Oscar Della Pasqua

Aims To develop a drug–disease model describing iron overload and its effect on ferritin response in patients affected by transfusion‐dependent haemoglobinopathies and investigate the contribution of interindividual differences in demographic and clinical factors on chelation therapy with deferiprone or deferasirox. Methods Individual and mean serum ferritin data were retrieved from 13 published studies in patients affected by haemoglobinopathies receiving deferiprone or deferasirox. A nonlinear mixed effects modelling approach was used to characterise iron homeostasis and serum ferritin production taking into account annual blood consumption, baseline demographic and clinical characteristics. The effect of chelation therapy was parameterised as an increase in the iron elimination rate. Internal and external validation procedures were used to assess model performance across different study populations. Results An indirect response model was identified, including baseline ferritin concentrations and annual blood consumption as covariates. The effect of chelation on iron elimination rate was characterised by a linear function, with different slopes for each drug (0.0109 [90% CI: 0.0079–0.0131] vs . 0.0013 [90% CI: 0.0008–0.0018] L/mg mo). In addition to drug‐specific differences in the magnitude of the ferritin response, simulation scenarios indicate that ferritin elimination rates depend on ferritin concentrations at baseline. Conclusion Modelling of serum ferritin following chronic blood transfusion enabled the evaluation of drug‐induced changes in iron elimination rate and ferritin production. The use of a semi‐mechanistic parameterisation allowed us to disentangle disease‐specific factors from drug‐specific properties. Despite comparable chelation mechanisms, deferiprone appears to have a significantly larger effect on the iron elimination rate than deferasirox.