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BACKGROUND: Diabetes is a complex, multi-symptomatic disease whose complications drives increases in healthcare costs as the diabetes prevalence grows rapidly world-wide. Real-world electronic health records (EHRs) coupled with patient biospecimens, biological understanding, and technologies can characterize emerging diagnostic autoimmune markers resulting from proteomic discoveries. METHODS: Circulating autoantibodies for C-terminal fragments of adiponectin receptor 1 (IgG-CTF) were measured by immunoassay to establish the reference range using midpoint samples from 1862 participants in a 20-year observational study of type 2 diabetes and cardiovascular arterial disease (CVAD) conducted by the Fairbanks Institute. The White Blood Cell elastase activity in these patients was assessed using immunoassays for Bikunin and Uristatin. Participants were assigned to four cohorts (healthy, T2D, CV, CV+T2D) based on analysis of their EHRs and the diagnostic biomarkers values and patient status were assessed ten-years post-sample. RESULTS: The IgG-CTF reference range was determined to be 75-821 ng/mL and IgG-CTF out-of-range values did not predict cohort or comorbidity as determined from the EHRs at 10 years after sample collection nor did IgG-CTF demonstrate a significant risk for comorbidity or death. Many patients at sample collection time had other conditions (hypertension, hyperlipidemia, or other risk factors) of which only hypertension, Uristatin and Bikunin values correlated with increased risk of developing additional comorbidities (odds ratio 2.58-13.11, P<0.05). CONCLUSIONS: This study confirms that retrospective analysis of biorepositories coupled with EHRs can establish reference ranges for novel autoimmune diagnostic markers and provide insights into prediction of specific health outcomes and correlations to other markers.
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Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for "hands-on" computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements "best-practice" methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.
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Acceso a la Información , Internet , Hígado/efectos de los fármacos , Toxicogenética , Benzbromarona/farmacología , Benzofuranos/farmacología , Humanos , Hígado/metabolismo , Hígado/patología , Omeprazol/toxicidad , Fenotipo , Transcriptoma , Triglicéridos/sangreRESUMEN
PURPOSE: Results of a study in which population-based body weight and body surface area (BSA) data were used for vial size optimization to reduce drug waste associated with administration of the i.v. anticancer agent olaratumab are reported. METHODS: A retrospective observational study was conducted to determine weight and BSA distributions in a large sample of U.S. oncology patients using data from a large electronic medical record database. Body weight and BSA values at the time of initial systemic anticancer therapy were used to compute olaratumab dose requirements in a cohort of patients with soft tissue sarcoma; those data were analyzed to derive estimates of drug waste likely to result from the use of various proposed olaratumab vial sizes in combination with an existing 500-mg size. Weight and BSA distributions were calculated for additional cohorts of patients with 7 other cancer types. RESULTS: Median weight values in men (n = 1,179) and women (n = 1,078) with soft tissue sarcoma were 82.55 kg (interquartile range [IQR], 72.58-95.53 kg) and 68.69 kg (IQR, 58.51-84.28 kg), respectively. Modeling of olaratumab dosing scenarios indicated that use of the 500-mg vial only would result in estimated average drug waste of 234 mg per patient per administration; analysis of various potential vial size combinations showed that waste could be reduced by 87.6% with the addition of a 190-mg vial size. CONCLUSION: Analysis of real-world patient weight and BSA data allowed olaratumab vial size optimization to enable maximal dosing flexibility with minimal drug waste.