In Silico Design of New Drugs for Myeloid Leukemia Treatment
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1 In Silico Design of New Drugs for Myeloid Leukemia Treatment Washington Pereira and Ihosvany Camps Computational Modeling Laboratory LaModel Exact Science Institute ICEx Federal University of Alfenas - UNIFAL-MG Alfenas. Minas Gerais. Brazil
2 Contents 1. Introduction 2. Materials and Methods Conclusions 5. Acknowledgments
3 Introduction In this work we use in silico tools like de novo drug design, molecular docking and absorption, distribution, metabolism and excretion (ADME) studies in order to develop new inhibitors for tyrosine-kinase protein (including its mutate forms) involved in myeloid leukemia disease. This disease is the first cancer directly associated with a genetic abnormality and is associated with hematopoietic stem cells that are manifested primarily with expansion myelopoiesis. Starting from a family of fragment and seeds from known reference drugs, a set of more than 6k molecules were generated. This first set was filtered using the Tanimoto similarity coefficient as criterion. The second set of more dissimilar molecules were then used in the docking and ADME studies. As a result, we obtain a group of molecule that inhibit the tyrosine-kinase family and have ADME properties better than the reference drugs used in the treatment of myeloid leukemia.
4 Materials and Methods Protein 1. Tyrosine-kinase in its wild form (1OPJ) 1 2. Mutated tyrosine-kinase 2 Softwares 1. Schrodinger Suite 3 2. Maestro interface 4 3. LigBuilder 5,6 Molecules 1. Grown/Linked from fragment database 6 2. Reference drugs: imatinib, dasatinib, nilotinib and ponatinib Steps 1. Prepare the protein 2. Grown/link new molecules (library no. 1) 3. Filter library no. 1 (library no. 2) 4. Calculate ADME properties 5. Dock (rigidly) library no. 2 and reference drugs 6. Dock (flexibly) best molecules from step 5 and reference drugs
5 Filtering To validate the structural diversity of the generated library we calculated a 2D linear hashed fingerprint with a 64-bit address space. Then, we used the Tanimoto metric to compute the similarity among all the molecules (if the Tanimoto coefficient of two structures is greater than 0.85, the structures are considered similar, and descarted)
6 Absorption, Distribution, Metabolism and Excretion Use of Lipinski s rule of five 7 : widely used descriptor to study the drugability of molecules. It predicts that a molecule will have poor absorption when: MW > 500Da QPlogPo/w > 5 HBDonor > 5 HBAcceptor > 10 Compound MW QPlogPo/w HBDonor* HBAcceptor * QPlogHERG Imatinib Dasatinib Nilotinib Ponatinib As they are average values, they can be non-integers. Red values = bad values! MW: molecular weight QPlogPo/w: octanol/water partition coefficient HBDonor: number of hydrogen bonds that would be donated by the solute to water molecules HBAcceptor: estimated number of hydrogen bonds that would be accepted by the solute from water molecules QPlogHERG: simulate the blockage of human ether-a-go-go herg K+ channels (cardiac side effects).
7 Absorption, Distribution, Metabolism and Excretion
8 Docking results: scores Table 1.1 Docking score (Gscore*) for the best molecules and for the references drugs (the lower the better). 1OPJ Molecule GScore Reference Imatinib Dasatinib Nilotinib Ponatinib GScore T315I Molecule GScore Reference Imatinib Dasatinib Nilotinib Ponatinib GScore T315A Molecule GScore * In kcal/mol Reference Imatinib Dasatinib Nilotinib Ponatinib GScore
9 Docking results: scores Table 1.2 Docking score (Gscore*) for the best molecules and for the references drugs. M244V Molecule GScore Reference Imatinib Dasatinib Nilotinib Ponatinib GScore E355G Molecule GScore Reference Imatinib Dasatinib Nilotinib Ponatinib GScore H396A Molecule GScore * In kcal/mol Reference Imatinib Dasatinib Nilotinib Ponatinib GScore
10 Docking results: interaction energies Docking results: interaction energies Complex HBondE a LipoE a ElectE a HBond b Good b Bad b Ugly b - b -cation HBondD c 1OPJ , 1.890, 1.975, 2.131, 2.167, OPJ+Imatinib , 1.895, 1.934, T315I , 2.097, T315I+Imatinib , 1.832, 2.029, T315A , 2.005, T315A+Nilotinib , 2.031, M244V , 2.029, 2.096, M244V+Nilotinib , 1.911, E355G , 1.756, 2.005, 2.058, E355G+Nilotinib , 2.018, H396A , 1.813, 1.983, 1.986, H396A+Nilotinib , 1.948, a In kcal/mol. b Number of contacts. c H-Bond distances, in Å.
11 Docking: 2D interactions 1OPJ 1OPJ+680 1OPJ+Imatinib
12 Docking: 2D interactions T315I T315I+781 T315I+Imatinib
13 Docking: 2D interactions M244V M244V+723 M244V+Nilotinb
14 Docking: 2D interactions E355G E355G+781 E355G+Nilotinb
15 Docking: 2D interactions H396A H396A+781 H396A+Nilotinb
16 Conclussion The myeloid leukemia is a fatal disease, so it is of great importance to keep the patients in chronic phase where they stay asymptomatic. The fragment based drug design method used in this work turns to be a good alternative to create drugs that can control this neoplasm. Based on the calculated GScore, the de novo designed molecules have better inhibitor capacity than the tyrosine-kinase inhibitors most used in the market. These molecules shown strong potential to become drugs capable to inhibit all mutations, mainly the T315I mutation, now the leading cause of deaths due to the difficulty of inhibitors to control it.
17 Acknowledgments
18 References [1] PDB ID: 1OPJ. B. Nagar, O. Hantschel, M. A. Young, K. Scheffzek, D. Veach, W. Bornmann, B. Clarkson, G. Superti-Furga, and J. Kuriyan, Cell 112, 859 (2003). [2] PDB ID: 3QRI. Wayne W. Chan, S. C. Wise, M. D. Kaufman, Y. M. Ahn, C. L. Ensinger, T. Haack, M. M. Hood, J. Jones, J. W. Lord, W. P. Lu, D. Miller, W. C. Patt, B. D. Smith, P. A. Petillo, T. J. Rutkoski, H. Telikepalli, L. Vogeti, T. Yao, L. Chun, R. Clark, P. Evangelista, L. C. Gavrilescu, K. Lazarides, V. M. Zaleskas, L. J. Stewart, R. A. V. Etten, and D. L. Flynn, Cancer Cell 19, 556 (2011). [3] Schrödinger suite: [4] Maestro, version 10.1, Schrödinger, LLC, New York, NY, [5] Ligbuilder site: [6] Y. Yuan, J. Pei, and L. Lai, J. Chem. Inf. Model. 51, 1083 (2011). [7] C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, Adv. Drug Delivery Rev. 46, 3 (2001).
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