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Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Differential Drug Target Selection in Blood Coagulation: What can we get from Computational Systems Biology Models?

Author(s): Mikhail A. Panteleev, Anna A. Andreeva and Alexey I. Lobanov*

Volume 26, Issue 18, 2020

Page: [2109 - 2115] Pages: 7

DOI: 10.2174/1381612826666200406091807

Price: $65

Abstract

Discovery and selection of the potential targets are some of the important issues in pharmacology. Even when all the reactions and the proteins in a biological network are known, how does one choose the optimal target? Here, we review and discuss the application of the computational methods to address this problem using the blood coagulation cascade as an example. The problem of correct antithrombotic targeting is critical for this system because, although several anticoagulants are currently available, all of them are associated with bleeding risks. The advantages and the drawbacks of different sensitivity analysis strategies are considered, focusing on the approaches that emphasize: 1) the functional modularity and the multi-tasking nature of this biological network; and 2) the need to normalize hemostasis during the anticoagulation therapy rather than completely suppress it. To illustrate this effect, we show the possibility of the differential regulation of lag time and endogenous thrombin potential in the thrombin generation. These methods allow to identify the elements in the blood coagulation cascade that may serve as the targets for the differential regulation of this system.

Keywords: Blood coagulation, thrombin generation, plasma clotting, mathematical model, antithrombotic, sensitivity.

