In Silico Hemostasis Modeling and Prediction

Dmitry Y. Nechipurenko; Aleksey M. Shibeko; Anastasia N. Sveshnikova; Mikhail A. Panteleev

doi:10.1055/a-1213-2117

Hämostaseologie, Table of Contents

Hamostaseologie 2020; 40(04): 524-535
DOI: 10.1055/a-1213-2117

Review Article

In Silico Hemostasis Modeling and Prediction

Dmitry Y. Nechipurenko‡^*

¹Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia

²Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia

³Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia

,

Aleksey M. Shibeko‡^*

²Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia

³Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia

,

Anastasia N. Sveshnikova

¹Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia

²Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia

³Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia

,

Mikhail A. Panteleev

¹Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia

²Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences, Moscow, Russia

³Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia

› Author Affiliations

Abstract

Computational physiology, i.e., reproduction of physiological (and, by extension, pathophysiological) processes in silico, could be considered one of the major goals in computational biology. One might use computers to simulate molecular interactions, enzyme kinetics, gene expression, or whole networks of biochemical reactions, but it is (patho)physiological meaning that is usually the meaningful goal of the research even when a single enzyme is its subject. Although exponential rise in the use of computational and mathematical models in the field of hemostasis and thrombosis began in the 1980s (first for blood coagulation, then for platelet adhesion, and finally for platelet signal transduction), the majority of their successful applications are still focused on simulating the elements of the hemostatic system rather than the total (patho)physiological response in situ. Here we discuss the state of the art, the state of the progress toward the efficient “virtual thrombus formation,” and what one can already get from the existing models.

