Informatics
ics infrastructure which involves: multiple scien- tific data repositories, a data warehouse, a report- ing system and the code to draw the funnels (Figure 3).
At Prosidion we use Browser, from Dotmatics Figure 2
Six-month snapshot of a lead optimisation project using a
trellis of monthly funnels. See Figure 1 for a detailed explanation of a single funnel and its annotations
question). This factor was removed by moving Assay 3 up to the top of the cascade and therefore eliminating the need for assay submission deci- sions. Secondly, the scientists performing the assay sometimes found it hard to locate the compounds. This was solved by sending a separate vial of the compound straight to Assay 3 without going via Assay 1 and 2. The third change was to run the assay once a week, regardless of volume of sub- mission. These changes were implemented during August, and the August and September funnels clearly signal the impact these changes have had on Assay 3 turnaround. Furthermore, since Assay 3 is an in vitro surrogate of the more complex in vivo Assay 5, there is a large knock-on effect on Assay 5 and 6, the data from which are available two months faster. Finally, by removing the gated selec- tion step between assay 2 and 3, most of the com- pounds are tested in all three frontline assays (1, 2 and 3), providing more scientific knowledge and understanding of the structure, activity and prop- erty relationships in the class of compounds, and therefore providing designers with better insight into how to design better compounds that have a greater chance of passing further down the cascade in the next cycle16,17.
Roll out and implementation The funnel diagrams have been rolled out as part of Prosidion’s centralised reporting system, mean- ing that everyone within research has access to them. They are updated automatically once a day, giving an up-to-date view of the cascade within a particular drug discovery project. The funnels are embedded in, and drawn from, a wider informat-
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(
www.dotmatics.com), as our reporting system. This is a highly versatile web-based reporting tool, which can be easily extended to either link out to or contain in-house functionality. The roll out of the funnel diagrams are a good example of this flexibility. In our distribution of Browser there is a tab placed at the top of each research project’s query page that allows users to go directly to the funnel diagrams for that project. This is in line with the aim of users only having to go to one cen- tralised location from which they can then link out to other tools. The funnels are embedded into Browser via a jsp page. This jsp page requests the data required to draw the funnels from our data warehouse. Our data warehouse is a centralised database of all our assay and compound data gath- ered together from several data sources and was implemented in collaboration with Raptor Infor- matics (
www.raptorinformatics.com). It provides us with a centralised repository that our reporting system can query efficiently. A set of materialised views written especially for the funnels, that refresh once a day, collate and pivot the relevant compound and assay attrition and timeline data out of our repositories into a format that can be easily translated into the funnel diagrams. Our main repository of chemical and biological data is ActivityBase™, from IDBS (
www.idbs.com). Finally the jsp page performs some additional for- matting and sends the data to a java applet, which draws the funnels.
Issues to be aware of
The benefits of simply and transparently visualis- ing turnaround time and attrition within a drug discovery project are, we believe, fairly self evi- dent. However, our experiences have highlighted that there are some issues which need to be con- sidered and navigated, ideally in advance of implementation.
The first of these is the choice of date from which the turnaround time is calculated. This is a simple problem to state, but it can be deceptively intricate to get right. Many of the issues stem directly from the perceptions of people working within the processes about how these data might be interpreted, used (and potentially abused). To illustrate the point, a database which tracks a test- ing workflow may have a number of date entries, such as those in Figure 4.
Drug Discovery World Winter 2011/12
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