Our Drug Transporter Sciences team at Cyprotex are delighted to announce publication of a review article in Expert Opinion on Drug Metabolism and Toxicology entitled ‘Studying the Right Transporter at the Right Time: An In Vitro Strategy for Assessing Drug-Drug Interaction Risk during Drug Discovery and Development’.
As our understanding of the role of drug transporters in clinical drug-drug interactions (DDIs) has developed, the list of transporters requiring in vitro study by regulators has grown to accommodate assessment of risk for new drugs. Currently, ten transporters require routine study prior to regulatory NDA submission. Getting the timing wrong for these investigations could result in in vitro data being generated either 1) too early in the drug discovery/development timeline and potentially becoming surplus to requirements if the investigational drug fails for reasons of poor pharmacokinetics (and efficacy) or toxicity, or 2) too late to influence finalisation of the clinical development plan resulting in perhaps unnecessary co‑medication exclusions that impact patient recruitment and thus delay clinical trials. In either case, there will be a cost and resource penalty, with the overall impact being considerably cheaper for the former compared with the latter. To minimise these development risks, project teams should study the right transporters at the right time for their investigational drug and the authors (Dr’s Robert Elsby, Hayley Atkinson, Philip Butler and Rob Riley) have tried to address this in their review article by proposing in vitro strategies that could be employed to either mitigate/remove transporter DDI risk during development through frontloading certain studies, or to manage (contextualise) DDI risk to patients in the clinical setting.
In the article, an overview of clinically relevant drug transporters and observed DDIs is provided, alongside presentation of key considerations/recommendations for in vitro study design when evaluating drugs as inhibitors or substrates of transporters. Guidance on identifying critical victim co‑medications and their clinically relevant disposition pathways, and using mechanistic static equations for quantitative prediction of DDI (demonstrating a 97% predictive accuracy for 28 statin DDIs) is also compiled. To truly alleviate or manage clinical risk, the industry would benefit from moving away from current regulatory qualitative basic static equation approaches to quantitative mechanistic DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention. Furthermore, such a mechanistic approach can be used towards either mitigating perpetrator DDI risk early during candidate selection, or managing clinical risk and aiding patient recruitment by informing labels and potentially providing an alternative to conducting costly clinical interaction studies with co-medications in the future.