Keywords

computational biology - mathematical modeling - thrombosis - hemostasis

Full Text

References

References
1 Xu Z, Kamocka M, Alber M, Rosen ED. Computational approaches to studying thrombus development. Arterioscler Thromb Vasc Biol 2011; 31 (03) 500-505
2 Shibeko AM, Panteleev MA. Untangling the complexity of blood coagulation network: use of computational modelling in pharmacology and diagnostics. Brief Bioinform 2016; 17 (03) 429-439
3 Dunster JL, Panteleev MA, Gibbins JM, Sveshnikova AN. Mathematical techniques for understanding platelet regulation and the development of new pharmacological approaches. Methods Mol Biol 2018; 1812: 255-279
4 Belyaev AV, Dunster JL, Gibbins JM, Panteleev MA, Volpert V. Modeling thrombosis in silico: frontiers, challenges, unresolved problems and milestones. Phys Life Rev 2018; 26–27: 57-95
5 Panteleev MA, Andreeva AA, Lobanov AI. Differential drug target selection in blood coagulation: what can we get from computational systems biology models?. Curr Pharm Des 2020; 26 (18) 2109-2115
6 Panteleev MA, Ananyeva NM, Ataullakhanov FI, Saenko EL. Mathematical models of blood coagulation and platelet adhesion: clinical applications. Curr Pharm Des 2007; 13 (14) 1457-1467
7 Ataullakhanov FI, Panteleev MA. Mathematical modeling and computer simulation in blood coagulation. Pathophysiol Haemost Thromb 2005; 34 (2–3): 60-70
8 Nesbitt WS, Mangin P, Salem HH, Jackson SP. The impact of blood rheology on the molecular and cellular events underlying arterial thrombosis. J Mol Med (Berl) 2006; 84 (12) 989-995
9 Swieringa F, Spronk HMH, Heemskerk JWM, van der Meijden PEJ. Integrating platelet and coagulation activation in fibrin clot formation. Res Pract Thromb Haemost 2018; 2 (03) 450-460
10 Panteleev MA, Dashkevich NM, Ataullakhanov FI. Hemostasis and thrombosis beyond biochemistry: roles of geometry, flow and diffusion. Thromb Res 2015; 136 (04) 699-711
11 Kovalenko TA, Panteleev MA, Sveshnikova AN. Substrate delivery mechanism and the role of membrane curvature in factor X activation by extrinsic tenase. J Theor Biol 2017; 435: 125-133
12 Panteleev MA, Saenko EL, Ananyeva NM, Ataullakhanov FI. Kinetics of factor X activation by the membrane-bound complex of factor IXa and factor VIIIa. Biochem J 2004; 381 (Pt 3): 779-794
13 Panteleev MA, Zarnitsina VI, Ataullakhanov FI. Tissue factor pathway inhibitor: a possible mechanism of action. Eur J Biochem 2002; 269 (08) 2016-2031
14 Podoplelova NA, Sveshnikova AN, Kotova YN. et al. Coagulation factors bound to procoagulant platelets concentrate in cap structures to promote clotting. Blood 2016; 128 (13) 1745-1755
15 Podoplelova NA, Sveshnikova AN, Kurasawa JH. et al. Hysteresis-like binding of coagulation factors X/Xa to procoagulant activated platelets and phospholipids results from multistep association and membrane-dependent multimerization. Biochim Biophys Acta 2016; 1858 (06) 1216-1227
16 Terentyeva VA, Sveshnikova AN, Panteleev MA. Kinetics and mechanisms of surface-dependent coagulation factor XII activation. J Theor Biol 2015; 382: 235-243
17 Zakharova NV, Artemenko EO, Podoplelova NA. et al. Platelet surface-associated activation and secretion-mediated inhibition of coagulation factor XII. PLoS One 2015; 10 (02) e0116665
18 Weisel JW, Nagaswami C. Computer modeling of fibrin polymerization kinetics correlated with electron microscope and turbidity observations: clot structure and assembly are kinetically controlled. Biophys J 1992; 63 (01) 111-128
19 Goldsmith HL, Turitto VT. Rheological aspects of thrombosis and haemostasis: basic principles and applications. ICTH-Report--Subcommittee on Rheology of the International Committee on Thrombosis and Haemostasis. Thromb Haemost 1986; 55 (03) 415-435
20 Tokarev AA, Butylin AA, Ermakova EA, Shnol EE, Panasenko GP, Ataullakhanov FI. Finite platelet size could be responsible for platelet margination effect. Biophys J 2011; 101 (08) 1835-1843
