Predicting Pharmacokinetics

There are a lot of challenges in drug discovery and medicinal chemistry, but it is safe to say that most of them stem from trying to make these compounds work inside human beings. Not surprising as we are putting small molecules into a highly complex system that no one fully understands and expect them to work to cure what ails without messing anything else up.

Well, no, that is not strictly true. We don’t expect that. We’d like it to happen, but it never does.

As the established wisdom goes, efficacy is easy, but PK is hard. With the cost of developing drugs, it is not surprising that people have tried to come up with some kind of predictor to help point us in the right direction. The most well known in medicinal chemistry circles is Lipinski’s Rule of 5, which even has its own Wikipedia entry. That gives me a warm and fuzzy feeling for some reason. Other examples exist, such as Gleeson’s ADMET rules of thumb. What they share is a relative simplicity and a way to relate what you are trying to achieve to something a chemist in the lab can control – in other words, what changes you can make to your molecule to make it more acceptable.

This came up with me now because, while tiudying my desk (yes, it does occasionally happen), I came across an article out of Pfizer from Ted Johnson on the Golden Triangle to optimize clearance and oral absorption. In the spirit of the previous mentioned, it attempts to bring properties of the molecule (in this case log D and molecular weight) and find trends to indicate how these relate to the important pharmacokinetic properties of clearance and oral bioavailibility. In fact, they did find some trends and call it the Golden Triangle Rule, where the compounds that display the best PK properties fall within a triangular area of a graph of MW against log D, with an apex at MW=450 and log D ~1.5 and a baseline at MW=200 with log D between -2.0 and 5.0. So again, we need low molecular weight compounds of just the right polarity, though it is interesting that a lower molecular weight gives you more leeway on the range of polarity you can get away with. Though the authors do point out there are compounds within the triangle that fail. This is just a guideline, despite the name.

These are all only predictions and the trend of screening compounds for undesirable properties earlier in the drug discovery program will continue, despite how practical results often ruin a perfectly good hypothesis.

Of course, the problem with a lot of these techniques is that in order to reach an optimum molecular weight you have to remove the parts of the molecule that give it its potency, making it perfectly acceptable but also perfectly useless. But that is what makes a medicinal chemist’s life so interesting.


One comment on “Predicting Pharmacokinetics

  1. JP says:

    Always a sticky subject… if I recall, Lipinski’s Rules were derived from an analysis of FDA approved drugs, but unfortunately they are applied in ways that often exclude many compounds and/or compound classes from even being screened. The disconnect is that screening gives you a lead compound, which is then optimized to created the compound that ultimately becomes a drug (and yes, typically falls within the Lipinski’s boundaries), but it doesn’t follow that all compounds to be screened need to subscribe to Lipinski. Also, it’s a self-fulfilling prophecy to say “we’re only going to make compounds like this because only these compounds can become drugs.” Anyhow, I’m glad there are other analyses that have been (and are being) done that look at a much wider range of issues (both in terms of compound properties and how these change in the “hit to drug” pipeline).

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