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Current Medicinal Chemistry

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ISSN (Online): 1875-533X

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Review Article

In Silico Modelling in the Development of Novel Radiolabelled Peptide Probes

Author(s): Janke Kleynhans, Hendrik Gerhardus Kruger*, Theunis Cloete, Jan Rijn Zeevaart and Thomas Ebenhan

Volume 27, Issue 41, 2020

Page: [7048 - 7063] Pages: 16

DOI: 10.2174/0929867327666200504082256

Price: $65

Abstract

This review describes the usefulness of in silico design approaches in the design of new radiopharmaceuticals, especially peptide-based radiotracers (including peptidomimetics). Although not part of the standard arsenal utilized during radiopharmaceutical design, the use of in silico strategies is steadily increasing in the field of radiochemistry as it contributes to a more rational and scientific approach. The development of new peptide-based radiopharmaceuticals as well as a short introduction to suitable computational approaches are provided in this review. The first section comprises a concise overview of the three most useful computeraided drug design strategies used, namely i) a Ligand-based Approach (LBDD) using pharmacophore modelling, ii) a Structure-based Design Approach (SBDD) using molecular docking strategies and iii) Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) predictions. The second section summarizes the challenges connected to these computer-aided techniques and discusses successful applications of in silico radiopharmaceutical design in peptide-based radiopharmaceutical development, thereby improving the clinical procedure in Nuclear Medicine. Finally, the advances and future potential of in silico modelling as a design strategy is highlighted.

Keywords: Computer-aided drug design, Ligand-based drug design, Structure-based drug design, positron emission tomography (PET), single photon emission tomography (SPECT), Absorption-distribution-metabolism-excretiontoxicity (ADMET)

« Previous Next »
[1]
Lauber, D.T.; Fülöp, A.; Kovács, T.; Szigeti, K.; Máthé, D.; Szijártó, A. State of the art in vivo imaging techniques for laboratory animals. Lab. Anim., 2017, 51(5), 465-478.
[http://dx.doi.org/10.1177/0023677217695852] [PMID: 28948893]
[2]
Vanhove, C.; Bankstahl, J.P.; Krämer, S.D.; Visser, E.; Belcari, N.; Vandenberghe, S. Accurate molecular imaging of small animals taking into account animal models, handling, anaesthesia, quality control and imaging system performance. EJNMMI Phys., 2015, 2(1), 31.
[http://dx.doi.org/10.1186/s40658-015-0135-y] [PMID: 26560138]
[3]
Osborne, D.R.; Kuntner, C.; Berr, S.; Stout, D. Guidance for efficient small animal imaging quality control. Mol. Imaging Biol., 2017, 19(4), 485-498.
[http://dx.doi.org/10.1007/s11307-016-1012-3] [PMID: 27738785]
[4]
Zaidi, H. Molecular Imaging of Small Animals: Instrumentation and Applications, 1st ed; Springer New York, 2014.
[http://dx.doi.org/10.1007/978-1-4939-0894-3]
[5]
Koba, W.; Jelicks, L.A.; Fine, E.J. MicroPET/SPECT/CT imaging of small animal models of disease. Am. J. Pathol., 2013, 182(2), 319-324.
[http://dx.doi.org/10.1016/j.ajpath.2012.09.025] [PMID: 23219729]
[6]
Sandhu, G.S.; Solorio, L.; Broome, A-M.; Salem, N.; Kolthammer, J.; Shah, T.; Shah, T.; Flask, C.; Duerk, J.L. Whole animal imaging. Wiley Interdiscip. Rev. Syst. Biol. Med., 2010, 2(4), 398-421.
[http://dx.doi.org/10.1002/wsbm.71] [PMID: 20836038]
[7]
Jelicks, L.A.; Lisanti, M.P.; Machado, F.S.; Weiss, L.M.; Tanowitz, H.B.; Desruisseaux, M.S. Imaging of small-animal models of infectious diseases. Am. J. Pathol., 2013, 182(2), 296-304.
[http://dx.doi.org/10.1016/j.ajpath.2012.09.026] [PMID: 23201133]
[8]
Golestani, R.; Wu, C.; Tio, R.A.; Zeebregts, C.J.; Petrov, A.D.; Beekman, F.J.; Dierckx, R.A.J.O.; Boersma, H.H.; Slart, R.H.J.A. Small-animal SPECT and SPECT/CT: application in cardiovascular research. Eur. J. Nucl. Med. Mol. Imaging, 2010, 37(9), 1766-1777.
[http://dx.doi.org/10.1007/s00259-009-1321-8] [PMID: 20069298]
[9]
Zimmer, E.R.; Parent, M.J.; Cuello, A.C.; Gauthier, S.; Rosa-Neto, P. MicroPET imaging and transgenic models: a blueprint for Alz-heimer’s disease clinical research. Trends Neurosci., 2014, 37(11), 629-641.
