Informatics


Future developments


As previously mentioned, showing just the average assay timelines in the funnels means that there is no indication of variation. In order to overcome this, each event rectangle could be populated with an error bar, or surrounded by spots to represent the individual compound times. For even further granu- larity, every compound could be plotted out in a sep- arate column of events, rather similar to the excel- lent ‘event-based analyses’ first reported by Petrillo2. However, we feel that each of these expansions of the funnels provides more information but compro- mises simplicity, and tends to erode the simultane- ous view of attrition and time. We therefore prefer to keep the funnels in their simple form with a click to expand function, where the error bars or chosen expansion can be easily switched on and off. A further possible extension of the funnel would be to track upstream through the conception and synthesis stages to registration. This would involve connecting the funnels up to a compound ideas database16,20,21. The funnels could then be used to gain insight into: how long it takes to decide to make a compound idea, how many ideas are actu- ally carried forward and whether the idea volume correlates with synthesis volume. Another event extension would be to integrate the funnels with an assay requesting system, in order to be able to directly visualise time for decision-making. Here, we have described a simple method of visualising attrition and speed in a single, easy-to- interpret view, as applied to the post-synthesis test cascade in drug discovery projects. However, given that speed and attrition are at the heart of the industry’s problems, it is trivial to envisage wider, more strategic applications of visual representation of these two issues simultaneously. For example, within a large company with numerous projects at various stages through the discovery and develop- ment process, one could readily apply the funnel diagram to represent real-time residence time and attrition across the portfolio. Similarly, a portfolio containing a number of in-licensing opportunities which are being evaluated in parallel at various stages of maturity could also be readily visualised in this way. Furthermore, the funnel diagram could find utility in cross-company benchmarking reports. Participating companies routinely com- pare their success/attrition rates and residence time per phase. These dimensions are usually compared separately, leaving questions about whether the ‘fast’ companies suffer higher attrition later, or operate a different attrition model. The funnel dia- gram would enable company speed and attrition profiles to be compared directly.


Drug Discovery World Winter 2011/12


In drug discovery we need to improve the speed and quality simultaneously. Going fast in the wrong direction is not fruitful and producing the highest quality too slowly will be commercially unattractive. Thus it is valuable to practising scien- tists, managers and leaders to be able to view speed and attrition simultaneously. Therefore we offer this simple funnel representation as a small yet scalable and, we believe, more widely applicable tool to visualise these two most important param- eters in addressing the challenges of improving drug discovery.


DDW


Continued from page 48


Dr Linda Hirons is currently CEO at Amethyst Informatics Ltd (linda.hirons@amethystinformat- ics.co.uk), having spent the past 3½ years working at Prosidion Ltd, a research-driven biotech compa- ny in Oxford, within the Research Technologies team as an informatics scientist. Previously she was a Post Doc at Lilly, using the KNIME pipelining technology to develop an inverse structure-based design tool. She obtained her PhD at Sheffield University under the supervision of Professor Peter Willett and Professor Chris Hunter, looking at activity fingerprints in DNA.


Dr Craig Johnstone joined (Astra)Zeneca Pharmaceuticals in 1994. He has worked in oncolo- gy, inflammation and cardiovascular research pro- grammes. As the Director of Chemistry, Cardiovascular & Gastrointestinal Research Area in the UK, he became interested in the improvement of the discovery process and in 2008, in addition to his line management role, he was appointed Value Chain Leader, CV&GI at AstraZeneca. He joined Prosidion in 2011, as Head of Medicinal Chemistry.


Colin Sambrook-Smith graduated in chemistry from the University of Bristol. He joined Courtaulds plc (later Akzo Nobel) and spent more than 10 years working on materials modelling and simulation. In 1999 he joined OSI Pharmaceuticals and focused on the structure- based design of kinase inhibitors for oncology. He transferred to Prosidion in 2006 and is Head of Research Technologies, responsible for informat- ics, computational chemistry and array chemistry.


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13 Carleysmith, SW et al (2009). Implementing lean sigma in pharmaceutical research and development: a review by practitioners. R&D Manag. 39, 95-105. 14 Uitdehaag, JCM (2011). The seven types of drug discovery waste: toward a new lean for the drug industry, Drug Discov. Today, 16, 369-371. 15Walker, SM and Davies, BJ (2011). Deploying continuous improvement across the drug discovery value chain, Drug Discov. Today, 16, 467-471. 16 Plowright, AT et al (2011). Hypothesis driven drug design: improving quality and effectiveness of the design- make-test-analyse cycle, Drug Discov. Today, (in press), doi:10.1016/j.drudis.2011. 09.012. 17 Gleeson, MP et al (2011). Probing the links between in vitro potency, ADMET and physicochemical parameters, Nat. Rev. Drug Disc., 10, 197-208. 18 Shook, J (2010). How to change a culture: lessons from NUMMI. MIT Sloan Manag. Rev. 51, 63-68. 19 Deming, WE (1993). The New Economics for Industry, Government, Education, second edition. 20 Lee, M et al (2011). DEGAS: Sharing and tracking target compound ideas with external collaborators, J. Chem. Inf. Model (in press), DOI: 10.1021/ci2003297. 21 Brodney, MD et al (2009). Project-focused activity and knowledge tracker: a unified data analysis, collaboration and workflow tool for medicinal chemistry project teams J. Chem. Inf. Model. 49, 2639-2649.


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