Abstract
Background: Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.
Methods: We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.
Results: The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.
Conclusion: Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.
Keywords: Molecular design, peptibody, mimetibody, phage display, biopanning, peptide, peptide-Fc fusion, immunoinformatics.
Graphical Abstract
[1]
Lau, J.L.; Dunn, M.K. Therapeutic peptides: Historical perspec- tives, current development trends, and future directions. Bioorg. Med. Chem., 2017, 26(10), 2700-2707.
[2]
Otvos, L., Jr; Vetter, S.W.; Koladia, M.; Knappe, D.; Schmidt, R.; Ostorhazi, E.; Kovalszky, I.; Bionda, N.; Cudic, P.; Surmacz, E.; Wade, J.D.; Hoffmann, R. The designer leptin antagonist peptide Allo-aca compensates for short serum half-life with very tight bind- ing to the receptor. Amino Acids, 2014, 46(4), 873-882.
[3]
Werle, M.; Bernkop-Schnurch, A. Strategies to improve plasma half life time of peptide and protein drugs. Amino Acids, 2006, 30(4), 351-367.
[4]
Cavaco, M.; Castanho, M.; Neves, V. Peptibodies: An elegant solution for a long-standing problem. Biopolymers, 2017, e23095.
[5]
Shimamoto, G.; Gegg, C.; Boone, T.; Queva, C. Peptibodies: A flexible alternative format to antibodies. MAbs, 2012, 4(5), 586-59.
[6]
McGregor, D.P. Discovering and improving novel peptide thera-peutics. Curr. Opin. Pharmacol., 2008, 8(5), 616-619.
[7]
Sleep, D.; Cameron, J.; Evans, L.R. Albumin as a versatile plat-form for drug half-life extension. Biochim. Biophys. Acta, 2013, 1830(12), 5526-5534.
[8]
Wu, B.; Lewis, L.D.; Harvey, R.D.; Rasmussen, E.; Gamelin, E.; Sun, Y.N.; Friberg, G.; Koyner, J.L.; Dowlati, A.; Maitland, M.L. A Pharmacokinetic and Safety Study of Trebananib, an Fc-Fusion Peptibody, in Patients With Advanced Solid Tumors and Varying Degrees of Renal Dysfunction. Clin. Pharmacol. Ther., 2017, 102(2), 313-320.
[9]
Wu, B.; Sun, Y.N. Pharmacokinetics of Peptide-Fc fusion proteins. J. Pharm. Sci., 2014, 103(1), 53-64.
[10]
Nichol, J.L. AMG 531: An investigational thrombopoiesis-stimulating peptibody. Pediatr. Blood Cancer, 2006, 47(5)(Suppl.), 723-725.
[11]
Bugelski, P.J.; Capocasale, R.J.; Makropoulos, D.; Marshall, D.; Fisher, P.W.; Lu, J.; Achuthanandam, R.; Spinka-Doms, T.; Kwok, D.; Graden, D.; Volk, A.; Nesspor, T.; James, I.E.; Huang, C. CNTO 530: molecular pharmacology in human UT-7EPO cells and pharmacokinetics and pharmacodynamics in mice. J. Biotechnol., 2008, 134(1-2), 171-180.
[12]
Huang, C. Receptor-Fc fusion therapeutics, traps, and MIMETI- BODY technology. Curr. Opin. Biotechnol., 2009, 20(6), 692-699.
[13]
Marquardt, A.; Muyldermans, S.; Przybylski, M. A synthetic camel anti-lysozyme peptide antibody (peptibody) with flexible loop structure identified by high-resolution affinity mass spec- trometry. Chemistry, 2006, 12(7), 1915-1923.
[14]
Szynol, A.; de Haard, J.J.; Veerman, E.C.; de Soet, J.J.; van Nieuw Amerongen, A.V. Design of a peptibody consisting of the antimi- crobial peptide dhvar5 and a llama variable heavy-chain antibody fragment. Chem. Biol. Drug Des., 2006, 67(6), 425-431.