[http://dx.doi.org/10.1016/j.tins.2014.07.002] [PMID: 25151336]
[10]
Jang, B-S. MicroSPECT and MicroPET imaging of small animals for drug development. Toxicol. Res., 2013, 29(1), 1-6.
[http://dx.doi.org/10.5487/TR.2013.29.1.001] [PMID: 24278622]
[11]
Doke, S.K.; Dhawale, S.C. Alternatives to animal testing: a review. Saudi Pharm. J., 2015, 23(3), 223-229.
[http://dx.doi.org/10.1016/j.jsps.2013.11.002] [PMID: 26106269]
[12]
Jean-Quartier, C.; Jeanquartier, F.; Jurisica, I.; Holzinger, A. In silico cancer research towards 3R. BMC Cancer, 2018, 18(1), 408.
[http://dx.doi.org/10.1186/s12885-018-4302-0] [PMID: 29649981]
[13]
Chen, K.; Chen, X. Design and development of molecular imaging probes. Curr. Top. Med. Chem., 2010, 10(12), 1227-1236.
[http://dx.doi.org/10.2174/156802610791384225] [PMID: 20388106]
[14]
Boudreau, R.J.; Efange, S.M. Computer-aided radiopharmaceutical design. Invest. Radiol., 1992, 27(8), 653-658.
[http://dx.doi.org/10.1097/00004424-199208000-00017] [PMID: 1428744]
[15]
Vermeulen, K.; Vandamme, M.; Bormans, G.; Cleeren, F. Design and challenges of radiopharmaceuticals. Semin. Nucl. Med., 2019, 49(5), 339-356.
[http://dx.doi.org/10.1053/j.semnuclmed.2019.07.001] [PMID: 31470930]
[16]
George, G.P.C.; Pisaneschi, F.; Nguyen, Q-D.; Aboagye, E.O. Positron emission tomographic imaging of CXCR4 in cancer: challenges and promises. Mol. Imaging, 2014, 13, 1-19.https://doi.org/10.2310%2F7290.2014.00041
[PMID: 25341373]
[17]
Tornesello, A.L.; Buonaguro, L.; Tornesello, M.L.; Buonaguro, F.M. New insights in the design of bioactive peptides and chelating agents for imaging and therapy in oncology. Molecules, 2017, 22(8), 1282.
[http://dx.doi.org/10.3390/molecules22081282] [PMID: 28767081]
[18]
Fani, M.; Maecke, H.R. Radiopharmaceutical development of radiolabelled peptides. Eur. J. Nucl. Med. Mol. Imaging, 2012, 39(Suppl. 1), S11-S30.
[http://dx.doi.org/10.1007/s00259-011-2001-z] [PMID: 22388624]
[19]
Piñero, J.; Furlong, L.I.; Sanz, F. In silico models in drug development: where we are. Curr. Opin. Pharmacol., 2018, 42, 111-121.
[http://dx.doi.org/10.1016/j.coph.2018.08.007] [PMID: 30205360]
[20]
Geldenhuys, W.J.; Gaasch, K.E.; Watson, M.; Allen, D.D.; Van der Schyf, C.J. Optimizing the use of open-source software applications in drug discovery. Drug Discov. Today, 2006, 11(3-4), 127-132.
[http://dx.doi.org/10.1016/S1359-6446(05)03692-5] [PMID: 16533710]
[21]
Honarparvar, B.; Govender, T.; Maguire, G.E.M.; Soliman, M.E.; Kruger, H.G. Integrated approach to structure-based enzymatic drug design: molecular modeling, spectroscopy, and experimental bioactivity. Chem. Rev., 2014, 114(1), 493-537.
[http://dx.doi.org/10.1021/cr300314q] [PMID: 24024775]
[22]
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171(2), 165-176.
[http://dx.doi.org/10.1016/j.cbi.2006.12.006] [PMID: 17229415]
[23]
Okarvi, S.M.; Maecke, H.R. 17- Radiolabeled peptides in medical imaging. Peptide Application in Biomedicine, Biotechnology and Bioengineering; Koutsopoulos, S., Ed.; Elsevier Science B.V.: Amsterdam, 2018, pp. 431-438.
[http://dx.doi.org/10.1016/B978-0-08-100736-5.00019-3]
[24]
Lau, J.L.; Dunn, M.K. Therapeutic peptides: Historical perspectives, current development trends, and future directions. Bioorg. Med. Chem., 2018, 26(10), 2700-2707.
