As covalent drug discovery continues to rise, the demand for metrics to help guide lead optimization is increasing.
Last year we discussed covalent ligand efficiency (CLE). In an open-access
paper just published in J. Med. Chem., Benjamin Horning, Brian Cook, and
colleagues at Vividion Therapeutics describe ligand reactivity efficiency
(LRE). (Benjamin presented LRE at the DDC meeting in 2024.)
A key challenge when developing covalent
ligands is maximizing specific reactivity towards the target of interest while
minimizing intrinsic reactivity towards other proteins; the two types of
reactivity are not the same, as we wrote about last year. For molecules that target
cysteine residues, intrinsic reactivity is usually determined by assessing
reactivity against the small molecule glutathione, which is abundant in cells.
For lead optimization more generally, a common
metric is lipophilic efficiency (LLE or LipE, see here and here), in which the
logP of a molecule is subtracted from the negative log of the IC50
(pIC50). More lipophilic molecules have higher logP values, so maximizing
LLE helps to minimize increases in lipophilicity.
By analogy, the researchers defined
LRE to help minimize increases in intrinsic reactivity. However, distinguishing
specific from intrinsic activity is not necessarily straightforward. As we previously
discussed, IC50 alone is an inappropriate measurement for covalent inhibitors;
the incubation time before the IC50 is measured is an essential variable. The most rigorous
value is kinact/KI, and although this ratio has
been historically time-consuming to determine, we described an easier method earlier
this year. Yet an even simpler measurement is the TE50(target, 1h),
the concentration of compound necessary to label 50% of a target after one hour,
which is a function of kinact/KI. The researchers
thus defined LRE as:
LRE = pTE50(target, 1h) – pTE50(GSH, 1h)
The variable in the second term, pTE50(GSH,
1h), is calculated from the reaction rate of the ligand with glutathione;
intrinsically reactive ligands have higher rates.
In the case of LLE, values above
5 or 6 are generally considered acceptable for advanced leads, and the same is
true for LRE. For example, a molecule with TE50(target, 1h) = 10 nM and
a (low) GSH reactivity of 0.01 M-1s-1 would have an LRE =
6.3. Also analogous to LLE, one can generate plots with pTE50(GSH, 1h) on the
x-axis and pTE50(target, 1h) on the y-axis to assess whether LRE
values are improving during a lead optimization campaign.
In my view, LRE is superior to previously
discussed CLE because it explicitly considers the time component. A one hour
incubation is practical; a ligand with kinact/KI =
10,000 M-1s-1 would have TE50(target, 1h) = 19
nM. Also, LRE is more intuitive for medicinal chemists than CLE due to its
similarity to LLE.
On the minus side, the researchers
note that some of the assumptions break down for ligands with high non-covalent
affinity (low KI). Also, some folks may take issue with metrics
that take the logarithm of a measurement that has units.
The researchers note another
alternative metric, the reactivity enhancement factor (REF), which I briefly
discussed here. REF is simply the ratio of the specific reactivity to the
intrinsic reactivity, which is conceptually simpler to me than LRE. Nonetheless, the
researchers state that LRE is commonly used at Vividion, which has put several
covalent drugs into the clinic, so clearly it can be useful. Whether REF, LRE,
or CLE, ultimately the choice of metric is less important than the ultimate
goal: maximizing specific reactivity while minimizing intrinsic reactivity.
This is a clear and concise explanation of this new LRE metric. We definitely live in interesting times with all of these new metrics and you do a great job of keeping track of them!
ReplyDeleteThanks Brett, I appreciate the kind words especially coming from someone who has written for Drug Hunter
ReplyDeleteMy take on LRE, Dan, is that it’s effectively the logarithm of k_inact/(K_i × k_GSH) and I don’t see it as having special value in design or even providing genuine insight. In a real life drug discovery project I would generate a log-log plot of k_inact/K_i against k_GSH which enables the trend in the data to be visualized and also makes it easy to see whether values of k_inact/K_i and k_GSH satisfy project acceptability criteria. It’s worth noting that medicinal chemists using LLE often (usually?) work from plots of pIC50 versus logP rather than tables of LLE values. The problem with compressing k_inact/K_i and k_GSH into a single metric is that it can lead to bad decisions (for example, you might conclude that a high value of k_inact/K_i allows you to get away a warhead that would otherwise be considered to be excessively reactive). This scenario is analogous to what I refer to as the Paul Leeson fallacy (that you can compensate for inadequate potency if lipophilicity is sufficiently low).
ReplyDeleteCLE is a very different beast (normalization is with respect to molecular size rather than GSH reactivity although György’s continued struggles with the rudiments of physical chemistry are very much in evidence in the study) and I’ll direct you toward my blog post on that metric:
https://fbdd-lit.blogspot.com/2025/11/covalent-ligand-efficiency.html
Hi Pete,
ReplyDeleteYes, as I mentioned in the post the researchers do suggest using these types of plots and they even include examples both in the graphical abstract and in Figure 5. Adding the diagonal lines for different LRE values, analogous to LLE, is a nice way to orient oneself on the plots.
What I was getting at when I mentioned the plots, Dan, is that if you’re using the plots you’re not really using the metric. While the reference lines are helpful the key question is whether you would do anything differently if reference lines were drawn with a different slope. My understanding is that the appropriate citation for plotting data with reference lines in this manner is this 2009 article from some Pfizer scientists:
Deletehttps://doi.org/10.1016/j.bmcl.2009.05.062