21 Czaja B, Gutierrez M, Závodszky G, de Kanter D, Hoekstra A, Eniola-Adefeso O. The influence of red blood cell deformability on hematocrit profiles and platelet margination. PLOS Comput Biol 2020; 16 (03) e1007716
22 Vahidkhah K, Diamond SL, Bagchi P. Platelet dynamics in three-dimensional simulation of whole blood. Biophys J 2014; 106 (11) 2529-2540
23 Tokarev AA, Butylin AA, Ataullakhanov FI. Platelet adhesion from shear blood flow is controlled by near-wall rebounding collisions with erythrocytes. Biophys J 2011; 100 (04) 799-808
24 van Rooij BJM, Závodszky G, Azizi Tarksalooyeh VW, Hoekstra AG. Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer. J R Soc Interface 2019; 16 (159) 20190148
25 Bark Jr DL, Ku DN. Platelet transport rates and binding kinetics at high shear over a thrombus. Biophys J 2013; 105 (02) 502-511
26 Mody NA, Lomakin O, Doggett TA, Diacovo TG, King MR. Mechanics of transient platelet adhesion to von Willebrand factor under flow. Biophys J 2005; 88 (02) 1432-1443
27 Belyaev AV. Long ligands reinforce biological adhesion under shear flow. Phys Rev E 2018; 97 (4–1): 042407
28 Nesbitt WS, Westein E, Tovar-Lopez FJ. et al. A shear gradient-dependent platelet aggregation mechanism drives thrombus formation. Nat Med 2009; 15 (06) 665-673
29 Receveur N, Nechipurenko D, Knapp Y. et al. Shear rate gradients promote a bi-phasic thrombus formation on weak adhesive proteins, such as fibrinogen in a VWF-dependent manner. Haematologica 2019; (e-pub ahead of print)
30 Fu H, Jiang Y, Yang D, Scheiflinger F, Wong WP, Springer TA. Flow-induced elongation of von Willebrand factor precedes tension-dependent activation. Nat Commun 2017; 8 (01) 324
31 Deng W, Wang Y, Druzak SA. et al. A discontinuous autoinhibitory module masks the A1 domain of von Willebrand factor. J Thromb Haemost 2017; 15 (09) 1867-1877
32 Schneider SW, Nuschele S, Wixforth A. et al. Shear-induced unfolding triggers adhesion of von Willebrand factor fibers. Proc Natl Acad Sci U S A 2007; 104 (19) 7899-7903
33 Belyaev AV, Panteleev MA, Ataullakhanov FI. Threshold of microvascular occlusion: injury size defines the thrombosis scenario. Biophys J 2015; 109 (02) 450-456
34 Stalker TJ, Traxler EA, Wu J. et al. Hierarchical organization in the hemostatic response and its relationship to the platelet-signaling network. Blood 2013; 121 (10) 1875-1885
35 Welsh JD, Poventud-Fuentes I, Sampietro S, Diamond SL, Stalker TJ, Brass LF. Hierarchical organization of the hemostatic response to penetrating injuries in the mouse macrovasculature. J Thromb Haemost 2017; 15 (03) 526-537
36 Yazdani A, Li H, Humphrey JD, Karniadakis GE. A general shear-dependent model for thrombus formation. PLOS Comput Biol 2017; 13 (01) e1005291
37 Tosenberger A, Ataullakhanov F, Bessonov N, Panteleev M, Tokarev A, Volpert V. Modelling of thrombus growth in flow with a DPD-PDE method. J Theor Biol 2013; 337: 30-41
38 Babushkina ES, Bessonov NM, Ataullakhanov FI, Panteleev MA. Continuous modeling of arterial platelet thrombus formation using a spatial adsorption equation. PLoS One 2015; 10 (10) e0141068
39 Tomaiuolo M, Stalker TJ, Welsh JD, Diamond SL, Sinno T, Brass LF. A systems approach to hemostasis: 2. Computational analysis of molecular transport in the thrombus microenvironment. Blood 2014; 124 (11) 1816-1823
40 Mirramezani M, Herbig BA, Stalker TJ. et al. Platelet packing density is an independent regulator of the hemostatic response to injury. J Thromb Haemost 2018; 16 (05) 973-983
41 Xu S, Xu Z, Kim OV, Litvinov RI, Weisel JW, Alber M. Model predictions of deformation, embolization and permeability of partially obstructive blood clots under variable shear flow. J R Soc Interface 2017; 14 (136) 14
42 Govindarajan V, Zhu S, Li R. et al. Impact of tissue factor localization on blood clot structure and resistance under venous shear. Biophys J 2018; 114 (04) 978-991