[http://dx.doi.org/10.1016/j.bmc.2017.06.052] [PMID: 28720325]
[25]
Fischman, A.J.; Babich, J.W.; Strauss, H.W. A ticket to ride: peptide radiopharmaceuticals. J. Nucl. Med., 1993, 34(12), 2253-2263.
[PMID: 8254420]
[26]
Fani, M.; Maecke, H.R.; Okarvi, S.M. Radiolabeled peptides: valuable tools for the detection and treatment of cancer. Theranostics, 2012, 2(5), 481-501.
[http://dx.doi.org/10.7150/thno.4024] [PMID: 22737187]
[27]
Sun, X.; Li, Y.; Liu, T.; Li, Z.; Zhang, X.; Chen, X. Peptide-based imaging agents for cancer detection. Adv. Drug Deliv. Rev., 2017, 110-111, 38-51.
[http://dx.doi.org/10.1016/j.addr.2016.06.007] [PMID: 27327937]
[28]
Okarvi, S.M. Peptide-based radiopharmaceuticals: future tools for diagnostic imaging of cancers and other diseases. Med. Res. Rev., 2004, 24(3), 357-397.
[http://dx.doi.org/10.1002/med.20002] [PMID: 14994368]
[29]
Makhouri, F.R.; Ghasemi, J.B. Combating diseases with computational strategies used for drug design and discovery. Curr. Top. Med. Chem., 2018, 18(32), 2743-2773.
[http://dx.doi.org/10.2174/1568026619666190121125106] [PMID: 30663568]
[30]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W. Jr. Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[31]
Wagh, N.K.; Zhou, Z.; Ogbomo, S.M.; Shi, W.; Brusnahan, S.K.; Garrison, J.C. Development of hypoxia enhanced 111In-labeled Bombesin conjugates: design, synthesis, and in vitro evaluation in PC-3 human prostate cancer. Bioconjug. Chem., 2012, 23(3), 527-537.
[http://dx.doi.org/10.1021/bc200600w] [PMID: 22296619]
[32]
Zeng, Y.; Ma, J.; Zhan, Y.; Xu, X.; Zeng, Q.; Liang, J.; Chen, X. Hypoxia-activated prodrugs and redox-responsive nanocarriers. Int. J. Nanomedicine, 2018, 13, 6551-6574.
[http://dx.doi.org/10.2147/IJN.S173431] [PMID: 30425475]
[33]
Reischl, G. Special issue: targets, tracers and translation novel radiopharmaceuticals boost nuclear medicine. Pharmaceuticals (Basel), 2019, 12(3), 111.
[http://dx.doi.org/10.3390/ph12030111] [PMID: 31323760]
[34]
Hori, H.; Nagasawa, H.; Uto, Y.; Ohkura, K.; Kirk, K.L.; Uehara, Y.; Shimamura, M. Design of hypoxia-targeting protein tyrosine kinase inhibitor using an innovative pharmacophore 2-methylene-4-cyclopentene-1,3-dione. Biochim. Biophys. Acta, 2004, 1697(1-2), 29-38.
[http://dx.doi.org/10.1016/j.bbapap.2003.11.011] [PMID: 15023348]
[35]
Rhenukadevi, J.; Nandhinidevi, G.; Bavanilatha, M.; Tharani, H.; Sathiyabama, R.; Vasumathi, S. Pharmacophore modelling of Bras-sicacea members as potent HIF (hypox inducible factor) inhibitors involved in cancer angiogenesis. Pharmacogn. J., 2018, 10(4), 798-802.
[http://dx.doi.org/10.5530/pj.2018.4.135]
[36]
Lu, X.; Yang, H.; Chen, Y.; Li, Q.; He, S-Y.; Jiang, X.; Feng, F.; Qu, W.; Sun, H. Sun, H. The development of pharmacophore mod-elling: generation and recent applications in drug discovery. Curr. Pharm. Des., 2018, 24(29), 3424-3439.
[http://dx.doi.org/10.2174/1381612824666180810162944] [PMID: 30101699]
[37]
Qing, X.; Lee, X.Y.; Tame, J.R.H.; Zhang, K.Y.J.; Maeyr, M.D.; Voet, A.R.D. Pharmacophore modelling: advances, limitations and current utility in drug discovery. J. Receptor Ligand Channel Res., 2014, 7, 81-92.
[http://dx.doi.org/10.2147/JRLCR.S46843 ]
[38]
Gupta, N.; Sitwala, N.; Patel, K. Pharmacophore modelling, validation, 3D virtual screening, docking, design and in silico ADMET simulation study of histone deacteylase class-1 inhibitors. Med. Chem. Res., 2014, 32(11), 4853-4864.
