Building of Mathematic Models for High-Throughput Screening

doi:10.7523/j.issn.2095-6134.2004.3.008

Abstract

Abstract:

In high throughput screening process, mathematic models are regarded as the foundations for large scale screening for compound library and automating the process. With thermodynamics and kinetics as the means, the article provides synthetically dynamic models by analyzing the principle of screening. Meanwhile, the article also analyzes three probable interactions between drugs and targets, and deduces corresponding mathematic models.

Key words: high-throughput screening of drugs, mathematic models, qualify and quantify

CLC Number:

R965.1

LI SongJun, LIU BaiLing, HU Jie. Building of Mathematic Models for High-Throughput Screening[J]. , 2004, 21(3): 333-339.

References

[1] L Hemmmila, S Webb. Time2resolved fluorometry: an overview of the labels and core technologies for drug screening applications. Drug Discov.Today, 1997, 2(9): 373-381

[2] H CLiu, Z MHe, Z Rosenwaks. Application of complementary DNA microarray (DNA chip) technology in the study of gene expression profiles during folliculognesis. Fertility and Sterility, 1999, 45(5): 533-543

[3] R Storer. Solution-phase synthesis in combinatorial chemistry: Applications in drug discovery.Drug Discov. Today, 1996, 1(6): 248-254

[4] D Josephr-Carthyk. Computational approaches to structure-based ligand design.Pharmacol. Therap., 1999, 84(2): 179-191

[5] Y Hashimoto. Structural development of biological response modifiers based on thalidomide. Bioorg. Med. Chem., 2002, 10(3): 461-479

[6] M A Sills. Future considerations in HTS: the acute effect of chronic dilemmas. 2.Drug Discov. Today, 1998, 3(7): 304-312

[7] A Ebneth. Ion channel screening technologies: will they revolutionize drug discovery. Drug Discov. Today, 2002, 7(4): 227

[8] S A Sundberg, A Chow, T Nikiforow, H GWada. Microchip-based systems for target validation and HTS. Drug Discov. Today, 2000, 5(12) (suppl): 92-103

[9] L F Richard. RPRπs approach to high-speed parallel synthesis for lead generation. Drug Discov. Today, 1998, 3(11): 511-515

[10] O Ramstrom, TBunyapaoboonsri, SLohmann, C Boone. Chemical biology of dynamic combinatorial libraries. Biochem. Biophys. Acta( BBA)/General subjects, 2002, 1572(2):178-866

[11] TJoaquim, GM Eric. Innovative approaches to novel antibacterial drug discovery. Curr. Opin. Biotech., 1997, 8(6): 757-762

[12] J Trias. The role of combichem in antibiotic discovery. Curr. Opin. Microbiol., 2001, 4(5): 520-525

[13] J Marshall, D Jamous, J Nilsson. Reconciling thermodynamic and dynamic methods of computation of water-mass transformation rates.Deep Sea Res. Part I. Oceanograph. Res. Papers, 1999, 46(4): 545-572

[14] J A Landro, L C A Taylor, W G Stirtan, D G Osterman, J Kristie. HTS in the new millennium: the role of pharmacology and flexibility. J.Pharmacol. Toxicol. Methods, 2000, 44(1): 273-289

[15] N Terrtt. Combinatorial chemistry. Drug Discov. Today, 1999, 4(4): 187-188

[16] D A Ahlquist, A P Shuber. Stool screening for colorectal cancer: evolution from occult blood to molecular markers. Clin. Chem. Acta, 2002,315(2) : 157-168

[17] D A Smith. Induction and drug development. Euro. J. Pharm. Sci., 2000, 11(3): 185-189

[18] 唐敖庆. 高分子反应统计理论. 北京:科学出版社, 1985. 2-10

[19] T Abe. Derivationof the lattice boltzmann method by meansof the discrete ordinate methodfor the Boltzmann equation.J. Compt. Phys., 1997,131(1): 241-246

[20] J Langowski, A Long. Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Deliv. Rev., 2002, 54(3): 407-415

[21] A P Shakeshaft, J A Bowman, R W F Sanson. Computers in community-based drug and alcohol clinical settings: are they acceptable to respondents. Drug Alcohol Depend., 1998, 50(2): 177-180