In silico augmentation of the drug development pipeline: Examples from the study of acute inflammation

Drug Development Research

Volumn 72, Issue 2, 2011, Pages 187-200

  An, Gary ^a,b   Bartels, John ^c   Vodovotz, Yoram ^b,d  

^a UNIVERSITY OF CHICAGO   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c Immunetrics   (United States)

^d UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

Author keywords

in silico clinical trial; inflammation; mathematical model; rational drug design; sepsis; trauma

Indexed keywords

DROTRECOGIN; TUMOR NECROSIS FACTOR ALPHA;

ARTICLE; CLINICAL TRIAL (TOPIC); COMPUTER MODEL; DRUG DESIGN; DRUG RESEARCH; HUMAN; INFLAMMATION; MATHEMATICAL MODEL; NONHUMAN; PERSONALIZED MEDICINE; TRANSLATIONAL RESEARCH;

EID: 79953894415     PISSN: 02724391     EISSN: 10982299     Source Type: Journal    
DOI: 10.1002/ddr.20415     Document Type: Article

References (116)

1. Alt W, Lauffenburger DA,. 1987. Transient behavior of a chemotaxis system modelling certain types of tissue inflammation. J Math Biol 24: 691-722. (Pubitemid 17031988)
2. Alverdy J, Zaborina O, Wu L,. 2005. The impact of stress and nutrition on bacterial-host interactions at the intestinal epithelial surface. Curr Opin Clin Nutr Metab Care 8: 205-209. (Pubitemid 40289483)
3. An G,. 2001. Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials. Shock 16: 266-273. (Pubitemid 33599113)
4. An G,. 2004. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit Care Med 32: 2050-2060. (Pubitemid 39372539)
5. An G,. 2008. Introduction of a agent based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 5: 11.
6. An G,. 2009a. Dynamic knowledge representation using agent based modeling: ontology instantiation and verification of conceptual models. In:, Maly I, editor. Systems biology: methods in molecular biology series. Totowa, NJ: Humana Press. p 445-468.
7. An G,. 2009b. A model of TLR4 signaling and tolerance using a qualitative, particle event-based method: introduction of spatially configured stochastic reaction chambers (SCSRC). Math Biosci 217: 43-52.
8. An G,. 2010. Closing the scientific loop: bridging correlation and causality in the petaflop age. Science Translational Med 2: 41ps34.
9. An G, Faeder JR,. 2009. Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Math Biosci 217: 53-63.
10. An G, Vodovotz Y,. 2008. Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient. J Burn Care Res 29: 277-285. (Pubitemid 351498820)
11. An G, Mi Q, Dutta-Moscato J, Solovyev A, Vodovotz Y,. 2009. Agent-based models in translational systems biology. WIRES 1: 159-171.
12. Angus DC, Yang L, Kong L, Kellum JA, Delude RL, Tracey KJ, Weissfeld L,. 2007. Circulating high-mobility group box 1 (HMGB1) concentrations are elevated in both uncomplicated pneumonia and pneumonia with severe sepsis. Crit Care Med 35: 1061-1067. (Pubitemid 46598956)
13. Arciero J, Rubin J, Upperman J, Vodovotz Y, Ermentrout GB,. 2010. Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis. PLoS ONE 5: e10066.
14. Bailey JE,. 1998. Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnol Prog 14: 8-20. (Pubitemid 28118259)
15. Borer JS, Simon LS,. 2005. Cardiovascular and gastrointestinal effects of COX-2 inhibitors and NSAIDs: achieving a balance. Arthritis Res Ther 7 (Suppl 4): S14-S22. (Pubitemid 41646250)
16. Bornholdt S,. 2001. Modeling genetic networks and their evolution: a complex dynamical systems perspective. Biol Chem 382: 1289-1299. (Pubitemid 32976488)
17. Bruggeman FJ, Westerhoff HV,. 2006. Approaches to biosimulation of cellular processes. J Biol Phys 32: 273-288. (Pubitemid 44825130)
18. Calabrese L,. 2006. The yin and yang of tumor necrosis factor inhibitors. Cleve Clin J Med 73: 251-256.
19. Chang S, Baratt A, Clermont G, Planquois JM, Yan SB, Williams M, Macias W,. 2006. Mathematical model predicting outcomes of sepsis patients treated with Xigris(R): ENHANCE trial. Shock 25: 70-71.
20. Chaouiya C,. 2007. Petri net modelling of biological networks. Brief Bioinform 8: 210-219. (Pubitemid 47336806)
