INTERVIEW WITH ROGER HAYES
“ ...microsampling is coming into the vernacular for bioanalysis. ”
INTERVIEW WITH ROGER HAYES MPI RESEARCH
This interview is a transcript taken from a podcast interview with Roger Hayes, Senior Vice President, DMPK, at MPI Research (Mattawan, MI, US).
In this short interview Roger shares his experience in microsampling and what drew him to the field. He discusses some of the key challenges still faced by the community.
Listen to the full audio of the interview here. Q 16
Q Q
Could you tell us more about your current work?
I’ve had the fortune of managing an organization that supports predominantly nonclininal bioanalysis drug metabolism and pharmacokinetic(PK); this includes management of an ADME group doing drug metabolism biodistribution and bioanalytical work in both large and small molecules. I also have responsibility for analytical work in supporting nonclinical toxicology as formulation support.
My current works spans the whole gamut of DMPK from discovery through to Phase I and then to Phase III for the bioanalysis. So it includes a lot of the bits and pieces that go to drug development.
What does microsampling mean to you?
Very simply and practically a microsample is a small aliquot that is less than 50 µl. That aliquot could be serum, plasma, blood or even cerebral spinal fluid.
What began your interest in microsampling?
My interest in microsampling actually occurred in parallel with the need to improve assay sensitivity.
It really kicked off for me in the 2000s when we were doing a lot of discovery screening involving new molecular entities that medicinal chemists would synthesize. They were very keen on understanding the pharmacokinetics. You didn’t have a lot of test articles to dose a lot of animals, which then led to finding techniques for even smaller volumes to reduce the amount of test articles being dosed, but also come up with good quality data.
And so serial sampling was the ultimate technique for data quality and so with those two components, small amount of test article with high quality data in a fast as possible scenario, this led to techniques and workflows that relied on small volume of sample.
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