[http://dx.doi.org/10.1007/s00044-014-1057-2]
[39]
Pal, S.; Kumar, V.; Kundu, B.; Bhattacharya, D.; Preethy, N.; Reddy, M.P.; Talukdar, A. Ligand-based pharmacophore modelling, virtual screening and molecular docking studies for discovery of potential Topoisomerase I inhibitors. Comput. Struct. Biotechnol. J., 2019, 17, 291-310.
[http://dx.doi.org/10.1016/j.csbj.2019.02.006] [PMID: 30867893]
[40]
Sivashanmugam, M. K N, S.; v, U. Virtual screening of natural inhibitors targeting ornithine decarboxylase with pharmacophore scaf-folding of DFMO and validation by molecular dynamics simulation studies. J. Biomol. Struct. Dyn., 2019, 37(3), 766-780.
[http://dx.doi.org/10.1080/07391102.2018.1439772] [PMID: 29436980]
[41]
Lee, Y.H.; Yi, G.S. Prediction of novel anoctamin1 (ANO1) inhibitors using 3D-QSAR pharmacophore modeling and molecular docking. Int. J. Mol. Sci., 2018, 19(10), 1-18.
[http://dx.doi.org/10.3390/ijms19103204] [PMID: 30336555]
[42]
Gupta, A.K.; Varshney, K.; Saxena, A.K. Toward the identification of a reliable 3D QSAR pharmacophore model for the CCK2 receptor antagonism. J. Chem. Inf. Model., 2012, 52(5), 1376-1390.
[http://dx.doi.org/10.1021/ci300094e] [PMID: 22530718]
[43]
Goodarzi, M.; Dejaegher, B.; Vander Heyden, Y. Feature selection methods in QSAR studies. J. AOAC Int., 2012, 95(3), 636-651.
[http://dx.doi.org/10.5740/jaoacint.SGE_Goodarzi] [PMID: 22816254]
[44]
Koutsoukas, A.; Simms, B.; Kirchmair, J.; Bond, P.J.; Whitmore, A.V.; Zimmer, S.; Young, M.P.; Jenkins, J.L.; Glick, M.; Glen, R.C.; Bender, A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J. Proteomics, 2011, 74(12), 2554-2574.
[http://dx.doi.org/10.1016/j.jprot.2011.05.011] [PMID: 21621023]
[45]
Yu, W.; MacKerell, A.D. Jr.Jr. Computer-aided drug design models. Methods Mol. Biol., 2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5] [PMID: 27873247]
[46]
Yousefienjad, S.; Hemmateenejad, B. Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom. Intell. Lab. Syst., 2015, 149, 177-204.
[http://dx.doi.org/10.1016/j.chemolab.2015.06.016]
[47]
Welling, M.M.; Hensbergen, A.W.; Bunschoten, A.; Velders, A.H.; Roestenberg, M.; Van Leeuwen, F.W.B. An updated on radiotracer development for molecular imaging of bacterial infections. Clin. Transl. Imaging, 2019, 7(2), 105-124.
[http://dx.doi.org/10.1007/s40336-019-00317-4]
[48]
Sood, D.; Kumar, N.; Singh, A.; Sakharkar, M.K.; Tomar, V.; Chandra, R. Antibacterial and pharmacological evaluation of fluoro-quinolones: a chemoinformatics approach. Genomics Inform., 2018, 16(3), 44-51.
[http://dx.doi.org/10.5808/GI.2018.16.3.44] [PMID: 30309202]
[49]
Quadir, M.A.; Wattoo, F.H.; Yaseen, M.; Atta, S.; Wattoo, M.H.S.; Ahmad, S.A.; Gulzar, A. In-vitro binding assay study of 99mTc-fluoroquinolones with E.coli, Salmonella and Ps. Aeruginosa. Alexandria. J. Med., 2015, 51(1), 47-52.
[http://dx.doi.org/10.1016/j.ajme.2014.09.004]
[50]
Meng, X-Y.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 2011, 7(2), 146-157.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[51]
Grinter, S.Z.; Zou, X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules, 2014, 19(7), 10150-10176.
[http://dx.doi.org/10.3390/molecules190710150] [PMID: 25019558]
[52]
Chen, H.; Lyne, P.D.; Giordanetto, F.; Lovell, T.; Li, J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model., 2006, 46(1), 401-415.
[http://dx.doi.org/10.1021/ci0503255] [PMID: 16426074]
[53]
Hevener, K.E.; Zhao, W.; Ball, D.M.; Babaoglu, K.; Qi, J.; White, S.W.; Lee, R.E. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J. Chem. Inf. Model., 2009, 49(2), 444-460.
[http://dx.doi.org/10.1021/ci800293n] [PMID: 19434845]
[54]
Cole, J.C.; Murray, C.W.; Nissink, J.W.M.; Taylor, R.D.; Taylor, R. Comparing protein-ligand docking programs is difficult. Proteins, 2005, 60(3), 325-332.