43 Nechipurenko DY, Receveur N, Yakimenko AO. et al. Clot contraction drives the translocation of procoagulant platelets to thrombus surface. Arterioscler Thromb Vasc Biol 2019; 39 (01) 37-47
44 Trifanov P, Kaneva V, Strijhak S. et al. Developing quasi-steady model for studying hemostatic response using supercomputer technologies. Supercomp Front Innovat 2018; 5: 67-72
45 Tosenberger A, Ataullakhanov F, Bessonov N, Panteleev M, Tokarev A, Volpert V. Modelling of platelet-fibrin clot formation in flow with a DPD-PDE method. J Math Biol 2016; 72 (03) 649-681
46 Leiderman K, Fogelson AL. The influence of hindered transport on the development of platelet thrombi under flow. Bull Math Biol 2013; 75 (08) 1255-1283
47 Khanin MA, Semenov VV. A mathematical model of the kinetics of blood coagulation. J Theor Biol 1989; 136 (02) 127-134
48 Sorensen EN, Burgreen GW, Wagner WR, Antaki JF. Computational simulation of platelet deposition and activation: I. Model development and properties. Ann Biomed Eng 1999; 27 (04) 436-448
49 Purvis JE, Chatterjee MS, Brass LF, Diamond SL. A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation. Blood 2008; 112 (10) 4069-4079
50 Sveshnikova AN, Ataullakhanov FI, Panteleev MA. Compartmentalized calcium signaling triggers subpopulation formation upon platelet activation through PAR1. Mol Biosyst 2015; 11 (04) 1052-1060
51 Shepelyuk TO, Panteleev MA, Sveshnikova AN. Computational modeling of quiescent platelet energy metabolism in the context of whole-body glucose turnover. Math Model Nat Phenom 2016; 11: 91-101
52 Obydennyi SI, Artemenko EO, Sveshnikova AN. et al. Mechanisms of increased mitochondria-dependent necrosis in Wiskott-Aldrich syndrome platelets. Haematologica 2020; 105 (04) 1095-1106
53 Shakhidzhanov SS, Shaturny VI, Panteleev MA, Sveshnikova AN. Modulation and pre-amplification of PAR1 signaling by ADP acting via the P2Y12 receptor during platelet subpopulation formation. Biochim Biophys Acta 2015; 1850 (12) 2518-2529
54 Martyanov AA, Balabin FA, Dunster JL, Panteleev MA, Gibbins JM, Sveshnikova AN. Control of platelet CLEC-2-mediated activation by receptor clustering and tyrosine kinase signaling. Biophys J 2020; 118 (11) 2641-2655
55 Sveshnikova AN, Balatskiy AV, Demianova AS. et al. Systems biology insights into the meaning of the platelet's dual-receptor thrombin signaling. J Thromb Haemost 2016; 14 (10) 2045-2057
56 Bergmann FT, Hoops S, Klahn B. et al. COPASI and its applications in biotechnology. J Biotechnol 2017; 261: 215-220
57 Burkhart JM, Vaudel M, Gambaryan S. et al. The first comprehensive and quantitative analysis of human platelet protein composition allows the comparative analysis of structural and functional pathways. Blood 2012; 120 (15) e73-e82
58 Makhoul S, Walter E, Pagel O. et al. Effects of the NO/soluble guanylate cyclase/cGMP system on the functions of human platelets. Nitric Oxide 2018; 76: 71-80
59 Dunster JL, Mazet F, Fry MJ, Gibbins JM, Tindall MJ. Regulation of early steps of GPVI signal transduction by phosphatases: a systems biology approach. PLOS Comput Biol 2015; 11 (11) e1004589
60 Dunster JL, Unsworth AJ, Bye AP. et al. Interspecies differences in protein expression do not impact the spatiotemporal regulation of glycoprotein VI mediated activation. J Thromb Haemost 2020; 18 (02) 485-496
61 Martyanov AA, Morozova DS, Sorokina MA. et al. Heterogeneity of integrin α_IIbβ₃ function in pediatric immune thrombocytopenia revealed by continuous flow cytometry analysis. Int J Mol Sci 2020; 21 (09) 21
62 Obydennyy SI, Sveshnikova AN, Ataullakhanov FI, Panteleev MA. Dynamics of calcium spiking, mitochondrial collapse and phosphatidylserine exposure in platelet subpopulations during activation. J Thromb Haemost 2016; 14 (09) 1867-1881
