Predicting cancer targets modulated by Ayurvedic medicines

The recent availability of databases that provide both phenotypic descriptions and the chemical structures of the constituent compounds in traditional Chinese and Indian medicines, have enabled Bender et al  (J. Chem. Inf. Model. 2013 (53) 661 – 673, DOI: 10.1021/ci3005513 , to develop a cool algorithm to predict the mode of action (MOA) of these compounds and to predict novel protein targets for cancer therapies.

Traditional medicine has been utilised by human for thousands of years and normally viewed as complementary or alternative to mainstream therapies.  However, both Chinese and traditional Indian medicine (Ayervedic) have provided us with important drugs for instance Artemisinin an antimalarial drug and reserpine an antihypertensive drug.

From 1981 to 2007, 67% of the pharmaceuticals or new molecular entities (NMEs) introduced into the market were natural product based or a derivative there of.  These natural products often have desirable properties which make them good drugs; they are soluble despite breaking Lipinski’s Rule of Five, they embody privileged structures that are more frequently found to bind a variety of proteins in different organisms, and they are safe and well-tolerated, often having been commonly used for centuries.

However, there are major challenges that need to be resolved that enable the development of a new drug from a traditional medicine.  These include the isolation of the active constituents, the synthesis of the active constituents, the elucidation of the mode of action and finally the development of the compound as a “drug”.

The recent availability of databases that provide chemical structures and their corresponding phenotypes have enabled Bender et al to predict the MOA of compounds found in TCM and Ayurveda addressing one of these major challenges.  First they developed a classifier using bioactive compounds from the ChEMBL database, ChEMBL biological targets, ECFP_4 fingerprints for each compound and a Naïve Bayes classifier.


Figure 1: The compounds were represented using the Extended Connectivity Fingerprints, with a diameter of 4 bonds ECFP_4.  The ECPF is derived from the Morgan algorithm and was implemented in Sitegic’s Pipeline Pilot (Accelrys Inc).  Each atom identifier contains topological information on the atom that includes the number of immediate heavy atoms, the atom’s mass, its charge, the number of hydrogens attached, the valance minus the number of hydrogens and whether it is part of a ring.

This was used to predict which compound (fingerprint) would inhibit each protein target.  Then by creating fingerprints for each traditional medicine compound they could predict which protein targets they would hit.  For example they predicted the protein targets for some of the active ingredients of Panaz ginseng

FP5Figure 2:  Predicted targets for some of the active ingredients in Panax Ginseng

Next they correlated different proteins targets with different phenotypes. Predicting which molecular targets were modulated by the compounds in each different phenotype.  This enabled them to identify the protein targets most frequently modulated by Ayurvedic medicines, with possible anti-tumour effects.  The 10 most enriched protein targets are shown in the table below. The progesterone receptor currently has over 10 inhibitors with FDA approval.  Other proteins identified by this methods include regulators of other well-known cancer targets.


Figure 3:  Top 10 cancer targets in predicted to be inhibited by Ayurvedic medicines.


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