1 Total size of the pipeline Citeline annual review 2012 ?? Adverse drug reactions (ADMET) Can Ayurvedic Treatment Help in Integrating with Modern Medicine?

NCI, Specs, IBScreen Databases ( Compounds) Compounds (CH 3 ) 3 N, Lipinski’s rule based screening 117 Compounds Pharmacophore based screening, Fit value ≥ 4 In vitro HTS compounds (ChemDiv, Tripose, ChemBridge) AChE activity < 0.35µM 8 compounds 75 Compounds 9 Compounds Docking based screening, GFS > 52 Blood Brain Barrier, Bioavailability & Toxicity based screening 2 Lead Compounds In vitro screening Flow chart showing different sequential virtual screening techniques 2 Finland Collaboration

3 In Vitro Analysis (Finland collaboration)  AChE Enzyme Assay based on Ellman’s Reagent reaction.  Data set consisted of 8 highly potent compounds (IC 50 < 0.35 µM) obtained from high-throughput in vitro screening consisting of 56,000 compounds for AChE inhibitory activity.  Compounds were taken from 3 Key Databases: ChemDiv Inc (San Diego, US), ChemBridge Corporation (San Diego, US) Tripos (US). Selection of Compounds for Pharmacophore Identification I (0.019 μM) II (0.069 μM) IV (0.204 μM) III (0.152 μM) VI (0.236 μM) V (0.229 μM) VII (0.315 μM) VIII (0.332 μM) #Comb. Chem. and High Throughput Screening 2010, 13,

4 Pharmacophore Model for Anti-Cholinesterase Inhibitors D72 Y121 W279 W84 F330 F288 F331 Y334 CD1 CD1 (IC 50 =0.019 μM) Active binding site residues of AChE enzyme in complex with CD1 molecule

5 Virtually screened Lead Compounds (Specs1 and Specs2) Interaction with AChE Enzyme IC 50 =3.279 μM IC 50 =5.986 μM

6 Compound Structure (Vendor ID)SourceGFS in AChE enzyme (kJ/mol) IC 50 (μM) for AChE inhibition IC 50 (μM) for BChE inhibition (ChemDiv (CD1), (ID= C ) High throughput in vitro screening (ChemDiv (CD2), (ID= E ) (Specs (Specs1), (ID=AF-399/ ) Virtual screening followed by in vitro validation Less inhibition Less inhibition (Specs (Specs2), (ID= AF-399/ ) Structure of the 4 lead compounds along with the AChE and BChE enzyme inhibition, and its GOLD fitness score

CompoundToxicity ab CNS b logS b % H_ O_Abs c AlogP c logD b logBB b PSA CD1 Skin sensitisation CD2None Specs1None Specs2None ADMET parameters of 4 leads obtained from ChemDiv & Specs database a Toxicity prediction using DEREK software; b Prediction using Schördinger software; c Prediction using Discovery studio (DS2.1) ADME module software; CNS= Predicted central nervous system activity on a -2 (inactive) to +2 (active) scale; LogS= Predicted aqueous solubility, log S on a –6.5 to 0.5; % H_O_Abs = Percent Human Oral Absorption; AlogP= Log of the octanol-water partition coefficient using Ghose and Crippen's method; LogD=The octanol-water partition coefficient calculated taking into account the ionization states of the molecule; LogBB= Predicted brain/blood partition coefficient on a -3 to 1.2 scale; PSA= Van der Waals surface area of polar nitrogen and oxygen atoms. 7

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Topics of Interest in Ayur Informatics Ayurvedic Drug Designing Biostatistics Computational Languages Distributed Electronic Medical Records Emergency Communications and Remote Monitoring Medicinal Plant Databases Medicinal Plant Genomics Molecular Modelling Networking Rural Clinics with Major Medical Facilities Pharmacogenomics Pharmacoproteomics Plant Datasets and Databases Remote Analysis of Diagnostic Data Remote Information Services & Decision Support Tools for Patient Care Telemedicine, Telemonitoring, Telediagnostics Use of Digital Radios and Personal Communication Services Visualization Technology for Visualizing the Human Anatomy Wireless Information Networks

BIOACTIVE COMPOUNDS FROM NATURAL SOURCES (Natural Products as Lead Compounds in Drug Discovery) Computational Approaches for the Discovery of Natural Lead Structures Wang, S. Q., Q. S. Du, K. Zhao, A. X. Li, D. Q. Wei, and K. C. Chou Virtual screening for finding natural inhibitor against cathepsin-L for SARS therapy. Amino Acids 33:129–135. Natural products were often viewed as being “too complex” but then have been recognized as a valuable source of interesting and diverse structures. Natural products show higher molecular weight than synthetic drugs, contain more oxygen atoms, fewer nitrogen atoms, three times more stereo centers, their degree of unsaturation is higher than in synthetic drugs and they incorporate less aromatic rings. Although natural products in general contain more rings than drugs, most of them are non-aromatic and part of single fused ring system.

Bioactive compounds from Natural Sources

The major challenge for in silico techniques is the increased molecular weight and the higher number of saturated bonds resulting in higher molecular flexibility. As a rule of thumb, docking and conformational sampling become slow and inaccurate when a molecule has more than seven rotatable bonds. However, these problems are not irresolvable, but have to be addressed while setting up a virtual screening run using natural products taking into account longer computational time. A too high degree of flexibility may also result in promiscuity, that is, a compound is fitted into a binding site in an implausible way, which stresses the need for visual inspection of virtual hits and selection of the most plausible virtual hits. Another putative practical problem is the higher number of stereo centers whose definitions are often neglected during database construction and therefore the programs have to enumerate all stereoisomers, which unnecessarily increase the (virtual) flexibility.

General workflow for Computer assisted strategy for Natural lead discovery (VS=Virtual screening), (NP= Natural product)

Computational workflow for the identification of the active metabolite/s within a natural (Ayurvedic) preparation.