Covalent ligand efficiency

Ligand efficiency (LE) was proposed more than two decades ago as “a useful metric for lead selection.” The concept is simple: divide the binding energy of a ligand by the number of non-hydrogen (or heavy) atoms (HA). The higher the number, the higher the binding energy per atom, and thus the more “efficiently” the ligand binds to the protein. LE is particularly useful in fragment-based lead discovery when prioritizing among differently sized hits to ensure that small, weak molecules are not overlooked. While some have criticized the metric’s dependence on standard state, drug hunters have repeatedly found it to be useful, as we’ve discussed here, here, and here.

 

Irreversible covalent drugs are a horse of a different color - or perhaps a different species entirely. Because of their two-step mechanism, binding followed by bonding, time is an essential parameter, and the proper way to characterize them is with the ratio k_inact/K_I. Is it possible to develop a covalent ligand efficiency metric? This is the task that György Ferenczy and György Keserű at HUN-REN Research Centre for Natural Sciences and Budapest University of Technology and Economics set for themselves in a recent (open-access) Drug Discovery Today paper.

 

As we wrote just a couple months ago, an important distinction for covalent drugs is specific vs chemical reactivity: you want the first to be high and the second to be low. For cysteine-reactive molecules, this distinction is often assessed by measuring the rate of reaction with the abundant cellular thiol glutathione (GSH). The researchers sought to incorporate this parameter into their definition of covalent ligand efficiency (CLE) as follows:

 

CLE = LE – LE(GSH) = (-1.4*log₁₀(IC_50,t)/HA) - (1.4*log₁₀(k^2nd_sur*t)/HA)

Where IC_50,t is half maximal inhibitory concentration at time “t” and k^2nd_sur is the second-order rate constant of the ligand reacting with a surrogate nucleophile such as GSH.

 

The researchers cataloged multiple covalent modifiers from the literature. Some had reported glutathione reactivity data. For the rest, the researchers estimated these values based on analogs. They went on to calculate CLE values for the protein-ligand pairs. Laudably, all of these data are provided in the supplementary data.

 

So, how useful would CLE have been in prior lead discovery campaigns? The researchers calculated CLE values for the BFL1 covalent fragment hits we wrote about here. The potencies of the six reported fragment hits varied, reflected in k_inact/K_I values, from 0.7 to 9.5 M^-1s^-1. But their CLE values spanned a narrower range, from 0.08 to 0.12. The fragment that was successfully optimized was one of the most potent, with a k_inact/K_I of 7.5 M^-1s^-1, but had a CLE of just 0.09. If anything, CLE would have deprioritized this fragment, at odds with the stated goal that “CLE is designed to support compound priorization.”

 

As we discussed earlier this year, the researchers previously proposed that covalent fragments may need to be larger than reversible fragments. If this is true, then normalizing for size may be less important for covalent ligands than for noncovalent ones, which can be very small and weak yet still valuable. Indeed, the researchers’ analysis of covalent ligands from the literature shows a smaller range of CLE values than LE values.

 

The researchers acknowledge other oddities too: “there is no smooth transition from CLE to LE as the reactivity of ligands decreases. Moreover, CLE can take negative values for compounds with low affinity and high reactivity.”

 

But for me, the biggest liability is the fact that – unlike LE for reversible binders or k_inact/K_I – the value of CLE depends on the time the measurement was taken. (In the paper, the researchers use a 1 hour incubation, so I would propose the annotation CLE_1h.) This makes it difficult to compare CLE values taken at different time points.

 

The first word of this blog is “practical,” and I’m not convinced this adjective applies to CLE, though I applaud the effort. The popularity of LE spawned a cottage industry of other metrics, some of which we summarized in a 2011 post. I confess that I had nearly forgotten about some of them, but I think they were a useful way for the field to grapple with what characteristics mattered. As covalent drug discovery becomes increasingly popular, perhaps we will see a similar proliferation of metrics. (Indeed, we already wrote about another one here.) It will be interesting to revisit these a decade hence to see which ones have caught on.