It's Corin here from Rowan. And today I want to look at how metabolism affects blood–brain-barrier permeability. So to do that, we're going to zoom in on this compound here, alixorexton or ALKS 2680, which is, you know, developed by Alkermes, close to Rowan in Waltham. And what this is, is an oral OX2R agonist. So just like the native neuropeptide orexin, this is an agonist for OX2R. And, you know, there's a whole biology and set of pathways here that we don't have time to get into. But essentially, this can be used for treating sleep related disorders like narcolepsy. And that's what it's in clinical trials for.
So this is a pretty cool molecule. You know, it's a small molecule agonist of something that natively is used to bind to peptides. It can actually get through the blood brain barrier. So it's, you know, CNS penetrant, which is quite nice. And it's just a really cool compound, too. So it has this cool-looking macrocycle here. And if we actually look at what the conformation looks like in 3D, previously I ran a conformational search on this compound, and you can see that there's this really nice twist boat here that is actually conserved among all the low energy conformers. So they're all boat-like here. And it's just a really nice example of linking your compounds together, designing something that can fit into the binding pocket well, but it doesn't have too many exposed hydrogen-bond donors or acceptors to get through the blood–brain barrier.
Now, I've talked a decent number of times about the blood–brain barrier, and we've put videos out on this before, but basically, designing drugs that can actually get into the brain is very, difficult because most of the usual handles that you have as a drug designer, so putting out hydrogen-bond donors, hydrogen-bond acceptors, other polar groups, these also prevent drugs from getting through this protective membrane, the blood-brain barrier. So you don't actually have these handles. So you have to be very, very selective and very, very judicious about what you're putting on your molecule to maintain potency without totally destroying your CNS bioavailability.
And so in this case, what we can see is, although this is a tightly binding compound, we do have this amide that's a little shielded. We have some ethers, which are pretty weak hydrogen bond acceptors. And then we have the sulfonamide. So we basically have only a single hydrogen bond donor. And that ends up being good enough to let it get through the blood–brain barrier.
So at Rowan, we've adapted this computational physics-based approach to predict unbound-brain-to-plasma partition coefficient. What this essentially does is say that you can use quantum mechanics to predict the state-corrected energy of solvation, and that compounds with a less negative energy of solvation are more likely to get through the blood–brain barrier. Specifically, what this is predicting is the unbound-brain-to-plasma partition coefficient, which is one of the key ways to quantify blood-brain barrier penetrance.
And so what we're able to do is we can draw this molecule, run a macro-pKa calculation, and then compute the solvation energy here, which we see is −16.6. And then we do some logistic regression against experimental Kp,uu data and find that this is predicted to be pretty blood–brain-barrier penetrant. So we have a score of 0.607. This is a logistic regression output, but basically what you can see here from the green color is that yes, this compound is likely to get into the brain, which is great because that's exactly what it's observed to do in the lab, and that's why this is a drug that's in clinical trials.
Okay, so all of that being said, what we want to do today is actually understand what if this compound gets metabolized a little bit? So what if this undergoes, you know, phase one metabolism? How will this affect the blood-brain barrier penetrance? And specifically, what I want to look at today is just looking at the effect of hydroxylation, specifically aromatic hydroxylation. So if we go back to the conformers here, you know, you can see that there's this sort of exposed aromatic group. It's relatively electron rich. You know, you have these C–H bonds just sticking out. And you can imagine, although I don't actually know if this is true, you can imagine that a CYP3A4, for instance, might like to hydroxylate one or both of these aromatic C–H bonds.
And so what I've done here is I've drawn the hydroxylated compound with one OH, sort of arbitrarily chosen to be para to the phenol. And then I've the dihydroxylated compound. So now this is looking more like a catechol with two OHs. And what we want to do here is understand the effects that this will have on blood–brain-barrier penetrance. So what we can do here is you can see obviously now the macro-pKa distribution is more complicated because we've added another acidic group. But nevertheless, we can go over here to this blood–brain-barrier tab and see that now the solvation energy is predicted to be much more negative and the blood–brain-barrier penetrance is predicted to be much less. And this sort of matches the rule of thumb that medicinal chemists are taught because we've added another hydrogen bond donor and another hydrogen bond acceptor. These are both things that are bad for CNS bioavailability. And so it's not surprising to see that, yes, this will be less blood-brain barrier penetrant than the parent compound.
If we go now to the dihydroxylated compound, we can see that the solvation energy is even more negative, so this is binding more tightly to water. Desolvation to get through the membrane via passive diffusion will be even harder. And the blood–brain-barrier penetrance will be even lower. So just remember for review, right, so the parent compound had a predicted score of 0.607. So, you know, decently likely to get into the brain according to our models. Now this is 0.33, and the dihydroxylated compound is even worse at 0.17. So it's very unlikely that this compound will get into the brain.
And again, you know, this makes sense, right? This is now we've added two hydrogen bond donors for total of three. We've added, you know, good hydrogen-bond acceptors. You could say maybe there'll be an intramolecular hydrogen bond in this diol. So the effect won't be quite as big as it might seem on first glance, but still I think it's very unlikely that this compound will get into the brain.
Now this could be either good or bad, right? If this is actually the active species that is giving the desired effect, which is sometimes known to happen, then you might actually want a compound like this to get into the brain. But more likely, metabolism is an off-target thing. We don't want the metabolites to do too much. And so it's probably good that these hydroxylated metabolites can't get into the brain. If we wanted to do a more exhaustive study of this topic, we could look at metabolism around different sites. So we could look at aliphatic hydroxylation, we could look at N-oxidation, or we could start even going into phase II drug metabolism, so glucuronidation, et cetera, et cetera. But I think this shows the point off pretty well for now.
The last thing I want to just note is that these calculations are quite fast. So while the sort of DFT-based workflow outlined here takes hours or days per compound, each one of these just takes about 10 minutes. And so you can run a whole litany of these if you want to study the effect of potential metabolism on drug properties, or if you just want to screen different variants of a compound to see how structural modifications will impact CNS bioavailability.