From data to models: reducing uncertainty in benefit risk assessment: application to chronic iron overload in children
Promotor: Prof.dr. M. Danhof, Co-promotor: O.E. Della Pasqua
- F. Bellanti
- 24 september 2015
- Thesis in Leiden Repository
Growing awareness about the relevance of formal evaluation of the efficacy and safety in children has resulted into important changes in the requirements for the approval of medicines for children. In this thesis a model-based approach is proposed to ensure more efficient use of the evidence available from historical data. The work presented provides a quantitative framework for the assessment of the benefit-risk balance prior to drug approval. Three central questions are addressed, which refer to the need to ensure that accurate inferences are made about the safety and efficacy of a therapeutic intervention, especially when considering long-term outcome. In brief, we demonstrate how modelling and simulations can be applied together with multi-criteria decision analysis to address essential clinical, scientific and regulatory questions. Focus is given to the opportunity for optimising experimental protocol design (evidence generation) and integrating existing knowledge about the drug and the disease in the target population (evidence synthesis). These concepts are illustrated by real-life examples on the treatment of transfusion-dependent haemoglobinopathies, but the proposed framework can be extrapolated to a broader range of diseases and conditions. We anticipate that some of the meta-analytical elements presented here will become embedded into daily practice in pharmaceutical R&D.