کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1361228 | 981459 | 2011 | 11 صفحه PDF | دانلود رایگان |

Pharmaceutical companies routinely collect data across multiple projects for common ADME endpoints. Although at the time of collection the data is intended for use in decision making within a specific project, knowledge can be gained by data mining the entire cross-project data set for patterns of structure–activity relationships (SAR) that may be applied to any project. One such data mining method is pairwise analysis. This method has the advantage of being able to identify small structural changes that lead to significant changes in activity. In this paper, we describe the process for full pairwise analysis of our high-throughput ADME assays routinely used for compound discovery efforts at Pfizer (microsomal clearance, passive membrane permeability, P-gp efflux, and lipophilicity). We also describe multiple strategies for the application of these transforms in a prospective manner during compound design. Finally, a detailed analysis of the activity patterns in pairs of compounds that share the same molecular transformation reveals multiple types of transforms from an SAR perspective. These include bioisosteres, additives, multiplicatives, and a type we call switches as they act to either turn on or turn off an activity.
Figure optionsDownload as PowerPoint slide
Journal: Bioorganic & Medicinal Chemistry - Volume 19, Issue 12, 15 June 2011, Pages 3739–3749