[15]
Molineux, G.; Newland, A. Development of romiplostim for the treatment of patients with chronic immune thrombocytopenia: from bench to bedside. Br. J. Haematol., 2010, 150(1), 9-20.
[16]
Scheen, A.J. Dulaglutide for the treatment of type 2 diabetes. Expert Opin. Biol. Ther., 2017, 17(4), 485-496.
[17]
Lenert, A.; Niewold, T.B.; Lenert, P. Spotlight on blisibimod and its potential in the treatment of systemic lupus erythematosus: evi- dence to date. Drug Des. Devel. Ther., 2017, 11, 747-757.
[18]
Foster, J.S.; Koul-Tiwari, R.; Williams, A.; Martin, E.B.; Richey, T.; Stuckey, A.; Macy, S.; Kennel, S.J.; Wall, J.S. Preliminary
characterization of a novel peptide-Fc-fusion construct for targeting
amyloid deposits. Amyloid., 2017, 24, (sup1), 26-27.
[19]
Zhu, W.; Sun, X.; Zhu, L.; Gan, Y.; Baiwu, R.; Wei, J.; Li, Z.; Li, R.; Sun, J. A Novel BLyS Peptibody Down-Regulates B Cell and T Helper Cell Subsets In vivo and Ameliorates Collagen-Induced Arthritis. Inflammation, 2016, 39(2), 839-848.
[20]
Torchia, J.; Weiskopf, K.; Levy, R. Targeting lymphoma with precision using semisynthetic anti-idiotype peptibodies. Proc. Natl. Acad. Sci. USA, 2016, 113(19), 5376-5381.
[21]
Scheinberg, M.A.; Hislop, C.M.; Martin, R.S. Blisibimod for treatment of systemic lupus erythematosus: with trials you become wiser. Expert Opin. Biol. Ther., 2016, 16(5), 723-733.
[22]
Monk, B.J.; Poveda, A.; Vergote, I.; Raspagliesi, F.; Fujiwara, K.; Bae, D.S.; Oaknin, A.; Ray-Coquard, I.; Provencher, D.M.; Karlan, B.Y.; Lhomme, C.; Richardson, G.; Rincon, D.G.; Coleman, R.L.; Marth, C.; Brize, A.; Fabbro, M.; Redondo, A.; Bamias, A.; Ma, H.; Vogl, F.D.; Bach, B.A.; Oza, A.M. Final results of a phase 3 study of trebananib plus weekly paclitaxel in recurrent ovarian can- cer (TRINOVA-1): Long-term survival, impact of ascites, and pro- gression-free survival-2. Gynecol. Oncol., 2016, 143(1), 27-34.
[23]
Mobergslien, A.; Peng, Q.; Vasovic, V.; Sioud, M. Cancer cell- binding peptide fused Fc domain activates immune effector cells and blocks tumor growth. Oncotarget, 2016, 7(46), 75940-75953.
[24]
SP. D.A.; Mahoney, M.R.; Van Tine, B.A.; Adkins, D.R.; Per-dekamp, M.T.; Condy, M.M.; Luke, J.J.; Hartley, E.W.; Antonescu, C.R.; Tap, W.D.; Schwartz, G.K. Alliance A091103 a phase II study of the angiopoietin 1 and 2 peptibody trebananib for thetreatment of angiosarcoma. Cancer Chemother. Pharmacol., 2015, 75(3), 629-638.
[25]
Sioud, M.; Westby, P.; Olsen, J.K.; Mobergslien, A. Generation of new peptide-Fc fusion proteins that mediate antibody-dependent cellular cytotoxicity against different types of cancer cells. Mol. Ther. Methods Clin. Dev., 2015, 2, 15043.
[26]
Foster, J.S.; Williams, A.D.; Macy, S.; Richey, T.; Stuckey, A.; Wooliver, D.C.; Koul-Tiwari, R.; Martin, E.B.; Kennel, S.J.; Wall, J.S. A peptide-Fc opsonin with pan-amyloid reactivity. Front. Immunol., 2017, 8, 1082.
