SYSTEMATIC APPROACH TO THE DESCRIPTION OF THE EVIDENCE SUPPORTING EMA REGULATORY OPINIONS ON ORPHAN MEDICINAL PRODUCTS

Objective:The study of rare diseases struggle with the difficulties ofachieving statistical demonstration in small populations using conven-tional statistics. The EU legislation determines that market access tonew treatments requires the same level of evidence for rare and highlyprevalent diseases, but the reality is that regulators often have to takedecisions on orphan medicinal products (OMP) based on limitedamounts of evidence.Objective: To describe which is the amount and type of informationon which the EMA has based opinions on OMP applications, with theintention to provide an standard that may later be used to model thepotential impact of new methodological and statistical approaches tothe study of OMP .Method:In the frame of an international FP7 collaboration (FP7-HEALTH-2013-INNOVATION-1‘Advances in Small Trials dEsignfor Regulatory Innovation and eXcellence’), a review of the regulatoryassessment strategies using public information on EMA treatmentapprovals is being conducted. To improve extrapolability of inferences,a diseases classification based on clinical disease characteristics thatare relevant for methodology has been proposed, built using a MultipleCorrespondence Analysis (MCA) to guide the proposal of disease clus-ters.Results:A diseases classification has been proposed based on MCAof 76 disease characteristics which are relevant for design of studies asapplied to 27 diverse medical conditions. Relevant dimensions explain-ing data were identified and potential clusters validated clinically; 6groups of diseases were proposed sharing similar methodologicalapproaches to their study. A total of 91 OMP have been approvedsince inforcement of the EU Regulation on OMP (EC141/2000) untilApril 2014, for a total of 105 medical indications, which have beenclassified into 6 groups.Conclusions:Data on EMA opinions on OMP have been systematisedaccording to a new diseases classification focused on methodology, inorder to describe the evidence supporting regulatory decisions.