QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

Parvin Kumar; Ashwani Kumar; Jayant Sindhu; Sohan Lal

doi:10.1055/a-0652-5290

Drug Research, Table of Contents

Drug Res (Stuttg) 2019; 69(03): 159-167
DOI: 10.1055/a-0652-5290

Original Article

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

Authors

Parvin Kumar

¹Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
Ashwani Kumar

²Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India
Jayant Sindhu

³K.M. Govt. College, Narwana, Haryana, India
Sohan Lal

¹Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India

Abstract

Human farnesyl pyrophosphate synthase (hFPPS) is a well-settled therapeutic target and it is an enzyme of the mevalonate pathway which catalyzes the biosynthesis of the C-15 isoprenoid farnesyl pyrophosphate. QSAR studies by using Monte Carlo method for human farnesyl pyrophosphate synthase inhibitors has been carried out using balance of correlation technique with Index of ideality correlation. For construction of QSAR models, six random splits were prepared from the data of 73 phosphonates and hybrid optimal descriptors procured from graph (HFG) and SMILES based notations were employed. The developed QSAR models have robustness, good fitting ability, generalizability and internal predictive ability. The external predictive ability has been certified by testing various precedents. The values of R², IIC, Q² and ∆R² _m for the best model are 0.9304, 0.9614, 0.9061 and 0.0861 respectively. The developed QSAR models met with the specified standards given in OECD guideline and applicability domain. The structural feature promoters for the end point increase and promoters for end point decrease have been extracted. The predicted pIC₅₀ for the new proposed compounds have also been reported.

Key words

Human farnesyl pyrophosphate synthase - Phosphonates - QSAR - IIC - CORAL - SMILES