21. Chaplain M, Anderson A,. 2004. Mathematical modelling of tumour-induced angiogenesis: network growth and structure. Cancer Treat Res 117: 51-75.
22. Chow CC, Clermont G, Kumar R, Lagoa C, Tawadrous Z, Gallo D, Betten B, Bartels J, Constantine G, Fink MP, et al. 2005. The acute inflammatory response in diverse shock states. Shock 24: 74-84. (Pubitemid 40946598)
23. Christensen C, Thakar J, Albert R,. 2007. Systems-level insights into cellular regulation: inferring, analysing, and modelling intracellular networks. IET Syst Biol 1: 61-77. (Pubitemid 46488240)
24. Clermont G, Bartels J, Kumar R, Constantine G, Vodovotz Y, Chow C,. 2004a. In silico design of clinical trials: a method coming of age. Crit Care Med 32: 2061-2070. (Pubitemid 39372540)
25. Clermont G, Chow CC, Constantine GM, Vodovotz Y, Bartels J,. 2004b. Mathematical and statistical modeling of acute inflammation. Classification, clustering, and data mining applications. Proceedings of the International Meeting of Classification Societies. New York: Springer-Verlag. p 457-467.
26. Clermont G, Vodovotz Y, Rubin J,. 2007. Equation-based models of dynamic biological systems. In:, Aird WC, editor. Endothelial biomedicine. Cambridge, MA: Cambridge University Press. p 1780-1785.
27. Csete ME, Doyle JC,. 2002. Reverse engineering of biological complexity. Science 295: 1664-1669. (Pubitemid 34202894)
28. Daun S, Rubin J, Vodovotz Y, Roy A, Parker R, Clermont G,. 2008. An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: results from parameter space reduction. J Theor Biol 253: 843-853.
29. Day J, Rubin J, Vodovotz Y, Chow CC, Reynolds A, Clermont G,. 2006. A reduced mathematical model of the acute inflammatory response. II. Capturing scenarios of repeated endotoxin administration. J Theor Biol 242: 237-256. (Pubitemid 44218998)
30. Dearden JC,. 2007. In silico prediction of ADMET properties: how far have we come? Expert Opin Drug Metab Toxicol 3: 635-639. (Pubitemid 351320634)
31. Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP,. 2010. Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS ONE 5: e9249.
32. Edelstein-Keshet L,. 1988. Mathematical models in biology. New York: Random House.
33. Ekins S, Mestres J, Testa B,. 2007a. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 152: 21-37. (Pubitemid 47339897)
34. Ekins S, Mestres J, Testa B,. 2007b. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152: 9-20. (Pubitemid 47339896)
35. Ermentrout GB, Edelstein-Keshet L,. 1993. Cellular automata approaches to biological modeling. J Theor Biol 160: 97-133.
36. Evans MC,. 2008. Recent advances in immunoinformatics: application of in silico tools to drug development. Curr Opin Drug Discov Dev 11: 233-241. (Pubitemid 351281063)
37. Faeder JR, Blinov ML, Goldstein B, Hlavacek WS,. 2005. Rule-based modeling of biochemical networks. Complexity 10: 22-41. (Pubitemid 40772240)
38. Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, Wofsy C, Goldstein B,. 2003. Investigation of early events in Fc epsilon RI-mediated signaling using a detailed mathematical model. J Immunol 170: 3769-3781. (Pubitemid 36359247)
39. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP,. 2009a. In silico simulation of corticosteroids effect on an NFkB-dependent physicochemical model of systemic inflammation. PLoS ONE 4: e4706.
40. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP,. 2009b. Modeling endotoxin-induced systemic inflammation using an indirect response approach. Math Biosci 217: 27-42.
41. Goldstein B, Faeder JR, Hlavacek WS, Blinov ML, Redondo A, Wofsy C,. 2002. Modeling the early signaling events mediated by FcepsilonRI. Mol Immunol 38: 1213-1219. (Pubitemid 35253755)
42. Goncharova LB, Tarakanov AO,. 2007. Molecular networks of brain and immunity. Brain Res Rev 55: 155-166. (Pubitemid 47422226)
43. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL,. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310: 987-991. (Pubitemid 41611904)
