About & Team

Motivation

The Center for Data-Driven Drug Development and Treatment Assessment (DATA) intends to establish a center for precompetitive research into drug design, drug treatment assessment, drug repositioning, patient phenotyping and quantitative pharmacovigilance using novel machine learning and artificial intelligence techniques. We will work with our partners from industry and state / professional organizations whose research efforts align with the Center’s focus and who will be active collaborators in shaping the future paradigm of integrated and cost-effective drug design, health monitoring and patient care.

Center for Data-Driven Drug Development and Treatment Assessment (DATA) will focus on six main areas with the goal of significantly accelerating overall drug development and assessment of the outcomes associated with these treatments, while reducing national health care expenditures. These research areas represent the general capabilities that the Center will offer, but can be readily extended to meet the needs of participating industry partners:

1. Development, testing, and validation of algebraic, machine learning, and artificial intelligence techniques for drug development, treatment monitoring and patient phenotyping with respect to drugs/treatments – State of the art algebraic, machine learning, and artificial intelligence techniques together with advances in treatment monitoring will be brought to bear on discovering new approaches for drug screening, drug repurposing/repositioning, estimation of patient phenotypes related to treatment outcomes/efficacy and exploring drug toxicity/adverse effects; using public, academic, and industry partner-provided patient and drug databases. These include advanced tensor algebraic techniques that were designed to systematically integrate auxiliary information currently ignored or under-utilized by extant methods. Additionally, novel machine learning-based methodologies for mobile/at-home patient monitoring will also be further developed.
2. Design and development of advanced artificial intelligence methods for treatment assessment. The Center will focus on the design and implementation of monitoring systems and algorithms that can continuously monitor the health of individuals receiving different types of drugs and treatments.
3. Provide an industry-wide and vendor-agnostic Secure Data Hub with third-party private search capabilities for the development, testing, validation and assessment of drugs and treatments – By applying advanced algebraic and statistical methods, the Center will create large, encrypted, and integrated databases of patient and pharmaceutical data for which the participating companies need only share full encryptions of their proprietary databases. Such databases enable third-party private searches, allowing partners to search securely and anonymously for data related to therapies or patient populations of interest.
4. Enable federated machine learning for drug design, health informatics and pharmacovigilance over encrypted databases – Center investigators have adapted several machine learning algorithms for use over encrypted data. The Center will further integrate these algorithms into a federated learning framework. Such technologies will enable machine learning over databases that cannot be integrated into the Secure Data Hub.
5. Enable data driven approaches to developing heath care policies with respect to drugs and treatments – Utilizing the patient phenotypes, health analytics, and comprehensive adverse event/drug interactions data developed and collected by the Center, policy makers, professional organizations, and patient advocacy organizations will be able to better revise standards of care and patient outreach methods to improve patient outcomes in response to drugs and treatments while reducing costs related to unnecessary or ineffective therapies.
6. Provide unique lab resources for development and validation of patient phenotypes and new drug discovery – University of Michigan provides unique high throughput in-vitro and in-vivo capabilities, facilitated by multiple labs, centers and cores, which will be made available as resources for developing new drugs informed by discovered patient phenotypes and post-market surveillance data, closing the loop on the pharmaceutical development pipeline.