Full Text

References

References
1 Park J, Rodionov D, De Schutter JW et al. Crystallographic and thermodynamic characterization of phenylaminopyridine bisphosphonates binding to human farnesyl pyrophosphate synthase. Plos One 2017; 12: e0186447.DIO: 10.1371/journal.pone.0186447.
2 Leung CY, Langille AM, Mancuso J. et al. Discovery of thienopyrimidine-based inhibitors of the human farnesyl pyrophosphate synthase-Parallel synthesis of analogs via a trimethylsilyl ylidene intermediate. Bioorgan Med Chem 2013; 21: 2229-2240
3 Park J, Leung CY, Matralis AN. et al. Pharmacophore Mapping of Thienopyrimidine-Based Monophosphonate (ThP-MP) Inhibitors of the Human Farnesyl Pyrophosphate Synthase. J Med Chem 2017; 60: 2119-2134
4 Szabo CM, Matsumura Y, Fukura S. et al. Inhibition of geranylgeranyl diphosphate synthase by bisphosphonates and diphosphates: A potential route to new bone antiresorption and antiparasitic agents. J Med Chem 2002; 45: 2185-2196
5 Lin YS, Park J, De Schutter JW. et al. Design and Synthesis of Active Site Inhibitors of the Human Farnesyl Pyrophosphate Synthase: Apoptosis and Inhibition of ERK Phosphorylation in Multiple Myeloma Cells. J Med Chem 2012; 55: 3201-3215
6 Hooff GP, Peters I, Wood WG. et al. Modulation of Cholesterol, Farnesylpyrophosphate, and Geranylgeranylpyrophosphate in Neuroblastoma SH-SY5Y-APP695 Cells: Impact on Amyloid Beta-Protein Production. Mol Neurobiol 2010; 41: 341-350
7 Russell RGG. Bisphosphonates: The first 40 years. Bone 2011; 49: 2-19
8 Tsoumpra MK, Muniz JR, Barnett BL. et al. The inhibition of human farnesyl pyrophosphate synthase by nitrogen-containing bisphosphonates. Elucidating the role of active site threonine 201 and tyrosine 204 residues using enzyme mutants. Bone 2015; 81: 478-486
9 De Schutter JW, Zaretsky S, Welbourn S. et al. Novel bisphosphonate inhibitors of the human farnesyl pyrophosphate synthase. Bioorg Med Chem Lett 2010; 20: 5781-5786
10 Dozier JK, Distefano MD. An enzyme-coupled continuous fluorescence assay for farnesyl diphosphate synthases. Anal BioChem 2012; 421: 158-163
11 Dhar MK, Koul A, Kaul S. Farnesyl pyrophosphate synthase: A key enzyme in isoprenoid biosynthetic pathway and potential molecular target for drug development. N Biotechnol 2013; 30: 114-123
12 Park J, Zielinski M, Magder A. et al. Human farnesyl pyrophosphate synthase is allosterically inhibited by its own product. Nature Communications 2017; 8: 14132
13 Gisselberg JE, Herrera Z, Orchard LM. et al. Specific Inhibition of the Bifunctional Farnesyl/Geranylgeranyl Diphosphate Synthase in Malaria Parasites via a New Small-Molecule Binding Site. Cell Chem Biol 2018; 25: 185-193
14 Fernandez D, Ramis R, Ortega-Castro J. et al. New insights into human farnesyl pyrophosphate synthase inhibition by second-generation bisphosphonate drugs. J Comput Aid Mol Des 2017; 31: 675-688
15 Fernandez D, Ortega-Castro J, Frau J. Theoretical study of the deposition and adsorption of bisphosphonates on the 001 hydroxyapatite surface: Implications in the pathological crystallization inhibition and the bone antiresorptive action. Appl Surf Sci 2017; 392: 204-214
16 Rodriguez JB, Falcone BN, Szajnman SH. Approaches for Designing new Potent Inhibitors of Farnesyl Pyrophosphate Synthase. Expert Opin Drug Dis 2016; 11: 307-320
17 Abdel-Hamid MK, Abdel-Hafez AA, El-Koussi NA. et al. Quantitative structure-activity relationship (QSAR) studies on a series of 1,3,4-thiadiazole-2-thione derivatives as tumor-associated carbonic anhydrase IX inhibitors. J Enzyme Inhib Med Chem 2009; 24: 722-729
18 Gao H, Katzenellenbogen JA, Garg R. et al. Comparative QSAR analysis of estrogen receptor ligands. Chem Rev 1999; 99: 723-744
19 Hadjipavlou-Litina D, Garg R, Hansch C. Comparative quantitative structure-activity relationship studies (QSAR) on non-benzodiazepine compounds binding to benzodiazepine receptor (BzR). Chem Rev 2004; 104: 3751-3794
20 Hansch C, Hoekman D, Gao H. Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions. Chem Rev 1996; 96: 1045-1076
21 Thareja S. Steroidal 5alpha-Reductase Inhibitors: A Comparative 3D-QSAR Study Review. Chem Rev 2015; 115: 2883-2894
22 Agatonovic-Kustrin S, Morton DW, Celebic D. QSAR: an in silico approach for predicting the partitioning of pesticides into breast milk. Comb Chem High Throughput Screen 2013; 16: 223-232
23 Aouidate A, Ghaleb A, Ghamali M. et al. 3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase. Comput Biol Chem 2018; 74: 201-211
24 Martin MB, Sanders JM, Kendrick H. et al. Activity of bisphosphonates against Trypanosoma brucei rhodesiense. J Med Chem 2002; 45: 2904-2914
