can use diversity/similarity-based techniques to select the final set For the example above, 64% of the starting compounds survive the compound acquisition process (around 64000 compounds in 11000 clusters), see Fig. 17.8. 

An acquisition fee paid by the acquiring company for the right to develop the acquired compound compound acquisition revenues). 

The implication of these problems is daunting and has led researchers to overlook some of the nasty details and to develop what might be called practical strategies . This situation is just another example of the heuristic nature of many of the computational methods employed in biological research in general and drug research in particular. What is described in this chapter is a practical procedure based on a soft, heuristic approach to the problem of compound acquisition that has been implemented and used at Pharmacia over the last several years. 

Before the formation of Pharmacia through the merger of Pharmacia Upjohn with Monsanto-Searle in 2000, each company had its own approaches to augment and diversify their compound collections, and these approaches set the stage for the procedure implemented at Pharmacia. Thus, it was felt that a brief description of the historical background to the present work would be informative and would afford additional insights that bear directly on the development of the compound acquisition strategy described here. 

Fig. 13.5 The compound acquisition scheme. The overall process depicted in the figure provides an efficient procedure for the selection and acquisition of compounds that optimally populate the PRCC with diverse, druglike molecules for suitable screening.

I 73 A Practical Strategy for Directed Compound Acquisition Corporate collection... 

According to our intention, to provide in this series on Methods and Principles in Medicinal Chemistry practice-oriented monographs, the book closes with a section on Chemoinformatics Applications. These are exemplified by G.M. Maggiora et al. in a chapter on A Practical Strategy for Directed Compound Acquisition , by... 

Maggiora GM, Shanmugasundaram V, Lajiness MS, et al. (2005) A practical strategy for directed compound acquisition. In T Oprea (ed), Chemoinformatics in Drug Discovery, pp. 317-332. Wiley-VCH, Weinheim. 

Dissimilarity analysis plays a major role in compound selection. Typical tasks include the selection of a maximally dissimilar subset of compounds from a large set or the identification of compounds that are dissimilar to an existing collection. Such issues have played a major role in compound acquisition in the pharmaceutical industry. A typical task would be to select a subset of maximally dissimilar compounds from a data set containing n molecules. This represents a non-trivial challenge because of the combinatorial problem involved in exploring all possible subsets. Therefore, other dissimilarity-based selection algorithms have been developed (Lajiness 1997). The basic idea of such approaches is to initially select a seed compound (either randomly or, better, based on dissimilarity to others), then calculate dissimilarity between the seed compound and all others and select the most dissimilar one. In the next step, the database compound most dissimilar to these two compounds is selected and added to the subset, and the process is repeated until a subset of desired size is obtained. 

Antimalarial compound acquisition sharing, lead optimization... 

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