44. Han JD,. 2008. Understanding biological functions through molecular networks. Cell Res 18: 224-237.
45. Hausheer FH, Kochat H, Parker AR, Ding D, Yao S, Hamilton SE, Petluru PN, Leverett BD, Bain SH, Saxe JD,. 2003. New approaches to drug discovery and development: a mechanism-based approach to pharmaceutical research and its application to BNP7787, a novel chemoprotective agent. Cancer Chemother Pharmacol 52 (Suppl 1): S3-S15. (Pubitemid 37047018)
46. Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W,. 2006. Rules for modeling signal-transduction systems. Sci STKE 2006 (344): re6.
47. Huang S,. 2002. Rational drug discovery: what can we learn from regulatory networks? Drug Discov Today 7 (Suppl): S163-S169.
48. Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB,. 2005. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310: 1646-1653. (Pubitemid 41780781)
49. Janes KA, Yaffe MB,. 2006. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 7: 820-828. (Pubitemid 44627475)
50. Jenwitheesuk E, Horst JA, Rivas KL, Van Voorhis WC, Samudrala R,. 2008. Novel paradigms for drug discovery: computational multitarget screening. Trends Pharmacol Sci 29: 62-71.
51. Kellum JA,. 2003. Hemoadsorption therapy for sepsis syndromes. Crit Care Med 31: 323-324. (Pubitemid 36151201)
52. Kellum JA, Kong L, Fink MP, Weissfeld LA, Yealy DM, Pinsky MR, Fine J, Krichevsky A, Delude RL, Angus DC,. 2007. Understanding the inflammatory cytokine response in pneumonia and sepsis: results of the Genetic and Inflammatory Markers of Sepsis (GenIMS) Study. Arch Intern Med 167: 1655-1663. (Pubitemid 47280347)
53. Khakar PS,. 2010. Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. Curr Top Med Chem 10: 116-126.
54. Kirchmair J, Distinto S, Schuster D, Spitzer G, Langer T, Wolber G,. 2008. Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr Med Chem 15: 2040-2053.
55. Kitano H,. 2002. Systems biology: a brief overview. Science 295: 1662-1664. (Pubitemid 34202893)
56. Klune JR, Dhupar R, Cardinal J, Billiar TR, Tsung A,. 2008. HMGB1: endogenous danger signaling. Mol Med 14: 476-484.
57. Kumar R, Chow CC, Bartels J, Clermont G, Vodovotz Y,. 2008. A mathematical simulation of the inflammatory response to anthrax infection. Shock 29: 104-111.
58. Kumar R, Clermont G, Vodovotz Y, Chow CC,. 2004. The dynamics of acute inflammation. J Theor Biol 230: 145-155. (Pubitemid 39078401)
59. Lagoa CE, Bartels J, Baratt A, Tseng G, Clermont G, Fink MP, Billiar TR, Vodovotz Y,. 2006. The role of initial trauma in the host's response to injury and hemorrhage: insights from a comparison of mathematical simulations and hepatic transcriptomic analysis. Shock 26: 592-600. (Pubitemid 44789634)
60. Lengeler JW,. 2000. Metabolic networks: a signal-oriented approach to cellular models. Biol Chem 381: 911-920.
61. Li H, Xuan J, Wang Y, Zhan M,. 2008a. Inferring regulatory networks. Front Biosci 13: 263-275. (Pubitemid 351606128)
62. Li NYK, Verdolini K, Clermont G, Mi Q, Hebda PA, Vodovotz Y,. 2008b. A patient-specific i n silico model of inflammation and healing tested in acute vocal fold injury. PLoS ONE 3: e2789.
63. Liao G, Zhang X, Clark DJ, Peltz G,. 2008. A genomic "roadmap" to "better" drugs. Drug Metab Rev 40: 225-239.
64. Mandal S, Moudgil M, Mandal SK,. 2009. Rational drug design. Eur J Pharmacol 625: 90-100.
65. Marathe DD, Sarkar J, Hurst KW, Inglis AM, Kellum JA, Angus DC, Vodovotz Y, Chang S,. 2010. Modeling of severe sepsis patients with community acquired pneumonia (CAP). Shock 33 (Suppl 1): 72-73.
66. Matzinger P,. 2002. The danger model: a renewed sense of self. Science 296: 301-305. (Pubitemid 34303674)
67. Medzhitov R,. 2008. Origin and physiological roles of inflammation. Nature 454: 428-435.
68. Merlot C,. 2008. In silico methods for early toxicity assessment. Curr Opin Drug Discov Dev 11: 80-85.
69. Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, Okonkwo DO, Verdolini Abbott K, Constantine G, An G,. 2010. Translational systems biology of inflammation: potential applications to personalized medicine. Personal Med 7: 549-559.