25 Szabo CM, Martin MB, Oldfield E. An investigation of bone resorption and Dictyostelium discoideum growth inhibition by bisphosphonate drugs. J Med Chem 2002; 45: 2894-2903
26 Sanders JM, Gomez AO, Mao J. et al. 3-D QSAR investigations of the inhibition of Leishmania major farnesyl pyrophosphate synthase by bisphosphonates. J Med Chem 2003; 46: 5171-5183
27 Prokopenko V, Kovalishyn V, Shevchuk M. et al. Design and synthesis of new potent inhibitors of farnesyl pyrophosphate synthase. Curr Drug Discov Technol 2014; 11: 133-144
28 Ling Y, Sahota G, Odeh S. et al. Bisphosphonate inhibitors of Toxoplasma gondi growth: In vitro, QSAR, and in vivo investigations. J Med Chem 2005; 48: 3130-3140
29 Kotsikorou E, Oldfield E. A quantitative structure-activity relationship and pharmacophore modeling investigation of aryl-X and heterocyclic bisphosphonates as bone resorption agents. J Med Chem 2003; 46: 2932-2944
30 C KMC Hudock MP, Zhang Y. et al Inhibition of geranylgeranyl diphosphate synthase by bisphosphonates: A crystallographic and computational investigation. J Med Chem 2008; 51: 5594-5607
31 Sanders JM, Ghosh S, Chan JM. et al. Quantitative structure-activity relationships for gammadelta T cell activation by bisphosphonates. J Med Chem 2004; 47: 375-384
32 Fernandez D, Ortega-Castro J, Frau J. Human farnesyl pyrophosphate synthase inhibition by nitrogen bisphosphonates: A 3D-QSAR study. J Comput Aided Mol Des. 2013; 27: 739-754
33 Liu QZ, Wang SS, Li X. et al 3D-QSAR, molecular docking, and ONIOM studies on the structure-activity relationships and action mechanism of nitrogen-containing bisphosphonates. Chem Biol Drug Des 2018; 91: 735-746
34 Trinh TX, Choi JS, Jeon H. et al. Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chem Res Toxicol. 2018; 31: 183-190
35 Stoickov V, Stojanovic D, Tasic I. et al QSAR study of 2,4-dihydro-3 H-1,2,4-triazol-3-ones derivatives as angiotensin II AT(1) receptor antagonists based on the Monte Carlo method. Struct Chem 2018; 29: 441-449
36 Prachayasittikul V, Worachartcheewan A, Toropova AP. et al. Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors. Sar Qsar Environ Res 2017; 28: 1-16
37 Kumar A, Chauhan S. QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method. Drug Res 2017; 67: 156-162
38 Kumar A, Chauhan S. Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors. Sar Qsar Environ Res 2017; 28: 179-197
39 Kumar A, Chauhan S. Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors. Arch Pharm 2017; 350: e1600268. DOI: 10.1002/ardp.201600268
40 Sokolović D, Stanković V, Toskić D. et al. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct Chem 2016; 27: 1511-1519
41 Veselinović JB, Veselinović AM, Toropova AP. et al. The Monte Carlo technique as a tool to predict LOAEL. Eur J Med Chem 2016; 116: 71-75
42 Veselinović AM, Veselinović JB, Toropov AA. et al. In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method. Int J Pharm 2015; 495: 404-409
43 Toropov AA, Rallo R, Toropova AP. Use of Quasi-SMILES and Monte Carlo Optimization to Develop Quantitative Feature Property/Activity Relationships (QFPR/QFAR) for Nanomaterials. Curr Top Med Chem 2015; 15: 1837-1844
44 Veselinovic AM, Milosavljevic JB, Toropov AA. et al. SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL. Eur J Pharm Sci 2013; 48: 532-541
45 Toropov AA, Toropova AP, Benfenati E. et al. QSPR/QSAR Analyses by Means of the CORAL Software: Results, Challenges, Perspectives. Quantitative Structure-Activity Relationships. In Drug Design, Predictive Toxicology, and Risk Assessment. Roy K. (eds) Hershey, IGI Global; 2015: 560-585
46 Nesmerak K, Toropov AA, Toropova AP. et al. SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides. Eur J Med Chem 2013; 67: 111-114
47 Toropov AA, Toropova AP, Benfenati E. Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions. Int J Mol Sci 2009; 10: 3106-3127
48 Kumar P, Kumar A. Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles. Drug Res (Stuttg) 2018; 68: 189-195
49 Veselinović J, Veselinović A, Toropov A. et al. Monte Carlo method based QSAR modeling of coumarin derivatives as potent HIV-1 integrase inhibitors and molecular docking studies of selected 4-phenyl hydroxycoumarins. Acta Fac Med Naiss 2014; 31: 95-103
50 Toropova AP, Toropov AA. CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Comput Biol Chem 2018; 72: 26-32
51 Toropova AP, Toropov AA. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicol Lett 2017; 268: 51-57
52 Toropova AP, Toropov AA, Martyanov SE. et al. CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna. Chemometr Intell Lab 2012; 110: 177-181
