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Predictive Software for Drug Design and Development

Recent Progress and Future Developments

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Pharmaceutical Development and Regulation

Abstract

The drug discovery and development process has become more quantitative and much more computationally intensive in recent years. For pharmaceutical and biotechnology companies, this has had two major implications. Firstly, there is now a much greater range of commercial software to support drug design and development. Secondly, a number of specialized companies have appeared that have written proprietary software, and which provide computational service support to the industry.

In the area of drug design, available software falls into four main categories: (i) tools for structure-based ligand design when a 3-dimensional receptor structure is available (from X-ray crystallography or high-field nuclear magnetic resonance spectrometry); (ii) software products for in silico screening of chemical compound collections against a 3-dimensional receptor structure, where this is available; (iii) computational tools for the design of inhibitors in the absence of a 3-dimensional structure, by drawing inferences about receptor structure from the properties of known inhibitors; and (iv) computational techniques for prediction of drug-like properties, i.e. the physical and metabolic attributes characteristic of successful drugs such as solubility, ability to cross biological barriers, and stability to metabolism.

A number of other trends are leading to greater computational intensity in drug development. Software is available that attempts to predict a range of toxicities, and also drug absorption, distribution, metabolism and elimination (ADME) properties from chemical structure. In another growth area, the established discipline of pharmacokinetics (prediction of drug concentrations in body compartments) is extending its range into pharmacodynamics (prediction of drug effects). Since drug effects are the result of interactions of xenobiotic agents with complex biological systems, this has led to attempts to create quantitative disease models, bringing the field of complex system theory into drug development. Ultimately the rate-limiting process in drug development, and the most expensive part, is the clinical trial. The promise that computational biology brings to drug development is the ability to bring these modeling tools to bear on the design and interpretation of clinical trials, to increase their success rate and cost effectiveness.

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Notes

  1. 1 The use of tradenames is for product identification purposes only and does not imply endorsement

References

  1. Babine RE, Bender SL. Molecular recognition of protein-ligand complexes: applications to drug design. Chem Rev 1997; 97: 1359–1472

    Article  PubMed  CAS  Google Scholar 

  2. Hopkins AL, Groom CR. The draggable genome. Nat Rev Drug Discov 2002; 1: 727–30

    Article  PubMed  CAS  Google Scholar 

  3. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 2002; 9: 67–103

    Article  PubMed  Google Scholar 

  4. Ghose A, Viswanadhan V, Wendoloski J. A knowledge-based approach in designing combinational or medicinal chemistry libraries for drug discovery. J Comb Chem 1999; 1: 55–68

    Article  PubMed  CAS  Google Scholar 

  5. Böhm HJ. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 1992; 6: 61–78

    Article  PubMed  Google Scholar 

  6. Mata P, Gillet VJ, Johnson AP, et al. SPROUT-3D structure generation using templates. J Chem Inf Comput Sci 1995; 35: 479–93

    Article  CAS  Google Scholar 

  7. Hansch C, Klein TE. Quantitative structure-activity relationships and molecular graphics in evaluation of enzyme-ligand interactions. Methods Enzymol 1991; 202: 512–43

    Article  PubMed  CAS  Google Scholar 

  8. Zupan J, Gasteiger J. Neural networks for chemists: an introduction. VCH Verlag: Weinheim, 1993

    Google Scholar 

  9. Islam MN, Song Y, Iskander MN. Investigation of structural requirements of anticancer activity at the paclitaxel/tubulin binding site using COMFA and COMSIA. J Mol Graph Model 2003; 21: 263–72

    Article  PubMed  CAS  Google Scholar 

  10. Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drag discovery and development settings. Adv Drug Delivery Rev 1997; 23: 3–25

    Article  CAS  Google Scholar 

  11. Lipinski CA. Chris Lipinski discusses life and chemistry after the rule of five. Drug Discov Today 2003; 8(1): 12–6

    Article  PubMed  Google Scholar 

  12. Abraham MH, Ibrahim A, Zissimos AM, et al. Application of hydrogen bonding calculations in property based drag design. Drug Discov Today 2002; 7: 1056–63

    Article  PubMed  CAS  Google Scholar 

  13. Raevsky OA. Hydrogen bond strength estimation by means of HYBOT. In: van de Waterbeemd H, Testa B, Folkers G, editors. Computer-assisted lead finding and optimization: current tools for medicinal chemistry. New York: Weinheim, 1997: 367–78

    Chapter  Google Scholar 

  14. Kleinöder T, Gasteiger J. PETRA Parameter estimation for the treatment of reactivity applications [online]. Available from URL: http://zabib.chemie.unierlangen.de/software/petra/intro.phtml [Accessed 2003 Sep 3]

  15. Veber DF, Johnson SR, Cheng H-Y, et al. Molecular properties that influence the oral bioavailability of drag candidates. J Med Chem 2002; 45: 2615–23

