Drug Discovery
Figure 1
An example of steps taken during a typical fragment library design
Database of potential fragments
Filter according to the following physico- chemical parameters:
Mwt >= 80 and <= 300 clogP <=3.0; HBA <= 3; HBD <= 3
Tractability Filter to select favourable and remove unwanted functionalities
Diversity analysis by clustering of Daylight Fingerprints with the dbclus algorithm at 0.66 Tanimoto level
Visual inspection of clusters and singletons. Selection of centroid or close relative and singletons
QC – Identity and purity analysis (>95%) Solubility measurement ( >1 mM in aqueous phosphate buffer)
Ro3 FRAGMENT LIBRARY
QC – Visual check of stock and re-analysis to maintain quality of library. Replacement of fragments as required.
physico-chemical properties such as MW, logP, pre- dicted solubility and flexibility.
Physico-chemical considerations
Scientists at Astex analysed a diverse set of frag- ment hits that were identified against a variety of targets and concluded that the hits obeyed, on aver- age, the ‘Rule of 3’ where MW was <300Da, cLogP was ≤3, and the numbers of hydrogen bond donors, hydrogen bond acceptors and rotatable bonds were all ≤3. In addition, a polar surface area of ≤60 Å2,14 was considered important for good cell permeabili- ty. These parameters have been widely accepted as providing a starting point for identifying ‘ideal’ fragments and tailored variants of them are used as filters for most fragment libraries.
What makes a library ideal? The design of a fragment library is critical to ensure that high quality hits are obtained and there have been a number of discussions in recent literature over what constitutes the ideal fragment library2,4,9-13. In this paper, we will discuss the main considerations, including the physico-chemi- cal properties of fragments, removal of unwanted chemical functionalities, medicinal chemistry tractability, overall diversity and the size of the library, with particular focus on the importance of aqueous solubility to the ultimate success of the fragment library screen. While the exact method- ology used to design proprietary fragment libraries varies, the steps followed are similar. We will illustrate this with the design of the Maybridge Ro3 Library, summarised as a flow- chart in Figure 1.
The starting point for fragment library design is a pool of available compounds, obtained from in- house collections and commercial sources. In order to computationally evaluate these compounds, files containing 2D connectivity information are gener- ated (eg SD files or SMILES strings) and the set of molecules is then ‘filtered’ against a number of
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A nearly linear relationship between molecular weight and binding efficiency was observed by Hajduk who retrospectively deconstructed 18 highly optimised inhibitors until the minimal bind- ing elements could be identified15. He elegantly showed that to obtain nM potency with a lead can- didate which rigorously obeys the rule of five, the initial fragment must have a MW of <250Da, unless its potency is greater than about 30µM. This fact, plus the higher probability of finding hits with smaller fragments, means that in many fragment libraries the upper MW limit is kept below 250Da. A lower MW limit of about 100Da is often applied; crystal soaking experiments having shown that compounds with a MW <100, at high concen- trations, will bind to most active sites16. Other libraries apply a lower limit of 150Da, as smaller fragments have a greater tendency to bind in mul- tiple orientations17. An alternative approach is reported by GSK, which has developed a fragment set for ‘reduced complexity screening’ with frag- ments incorporating <22 heavy atoms18. This method ensures compounds containing heavy atoms such as bromine, which are useful for fur- ther synthetic manipulation, are not excluded.
In-silico filters
Solubility prediction models may be used to remove compounds with predicted poor aqueous solubility19-21. Vernalis reports that 88% of frag- ments in its first SeeDs library were correctly pre- dicted to have a solubility ≥2mM using a linear model validated for small drug like compounds22. Molecules containing undesirable functionality from a medicinal chemistry point of view, such as reactive groups and known toxic motifs, are filtered out while fragments containing chemical function- ality which allows rapid chemical evolution and optimisation of the fragment hits are positively
Drug Discovery World Winter 2011/12
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