LLE vs LELP

Besides ligand efficiency (LE), a slew of other metrics has been proposed to help evaluate what compounds to take forward in drug discovery. As seen in our poll and a recent round-table discussion, lipophilic ligand efficiency (LLE) is quite popular:

LLE = pIC₅₀ (or pK_i) – ClogP (or logD)

However, because this metric is not size-adjusted, it is not particularly useful for evaluating fragments, which often have low potency. In contrast, the metric LELP accounts for size:

LELP = logP / LE (where LE = ligand efficiency)

In a recent issue of J. Med. Chem., György Keserű and colleagues evaluate how these two metrics compare in a variety of settings.

The authors examine eight different compound sets: fragment hits and derived leads, HTS hits and derived leads, leads that subsequently became drugs (ie, “successful leads”), development candidates, compounds that entered phase II trials, and drugs on the market. Not surprisingly, drugs and phase II compounds had better LLE and LELP scores than other molecules. Also not surprisingly, fragments scored misleadingly poorly on the basis of LLE but well on the basis of LELP. What is perhaps unexpected, though, is that LELP was better at identifying successful leads than was LLE. Moreover, when compounds were evaluated for pharmacokinetic and safety parameters, LELP was more effective at predicting problems than was LLE. The authors state:

In summary, evaluation of pharmacokinetic and safety parameters revealed that LELP has benefits over LLE, as compounds with acceptable in vitro ADMET profiles are discriminated from compounds with significant liabilities.

Despite these potential advantages, LELP doesn’t seem to be widely used, perhaps because it is less intuitive than some of the other metrics. Indeed, it would be interesting to see these studies repeated using LLE_AT, which also takes lipophilicity into account but has the same scale as LE.

Privilege or selectivity?

Fragment selectivity is something we’ve covered before (see here and here). Sarah Barelier and Isabelle Krimm at the Université de Lyon have published on this topic (see here), and in a recent issue of Current Opinion in Chemical Biology they review the subject and its implications.

The authors document what many investigators have independently observed: some fragments, as expected theoretically, are less selective than larger molecules, but other fragments are quite selective.

They also note that fragments that bind to any protein tend to be slightly more lipophilic than fragments that don’t bind to any target proteins, suggesting that hydrophobic interactions are important:

Hydrophobic interactions play a major role in protein–ligand interactions and are known to be non-directional, thus allowing binding to a multitude of pockets in different conformations. By contrast, hydrogen bonds were shown to confer specificity but do not always add much binding free energy. This is due to the cost of desolvating both the donor and acceptor of the hydrogen bond, which can nearly equal the benefit of the hydrogen bond formation. Therefore, if the hydrogen bond acceptors or donors are not satisfied in the complex, it is likely that more hydrophobic fragments will be preferred.

This observation—lipophilicity for binding energy, hydrogen bonds for specificity—is consistent with the recent publication from Mike Hann and Andrew Leach, which finds that promiscuity increases with increasing lipophilicity.

One figure in the Barelier and Krimm paper shows 30 fragment-like “privileged scaffolds” that should bind to multiple proteins. What struck me is these molecules’ overwhelmingly planar character: more than half are completely aromatic (such as quinoline and indole), and only one is completely aliphatic. Barelier and Krimm note that:

The low specificity of these molecules is probably owing to their rigid and aromatic structures, well-adapted to protein hydrophobic pockets where π-stacking with phenylalanine and tyrosine are commonly observed.

This reminds me of Tony Giannetti’s talk at the FBLD San Diego meeting earlier this month, where he also noted that fragment hits tend to be relatively flat. Of course, given the negative correlation between aromaticity and good pharmaceutical properties, just because aromatics are frequent hits doesn’t mean they are necessarily the best hits – they may be tricks rather than treats. All of which comes back to a key question for library design: do you focus on the flat “privileged” scaffolds that will likely have high hit rates in your assay but may have baggage, or on the more three-dimensional compounds that may have lower hit rates but may ultimately be more developable?

Fragment specificity

A frequent topic in fragment roundtable discussions concerns specificity: do fragments hit lots of targets, or just a few? Isabelle Krimm and colleagues at the Université de Lyon in France studied this question experimentally and report their results in a recent issue of J. Med. Chem. The paper provides data for the ongoing debate of whether and how much specificity a fragment should exhibit before being pursued for further lead development.

The researchers assembled a diverse set of 150 fragments and used NMR techniques to determine whether they bind to five different proteins. Three of the proteins, Bcl-xL, Bcl-w, and Mcl-1 are related members of the Bcl-2 family of antiapoptotic proteins, and at least the first of these has been successfully targeted using fragment-based methods. The fourth protein, PRDX5, has proven to be much less yielding to inhibitor discovery, while the fifth, human serum albumin (HSA), binds a wide variety of small molecules.

After applying 1D-NMR techniques (WaterLOGSY and STD) to all of their fragments against each of the five proteins, the researchers used more rigorous but less sensitive 2D-NMR (HSQC) to determine the binding sites of the hits. (This later study revealed, in agreement with previous results from the same lab, that the fragments all bind in the “hot spots” or active sites of the proteins.)

More than two-thirds of the fragments bound to at least one protein, a rather high hit rate. However, the hit rates for each protein varied considerably, with only 7 hits for PRDX5 and 72 for HSA (with a close second of 71 for Bcl-xL). Within the Bcl-2 family there was little specificity observed: Mcl-1, with 29 hits, shared all but one hit with either Bcl-xL or Bcl-2 or both; such non-specificity among related proteins has been discussed previously. In the case of HSA and Bcl-xL, although both proteins had similar numbers of hits, just over half of these were in common, demonstrating that fragment specificity is not difficult even with small-molecule sponges such as HSA. That said, many fragments were remarkably nonspecific, with 22 hitting four of the 5 proteins. Amazingly, all 7 of the hits against PRDX5 also hit all four other proteins.

The physicochemical properties of the fragments that hit one or more proteins were compared with those of the library as a whole, and although most of the parameters were similar, the ClogP values (a measure of hydrophobicity) were considerably higher for hits, and highest of all for the non-specific hits.

These findings are more evidence that, as predicted almost a decade ago, fragments can bind to more proteins than can larger, more complex molecules. The follow-up question, how much does this matter, is still up for debate. There are plenty of examples of developing specific inhibitors from non-specific starting points during the course of fragment optimization. But how non-specific is too non-specific? Would you feel comfortable pursuing any of the fragments that hit all of the proteins?