70. Mi Q, RiviŠre B, Clermont G, Steed DL, Vodovotz Y,. 2007. Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-á1. Wound Rep Reg 15: 617-682.
71. Michelson S, Sehgal A, Friedrich C,. 2006. In silico prediction of clinical efficacy. Curr Opin Biotechnol 17: 666-670. (Pubitemid 44791904)
72. Montague G, Morris J,. 1994. Neural-network contributions in biotechnology. Trends Biotechnol 12: 312-324. (Pubitemid 24263703)
73. Musante CJ, Lewis AK, Hall K,. 2002. Small-and large-scale biosimulation applied to drug discovery and development. Drug Discov Today 7 (Suppl): S192-S196.
74. Nathan C,. 2002. Points of control in inflammation. Nature 420: 846-852. (Pubitemid 36019637)
75. Neves SR, Iyengar R,. 2002. Modeling of signaling networks. BioeEssays 24: 1110-1117. (Pubitemid 35464942)
76. Nielsen UB, Schoeberl B,. 2005. Using computational modeling to drive the development of targeted therapeutics. I. Drugs 8: 822-826. (Pubitemid 41479279)
77. Nikiforova VJ, Willmitzer L,. 2007. Network visualization and network analysis. EXS 97: 245-275.
78. Oviedo JA, Wolfe MM,. 2001. Clinical potential of cyclo-oxygenase-2 inhibitors. BioDrugs 15: 563-572.
79. Pauli I, Timmers LF, Caceres RA, Soares MB, de Azevedo WFJ,. 2008. In silico and in vitro: identifying new drugs. Curr Drug Targets 9: 1054-1061.
80. Pinsky MR,. 2004. Dysregulation of the immune response in severe sepsis. Am J Med Sci 328: 220-229. (Pubitemid 39372464)
81. Prince JM, Levy RM, Bartels J, Baratt A, Kane III JM, Lagoa C, Rubin J, Day J, Wei J, Fink MP, et al. 2006. In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice. Mol Med 12: 88-96. (Pubitemid 44454096)
82. Reynolds A, Rubin J, Clermont G, Day J, Vodovotz Y, Ermentrout GB,. 2006. A reduced mathematical model of the acute inflammatory response. I. Derivation of model and analysis of anti-inflammation. J Theor Biol 242: 220-236. (Pubitemid 44218999)
83. Rho S, You S, Kim Y, Hwang D,. 2008. From proteomics toward systems biology: integration of different types of proteomics data into network models. BMB Rep 41: 184-193. (Pubitemid 351518990)
84. RiviŠre B, Epshteyn Y, Swigon D, Vodovotz Y,. 2009. A simple mathematical model of signaling resulting from the binding of lipopolysaccharide with Toll-like receptor 4 demonstrates inherent preconditioning behavior. Math Biosci 217: 19-26.
85. Rosenberg AL,. 2002. Recent innovations in intensive care unit risk-prediction models. Curr Opin Crit Care 8: 321-330.
86. Rovira X, Pin JP, Giraldo J,. 2010. The asymmetric/symmetric activation of GPCR dimers as a possible mechanistic rationale for multiple signalling pathways. Trends Pharmacol Sci 31: 15-21.
87. Rychly DJ, DiPiro JT,. 2005. Infections associated with tumor necrosis factor-alpha antagonists. Pharmacotherapy 25: 1181-1192. (Pubitemid 41242578)
88. Sanga S, Sinek JP, Frieboes HB, Ferrari M, Fruehauf JP, Cristini V,. 2006. Mathematical modeling of cancer progression and response to chemotherapy. Expert Rev Anticancer Ther 6: 1361-1376. (Pubitemid 44697162)
89. Santos CC, Zhang H, Liu M, Slutsky AS,. 2005. Bench-to-bedside review: biotrauma and modulation of the innate immune response. Crit Care 9: 280-286. (Pubitemid 40796805)
90. Schlag G, Redl H,. 1996. Mediators of injury and inflammation. World J Surg 20: 406-410. (Pubitemid 26138586)
91. Solovyev A, Mikheev M, Zhou L, Dutta-Moscato J, Ziraldo C, An G, Vodovotz Y, Mi Q,. 2010. SPARK: a framework for multi-scale agent-based biomedical modeling. Int J Agent Technologies and Systems (in press).
92. Stoiser B, Knapp S, Thalhammer F, Locker GJ, Kofler J, Hollenstein U, Staudinger T, Wilfing A, Frass M, Burgmann H,. 1998. Time course of immunological markers in patients with the systemic inflammatory response syndrome: evaluation of sCD14, sVCAM-1, sELAM-1, MIP-1 alpha and TGF-beta 2. Eur J Clin Invest 28: 672-678. (Pubitemid 28380365)
93. Stransky B, Barrera J, Ohno-Machado L, De Souza SJ,. 2007. Modeling cancer: integration of "omics" information in dynamic systems. J Bioinform Comput Biol 5: 977-986. (Pubitemid 47371619)
