Cheminformatics
Figure 3: This graph shows the compounds generated by three generations of transformations starting with the lead compound for the project that yielded the drug Duloxetine. Error bars show the uncertainty of the overall score for each compound due to the uncertainties in the underlying data. Only the top 10% of generations 1 and 2 were used as the basis for subsequent generations. The compounds are coloured by generation: Red is the parent, yellow generation 1, light blue generation 2 and dark blue generation 3. The drug Duloxetine was present in generation 3 and is shown by the green diamond
number subjected to detailed consideration is a major challenge. Visualisation of the compound data may help, but is unlikely to be sufficient given the complexity of the data and objectives along with the uncertain- ties discussed above11. However, a solution is pro- vided by ‘multi-parameter optimisation’ that can integrate all of this data into an assessment of the overall quality of a compound against a profile of property criteria. One example is the probabilistic scoring algorithm implemented in StarDrop3, which allows the user to define a scoring profile that represents the goals for an ideal compound (see Figure 2). For each property, the user defines the desired outcome and the importance of that criteri- on to ensure that the overall score reflects the acceptable trade-offs between different properties. A score is then calculated for each compound, reflecting the likelihood of the compound success- fully achieving the overall profile. An uncertainty in each score, due to the uncertainties in the underly- ing data, can also be calculated, to clearly identify when compounds can be confidently distinguished. Plotting this information in a ‘chemical space’ which reflects the diversity of the chemistry being explored allows ‘hot spots’ to be quickly identified in which high quality compounds are most likely to be found. An example of a chemical space can be seen in Figure 5.
Drug Discovery World Fall 2011
Illustrative example: from lead to drug A number of examples have been published that illustrate how this approach could be used to aid the search for high quality compounds7-9. Here we will summarise one example describing a retro- spective application to the lead molecule that ulti- mately gave rise to the drug Duloxetine was used as a starting point.
The application of a set of 206 transforma- tions produced 172 child compounds, which sug- gests that three generations would create approx- imately 1.7 million child compounds. Therefore, three generations were applied, but only the top- scoring 10% of the compounds in each of gener- ations one and two were used as the basis for subsequent generations. The scores were generat- ed using predictions from QSAR models of inhi- bition of the serotonin transporter and key ADME properties.
The resulting data set contained 2,208 com- pounds out of the potential ~1.7 million and the scores for these compounds are plotted in Figure 3. From this it can be seen that the score typically increases with generation – the score for the initial lead is 0.09 and the averages for the compounds in subsequent generations are 0.32, 0.44 and 0.53 respectively – indicating that the compounds’ overall quality are improv- ing. However, as the results from multiple
References 1Van de Waterbeemd, H, Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery. 2003;2:192-204. 2 Ekins, S, Boulanger, B, Swaan, P, Hupcey, M. Towards a new age of virtual ADME/TOX and multidimensional drug discovery. J. Comp. Aided Mol. Design. 2001;16:381-401. 3 Segall, M, Champness, E, Obrezanova, O, Leeding, C. Beyond Profiling: Using ADMET models to guide decisions. Chemistry & Biodiversity. 2009;6:2144-2151. 4 Chadwick, AT, Segall, MD. Overcoming psychological barriers to good discovery decisions. Drug Discovery Today. 2010;15((13/14)): 561-569. 5 Schneider, G, Fechner, U. Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery. 2005;4(8):649-663. 6 Hartenfeller, M, Schneider, G. Enabling future drug discovery by de novo design. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2011. 7 Stewart, K, Shiroda, M, James, C. Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg. Med. Chem. 2006;14:7011-22. 8 Ekins, S, Honeycutt, J, Metz, J. Evolving molecules using multi- objective optimization: applying to ADME/Tox. Drug Discov. Today. 2010;15:451-60. 9 Segall, M, Champness, E, Leeding, C, Lilien, R, Mettu, R, Stevens, B. A new Generation of Possibilities: Applying med chem transformations to guide the search for high quality leads and candidates. [Internet]. 2010 [cited 2010 March 7]. Available from:
http://www.optibrium.com/co mmunity/publications/112-a- new-generation-of-possibilities. 10 Segall, MD. Why is it still Drug Discovery? European Biopharmaceutical review. 2008.
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