Neurological and neurodegenerative disorders, including Alzheimer?s disease (AD), Parkinson?s disease (PD), Huntington?s disease (HD) and amyotrophic lateral sclerosis (ALS), are characterized by heterogeneous disease progression. As a group, these complex diseases are influenced by an interplay between several genetic and environmental factors. Drug development for these diseases could benefit from a framework that selects more homogeneous participants for inclusion in clinical trials, but current methods have largely failed in this regard. In ALS, both slowly and rapidly progressing patients have been identified as confounding statistical analyses of clinical trials. Statistically speaking, heterogeneous disease progression rates in ALS clinical trials contribute to both trial arm misbalances and high variances of study populations. These issues increase the size, duration and cost of clinical trials in neurological diseases and contribute to high failure rates. In our successful phase 1 SBIR grant, we used ALS as a model disease to develop our neurological disease product prototype based on machine learning disease progression models that improves trial arm randomization and stratifies participants according to disease progression. We worked with a clinical trialist to develop a user interface capable of returning a randomization group assignment in real-time. Our ForecastOne Trials prototype product has passed IRB review and will be used in a 12-site clinical trial starting in fall, 2017. The objectives of this phase 2 SBIR are to convert the ALS neurological product prototype into a commercializable, robust, scalable, market-ready product with a versatile API and to develop a pipeline of models and applications that will expand our product offerings of drug development and clinical trial optimization tools for additional neurodegenerative indications.
Aim 1 : Construct a robust, scalable platform infrastructure for the neurological product based on a central Application Programming Interface (API) that receives user input and returns actionable information in real-time. The prototype ForecastOne Trials tool will be developed into a robust, scalable 21 CFR part 11 compliant product using ALS as a first indication that allows the rapid deployment of additional applications and disease indications.
Aim 2 : Develop a pipeline of neurodegenerative models and applications to populate the neurological product. We will add additional disease indications to the product created in Aim 1, including AD, PD and HD. The suite of marketable products that we create following this phase 2 grant will answer drug development needs of a full portfolio of neurodegenerative diseases. Ultimately, we see a series of machine learning models and applications aimed at solving drug development issues for multiple disease areas, including oncology, cardiology, infectious diseases, metabolic disorders, autoimmune diseases and respiratory diseases, etc. These models and applications will vastly increase the speed and efficiency of drug development, resulting in faster, cheaper, more efficient drug trials that yield numerous new medications to ease human pain and suffering.
This work builds on ForecastOne Trials, a prototype drug development tool created during our successful phase 1 SBIR grant using machine-learning generated predictions of patient disease progression. The phase 2 program will result in a suite of marketable products designed to answer drug development needs of a full portfolio of neurodegenerative diseases. These models and applications will vastly increase the speed and efficiency of drug development in multiple disease areas, resulting in faster, cheaper, more efficient drug trials that yield numerous new medications to ease human pain and suffering.
