DOI: 10.25934/pr00007587
title: Model-Informed Drug Development (MIDD) in Alzheimer's Disease

description: Recent estimates indicate that the number of people worldwide living with dementia is expected to rise to 139 million cases in 2050. Alzheimer's disease (AD) accounts for an estimated 60% to 80% of cases of dementia. Despite the looming public health crisis due to AD, therapies available in the U.S. for the last two decades only treated the symptoms of the disease. There is a pressing need for effective therapeutic interventions and there is a growing interest in intervening earlier in the disease process. This requires a robust understanding of disease progression across all stages of disease. The Critical Path for Alzheimer’s Disease (CPAD) is a nonprofit, pre-competitive consortium of the Critical Path Institute (C-Path). CPAD focuses on integrating precompetitive patient-level data from high quality Alzheimer’s disease (AD) clinical trials and observational studies, and transforming those data into actionable knowledge to advance novel regulatory-grade quantitative drug development tools.  We develop mathematical models of disease progression that will enable a user to simulate clinical trials in Alzheimer’s disease. The trial simulations will allow the user to identify the appropriate patients for a trial as well as understand how the trial should be designed in terms of number of patients. The simulations are meant to help companies design their trials more effectively, reducing trial cost and time, and reduced patient burden. 
We plan to  develop mathematical models of disease progression that will enable a user to simulate clinical trials in Alzheimer’s disease. Modelling approaches for disease progression models will utilize mixed-effects modeling as it readily has several advantages including incorporation of fixed observable effects to explain variability, incorporation of random unexplained variability between subjects with flexibility to include additional hierarchies (inter-study variability), flexibility in handling repeated measurements within individuals with different amount of and different spacing of time points, and ability to easily generate predicative simulations through Monte Carlo sampling. We will incorporate Clinical Outcomes Assessments and biomarkers (dependent variables). The selection of these dependent variables will be informed by the availability of relevant information, as well as the clinical meaningfulness, interpretability, as well as regulatory and drug development relevance of each proposed dependent variable. We will include apolipoprotein E4 allele (APOE4), sex, baseline age, baseline severity, other genetic and demographic data, as well as multiple biomarker candidates as covariates. The trial simulations will allow the user to identify the appropriate patients for a trial as well as understand how the trial should be designed in terms of number of patients. The simulations are meant to help companies design their trials more effectively. The trial simulation tool will benefit patients by reassuring them that they will be able to enroll in more efficiently designed clinical trials in the future.