How you move compounds may matter

Dispensing Processes Impact Apparent Biological Activity

From the desk of Sean Ekins, VP Science, Collaborative Drug Discovery, Inc.

pipet tips over plate

I have several collaborations that are well outside my CDD role and one of these may be of broader interest to CDD users. A recent PLOS ONE article “Dispensing Processes Impact Apparent Biological Activity as Determined by Computational and Statistical Analyses”, by myself in collaboration with Joe Olechno (Labcyte, Inc) and Antony J. Williams (Royal Society of Chemistry) suggests that how you dispense molecules may influence the results you obtain for your experiments.

Using data from Astra Zeneca patents we looked at the correlation of calculated physical properties with IC50 values obtained after serial dilution with tip-based methods or via acoustic dispensing (using a Labcyte, Inc device). In addition we analyzed more complex methods for modeling the data using pharmacophores (Discovery Studio, Accelrys). While the paper goes into far more detail, the bottom line is that compounds were more active when using the acoustic dispensing approach and that the pharmacophore for this data more closely resembled the X-ray structure derived pharmacophores, and showed the importance of hydrophobic features.

The story has been featured in several blogs by Derek Lowe (“Drug Assay Numbers, All Over the Place”), and Antony Williams and I have provided more background in “What data do we trust now in the world of high-throughput screening and public compound databases” on my own blogs about the process (“Press Releases That Morph“), the rejections (“What It Took to Get a Paper Out“) and the importance of making people aware of the issue we have highlighted.

While this is really only the beginning of the story, it is important to consider alongside a whole array of other issues that may impact your data. In addition making sure we annotate our data with as much information – such as how we dispensed the compounds, may be useful to others in the future that need to model or analyze the data.