Fragments vs LTA4H: LipE in action

Three years ago we described the discovery of LYS006, an inhibitor of leukotriene A4 hydrolase (LTA4H) from Novartis currently in phase 2 clinical trials. Companies often pursue multiple chemical series for important targets, and in a recent J. Med. Chem. paper Gebhard Thoma and colleagues describe another fragment-derived lead against LTA4H.

 

A biochemical high-throughput screen yielded compound 2, which is quite potent for a fragment-sized molecule. However, despite good ligand efficiency, the LipE (or LLE) was less impressive due to the high lipophilicity of the fragment. (Note that throughout the paper LipE is calculated based on measured logD rather than logP.) A co-crystal structure revealed that it bound in a similar fashion to other previously characterized LTA4H inhibitors such as compound 1, derived from LYS006 and reported in a J. Med. Chem. paper last year. Adopting elements from these led eventually to compound 12, which though less potent was also much less lipophilic and more soluble while still remaining fragment-sized.

 

 

Continuing to borrow from the rich literature around this target, the researchers added a basic amine group to get to the very potent compound 14. This was metabolically unstable, but further optimization led to compound 3.

 

Compound 3 was profiled extensively in a battery of tests. In addition to good biochemical potency, it showed mid-nanomolar activity in a human whole blood assay and was also active in other assays, including a mouse arthritis model. Other attractive features included a clean profile against a plethora of off-targets, good oral bioavailability in mice, rats, and dogs, and a predicted human oral dose of 40 mg once daily. However, a two week toxicology study in rats and dogs was “slightly less favorable” than compound 1.

 

This is a lovely example of property and structure-guided drug design, and the researchers are refreshingly open about borrowing elements from other molecules, even from outside Novartis. Interestingly, a crystal structure of compound 3 bound to LTA4H revealed that while the overall binding mode was similar to compound 1, which contains the same left-hand portion, the pyrazole and pyridine rings rotated 180º to make different hydrogen-bond interactions. Another reminder that despite our leaps in predictive capability, molecules can still provide many surprises.

Myriad metrics – but which are useful?

Practical Fragments recently introduced WTF as a light-hearted jab at the continuing proliferation of metrics to evaluate molecules, but there is an underlying problem: which ones are useful? In a provocative paper just published online in Bioorg. Med. Chem. Lett. (and also discussed over at In the Pipeline) Michael Shultz asks:

If one molecular change can theoretically alter 18 parameters, two shapes, the rules of 5, 3/75, 4/400 and ‘two thumbs’ while simultaneously affecting at least nine composite parameters and countless different methods of representing data, how is a practicing medicinal chemist to know if any specific modification was actually beneficial?

Shultz focuses on three parameters in depth: ligand efficiency (LE), ligand-efficiency-dependent lipophilicity (LELP), and lipophilic ligand efficiency (LLE, also referred to as lipophilic efficiency or LipE). He conducts a number of thought experiments to see how these metrics change when, for example, a methyl group is changed to a t-butyl group or a methyl sulfone. He also examines how the metrics perform against historical data from Novartis lead-optimization programs.

One problem with LE is that, although it was introduced to normalize potency and size, it is still highly dependent on number of heavy atoms (heavy atom count, or HAC): addition of one atom to a small fragment will have a more dramatic effect on LE than addition of one atom to a larger molecule. This has led to metrics in which larger molecules are treated more leniently, but because of the way all these metrics are mathematically defined, none achieve completely size-dependent normalization.

More seriously, LE ignores lipophilicity, which seems to be correlated with all sorts of deleterious properties. With a nod to Mike Hann’s “molecular obesity,” Shultz notes that the widely used body mass index (BMI) “cannot distinguish between the truly obese and professional athletes of identical height and weight. Similarly, HAC based composite parameters such as LE cannot distinguish between ‘lean molecular mass’ and groups of real molecular obesity.”

LELP addresses this shortcoming by incorporating clogP, but it has problems of its own. For example, “the effects of lipophilicy are magnified as molecular size increases.” More alarmingly, as clogP approaches zero, LELP becomes increasingly insensitive to both size and potency; a femtomolar binder would have the same LELP as a millimolar binder when clogP = 0.

In contrast to both LE and LELP, LipE (or LLE) is size-independent, so a change in potency or lipophilicity will produce the same change in LipE no matter the size of the initial molecule. Shultz uses data from two lead optimization programs to show that LipE behaves better than LE or LELP. This is in contrast to a previous report that suggested LELP to be superior to LipE, albeit against a different data set.

Shultz further notes that LipE can be thought of as the tendency of a molecule to bind to a specific protein rather than to bulk octanol:

LipE = pK_i - clopP = log [EI]/([E][I]) – log ([I_octanol]/[I_water])

where E stands for protein and I stands for inhibitor

Although this is a simple consequence of the math, it is a nice way of visualizing an otherwise abstract number. Moreover, it suggests that optimizing for LipE could optimize for enthalpic interactions, a topic Shultz explores in depth in a companion paper.

Overall Shultz raises some excellent points, but I still believe there is value in LE (and LLE_AT), particularly in the context of fragments, which usually have low affinity. Ligand efficiency can prioritize molecules that might otherwise be overlooked. For example, it is hard to get too excited over a 1 mM binder, but if the hit has only 8 heavy atoms it could be valuable.

Turning to my own miniature thought experiment, fragments 1 and 2 have very similar LipE values, but the LE of Fragment 1 is better, and arguably makes a more attractive fragment hit.

Of course, in the end, rules should not be followed slavishly; the most lucrative drug of all time, Pfizer’s atorvastatin, violates Lipinski’s rule of five. Papers like this are important to highlight the problems and inconsistencies that underlie some of our metrics. Ultimately I’ll take biological data and the intuition of a good medicinal chemist over any and every rule of thumb.

What do you think? What role should LE, LELP, and LipE play in drug discovery?