[http://dx.doi.org/10.1002/prot.20497] [PMID: 15937897]
[55]
Batool, M.; Ahmad, B.; Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci., 2019, 20(11), 2783.
[http://dx.doi.org/10.3390/ijms20112783] [PMID: 31174387]
[56]
Triballeau, N.; Acher, F.; Brabet, I.; Pin, J.P.; Bertrand, H.O. Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem., 2005, 48(7), 2534-2547.
[http://dx.doi.org/10.1021/jm049092j] [PMID: 15801843]
[57]
Braga, R.C.; Andrade, C.H. Assessing the performance of 3D pharmacophore models in virtual screening: how good are they? Curr. Top. Med. Chem., 2013, 13(9), 1127-1138.
[http://dx.doi.org/10.2174/1568026611313090010] [PMID: 23651486]
[58]
Vyas, V.K.; Ukawala, R.D.; Ghate, M.; Chintha, C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J. Pharm. Sci., 2012, 74(1), 1-17.
[http://dx.doi.org/10.4103/0250-474X.102537] [PMID: 23204616]
[59]
Muhammed, M.T.; Aki-Yalcin, E. Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem. Biol. Drug Des., 2019, 93(1), 12-20.
[http://dx.doi.org/10.1111/cbdd.13388] [PMID: 30187647]
[60]
Wedemeyer, M.J.; Mueller, B.K.; Bender, B.J.; Meiler, J.; Volkman, B.F. Modeling the complete chemokine-receptor interaction. Methods Cell Biol., 2019, 149, 289-314.
[http://dx.doi.org/10.1016/bs.mcb.2018.09.005] [PMID: 30616825]
[61]
Fakhar, Z.; Naiker, S.; Alves, C.N.; Govender, T.; Maguire, G.E.M.; Lameira, J.; Lamichhane, G.; Kruger, H.G.; Honarparvar, B. A comparative modeling and molecular docking study on Mycobacterium tuberculosis targets involved in peptidoglycan biosynthesis. J. Biomol. Struct. Dyn., 2016, 34(11), 2399-2417.
[http://dx.doi.org/10.1080/07391102.2015.1117397] [PMID: 26612108]
[62]
Davis, M.I.; Bennett, M.J.; Thomas, L.M.; Bjorkman, P.J. Crystal structure of prostate-specific membrane antigen, a tumor marker and peptidase. Proc. Natl. Acad. Sci. USA, 2005, 102(17), 5981-5986.
[http://dx.doi.org/10.1073/pnas.0502101102] [PMID: 15837926]
[63]
Wu, B.; Chien, E.Y.T.; Mol, C.D.; Fenalti, G.; Liu, W.; Katritch, V.; Abagyan, R.; Brooun, A.; Wells, P.; Bi, F.C.; Hamel, D.J.; Kuhn, P.; Handel, T.M.; Cherezov, V.; Stevens, R.C. Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide an-tagonists. Science, 2010, 330(6007), 1066-1071.
[http://dx.doi.org/10.1126/science.1194396] [PMID: 20929726]
[64]
Li, J.; Fukase, Y.; Shang, Y.; Zou, W.; Munoz-Felix, ; Buitrago, L.; van, Agthoven; Zhang, Y.; Hara, R.; Tanaka, Y.; Okamoto, R.; Yasui, T.; Nakahata, T.; Imaeda, T.; Aso, K.; Zhou, Y.; Locuson, C.; Nesic, D.; Duggan, M.; Takagi, J.; Vaughan, RD.; Walz, T.; Hodivala-Dilke, K.; Teitelbaum, SL.; Arnaout, MA.; Filizola, M.; Foley, MA; Coller, B.S. Integrin AlphaVBeta3 ectodomain bound to antagonist TDI-4161. Acs Pharmacol. Transl. Sci., 2019, 2, 387-40.
[http://dx.doi.org/10.2210/pdb6mk0/pdb]
[65]
Dong, X.; Mi, L-Z.; Zhu, J.; Wang, W.; Hu, P.; Luo, B-H.; Springer, T.A. α(V)β(3) integrin crystal structures and their functional implications. Biochemistry, 2012, 51(44), 8814-8828.
[http://dx.doi.org/10.1021/bi300734n] [PMID: 23106217]
[66]
Muller, Y.A.; Li, B.; Christinger, H.W.; Wells, J.A.; Cunningham, B.C.; de Vos, A.M. Vascular endothelial growth factor: crystal structure and functional mapping of the kinase domain receptor binding site. Proc. Natl. Acad. Sci. USA, 1997, 94(14), 7192-7197.