[27]
Zhou, P.; Wang, C.; Ren, Y.; Yang, C.; Tian, F. Computational peptidology: A new and promising approach to therapeutic peptide design. Curr. Med. Chem., 2013, 20(15), 1985-1996.
[28]
Otvos, L., Jr; Haspinger, E.; La Russa, F.; Maspero, F.; Graziano, P.; Kovalszky, I.; Lovas, S.; Nama, K.; Hoffmann, R.; Knappe, D.; Cassone, M.; Wade, J.; Surmacz, E. Design and development of a peptide-based adiponectin receptor agonist for cancer treatment. BMC Biotechnol., 2011, 11, 90.
[29]
Wu, Z.; Zhou, P.; Li, X.; Wang, H.; Luo, D.; Qiao, H.; Ke, X.; Huang, J. Structural characterization of a recombinant fusion protein by instrumental analysis and molecular modeling. PLoS One, 2013, 8(3), e57642.
[30]
Obexer, R.; Walport, L.J.; Suga, H. Exploring sequence space: Harnessing chemical and biological diversity towards new peptide leads. Curr. Opin. Chem. Biol., 2017, 38, 52-61.
[31]
Ashby, M.; Petkova, A.; Gani, J.; Mikut, R.; Hilpert, K. Use of Peptide Libraries for Identification and optimization of novel antimicrobial peptides. Curr. Top. Med. Chem., 2017, 17(5), 537-553.
[32]
Cwirla, S.E.; Balasubramanian, P.; Duffin, D.J.; Wagstrom, C.R.; Gates, C.M.; Singer, S.C.; Davis, A.M.; Tansik, R.L.; Mattheakis, L.C.; Boytos, C.M.; Schatz, P.J.; Baccanari, D.P.; Wrighton, N.C.; Barrett, R.W.; Dower, W.J. Peptide agonist of the thrombopoietin receptor as potent as the natural cytokine. Science, 1997, 276(5319), 1696-1699.
[33]
Zhang, Y.; He, B.; Liu, K.; Ning, L.; Luo, D.; Xu, K.; Zhu, W.; Wu, Z.; Huang, J.; Xu, X. A novel peptide specifically binding to VEGF receptor suppresses angiogenesis in vitro and in vivo. Signal Transduct. Target. Ther., 2017, 2, 17010.
[34]
Li, T.; Tu, W.; Liu, Y.; Zhou, P.; Cai, K.; Li, Z.; Liu, X.; Ning, N.; Huang, J.; Wang, S.; Huang, J.; Wang, H. A potential therapeutic peptide-based neutralizer that potently inhibits Shiga toxin 2 in vitro and in vivo. Sci. Rep., 2016, 6, 21837.
[35]
He, B.; Mao, C.; Ru, B.; Han, H.; Zhou, P.; Huang, J. Epitope mapping of metuximab on CD147 using phage display and molecular docking. Comput. Math. Methods Med., 2013, 2013, 983829.
[36]
Ning, L.; Li, Z.; Bai, Z.; Hou, S.; He, B.; Huang, J.; Zhou, P. Computational design of antiangiogenic peptibody by fusing human IgG1 Fc fragment and HRH peptide: Structural modeling, energetic analysis, and dynamics simulation of its binding potency to VEGF Receptor. Int. J. Biol. Sci., 2018, 14(8), 930-937.
[37]
Menendez, A.; Scott, J.K. The nature of target-unrelated peptides recovered in the screening of phage-displayed random peptide libraries with antibodies. Anal. Biochem., 2005, 336(2), 145-157.
[38]
Vodnik, M.; Zager, U.; Strukelj, B.; Lunder, M. Phage display: selecting straws instead of a needle from a haystack. Molecules, 2011, 16(1), 790-817.
[39]
Zade, H.M.; Keshavarz, R.; Shekarabi, H.S.Z.; Bakhshinejad, B. Biased selection of propagation-related TUPs from phage display peptide libraries. Amino Acids, 2017, 49(8), 1293-1308.
