INTRODUCTION

1. Pharmacophore modeling and 3D-QSAR of1-[4-(4,6-substituted-1,3,5-triazin-2-yl)phenyl]-3-(4-substituted phenyl) urea class for design of dual PI3K and mTOR inhibitors. Abubakar Danjuma Abdullahi1, Nadia Hanis Binti Abdul Samat1,Abdul Razak Kasmuri1, Farahidah bt. Mohamed1 and Roslan Harun 2 1Department of Basic Medical Sciences, Faculty of Pharmacy, International Islamic University Malaysia, Kuantan, 25200, Malaysia. 2UKM Medical Molecular Biology Institute (UMBI), University Kebangsaan Malaysia (National University of Malaysia), Kuala Lumpur, 56000, Malaysia. METHODOLOGY INTRODUCTION Docking Results of GQSAR Compounds 1E7V_Int_CID_46885277_P15 • The PI3K/AKT/mTOR pathway plays important roles in cellular proliferation, survival and growth. • Many members of this pathway including PI3K and mTOR are frequently abnormal in a variety of cancers which makes them attractive targets for development of targeted therapy. • MTORC1 promote protein translation through phosphorylation of S6 kinase (S6K) and inhibitory 4E-binding proteins (4EBPs). • Blocking of mTORC1 using rapamycin and related rapalogs did not completely shut down mTORC1 and had little effect on mTORC2 in the majority of treated cells. • Furthermore blocking of mTORC1 is associated with hyperactive activation of PI3K through phosphorylation of Akt at serine 473. • Therefore complete shut down of PI3K/Akt/mTOR pathway at the level of PI3K and mTOR could be the best option. • ATP competative inhibitors could disrupt mTORC1 signalling through blocking both S6K as well as 4EBP. • ATP competative inhibitors could also disrupts mTORC2-mediated phasphorylation at serine 473, leading to abrogation of Akt feedback activation. • In this study we describe lead optimization through 3D pharmacopore modeling and 3D QSAR modeling of di-(3-oxa-8-azabicyclo[3.2.1]octan-8-yl)-arylureidophenyl-triazinesPI3K, mTOR inhibitors. The methods is compared to group QSAR, a new method. Scoring 3D Pharmacopore 1. AARRN 2. AAARN 3. AAARN Top Scored Hypotheses Superimposed with Active Ligands 1. AARRN 2. AAARN 3. AAARN 1) 1E7V 1E8W_Int_CID_46884525_P6.png - SCORE- -34.034733 1E7V_Int_CID_46885277_P15.png- SCORE- -33.062528 2)1E8W 1E8W_Int_CID_46884541_P26.png - SCORE- -26.230357 1E8W_Int_CID_46885277_P1.png - SCORE- -36.969444 DISCUSSSION RESULTS • Five compounds with highest activity were selected for generation of common pharmacopore hypotheses. • 2 top ranking hypotheses were designed and used for screening 3D database. Preliminary shows retrieval of many actives. • For the GQSAR ↑ Polar surface area was found to increase for both PI3K and mTOR activities. • ↑ Polar surface area was conducive for PI3K activities. • GQSAR resulted in generation of 87 active compounds. PI3K/Akt/mTOR Pathway Modified from Discovery on targets Structures of 35 Ligands 3D QSAR Results – Pictorial Views Second Rank QSAR Model with highly Active Ligand Top Rank QSAR Model Highly Active Ligand mTOR- 1E8W PI3K-gamma - 1E7V Docking Results CID_46884525_P5 bound to 1E8W CID_46884540_P8 bound to 1E7V REFERENCES METHODOLOGY Pharmacopore Modeling and 3D QSAR Workflow Group QSAR (GQSAR) Workflow Hann C.L. and Rudin C.M. 2007. Fast, hungry and unstable: finding the Achilles’ heel of small-cell lung cancer. Trends in Molecular Medicine (In Press). 1471-4914. Tingjun Hou and Xiaojie Xu (2004). Recent Development and Application of Virtual Screening in Drug Discovery: An Overview. Current Pharmaceutical Design: 10; 1011-1033. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, et al. (2007). Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9: 166–80. 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Expert Opin Ther Targets 2007 ; 11 : 411 -21. http://www.dharmacon.com http://www.ambion.com/ http://www.promega.com/ http://www.bio-rad.com/ Group QSAR (GQSAR) Results • Ligand preparation and generation of conformers • Creating pharmacopore sites Top 20 ligands of GQSAR • Find common pharmacopores • Docking • Scoring pharmacopore hypotheses • +/- 3D database Screening 1) 1E7V 1E8W_Int_CID_46884525_P6.png - SCORE- -34.034733 1E7V_Int_CID_46885277_P15.png- SCORE- -33.062528 2)1E8W 1E8W_Int_CID_46884541_P26.png - SCORE- -26.230357 1E8W_Int_CID_46885277_P1.png - SCORE- -36.969444 • Building 3D QSAR models + 3D database screening • Creating a 3D database ACKNOWLEDGEMENT This research is being funded by Ministry of Science and Technology Malaysia eScience Fund (Registration number 02-01-02-SF0399) and International Islamic University Malaysia Research Matching Grant (Project ID: RMGS10-003-0013).