[http://dx.doi.org/10.1073/pnas.94.14.7192] [PMID: 9207067]
[67]
Schöppe, J.; Ehrenmann, J.; Klenk, C.; Rucktooa, P.; Schütz, M.; Doré, A.S.; Plückthun, A. Crystal structures of the human neurokinin 1 receptor in complex with clinically used antagonists. Nat. Commun., 2019, 10(1), 17.
[http://dx.doi.org/10.1038/s41467-018-07939-8] [PMID: 30604743]
[68]
Sung, M-T.; Lai, Y-T.; Huang, C-Y.; Chou, L-Y.; Shih, H-W.; Cheng, W-C.; Wong, C-H.; Ma, C. Crystal structure of the mem-brane-bound bifunctional transglycosylase PBP1b from Escherichia coli. Proc. Natl. Acad. Sci. USA, 2009, 106(22), 8824-8829.
[http://dx.doi.org/10.1073/pnas.0904030106] [PMID: 19458048]
[69]
Kroemer, R.T. Structure-based drug design: docking and scoring. Curr. Protein Pept. Sci., 2007, 8(4), 312-328.
[http://dx.doi.org/10.2174/138920307781369382] [PMID: 17696866]
[70]
de Ruyck, J.; Brysbaert, G.; Blossey, R.; Lensink, M.F. Molecular docking as a popular tool in drug design, an in silico travel. Adv. Appl. Bioinform. Chem., 2016, 9, 1-11.
[http://dx.doi.org/10.2147/AABC.S105289] [PMID: 27390530]
[71]
Khanapur, S.; Paul, S.; Shah, A.; Vatakuti, S.; Koole, M.J.B.; Zijlma, R.; Dierckx, R.A.J.O.; Luurtsema, G.; Garg, P.; Van Waarde, A.; Elsinga, P.H. Development of [18F]-labeled pyrazolo [4,3-e]-1,2,3-triazolo[1,5-c]pyrimidine (SCH442416) analogs for the imaging of cerebral adenosine A2A receptors with `phy. J. Med. Chem., 2015, 57, 6765-6780.
[http://dx.doi.org/10.1021/jm500700y] [PMID: 25061687]
[72]
Zang, L.; Villalobos, A. Strategies to facilitate the discovery of novel CNS PET ligands. EJNMMI Radiopharm. Chem, 2016, 1, 13.
[http://dx.doi.org/10.1186/s41181-016-0016-2] [PMID: 29564389]
[73]
Di, L. Strategic approaches to optimizing peptide ADME properties. AAPS J., 2015, 17(1), 134-143.
[http://dx.doi.org/10.1208/s12248-014-9687-3] [PMID: 25366889]
[74]
Diller, D.J.; Swanson, J.; Bayden, A.S.; Jarosinski, M.; Audie, J. Rational, computer-enabled peptide drug design: principles, methods, applications and future directions. Future Med. Chem., 2015, 7(16), 2173-2193.
[http://dx.doi.org/10.4155/fmc.15.142] [PMID: 26510691]
[75]
Fuchs, J-A.; Grisoni, F.; Kossenjans, M.; Hiss, J.A.; Schneider, G. Lipophilicity prediction of peptides and peptide derivatives by con-sensus machine learning. MedChemComm, 2018, 9(9), 1538-1546.
[http://dx.doi.org/10.1039/C8MD00370J] [PMID: 30288227]
[76]
van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2(3), 192-204.
[http://dx.doi.org/10.1038/nrd1032] [PMID: 12612645]
[77]
Moroy, G.; Martiny, V.Y.; Vayer, P.; Villoutreix, B.O.; Miteva, M.A. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov. Today, 2012, 17(1-2), 44-55.
[http://dx.doi.org/10.1016/j.drudis.2011.10.023] [PMID: 22056716]
[78]
Norinder, U.; Bergström, C.A.S. Prediction of ADMET Properties. ChemMedChem, 2006, 1(9), 920-937.
[http://dx.doi.org/10.1002/cmdc.200600155] [PMID: 16952133]
[79]
Adeowo, F.Y.; Honarparvar, B.; Skelton, A.A. Density functional theory study on the complexation of NOTA as a bifunctional chelator with radiometal ions. J. Phys. Chem. A, 2017, 121(32), 6054-6062.
[http://dx.doi.org/10.1021/acs.jpca.7b01017] [PMID: 28737914]
[80]
von Hacht, J.L.; Erdmann, S.; Niederstadt, L.; Prasad, S.; Wagener, A.; Exner, S.; Beindorff, N.; Brenner, W.; Grötzinger, C. Increasing molar activity by HPLC purification improves 68Ga-DOTA-NAPamide tumor accumulation in a B16/F1 melanoma xenograft model. PLoS One, 2019, 14(6)e0217883
[http://dx.doi.org/10.1371/journal.pone.0217883] [PMID: 31163066]
[81]
Nics, L.; Steiner, B.; Klebermass, E-M.; Philippe, C.; Mitterhauser, M.; Hacker, M.; Wadsak, W. Speed matters to raise molar radioac-tivity: fast HPLC shortens the quality control of C-11 PET-tracers. Nucl. Med. Biol., 2018, 57, 28-33.