[40]
Derda, R.; Tang, S.K.; Li, S.C.; Ng, S.; Matochko, W.; Jafari, M.R. Diversity of phage-displayed libraries of peptides during panning and amplification. Molecules, 2011, 16(2), 1776-1803.
[41]
Huang, J.; Ru, B.; Dai, P. Bioinformatics resources and tools for phage display. Molecules, 2011, 16(1), 694-709.
[42]
He, B.; Chai, G.; Duan, Y.; Yan, Z.; Qiu, L.; Zhang, H.; Liu, Z.; He, Q.; Han, K.; Ru, B.; Guo, F.B.; Ding, H.; Lin, H.; Wang, X.; Rao, N.; Zhou, P.; Huang, J. BDB: biopanning data bank. Nucleic Acids Res., 2016, 44(D1), D1127-D1132.
[43]
Huang, J.; Ru, B.; Zhu, P.; Nie, F.; Yang, J.; Wang, X.; Dai, P.; Lin, H.; Guo, F.B.; Rao, N. MimoDB 2.0: A mimotope database and beyond. Nucleic Acids Res., 2012, 40, D271-D277.
[44]
Ru, B.; Huang, J.; Dai, P.; Li, S.; Xia, Z.; Ding, H.; Lin, H.; Guo, F.; Wang, X.; Mimo, D.B. A new repository for mimotope data derived from phage display technology. Molecules, 2010, 15(11), 8279-8288.
[45]
Huang, J.; Ru, B.; Li, S.; Lin, H.; Guo, F.B. SAROTUP: scanner and reporter of target-unrelated peptides. J. Biomed. Biotechnol., 2010, 2010, 101932.
[46]
Ru, B.; Hoen, P.A.; Nie, F.; Lin, H.; Guo, F.B.; Huang, J. PhD7Faster: Predicting clones propagating faster from the Ph.D.-7 phage display peptide library. J. Bioinform. Comput. Biol., 2014, 12(1), 1450005.
[47]
He, B.; Kang, J.; Ru, B.; Ding, H.; Zhou, P.; Huang, J. SABinder: A web service for predicting streptavidin-binding peptides. BioMed Res. Int., 2016, 2016, 9175143.
[48]
Li, N.; Kang, J.; Jiang, L.; He, B.; Lin, H.; Huang, J. PSBinder: A
web service for predicting polystyrene surface-binding peptides. Biomed. Res. Int, 2017, 2017, (2017), 5.
[49]
Huang, J.; Derda, R.; Huang, Y. Phage display informatics. Comput. Math. Methods Med., 2013, 2013, 698395.
[50]
Vanhee, P.; van der Sloot, A.M.; Verschueren, E.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Computational design of peptide ligands. Trends Biotechnol., 2011, 29(5), 231-239.
[51]
Zhou, P.; Zhang, S.; Wang, Y.; Yang, C.; Huang, J. Structural modeling of HLA-B*1502/peptide/carbamazepine/T-cell receptor complex architecture: Implication for the molecular mechanism of carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis. J. Biomol. Struct. Dyn., 2016, 34(8), 1806-1817.
[52]
Yang, C.; Zhang, S.; He, P.; Wang, C.; Huang, J.; Zhou, P. Selfbinding peptides: Folding or binding? J. Chem. Inf. Model., 2015, 55(2), 329-342.
[53]
Zhou, P.; Wang, C.; Tian, F.; Ren, Y.; Yang, C.; Huang, J. Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity. J. Comput. Aided Mol. Des., 2013, 27(1), 67-78.
[54]
Zhou, P.; Huang, J.; Tian, F. Specific noncovalent interactions at protein-ligand interface: Implications for rational drug design. Curr. Med. Chem., 2012, 19(2), 226-238.
[55]
Heurich, M.; Altintas, Z.; Tothill, I.E. Computational design of peptide ligands for ochratoxin A. Toxins (Basel), 2013, 5(6), 1202-1218.
[56]
Sun, J.; Feng, J.; Li, Y.; Shen, B. A novel BLyS antagonist peptide designed based on the 3-D complex structure of BCMA and BLyS. Biochem. Biophys. Res. Commun., 2006, 346(4), 1158-1162.