[1]
Kurata M, Yamamoto K, Moriarity BS, Kitagawa M, Largaespada DA. CRISPR/Cas9 library screening for drug target discovery. J Hum Genet 2018; 63(2): 179-86.
[http://dx.doi.org/10.1038/s10038-017-0376-9] [PMID: 29158600]
[2]
Baig AM. Innovative methodology in the discovery of novel drug targets in the free-living amoebae. Curr Drug Targets 2019; 20(1): 60-9.
[http://dx.doi.org/10.2174/1389450119666180426100452] [PMID: 29697029]
[3]
Hu Y, Zhao T, Zhang N, Zhang Y, Cheng L. A review of recent advances and research on drug target identification methods. Curr Drug Metab 2019; 20(3): 209-16.
[http://dx.doi.org/10.2174/1389200219666180925091851] [PMID: 30251599]
[4]
Sinauridze EI, Panteleev MA, Ataullakhanov FI. Anticoagulant therapy: basic principles, classic approaches and recent developments. Blood Coagul Fibrinolysis 2012; 23(6): 482-93.
[http://dx.doi.org/10.1097/MBC.0b013e328355c9cb] [PMID: 22732252]
[5]
Bickmann JK, Baglin T, Meijers JCM, Renné T. Novel targets for anticoagulants lacking bleeding risk. Curr Opin Hematol 2017; 24(5): 419-26.
[http://dx.doi.org/10.1097/MOH.0000000000000367] [PMID: 28731874]
[6]
Buller HR, Lensing AW, Prins MH, et al. Einstein-DVT Dose-Ranging Study investigators. A dose-ranging study evaluating once-daily oral administration of the factor Xa inhibitor rivaroxaban in the treatment of patients with acute symptomatic deep vein thrombosis: the Einstein-DVT Dose-Ranging Study. Blood 2008; 112(6): 2242-7.
[http://dx.doi.org/10.1182/blood-2008-05-160143] [PMID: 18621928]
[7]
Hart RG, Sharma M, Mundl H, et al. NAVIGATE ESUS Investigators. Rivaroxaban for stroke prevention after embolic stroke of undetermined source. N Engl J Med 2018; 378(23): 2191-201.
[http://dx.doi.org/10.1056/NEJMoa1802686] [PMID: 29766772]
[8]
Hong KS, Kwon SU, Lee SH, et al. Phase 2 exploratory clinical study to assess the effects of xarelto (rivaroxaban) versus warfarin on ischemia, bleeding, and hospital stay in acute cerebral infarction patients with non-valvular atrial fibrillation (triple axel) study group. rivaroxaban vs warfarin sodium in the ultra-early period after atrial fibrillation-related mild ischemic stroke: a randomized clinical trial. JAMA Neurol 2017; 74(10): 1206-15.
[http://dx.doi.org/10.1001/jamaneurol.2017.2161] [PMID: 28892526]
[9]
Kupó P, Szakács Z, Solymár M, et al. Direct anticoagulants and risk of myocardial infarction, a multiple treatment network meta-analysis. Angiology 2020; 71(1): 27-37.
[http://dx.doi.org/10.1177/0003319719874255] [PMID: 31533437]
[10]
Al-Shahi Salman R, Law ZK, Bath PM, Steiner T, Sprigg N. Haemostatic therapies for acute spontaneous intracerebral haemorrhage. Cochrane Database Syst Rev 2018; 17: : 4CD005951
[http://dx.doi.org/10.1002/14651858.CD005951.pub4] [PMID: 29664991]
[11]
Tarandovskiy ID, Balandina AN, Kopylov KG, et al. Investigation of the phenotype heterogeneity in severe hemophilia A using thromboelastography, thrombin generation, and thrombodynamics. Thromb Res 2013; 131(6): e274-80.
[http://dx.doi.org/10.1016/j.thromres.2013.04.004] [PMID: 23611257]
[12]
Panteleev MA, Ananyeva NM, Ataullakhanov FI, Saenko EL. Mathematical models of blood coagulation and platelet adhesion: clinical applications. Curr Pharm Des 2007; 13(14): 1457-67.
[http://dx.doi.org/10.2174/138161207780765936] [PMID: 17504167]
[13]
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.
[http://dx.doi.org/10.1016/j.plrev.2018.02.005]
[14]
Shibeko AM, Panteleev MA. Untangling the complexity of blood coagulation network: use of computational modelling in pharmacology and diagnostics. Brief Bioinform 2016; 17(3): 429-39.
[http://dx.doi.org/10.1093/bib/bbv040] [PMID: 26116831]
[15]
Ataullakhanov FI, Panteleev MA. Mathematical modeling and computer simulation in blood coagulation. Pathophysiol Haemost Thromb 2005; 34(2-3): 60-70.
[http://dx.doi.org/10.1159/000089927] [PMID: 16432308]
[16]
Andreeva Anna A, Anand M, Lobanov Alexey I, Nikolaev Andrey V, Panteleev Mikhail A, Susree M. Mathematical modelling of platelet rich plasma clotting. Pointwise unified model. Russ J Numer Anal Math Model 2018; 33: 265.
[http://dx.doi.org/10.1515/rnam-2018-0022]
[17]
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(9): 1751-61.
[http://dx.doi.org/10.1016/j.bpj.2010.01.027] [PMID: 20441738]
[18]
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(8): 1989-2025.
[http://dx.doi.org/10.1007/s11538-018-0440-4] [PMID: 29948884]
[19]
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(3): 535-46.
[http://dx.doi.org/10.1213/ANE.0000000000001361] [PMID: 27541717]
[20]
Brummel-Ziedins KE, Orfeo T, Gissel M, Mann KG, Rosendaal FR. Factor Xa generation by computational modeling: an additional discriminator to thrombin generation evaluation. PLoS One 2012; 7(1): e29178
