Via Ash Jogalekar on Twitter, I came across this new paper from researchers at AstraZeneca (and collaborators in Sweden, the UK, and Denmark) on the synthesis and activity of some plasmin inhibitors. Plasmin is an anticoagulation target, and has a lysine-binding site in its Kringle-1 domain (yeah, that’s the real name) that is the site of action for tranexamic acid. The AZ team had discovered some small piperdinyl heterocycles that hit the same binding site, and you can easily see how they might (TXA is at the left, and the prototype AZ compound is at the right).
They produced a set of 16 simple analogs of that first isoxazolone/hydroxyisoxazole, adding methyls, changing the ring size of the piperidine or putting a double bond into it, moving the heteroatoms around, adding a nitrogen or swapping the oxygen for a sulfur, and so on – classic medicinal chemistry on a small scaffold, and perfectly reasonable stuff. The efficacy in the clotting assay matched very well with the Kringle-1 binding affinity, so everything holds together just fine (three or four of them had similar or better potency to the lead, and the others were slightly to noticeably worse). The interesting part is when they went back with this set of compounds and used all the computational tools at their disposal to try to see if anything could predict their affinities or rank-order them well.
They went after it with classic QSAR descriptors, three-dimensional similarity scores, ligand geometries from protein-bound crystallographic data, docking programs, FEP (free energy perturbation), and MM/GBSA. Here’s how all these techniques performed: the QSAR models using simple descriptors, with or without principle components analysis, were of no use at all. Ligand-centric QSAR using molecular shape scoring was equally worthless. The structure-based QSAR calculations also failed, because everything came out as too similar in shape and electrostatic potential. Moving on to the docking programs, the poses for the various compound all came out very similar, but the scoring functions (that try to determine the effect of hydrogen bonds, electrostatic effects, and so on) gave very poor results across the board. The FEP calculations likewise gave an extremely poor fit, as did the MM/GBSA calculations, which gave a very small trend in the wrong direction entirely.
This is not an impressive set of outcomes, clearly, and these are not large or complex molecules. That in itself might be part of the problem, since relatively high ligand efficiency means that most parts of the structure are important. But still. The authors believe that part of the problem is the heteroaromatic group, and the charged/zwitterionic nature of the compounds. But we certainly make an awful lot of heterocycles in this business, and charged interactions are some of the bread-and-butter of medicinal chemistry (and biology!) It may be that some of the techniques in this paper have been applied suboptimally, and I’m sure that if this is the case (or many even if it isn’t) we’ll hear about it. But for now: one would hope for better.