[57]
Wang, S.H.; Yu, J. Structure-based design for binding peptides in anti-cancer therapy. Biomaterials, 2018, 156, 1-15.
[58]
London, N.; Raveh, B.; Movshovitz-Attias, D.; Schueler-Furman, O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions? Proteins, 2010, 78(15), 3140-3149.
[59]
Sedan, Y.; Marcu, O.; Lyskov, S.; Schueler-Furman, O. Peptiderive server: derive peptide inhibitors from protein-protein interactions. Nucleic Acids Res., 2016, 44(W1), W536-W541.
[60]
Zhao, Y.; Hao, X.; Feng, J.; Shen, B.; Wei, J.; Sun, J. The comparison of BLyS-binding peptides from phage display library and computer-aided design on BLyS-TACI interaction. Int. Immunopharmacol., 2015, 24(2), 219-223.
[61]
Usmani, S.S.; Bedi, G.; Samuel, J.S.; Singh, S.; Kalra, S.; Kumar, P.; Ahuja, A.A.; Sharma, M.; Gautam, A.; Raghava, G.P.S. THPdb: Database of FDA-approved peptide and protein therapeutics. PLoS One, 2017, 12(7), e0181748.
[62]
Liu, S.; Fan, L.; Sun, J.; Lao, X.; Zheng, H. Computational resources and tools for antimicrobial peptides. J. Pept. Sci., 2017, 23(1), 4-12.
[63]
Waghu, F.H.; Barai, R.S.; Gurung, P.; Idicula-Thomas, S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res., 2016, 44(D1), D10941097.
[64]
Agrawal, P.; Bhalla, S.; Usmani, S.S.; Singh, S.; Chaudhary, K.; Raghava, G.P.; Gautam, A. CPPsite 2.0: A repository of experimentally validated cell-penetrating peptides. Nucleic Acids Res., 2016, 44(D1), D1098-D1103.
[65]
Tyagi, A.; Tuknait, A.; Anand, P.; Gupta, S.; Sharma, M.; Mathur, D.; Joshi, A.; Singh, S.; Gautam, A.; Raghava, G.P. CancerPPD: A database of anticancer peptides and proteins. Nucleic Acids Res., 2015, 43, D837-D843.
[66]
Kumar, R.; Chaudhary, K.; Sharma, M.; Nagpal, G.; Chauhan, J.S.; Singh, S.; Gautam, A.; Raghava, G.P. AHTPDB: A comprehensive platform for analysis and presentation of antihypertensive peptides. Nucleic Acids Res., 2015, 43, D956-D962.
[67]
Mehta, D.; Anand, P.; Kumar, V.; Joshi, A.; Mathur, D.; Singh, S.; Tuknait, A.; Chaudhary, K.; Gautam, S.K.; Gautam, A.; Varshney, G.C.; Raghava, G.P. ParaPep: A web resource for experimentally validated antiparasitic peptide sequences and their structures. Database (Oxford), 2014, 2014, pii bau051.
[68]
Gautam, A.; Chaudhary, K.; Singh, S.; Joshi, A.; Anand, P.; Tuknait, A.; Mathur, D.; Varshney, G.C.; Raghava, G.P. Hemolytik: A database of experimentally determined hemolytic and nonhemolytic peptides. Nucleic Acids Res., 2014, 42, D444-D449.
[69]
Novkovic, M.; Simunic, J.; Bojovic, V.; Tossi, A.; Juretic, D. DADP: The database of anuran defense peptides. Bioinformatics, 2012, 28(10), 1406-1407.
[70]
Vijayakumar, S. PTV, L. ACPP: A web server for prediction and design of anti-cancer peptides. Int. J. Pept. Res. Ther., 2015, 21(1), 99-106.
[71]
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7(13), 16895-16909.
[72]
Tang, H.; Su, Z.D.; Wei, H.H.; Chen, W.; Lin, H. Prediction of cell-penetrating peptides with feature selection techniques. Biochem. Biophys. Res. Commun., 2016, 477(1), 150-154.
