Abstract
Currently, go/no-go decision making in proof-of-concept (POC) multiple sclerosis (MS) trials for promising drug/dose selection are predominantly qualitative in nature. POC trials employ placebo corrected magnetic resonance imaging lesion counts (MRI-T2 counts) as endpoint, whereas, phase 3 trials employ annualized relapse rate at 24 months (ARR-24) as the efficacy endpoint. The objective of the current investigation is to provide a quantitative framework that can aid informed decision making in MS clinical drug development. Blinded summary level data on MRI-T2 lesions at 12 months and aggregate ARR-24 across six clinical development programs digitized from a Food and Drug Administration (FDA)’s 2012 science day presentation were utilized to develop a pharmaco-statistical model linking the MRI-T2 lesions at 12 months with ARR-24. The developed MRI-T2-ARR-24 model was further evaluated by clinical trial simulations and was used to predict the probability of phase 3 clinical trial success given the MRI results in POC trial. The MRI-T2-ARR-24 model suggested that for a unit increase in the MRI-T2 counts, the mean predicted ARR-24 increased by 10%. The model correctly predicted the trial outcomes of four out of the six published MS trials with individual trial predicted ARR-24 values within ± 60% bias. Clinical trial simulations indicated that at least 60% reduction in MRI-T2 counts from placebo in proof-of-concept trials (at any dose or regimen) is needed to achieve a minimum of 80% probability of technical success in the phase 3 trial. Given the competitive landscape in the MS drug development, the decision tool kit could aid in reducing the failure rate in MS phase 3 trials and provide a quantitative framework for more informed dose selection. Further it is anticipated that for significant formulation changes post approval, the MRI-T2-ARR-24 model may be used for bridging efficacy (ARR-24) based only on MRI-T2 data.