    Article  PubMed  CAS  Google Scholar 

  16. Penzotti JE, Lamb ML, Evenson E, et al. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem 2002; 45: 1737–40

    Article  PubMed  CAS  Google Scholar 

  17. Potts RO, Guy RH. A predictive algorithm for skin permeability: the effects of molecular size and hydrogen bond activity. Pharm Res 1995; 12: 1628–33

    Article  PubMed  CAS  Google Scholar 

  18. Prausnitz MR, Noonan JS. Permeability of cornea, sciera and conjunctiva: a literature analysis for drag delivery to the eye. J Pharm Sci 1998; 87: 1479–88

    Article  PubMed  CAS  Google Scholar 

  19. Seelig A, Gottschlich R, Devant RM. A method to determine the ability of drags to diffuse through the blood-brain barrier. Proc Natl Acad Sci U S A 1994; 91: 68–72

    Article  PubMed  CAS  Google Scholar 

  20. Hodgson J. ADMET: turning chemicals into drags. Nat Biotechnol 2001; 19: 722–6

    Article  PubMed  CAS  Google Scholar 

  21. Accelrys. Software for pharmaceutical, chemical, and materials research [online]. Available from URL: http://www.accelrys.com/ [Accessed 2003 Sep 3]

  22. Cyprotex. Science and technology [online]. Available from URL: http://www.cyprotex.com/ [Accessed 2003 Sep 3]

  23. Brüstle M, Beck B, Schindler T, et al. Descriptors, physical properties and drug-likeness. J Med Chem 2002; 45(16): 3345–55

    Article  PubMed  Google Scholar 

  24. Jackson RC. Computer models in preclinical and clinical drug development. Boca Raton (FL): CRC Press, 1996

    Google Scholar 

  25. Conolly RB, Andersen ME. Biologically based pharmacodynamic models: tools for toxological research and risk assessment. Ann Rev Pharmacol Toxicol 1991; 31: 503–23

    Article  CAS  Google Scholar 

  26. Amstein R, Bühler FR, Gasser D, et al. RIDO™/RIDO PLUS: an interactive computer-based guide to improve and shorten clinical drug development. Basel: European Centre of Pharmaceutical Medicine, 1998

    Google Scholar 

  27. Werner E. Systems biology: the new darling of drug discovery? Drug Discov Today 2002; 7: 947–9

    Article  PubMed  Google Scholar 

  28. Wodarz D, Nowak MA. Mathematical models of HIV pathogenesis and treatment. Bioessays 2002; 24: 1178–87

    Article  PubMed  Google Scholar 

  29. Jackson RC. A pharmacokinetic-pharmacodynamic model of chemotherapy of human immunodeficiency virus infection that relates development of resistance to treatment intensity. J Pharmacokinet Biopharm 1997; 25: 713–30

    PubMed  CAS  Google Scholar 

  30. Tyson JJ, Csikasz-Nagy A, Novak B. The dynamics of cell cycle regulation. Bioessays 2002 Dec; 24(12): 1095–109

    Article  PubMed  CAS  Google Scholar 

  31. Chaplain MA. Mathematical modelling of angiogenesis. J Neurooncol 2000; 50: 37–51

    Article  PubMed  CAS  Google Scholar 

  32. Entelos. Science and technology [online]. Available from URL: http://www.entelos.com/science/index.html [Accessed 2003 Sep 3]

  33. Entelos Inc, and Johnson & Johnson. Entelos®PhysioLab® technology used to evaluate compound for diabetes [online]. Available from URL: http://www.entelos.com/news/pressArchive/press47.html [Accessed 2003 Sep 3]

  34. Neves SR, Iyengar R. Modelling of signalling networks. Bioessays 2002; 24(12): 1110–7

    Article  PubMed  CAS  Google Scholar 

  35. Greco WR, Bravo G, Parsons JC. The search for synergy: a critical review from a response surface perspective. Pharmacol Rev 1995: 47; 331–85

    PubMed  CAS  Google Scholar 

  36. Chou TC, Talalay P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul 1984; 22: 27–55

    Article  PubMed  CAS  Google Scholar 

  37. Bunow B, Weinstein JN. COMBO: a new approach to the analysis of drug combinations in vitro. Ann N Y Acad Sci 1990; 616: 490–4

    Article  Google Scholar 

  38. Pharsight®. Pharsight® Trial Simulator® quick tour [online]. Available from URL: http://www.pharsight.com/literature/pts_web_tour.pdf [Accessed 2003 Sep 3]

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Acknowledgements

Preparation of this manuscript was supported by Cyclacel Ltd, Dundee, UK. The author has no financial interest in any of the software discussed in this review.

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  1. Cyclacel Ltd, James Lindsay Place, Dundee, DD1 5JJ, UK

    Robert C. Jackson

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  1. Robert C. Jackson
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Correspondence to Robert C. Jackson.

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Jackson, R.C. Predictive Software for Drug Design and Development. Pharm Dev Regul 1, 159–168 (2003). https://doi.org/10.1007/BF03257375

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