[73]
Gupta, S.; Sharma, A.K.; Shastri, V.; Madhu, M.K.; Sharma, V.K. Prediction of anti-inflammatory proteins/peptides: An in silico approach. J. Transl. Med., 2017, 15(1), 7.
[74]
Xu, C.; Ge, L.; Zhang, Y.; Dehmer, M.; Gutman, I. Computational prediction of therapeutic peptides based on graph index. J. Biomed. Inform., 2017, 75, 63-69.
[75]
Levin, D.; Golding, B.; Strome, S.E.; Sauna, Z.E. Fc fusion as a platform technology: Potential for modulating immunogenicity. Trends Biotechnol., 2015, 33(1), 27-34.
[76]
Salfeld, J.G. Isotype selection in antibody engineering. Nat. Biotechnol., 2007, 25(12), 1369-1372.
[77]
Saxena, A.; Wu, D. Advances in Therapeutic fc engineering - modulation of IgG-associated effector functions and serum halflife. Front. Immunol., 2016, 7, 580.
[78]
Rodriguez, L.F.; Bustos, R.H.; Zapata, C.D.; Garcia, J.C.; Jauregui, E.; Ashraf, G. Immunogenicity in protein and peptide basedtherapeutics: An overview. Curr. Protein Pept. Sci., 2017, 19(10), 958-971.
[79]
Hermanson, T.; Bennett, C.L.; Macdougall, I.C. Peginesatide for the treatment of anemia due to chronic kidney disease - an unfulfilled promise. Expert Opin. Drug Saf., 2016, 15(10), 1421-1426.
[80]
Koren, E.; De Groot, A.S.; Jawa, V.; Beck, K.D.; Boone, T.; Rivera, D.; Li, L.; Mytych, D.; Koscec, M.; Weeraratne, D.; Swanson, S.; Martin, W. Clinical validation of the “in silico” prediction of immunogenicity of a human recombinant therapeutic protein. Clin. Immunol., 2007, 124(1), 26-32.
[82]
Zhang, L.; Udaka, K.; Mamitsuka, H.; Zhu, S. Toward more accurate pan-specific MHC-peptide binding prediction: A review of current methods and tools. Brief. Bioinform., 2012, 13(3), 350-364.
[83]
Tang, Q.; Nie, F.; Kang, J.; Ding, H.; Zhou, P.; Huang, J. NIEluter: Predicting peptides eluted from HLA class I molecules. J. Immunol. Methods, 2015, 422, 22-27.
[84]
Moise, L.; Gutierrez, A.; Kibria, F.; Martin, R.; Tassone, R.; Liu, R.; Terry, F.; Martin, B.; De Groot, A.S. iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines. Hum. Vaccin. Immunother., 2015, 11(9), 2312-2321.
[85]
Vita, R.; Overton, J.A.; Greenbaum, J.A.; Ponomarenko, J.; Clark, D.; Cantrell, J.R.; Wheeler, D.K.; Gabbard, J.L.; Hix, D.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB) 3.0. Nucleic Acids Res., 2015, 43, D405-D412.
[86]
Andreatta, M.; Nielsen, M. Gapped sequence alignment using artificial neural networks: Application to the MHC class I system. Bioinformatics, 2016, 32(4), 511-517.
[87]
Potocnakova, L.; Bhide, M.; Pulzova, L.B. An introduction to BCell epitope mapping and in silico epitope prediction. J. Immunol. Res., 2016, 2016, 6760830.
Related Books
Localized Micro/Nanocarriers for Programmed and On-Demand Controlled Drug Release
Mixed Polymeric Micelles for Osteosarcoma Therapy: Development and Characterization
A Comprehensive Text Book on Self-emulsifying Drug Delivery Systems
Nanomaterials: Evolution and Advancement towards Therapeutic Drug Delivery (Part II)
Nanomaterials: Evolution and Advancement Towards Therapeutic Drug Delivery (Part I)
Article Metrics
93
16