94. Tekirian TL, Thomas SN, Yang A,. 2007. Advancing signaling networks through proteomics. Expert Rev Proteomics 4: 573-583. (Pubitemid 47347602)
95. Torres A, Bentley T, Bartels J, Sarkar J, Barclay D, Namas R, Constantine G, Zamora R, Puyana JC, Vodovotz Y,. 2009. Mathematical modeling of post-hemorrhage inflammation in mice: studies using a novel, computer-controlled, closed-loop hemorrhage apparatus. Shock 32: 172-178.
96. Ulloa L, Brunner M, Ramos L, Deitch EA,. 2009. Scientific and clinical challenges in sepsis. Curr Pharm Des 15: 1918-1935.
97. van de Waterbeemd H,. 2009. Improving compound quality through in vitro and in silico physicochemical profiling. Chem Biodivers 6: 1760-1766.
98. Vaz RJ, Zamora I, Li Y, Reiling S, Shen J, Cruciani G,. 2010. The challenges of in silico contributions to drug metabolism in lead optimization. Expert Opin Drug Metab Toxicol 6: 851-861.
99. Vedani A, Dobler M, Lill MA,. 2006. The challenge of predicting drug toxicity in silico. Basic Clin Pharmacol Toxicol 99: 195-208. (Pubitemid 44321483)
100. Vincent JL, Ferreira F, Moreno R,. 2000. Scoring systems for assessing organ dysfunction and survival. Crit Care Clin 16: 353-366. (Pubitemid 30193854)
101. Vodovotz Y,. 2010. Translational systems biology of inflammation and healing. Wound Repair Regen 18: 3-7.
102. Vodovotz Y, An G,. 2009. Systems biology and inflammation. In:, Yan Q, editor. Systems biology in drug discovery and development: methods and protocols. Totowa, NJ: Springer Science and Business Media. p 181-201.
103. Vodovotz Y, Chow CC, Bartels J, Lagoa C, Prince J, Levy R, Kumar R, Day J, Rubin J, Constantine G, et al. 2006. In silico models of acute inflammation in animals. Shock 26: 235-244.
104. Vodovotz Y, Clermont G, Chow C, An G,. 2004. Mathematical models of the acute inflammatory response. Curr Opin Crit Care 10: 383-390. (Pubitemid 39313509)
105. Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, Hotchkiss J, Ta'asan S, Neugebauer E, An G,. 2007. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 22: 77-84. (Pubitemid 46420756)
106. Vodovotz Y, Csete M, Bartels J, Chang S, An G,. 2008. Translational systems biology of inflammation. PLoS Comput Biol 4: 1-6.
107. Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO, An G,. 2009. Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 217: 1-10.
108. Vodovotz Y, Constantine G, Faeder J, Mi Q, Rubin J, Sarkar J, Squires R, Okonkwo DO, Gerlach J, Zamora R, et al. 2010. Translational systems approaches to the biology of inflammation and healing. Immunopharmacol Immunotoxicol 32: 181-195.
109. Vogels TP, Rajan K, Abbott LF,. 2005. Neural network dynamics. Annu Rev Neurosci 28: 357-376. (Pubitemid 41102238)
110. Wang E, Lenferink A, O'Connor-McCourt M,. 2007. Cancer systems biology: exploring cancer-associated genes on cellular networks. Cell Mol Life Sci 64: 1752-1762. (Pubitemid 47093012)
111. Wang H, Yang H, Czura CJ, Sama AE, Tracey KJ,. 2001. HMGB1 as a late mediator of lethal systemic inflammation. Am J Respir Crit Care Med 164 (Pt 1): 1768-1773. (Pubitemid 33152308)
112. Wen B, Fitch WL,. 2009. Analytical strategies for the screening and evaluation of chemically reactive drug metabolites. Expert Opin Drug Metab Toxicol 5: 39-55.
113. Wishart DS,. 2005. Bioinformatics in drug development and assessment. Drug Metab Rev 37: 279-310. (Pubitemid 40677506)
114. Yende S, D'Angelo G, Kellum JA, Weissfeld L, Fine J, Welch RD, Kong L, Carter M, Angus DC,. 2008. Inflammatory markers at hospital discharge predict subsequent mortality after pneumonia and sepsis. Am J Respir Crit Care Med 177: 1242-1247. (Pubitemid 351860933)
115. Young SS, Lam RL, Welch WJ,. 2002. Initial compound selection for sequential screening. Curr Opin Drug Discov Dev 5: 422-427. (Pubitemid 36285648)
116. Zoete V, Grosdidier A, Michielin O,. 2009. Docking, virtual high throughput screening and in silico fragment-based drug design. J Cell Mol Med 13: 238-248.