AUTHOR=Foksinska Aleksandra , Crowder Camerron M. , Crouse Andrew B. , Henrikson Jeff , Byrd William E. , Rosenblatt Gregory , Patton Michael J. , He Kaiwen , Tran-Nguyen Thi K. , Zheng Marissa , Ramsey Stephen A. , Amin Nada , Osborne John , UAB Precision Medicine Institute , Might Matthew , Barnes Stephen , Byrd William E. , Chen Mei-Jan , Crouse Andrew B. , Crowder Camerron M. , Crumbley Mary E. , Eckenrode Madeline , Fargason Crayton A. , Fehrmann Nathaniel , Foksinska Aleksandra , He Kaiwen , Huls Forest , Jarrell Matthew , Jenkins Lindsay , McCalley Meg , Might Matthew , Osborn Tamsyn , Patton Michael J. , Pollard Elizabeth , Rosenblatt Gregory , Rucka Sienna , Southern Nicholas T. , Tran-Nguyen Thi K. , Tinglin Jillian , Whitlock Jordan H. TITLE=The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren JOURNAL=Frontiers in Artificial Intelligence VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.910216 DOI=10.3389/frai.2022.910216 ISSN=2624-8212 ABSTRACT=

There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.