This paper is the second paper published by the Bourne and Xie group in “Drug Discovery Using Chemical Systems Biology”. It’s a very interesting topic: the authors explain how they used chemical systems biology to discover entacapone and tolcapone, commercial available drugs for the treatment of Parkinson’t disease, are good candidates for Multi-Drug and Extensively Drug Resistant Tuberculosis (MDR-TB and XDR-TB). These drugs can inhibit the enzyme InhA, which has a similar binding sites with COMT which is their primary target for treatment of the Parkinson’s disease. InhA is essential for type II fatty acid biosynthesis and the subsequent synthesis of the bacterial cell wall. It is the common target of the anti-tubercular drugs. Their discoveries are validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone.
The authors describe their strategy as follows:
1. The binding sites of a commercially available drug is extracted or predicted from a 3D structure or model of the target protein.
2. Off-targets with similar ligand binding sites are identified across the proteome using an efficient and accurate functional site search algorithm.
3. Atomic interactions between the putative off-targets and the drug are evaluated using protein-ligand docking. Only those off-targets that do not experience serious atomic clashes with the drug are selected for further analysis.
4. The drug is further optimized to enhance its potency, selectivity and ADME properties by taking into account both the primary target and the off-targets across the genome.
In short, the authors try to find other targets of a drug in the whole human proteome, based on binding site similarity, and docking. Then they optimize the drug based on all the targets in the proteome.
The result is interesting and inspiring. Using this strategy, they found what is described earlier: entacapone and tolcapone, drugs currently in the market for Parkinson’s disease can be good candidates for treatment of TB.
The authors elaborated on details of their research. The primary target of these drugs is human catehol-O-mehyltransferase (COMT). Using the SOIPPA algorithm, developed by the same group, they detect the common binding sites among proteins. Then they docked entacapone and tolcapone into these proteins and find that InhA were highly ranked.
Interestingly, the authors pointed out that when if comparing 2D similarity, these two drugs are not similar the current known InhA inhibitors. When docking 20K drug-like compounds into InhA, entacapone ranked very low. In other words, using virtual screening, they will not be found as potential InhA inhibitors. The logP of these two drugs violates the Linpinski’s rule of 5, and is very different from current aanti-tubercular drugs. So these two drugs would be quite unlikely to be selected as lead compounds for the inhibition of InhA, using common drug discovery methods.
The authors also compared the difference of the binding poses of the compounds to COMT and InhA, and pointed out possible ways to optimize them so that they can have weaker affinity to the original target COMT.
To summarize, this paper successfully presents a case study of using chemical systems biology, in particular using protein-ligand interactions, to assist drug discovery in a new multi-target-multi-drug paradigm.