[http://dx.doi.org/10.1016/j.nucmedbio.2017.11.006] [PMID: 29227813]
[82]
Jansen, D.R.; Krijger, G.C.; Wagener, J.; Senwedi, R.M.; Gabanamotse, K.; Kgadiete, M.; Kolar, Z.I.; Zeevaart, J.R. Blood plasma model predictions for the proposed bone-seeking radiopharmaceutical [(117m)Sn]Sn(IV)-N,N′,N′-trimethylenephosphonate-poly(ethyleneimine). J. Inorg. Biochem., 2009, 103(9), 1265-1272.
[http://dx.doi.org/10.1016/j.jinorgbio.2009.07.007] [PMID: 19665234]
[83]
Zeevaart, J.R.; Jarvis, N.V.; Louw, W.K.A.; Jackson, G.E.; Cukrowski, I.; Mouton, C.J. Metal-ion speciation in blood plasma incorpo-rating the bisphosphonate, 1-hydroxy-4-aminopropilydenediphosphonate (APD), in therapeutic radiopharmaceuticals. J. Inorg. Biochem., 1999, 73(4), 265-272.
[http://dx.doi.org/10.1016/S0162-0134(99)00027-6] [PMID: 10376350]
[84]
Price, E.W.; Orvig, C. Matching chelators to radiometals for radiopharmaceuticals. Chem. Soc. Rev., 2014, 43(1), 260-290.
[http://dx.doi.org/10.1039/C3CS60304K] [PMID: 24173525]
[85]
Gniazdowska, E.; Koźmiński, P.; Halik, P.; Bajda, M.; Czarnecka, K.; Mikiciuk-Olasik, E.; Masłowska, K.; Rogulski, Z.; Cheda, Ł.; Kilian, K.; Szymański, P. Synthesis, physicochemical and biological evaluation of tacrine derivative labeled with technetium-99m and gallium-68 as a prospective diagnostic tool for early diagnosis of Alzheimer’s disease. Bioorg. Chem., 2019, 91103136
[http://dx.doi.org/10.1016/j.bioorg.2019.103136] [PMID: 31374521]
[86]
Motaleb, M.A.; El-Safoury, D.M.; Abd-Alla, W.H.; Awad, G.A.S.; Sakr, T.M. Radiosynthesis, molecular modeling studies and bio-logical evaluation of 99mTc-Ifosfamide complex as a novel probe for solid tumor imaging. Int. J. Radiat. Biol., 2018, 94(12), 1134-1141.
[http://dx.doi.org/10.1080/09553002.2019.1524945] [PMID: 30373490]
[87]
Rashed, H.M.; Ibrahim, I.T.; Motaleb, M.A. 99mTc-hexoprenaline and 131I-dapoxetine: preparation, in silico modelling and biological evaluation as promising lung scintigraphy radiopharmaceuticals. J. Radioanal. Nucl. Chem., 2017, 314(2), 1297-1307.
[http://dx.doi.org/10.1007/s10967-017-5500-y]
[88]
Sanad, M.H.; Ibrahim, A.A. Preparation and biological evaluation for 99mTcN-histamine as a model for brain imaging: in silico study and preclinical evaluation. Radiochim. Acta, 2018, 106(3), 229-238.
[http://dx.doi.org/10.1515/ract-2017-2804]
[89]
Fang, Y.; Wang, D.; Xu, X.; Dava, G.; Liu, J.; Li, X.; Xue, Q.; Wang, H.; Zhang, J.; Zhang, H. Preparation, in vitro and in vivo eval-uation, and molecular dynamics (MD) simulation studies of novel F-18 labeled tumour imaging agents targeting focal adhesion kinase (FAK). RSC Advances, 2018, 8, 10333-10345.
[http://dx.doi.org/10.1039/C8RA00652K]
[90]
Limpachayaporn, P.; Schäfers, M.; Haufe, G. Isatin sulfonamides: potent caspases-3 and -7 inhibitors, and promising PET and SPECT radiotracers for apoptosis imaging. Future Med. Chem., 2015, 7(9), 1173-1196.