63 Kotova YN, Ataullakhanov FI, Panteleev MA. Formation of coated platelets is regulated by the dense granule secretion of adenosine 5'diphosphate acting via the P2Y12 receptor. J Thromb Haemost 2008; 6 (09) 1603-1605
64 Flamm MH, Colace TV, Chatterjee MS. et al. Multiscale prediction of patient-specific platelet function under flow. Blood 2012; 120 (01) 190-198
65 Levine SN. Enzyme amplifier kinetics. Science 1966; 152 (3722): 651-653
66 Ratto N, Tokarev A, Chelle P, Tardy-Poncet B, Volpert V. Clustering of thrombin generation test data using a reduced mathematical model of blood coagulation. Acta Biotheor 2020; 68 (01) 21-43
67 Zavyalova EG, Ustinov NB, Kopylov AM. Exploring the efficiency of thrombin inhibitors with a quantitative model of the coagulation cascade. FEBS Lett 2020; 594 (06) 995-1004
68 Link KG, Stobb MT, Sorrells MG. et al. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A. J Thromb Haemost 2020; 18 (02) 306-317
69 Siekmann I, Bjelosevic S, Landman K, Monagle P, Ignjatovic V, Crampin EJ. Mathematical modelling indicates that lower activity of the haemostatic system in neonates is primarily due to lower prothrombin concentration. Sci Rep 2019; 9 (01) 3936
70 Mitrophanov AY, Szlam F, Sniecinski RM, Levy JH, Reifman J. Controlled multifactorial coagulopathy: effects of dilution, hypothermia, and acidosis on thrombin generation in vitro. Anesth Analg 2020; 130 (04) 1063-1076
71 Lo K, Denney WS, Diamond SL. Stochastic modeling of blood coagulation initiation. Pathophysiol Haemost Thromb 2005; 34 (2–3): 80-90
72 Balandina AN, Shibeko AM, Kireev DA. et al. Positive feedback loops for factor V and factor VII activation supply sensitivity to local surface tissue factor density during blood coagulation. Biophys J 2011; 101 (08) 1816-1824
73 Dashkevich NM, Ovanesov MV, Balandina AN. et al. Thrombin activity propagates in space during blood coagulation as an excitation wave. Biophys J 2012; 103 (10) 2233-2240
74 Diamond SL, Anand S. Inner clot diffusion and permeation during fibrinolysis. Biophys J 1993; 65 (06) 2622-2643
75 Panteleev MA, Ovanesov MV, Kireev DA. et al. Spatial propagation and localization of blood coagulation are regulated by intrinsic and protein C pathways, respectively. Biophys J 2006; 90 (05) 1489-1500
76 Parunov LA, Fadeeva OA, Balandina AN. et al. Improvement of spatial fibrin formation by the anti-TFPI aptamer BAX499: changing clot size by targeting extrinsic pathway initiation. J Thromb Haemost 2011; 9 (09) 1825-1834
77 Shibeko AM, Lobanova ES, Panteleev MA, Ataullakhanov FI. Blood flow controls coagulation onset via the positive feedback of factor VII activation by factor Xa. BMC Syst Biol 2010; 4: 5
78 Zhalyalov AS, Panteleev MA, Gracheva MA, Ataullakhanov FI, Shibeko AM. Co-ordinated spatial propagation of blood plasma clotting and fibrinolytic fronts. PLoS One 2017; 12 (07) e0180668
79 Anand M, Rajagopal K, Rajagopal KR. A model for the formation, growth, and lysis of clots in quiescent plasma. A comparison between the effects of antithrombin III deficiency and protein C deficiency. J Theor Biol 2008; 253 (04) 725-738
80 Kuharsky AL, Fogelson AL. Surface-mediated control of blood coagulation: the role of binding site densities and platelet deposition. Biophys J 2001; 80 (03) 1050-1074
81 Ataullakhanov FI, Guria GT, Sarbash VI, Volkova RI. Spatiotemporal dynamics of clotting and pattern formation in human blood. Biochim Biophys Acta 1998; 1425 (03) 453-468
82 Hockin MF, Jones KC, Everse SJ, Mann KG. A model for the stoichiometric regulation of blood coagulation. J Biol Chem 2002; 277 (21) 18322-18333
83 Beltrami E, Jesty J. Mathematical analysis of activation thresholds in enzyme-catalyzed positive feedbacks: application to the feedbacks of blood coagulation. Proc Natl Acad Sci U S A 1995; 92 (19) 8744-8748
84 Beltrami E, Jesty J. The role of membrane patch size and flow in regulating a proteolytic feedback threshold on a membrane: possible application in blood coagulation. Math Biosci 2001; 172 (01) 1-13
