Changing Paradigms in Drug Discovery
Donnersbergstrasse 9, D-67256 Weisenheim am Sand, Germany.
Within the past few decades the strategies of drug design changed significantly [1,2]. Whereas chemistry, biological activity hypotheses, and animal experiments dominated drug research, especially in its "golden age", from the sixties to the eighties of the last century, many new technologies developed over the past twenty years. A vast amount of new drugs was expected to result from combinatorial chemistry and high-throughput screening. In the meantime, most groups learned that this is not the case; the yield of new drug candidates was relatively poor and the number of NCEs is steadily declining. It is now evident that blind synthesis and screening are just a waste of resources: "when trying to find a needle in a haystack, the best strategy might not be to increase the size of the haystack" [3].
Virtual screening selects compounds or libraries that are either lead-like, drug-like, have a good potential of oral bioavailability, or are similar to a lead, by sets of rules (e.g. the Lipinski rule of five, defining the potential of a compound to be orally bioavailable: MW < 500, log P < 5, H donors < 5, H acceptors < 10), "garbage filters" (to eliminate undesirable compounds), neural nets (e.g. defining drug-like character), pharmacophore analyses, similarity analyses, or docking and scoring [4,5].
In the future, the rational design of ligands will start from a protein 3D structure and a series of building blocks. Computer programs will screen and assemble these building blocks to molecules which fit the binding site, to end up with a relatively small number of compounds that are highly active and synthetically easily accessible leads for further optimization [6].
Successful applications of virtual screening, 3D structure-based design and docking demonstrate the value of these techniques in the selection and rational design of high-affinity protein ligands [7]. But high affinity to a disease-relevant target is only a necessary property of a drug candidate, not a sufficient one; in addition, a drug must be orally bioavailable, it should have favorable pharmacokinetics and should lack serious side effects. The presentation will also discuss the following questions:
- what are the reasons for the productivity gap between R&D costs and the number of NCE's?
- is there a "druggable genome"?
- is target focus always best?
- is poor ADME the main problem in clinical development?
- what's wrong and could we do better?
References
[1] Kubinyi, H., Random vs. rational drug discovery, Curr. Drug Discov., Oct. 2001, pp. 9-11.
[2] Kubinyi, H., Drug Research - Myths, Hypes and Reality, Nature Rev. Drug Discov. 2, 665-668 (2003).
[3] Lahana, R., How many leads from HTS? Drug Discov. today 4, 447-448 (1999).
[4] Böhm, H.-J., and Schneider, G., Eds., Virtual Screening for Bioactive Molecules (volume 10 of Methods and Principles in Medicinal Chemistry, Mannhold, R., Kubinyi, H., and Timmerman, H., Eds.), Wiley-VCH, Weinheim, 2000.
[5] Böhm, H.-J., and Schneider, G., Eds., Protein-Ligand Interactions. From Molecular Recognition to Drug Design, (volume 19 of Methods and Principles in Medicinal Chemistry, Mannhold, R., Kubinyi, H., and Timmerman, H., Eds.), Wiley-VCH, Weinheim, 2003.
[6] Kubinyi, H., Changing paradigms in drug discovery, The Chemical Theatre of Biological Systems, Bozen, Italy, 2004 (www.kubinyi.de/kubinyi-paradigms-bozen-2004.pdf; in press)
[7] Kubinyi, H., Success stories of computer-aided design, in: Computer Applications in Pharmaceutical Research and Development, Sean Ekins, Ed., Wiley, New York, in press.