[http://dx.doi.org/10.4155/fmc.15.52] [PMID: 26132525]
[91]
Sakr, T.M.; Khedr, M.A.; Rashed, H.M.; Mohamed, M.E. In Silico-based repositioning of phosphinothricin as a novel technetium-99m imaging probe with potential anti-cancer activity. Molecules, 2018, 23(2)E496
[http://dx.doi.org/10.3390/molecules23020496] [PMID: 29473879]
[92]
Cai, Z.; Ouyang, Q.; Zeng, D.; Nguyen, K.N.; Modi, J.; Wang, L.; White, A.G.; Rogers, B.E.; Xie, X.Q.; Anderson, C.J. 64Cu-labeled somatostatin analogues conjugated with cross-bridged phosphonate-based chelators via strain-promoted click chemistry for PET imaging: in silico through in vivo studies. J. Med. Chem., 2014, 57(14), 6019-6029.
[http://dx.doi.org/10.1021/jm500416f] [PMID: 24983404]
[93]
Yang, Y.; Zhang, X.; Cui, M.; Zhang, J.; Guo, Z.; Li, Y.; Zhang, X.; Dai, J.; Liu, B. Preliminary characterization and in vivo studies of structurally identical 18F- and 125I-labeled benzyloxybenzenes for PET/SPECT imaging of ß-amyloid plaques. Sci. Rep., 2015, 5, 12084.
[http://dx.doi.org/10.1038/srep12084] [PMID: 26170205]
[94]
Mindt, T.; Struthers, H.; Garcia-Garayoa, E.; Desbouis, D.; Schibli, R. Strategies for the development of novel tumor targeting techne-tium and rhenium radiopharmaceuticals. Chimia (Aarau), 2007, 61, 725-731.
[http://dx.doi.org/10.2533/chimia.2007.725]
[95]
Khurana, H.; Meena, V.K.; Prakash, S.; Chuttani, K.; Chadha, N.; Jaswal, A.; Dhawan, D.K.; Mishra, A.K.; Hazari, P.P. Preclinical evaluation of a potential GSH Ester based Pet/CT imaging probe DT (GSHMe)2 to detect gamma glutamyl transferase overexpressing tumors. PLoS One, 2015, 10(7)e0134281
[http://dx.doi.org/10.1371/journal.pone.0134281] [PMID: 26221728]
[96]
Watkins, G.A.; Jones, E.F.; Scott Shell, M.; VanBrocklin, H.F.; Pan, M-H.; Hanrahan, S.M.; Feng, J.J.; He, J.; Sounni, N.E.; Dill, K.A.; Contag, C.H.; Coussens, L.M.; Franc, B.L. Development of an optimized activatable MMP-14 targeted SPECT imaging probe. Bioorg. Med. Chem., 2009, 17(2), 653-659.
[http://dx.doi.org/10.1016/j.bmc.2008.11.078] [PMID: 19109023]
[97]
Aalto, K.; Autio, A.; Kiss, E.A.; Elima, K.; Nymalm, Y.; Veres, T.Z.; Marttila-Ichihara, F.; Elovaara, H.; Saanijoki, T.; Crocker, P.R.; Maksimow, M.; Bligt, E.; Salminen, T.A.; Salmi, M.; Roivainen, A.; Jalkanen, S. Siglec-9 is a novel leukocyte ligand for vascular ad-hesion protein-1 and can be used in PET imaging of inflammation and cancer. Blood, 2011, 118(13), 3725-3733.
[http://dx.doi.org/10.1182/blood-2010-09-311076] [PMID: 21821708]
[98]
Lipiński, P.F.J.; Garnuszek, P.; Maurin, M.; Stoll, R.; Metzler-Nolte, N.; Wodyński, A.; Dobrowolski, J.C.; Dudek, M.K.; Orzełowska, M.; Mikołajczak, R. Structural studies on radiopharmaceutical DOTA-minigastrin analogue (CP04) complexes and their interaction with CCK2 receptor. EJNMMI Res., 2018, 8(1), 33.
[http://dx.doi.org/10.1186/s13550-018-0387-3] [PMID: 29663167]
[99]
Banerjee, S.R.; Foss, C.A.; Castanares, M.; Mease, R.C.; Byun, Y.; Fox, J.J.; Hilton, J.; Lupold, S.E.; Kozikowski, A.P.; Pomper, M.G. Synthesis and evaluation of technetium-99m- and rhenium-labeled inhibitors of the prostate-specific membrane antigen (PSMA). J. Med. Chem., 2008, 51(15), 4504-4517.
[http://dx.doi.org/10.1021/jm800111u] [PMID: 18637669]
[100]
Kaul, A.; Tiwari, A.J.; Vasrhney, R.; Mishra, A.K. Synthesis, in silico screening and preclinical evaluation studies of a hexapeptide analogue for its antimicrobial efficacy. RSC Advances, 2015, 5(118), 97180.
[http://dx.doi.org/10.1039/C5RA14936C]

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