Quantitative Prediction of Drug-Drug Interactions with Common Statin Co-meds: A Framework for Decision-making within Drug Discovery/Development
The first webinar in this series on drug transporters was presented by Hayley Atkinson from Cyprotex.
About the Webinar
Due to polypharmacy, drug-drug interactions (DDIs) continue to account for 5% of UK hospital admissions and as such remain a major regulatory concern. This is particularly true for common co-meds such as statins which due to their prescribing prevalence in patients with co-morbidities have high potential for DDI. Within Drug Discovery and Development we are moving away from relatively simple hazard identification of DDI potential using basic static equations detailed in regulatory guidance to actual risk analysis and mitigation using quantitative prediction of DDI. Using mechanistic static equations we can predict the AUC change of each statin due to inhibition of its critical enzyme/transporter pathway(s) so that the clinical team can make a decision on whether any potential DDI is simply a pharmacokinetic DDI or a clinically significant DDI requiring intervention. The statin mechanistic equation model requires very few in vitro input parameters and can be utilised towards aiding preclinical development candidate selection and towards reducing unexpected clinical findings in patients providing a useful tool in Drug Discovery and Early Development.
About the Speaker
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Hayley Atkinson PhD Hayley Atkinson is an Associate Principal Scientist in the permeability and drug transporter team at Cyprotex, UK where she acts as deputy scientific lead for drug transporter studies. Her research interests include prediction of transporter mediated drug-drug interactions with improved in vitro assays. Prior to joining Cyprotex in 2013, Hayley received her PhD from the University of Liverpool under Prof. Munir Pirmohamed where she conducted research on anti-epileptic drug transporters at the blood-brain barrier in the context of drug resistance. |
Watch the webinar to learn more!
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