In the last decade or so, there has been a significant development in the technology that provides a wealth of experimental in-vivo and in-vitro data. This data not only covers toxicity and ADME endpoints but also includes a multiple pharmacokinetic and pharmacodynamic properties as well. The surge in data has led to better methodologies by which project teams pick the best compounds to nominate as clinical candidates. However without a sophisticated tool it is difficult to make sense of each data point and the wealth of data challenges teams to focus simultaneously on all properties at the simultaneously. We have developed a probabilistic multi-parametric scoring methodology using data based on Abbott's proprietary in-vivo and in-vitro assay data as well as in silico ADMET models. This method incorporates error in the data to assign probability of success of compounds considering multiple properties simultaneously. We have used TIBCO Spotfire to deploy this tool to the Abbott Discovery community as an   easy to use GUI as well as it has very useful visualizations to support decision making process. This talk will focus on the technology and how it has enabled medicinal chemists in making better informed data driven decisions.

ALOHA-MPS: A novel compound scoring and selection tool

245th National Meeting (2013)

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