[http://dx.doi.org/10.1371/journal.pone.0029178] [PMID: 22247769]
[21]
Danforth CM, Orfeo T, Mann KG, Brummel-Ziedins KE, Everse SJ. The impact of uncertainty in a blood coagulation model. Math Med Biol 2009; 26(4): 323-36.
[http://dx.doi.org/10.1093/imammb/dqp011] [PMID: 19451209]
[22]
Luan D, Zai M, Varner JD. Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies. PLOS Comput Biol 2007; 3(7): e142
[http://dx.doi.org/10.1371/journal.pcbi.0030142] [PMID: 17658944]
[23]
Makin JG, Narayanan S. A hybrid-system model of the coagulation cascade: simulation, sensitivity, and validation. J Bioinform Comput Biol 2013; 11(5): 1342004
[http://dx.doi.org/10.1142/S0219720013420043] [PMID: 24131053]
[24]
Link KG, Stobb MT, Di Paola J, et al. A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow. PLoS One 2018; 13(7): e0200917
[http://dx.doi.org/10.1371/journal.pone.0200917] [PMID: 30048479]
[25]
Hemker HC, Kremers R. Data management in thrombin generation. Thromb Res 2013; 131(1): 3-11.
[http://dx.doi.org/10.1016/j.thromres.2012.10.011] [PMID: 23158401]
[26]
Al Dieri R, Peyvandi F, Santagostino E, et al. The thrombogram in rare inherited coagulation disorders: its relation to clinical bleeding. Thromb Haemost 2002; 88(4): 576-82.
[http://dx.doi.org/10.1055/s-0037-1613258] [PMID: 12362226]
[27]
Sinauridze EI, Vuimo TA, Tarandovskiy ID, et al. Thrombodynamics, a new global coagulation test: Measurement of heparin efficiency. Talanta 2018; 180: 282-91.
[http://dx.doi.org/10.1016/j.talanta.2017.12.055] [PMID: 29332812]
[28]
Link KGA, Stobb MT, Sorrells MG, et al. A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia. J Thromb Haemost 2019. Epub ahead of print
[http://dx.doi.org/10.1111/jth.14653]
[29]
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): 2461-73.
[http://dx.doi.org/10.1016/j.bpj.2018.11.009] [PMID: 30514632]
[30]
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(1): 3936.
[http://dx.doi.org/10.1038/s41598-019-40435-7] [PMID: 30850652]
[31]
Patra S, Mohapatra A. Motif discovery in biological network using expansion tree. J Bioinform Comput Biol 2018; 16(6): 1850024
[http://dx.doi.org/10.1142/S0219720018500245] [PMID: 30415600]
[32]
Kurata H, El-Samad H, Iwasaki R, et al. Module-based analysis of robustness tradeoffs in the heat shock response system. PLOS Comput Biol 2006; 2(7): e59
[http://dx.doi.org/10.1371/journal.pcbi.0020059] [PMID: 16863396]
[33]
He S, Liu YJ, Ye FY, Li RP, Dai RJ. A new grid- and modularity-based layout algorithm for complex biological networks. PLoS One 2019; 14(8): e0221620
[http://dx.doi.org/10.1371/journal.pone.0221620] [PMID: 31465473]
[34]
Fadeeva OA, Panteleev MA, Karamzin SS, Balandina AN, Smirnov IV, Ataullakhanov FI. Thromboplastin immobilized on polystyrene surface exhibits kinetic characteristics close to those for the native protein and activates in vitro blood coagulation similarly to thromboplastin on fibroblasts. Biochemistry (Mosc) 2010; 75(6): 734-43.
[http://dx.doi.org/10.1134/S0006297910060088] [PMID: 20636265]
[35]
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(9): 1825-34.
[http://dx.doi.org/10.1111/j.1538-7836.2011.04412.x] [PMID: 21696535]
[36]
Ovanesov MV, Panteleev MA, Sinauridze EI, et al. Mechanisms of action of recombinant activated factor VII in the context of tissue factor concentration and distribution. Blood Coagul Fibrinolysis 2008; 19(8): 743-55.
[http://dx.doi.org/10.1097/MBC.0b013e3283104093] [PMID: 19002040]
[37]
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.
[http://dx.doi.org/10.1186/1752-0509-4-5] [PMID: 20102623]
[38]
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(8): 1816-24.
[http://dx.doi.org/10.1016/j.bpj.2011.08.034] [PMID: 22004734]
[39]
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): 2233-40.
[http://dx.doi.org/10.1016/j.bpj.2012.10.011] [PMID: 23200057]
[40]
Ovanesov MV, Ananyeva NM, Panteleev MA, Ataullakhanov FI, Saenko EL. Initiation and propagation of coagulation from tissue factor-bearing cell monolayers to plasma: initiator cells do not regulate spatial growth rate. J Thromb Haemost 2005; 3(2): 321-31.
[http://dx.doi.org/10.1111/j.1538-7836.2005.01128.x] [PMID: 15670039]
[41]
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(5): 1489-500.
[http://dx.doi.org/10.1529/biophysj.105.069062] [PMID: 16326897]
[42]
Hoffman M, Monroe DM, Oliver JA, Roberts HR. Factors IXa and Xa play distinct roles in tissue factor-dependent initiation of coagulation. Blood 1995; 86(5): 1794-801.
[http://dx.doi.org/10.1182/blood.V86.5.1794.bloodjournal8651794] [PMID: 7655009]

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