53 Toropova AP, Toropov AA, Lombardo A. et al. CORAL: QSAR Models for Acute Toxicity in Fathead Minnow (Pimephales promelas). J Comput Chem 2012; 33: 1218-1223
54 Kumar P. A novel, facile, simple and convenient oxidative aromatization of Hantzsch 1, 4-dihydropyridines to pyridines using polymeric iodosobenzene with KBr. Journal of Heterocyclic Chemistry 2010; 47: 1429-1433
55 Kumar P, Kuamr A, Mohan LJ. et al. Heterocyclic Systems Containing Bridgehead Nitrogen Atom: Synthesis and Evaluation of Biological Activity of Imidazo [2, 1-b]-1, 3, 4-thiadiazolo [2, 3-c]-s-triazoles, s-Triazolo [3, 4-b]-1, 3, 4-thiadiazolo [3, 2-b] imidazo [4, 5-b] quinoxaline and bis-(s-Triazolo [3, 4-b]-1, 3, 4-thiadiazolo [3, 2-b][imidazo [4, 5-b]-cyclohexane]-5a, 6a-diene). Bulletin of the Korean Chemical Society 2010; 31: 3304-3308
56 Kumar P, Kumar A, Hussain K. Iodobenzene diacetate (IBD) catalyzed an quick oxidative aromatization of Hantzsch-1, 4-dihydropyridines to pyridines under ultrasonic irradiation. Ultrasonics sonochemistry 2012; 19: 729-735
57 Kumar A, Jain S, Parle M. et al. 3-Aryl-1-phenyl-1 H-pyrazole derivatives as new multitarget directed ligands for the treatment of Alzheimer's disease, with acetylcholinesterase and monoamine oxidase inhibitory properties. EXCLI Journal 2013; 12: 1030
58 Kumar A, Goyal R, Kumar S. et al. Estrogenic and anti-Alzheimer’s studies of Zingiber officinalis as well as Amomum subulatum Roxb.: the success story of dry techniques. Med Chem Res 2015; 24: 1089-1097
59 Kumar P, Bhatia R, Khanna R. et al. Synthesis of some benzothiazoles by developing a new protocol using urea nitrate as a catalyst and their antimicrobial activities. Journal of Sulfur Chemistry 2017; 38: 585-596 DOI: 10.1080/17415993.2017.1334781
60 Kumar P, Duhan M, Kadyan K. et al. Synthesis of novel inhibitors of α-amylase based on thiazolidine-4-one skeleton containing pyrazole moiety and their configurational studies. MedChemComm 2017; 8: 1468-1476
61 Kumar P, Kadyan K, Duhan M. et al. Design, synthesis, conformational and molecular docking study of some novel acyl hydrazone based molecular hybrids as antimalarial and antimicrobial agents. Chemistry Central Journal 2017; 11: 115 DOI: 10.1186/s13065-017-0344-7
62 Kumar R, Khanna R, Kumar P. et al. Synthesis of Some 4-Quinolinyl Pyridines and their Antimicrobial and Docking Studies. Journal of Heterocyclic Chemistry 2017; 54: 2740-2747
63 Kumar P, Duhan M, Kadyan K. et al. Multicomponent Synthesis of Some Molecular Hybrid Containing Thiazole Pyrazole as Apoptosis Inducer. Drug Res (Stuttg) 2018; 68: 72-79
64 O'Boyle NM, Banck M, James CA. et al. Open Babel: An open chemical toolbox. J Cheminform 2011; 3: 33
65 Toropov AA, Toropova AP, Benfenati E. et al. The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method. Struct Chem 2013; 24: 1369-1381
66 Toropova AP, Toropov AA. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?. Sci Total Environ 2017; 586: 466-472
67 Toropov AA, Toropova AP. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?. Mutat Res 2017; 819: 31-37
68 Toropova AP, Toropov AA. CORAL: Monte Carlo Method to Predict Endpoints for Medical Chemistry. Mini Reviews in Medicinal Chemistry 2018; 18: 382-391
69 Toropov AA, Carbó-Dorca R, Toropova AP. Index of Ideality of Correlation: new possibilities to validate QSAR: a case study. Struct Chem 2018; 29: 33-38
70 Toropova AP, Toropov AA, Veselinovic JB. et al. QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. Eur J Med Chem 2014; 77: 298-305
71 Toropova AP, Toropov AA, Benfenati E. et al. CORAL: quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats. J Comput Chem 2011; 32: 2727-2733
72 Roy K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin Drug Discov 2007; 2: 1567-1577
73 Roy PP, Leonard JT, Roy K. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems 2008; 90: 31-42
74 Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model 2002; 20: 269-276
75 Ojha PK, Roy K. Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection. Chemometrics and Intelligent Laboratory Systems 2011; 109: 146-161
76 OECD. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models; 2014 https://doi.org/10.1787/9789264085442-en.
77 Roy PP, Roy K. QSAR Studies of CYP2D6 Inhibitor Aryloxypropanolamines Using 2D and 3D Descriptors. Chemical Biology & Drug Design 2009; 73: 442-455
78 Ojha PK, Mitra I, Das RN. et al. Further exploring rm2 metrics for validation of QSPR models. Chemometrics and Intelligent Laboratory Systems. 2011; 107: 194-205

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