85 Kastrup CJ, Shen F, Runyon MK, Ismagilov RF. Characterization of the threshold response of initiation of blood clotting to stimulus patch size. Biophys J 2007; 93 (08) 2969-2977
86 Panteleev MA, Balandina AN, Lipets EN, Ovanesov MV, Ataullakhanov FI. Task-oriented modular decomposition of biological networks: trigger mechanism in blood coagulation. Biophys J 2010; 98 (09) 1751-1761
87 Diamandis P, Wildenhain J, Clarke ID. et al. Chemical genetics reveals a complex functional ground state of neural stem cells. Nat Chem Biol 2007; 3 (05) 268-273
88 Kuprash AD, Shibeko AM, Vijay R. et al. Sensitivity and robustness of spatially dependent thrombin generation and fibrin clot propagation. Biophys J 2018; 115 (12) 2461-2473
89 Mitrophanov AY, Wolberg AS, Reifman J. Kinetic model facilitates analysis of fibrin generation and its modulation by clotting factors: implications for hemostasis-enhancing therapies. Mol Biosyst 2014; 10 (09) 2347-2357
90 Mitrophanov AY, Szlam F, Sniecinski RM, Levy JH, Reifman J. A step toward balance: thrombin generation improvement via procoagulant factor and antithrombin supplementation. Anesth Analg 2016; 123 (03) 535-546
91 Sinauridze EI, Gorbatenko AS, Seregina EA, Lipets EN, Ataullakhanov FI. Moderate plasma dilution using artificial plasma expanders shifts the haemostatic balance to hypercoagulation. Sci Rep 2017; 7 (01) 843
92 Brummel-Ziedins KE, Gissel M, Neuhaus J. et al; INSIGHT SMART, ESPRIT Study Groups. In silico thrombin generation: Plasma composition imbalance and mortality in human immunodeficiency virus. Res Pract Thromb Haemost 2018; 2 (04) 708-717
93 Gupta S, Bravo MC, Heiman M. et al. Mathematical model of thrombin generation and bleeding phenotype in Amish carriers of Factor IX:C deficiency vs. controls. Thromb Res 2019; 182: 43-50
94 Brummel-Ziedins KE, Whelihan MF, Gissel M, Mann KG, Rivard GE. Thrombin generation and bleeding in haemophilia A. Haemophilia 2009; 15 (05) 1118-1125
95 Brummel-Ziedins KE, Vossen CY, Butenas S, Mann KG, Rosendaal FR. Thrombin generation profiles in deep venous thrombosis. J Thromb Haemost 2005; 3 (11) 2497-2505
96 Brummel-Ziedins K, Vossen CY, Rosendaal FR, Umezaki K, Mann KG. The plasma hemostatic proteome: thrombin generation in healthy individuals. J Thromb Haemost 2005; 3 (07) 1472-1481
97 Chelle P, Morin C, Montmartin A, Piot M, Cournil M, Tardy-Poncet B. Evaluation and calibration of in silico models of thrombin generation using experimental data from healthy and haemophilic subjects. Bull Math Biol 2018; 80 (08) 1989-2025
98 Duarte RCF, Ferreira CN, Rios DRA, Reis HJD, Carvalho MDG. Thrombin generation assays for global evaluation of the hemostatic system: perspectives and limitations. Rev Bras Hematol Hemoter 2017; 39 (03) 259-265
99 van Veen JJ, Gatt A, Makris M. Thrombin generation testing in routine clinical practice: are we there yet?. Br J Haematol 2008; 142 (06) 889-903
100 Liang Y, Woodle SA, Shibeko AM, Lee TK, Ovanesov MV. Correction of microplate location effects improves performance of the thrombin generation test. Thromb J 2013; 11 (01) 12
101 Bannish BE, Keener JP, Fogelson AL. Modelling fibrinolysis: a 3D stochastic multiscale model. Math Med Biol 2014; 31 (01) 17-44
102 Bannish BE, Chernysh IN, Keener JP, Fogelson AL, Weisel JW. Molecular and physical mechanisms of fibrinolysis and thrombolysis from mathematical modeling and experiments. Sci Rep 2017; 7 (01) 6914
103 Shibeko AM, Woodle SA, Mahmood I, Jain N, Ovanesov MV. Predicting dosing advantages of factor VIIa variants with altered tissue factor-dependent and lipid-dependent activities. J Thromb Haemost 2014; 12 (08) 1302-1312
104 Piebalgs A, Gu B, Roi D, Lobotesis K, Thom S, Xu XY. Computational simulations of thrombolytic therapy in acute ischaemic stroke. Sci Rep 2018; 8 (01) 15810