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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.

A role for hereditary influences in the susceptibility for chronic obstructive pulmonary disease (COPD) is widely recognized. Cytotoxic lymphocytes are implicated in COPD pathogenesis, and functions of these leukocytes are modulated by interactions between their killer cell Ig-like receptors (KIR) and human leukocyte antigen–Class I (HLA–Class I) molecules on target cells. We hypothesized HLA–Class I and KIR inheritance affect risks for COPD. HLA–Class I alleles and KIR genotypes were defined by candidate gene analyses in multiple cohorts of patients with COPD (total n = 392) and control smokers with normal spirometry (total n = 342). Compared with controls, patients with COPD had overrepresentations of HLA-C*07 and activating KIR2DS1, with underrepresentations of HLA-C*12. Particular HLA-KIR permutations were synergistic; e.g., the presence of HLA-C*07 + KIR2DS1 + HLA-C12null versus HLAC*07null + KIR2DS1null + HLA-C12 was associated with COPD, especially among HLA-C1 allotype homozygotes. Cytotoxicity of COPD lymphocytes was more enhanced by KIR stimulation than those of controls and was correlated with lung function. These data show HLA-C and KIR polymorphisms strongly influence COPD susceptibility and highlight the importance of lymphocyte-mediated cytotoxicity in COPD pathogenesis. Findings here also indicate that HLA-KIR typing could stratify at-risk patients and raise possibilities that HLA-KIR axis modulation may have therapeutic potential.

Developing an accurate and interpretable model to predict an individual’s risk for Coronavirus Disease 2019 (COVID-19) is a critical step to efficiently triage testing and other scarce preventative resources. To aid in this effort, we have developed an interpretable risk calculator that utilized de-identified electronic health records (EHR) from the University of Alabama at Birmingham Informatics for Integrating Biology and the Bedside (UAB-i2b2) COVID-19 repository under the U-BRITE framework. The generated risk scores are analogous to commonly used credit scores where higher scores indicate higher risks for COVID-19 infection. By design, these risk scores can easily be calculated in spreadsheets or even with pen and paper. To predict risk, we implemented a Credit Scorecard modeling approach on longitudinal EHR data from 7,262 patients enrolled in the UAB Health System who were evaluated and/or tested for COVID-19 between January and June 2020. In this cohort, 912 patients were positive for COVID-19. Our workflow considered the timing of symptoms and medical conditions and tested the effects by applying different variable selection techniques such as LASSO and Elastic-Net. Within the two weeks before a COVID-19 diagnosis, the most predictive features were respiratory symptoms such as cough, abnormalities of breathing, pain in the throat and chest as well as other chronic conditions including nicotine dependence and major depressive disorder. When extending the timeframe to include all medical conditions across all time, our models also uncovered several chronic conditions impacting the respiratory, cardiovascular, central nervous and urinary organ systems. The whole pipeline of data processing, risk modeling and web-based risk calculator can be applied to any EHR data following the OMOP common data format. The results can be employed to generate questionnaires to estimate COVID-19 risk for screening in building entries or to optimize hospital resources.

BackgroundNo medical treatment has proven efficacy for acute exacerbations of idiopathic pulmonary fibrosis (AE-IPF), and this syndrome has a very high mortality. Based on data indicating humoral autoimmune processes are involved in IPF pathogenesis, we treated AE-IPF patients with an autoantibody reduction regimen of therapeutic plasma exchange, rituximab, and intravenous immunoglobulin. This study aimed to identify clinical and autoantibody determinants associated with survival after autoantibody reduction in AE-IPF.MethodsTwenty-four(24) AE-IPF patients received the autoantibody reduction regimen. Plasma anti-epithelial autoantibody titers were determined by HEp-2 indirect immunofluorescence assays in 22 patients.ResultsMean age of the patients was 70 + 7 years old, and 70% were male. Beneficial clinical responses that occurred early during therapy were a favorable prognostic indicator: supplemental O2 flows needed to maintain resting SaO2>92% significantly decreased and/or walk distances increased among all 10 patients who survived for at least one year. Plasma anti-HEp-2 autoantibody titers were ~-three-fold greater in survivors compared to non-survivors (p1:160 were present in 75% of the evaluable one-year survivors, compared to 29% of non-survivors, and 10 of 12 patients (83%) with anti-HEP-2 titers ConclusionsAutoantibody reduction therapy is associated with rapid reduction of supplemental oxygen requirements and/or improved ability to ambulate in many AE-IPF patients. Facile anti-epithelial autoantibody assays may help identify those most likely to benefit from these treatments.

BackgroundThe processes that result in progression of idiopathic pulmonary fibrosis (IPF) remain enigmatic. Moreover, the course of this disease can be highly variable and difficult to accurately predict. We hypothesized analyses of body mass index (BMI), a simple, routine clinical measure, may also have prognostic value in these patients, and might provide mechanistic insights. We investigated the associations of BMI changes with outcome, plasma adipokines, and adaptive immune activation among IPF patients.MethodsData were analyzed in an IPF discovery cohort (n = 131) from the University of Pittsburgh, and findings confirmed in patients from the University of Alabama at Birmingham (n = 148). Plasma adipokines were measured by ELISA and T-cell phenotypes determined by flow cytometry.ResultsTransplant-free one-year survivals in subjects with the greatest rates of BMI decrements, as percentages of initial BMI (>0.68%/month), were worse than among those with more stable BMI in both discovery (HR = 1.8, 95%CI = 1.1-3.2, p = 0.038) and replication cohorts (HR = 2.5, 95%CI = 1.2-5.2, p = 0.02), when adjusted for age, baseline BMI, and pulmonary function. BMI decrements >0.68%/month were also associated with greater mortality after later lung transplantations (HR = 4.6, 95%CI = 1.7-12.5, p = 0.003). Circulating leptin and adiponectin levels correlated with BMI, but neither adipokine was prognostic per se. BMI decrements were significantly associated with increased proportions of circulating end-differentiated (CD28null) CD4 T-cells (CD28%), a validated marker of repetitive T-cell activation and IPF prognoses.ConclusionsIPF patients with greatest BMI decrements had worse outcomes, and this effect persisted after lung transplantation. Weight loss in these patients is a harbinger of poor prognoses, and may reflect an underlying systemic process, such as adaptive immune activation.