On the agenda today is a fascinating paper titled: predicting new molecular targets for known drugs, Keiser et al., Nature 2009 [PMC]. This is one of my favorite papers of the past two years. I have to admit that it took my quite a while to really understand the potential of the methods and findings of the paper and I was torn as to whether to include the paper in the class because it is likely a bit much to digest in a single class period; however, the findings are so cool and the implications of the method are so far-reaching that I ultimately decided I couldn’t pass up the opportunity to do this paper for the class. Let’s dive in…
The idea is simple. Drugs work because they have an action at a molecular target. However, most drugs have side effects and we don’t always know why. Moreover, while we often presume that drugs work because they hit a particular target, this may not actually be true. There are generally several drugs of a particular class but one particular drug may be better for treating a disease. This could be explained by a number of factors and one potential possibility is that a given drug has a mix of activities that is a perfect fit for a particular disorder. The problem is that we don’t necessarily know what that polypharmacological mix is. If there was a computation model that could help us predict appropriate polypharmacologies and off-target hits at the same time we could potentially make large leaps forward in terms of drug development.
This is, more or less, what the author’s set out to do:
The creation of target-specific ‘magic bullets’ has been a therapeutic goal since Ehrlich, and a pragmatic criterion in drug design for 30 years. Still, several lines of evidence suggest that drugs may have many physiological targets. Psychiatric medications, for instance, notoriously act through multiple molecular targets, and this ‘polypharmacology’ is probably therapeutically essential. Recent kinase drugs, such as Gleevec and Sutent, although perhaps designed for specificity, modulate several targets, and these ‘off-target’ activities may also be essential for efficacy. Conversely, anti-Parkinsonian drugs such as Permax and Dostinex activate not only dopamine receptors but also 5-HT2B serotonin receptors, thereby causing valvular heart disease and severely restricting their use.
Drug polypharmacology has inspired efforts to predict and characterize drug–target associations. Several groups have used phenotypic and chemical similarities among molecules to identify those with multiple targets, and early drug candidates are screened against molecular target panels. To predict new targets for established drugs, a previous group looked for side-effects shared between two molecules, whereas another group linked targets by drugs that bind to more than one of them. Indeed, using easily accessible associations, one can map 332 targets by the 290 drugs that bind to at least two of them, resulting in a network with 972 connections (Fig. 1a, below). It seemed interesting to calculate a related map that predicts new off-target effects.
So what does this network look like?
Panel A is a network of known drugs with known targets linked together. It shows, basically, some pharmacological promiscuity between receptors that would be expected by any pharmacologist. For instance, 5HT receptor drugs tend to hit other 5HT receptors and these drugs also tend to have effects at other GPCRs within the amine ligand class. Nuclear receptors and their ligands also group together and on and on. Where this really gets interesting is in panel B: