Background: Neighborhood characteristics are robust predictors of overall health and mortality risk for residents. Though there has been some investigation of the role that molecular indicators may play in mediating neighborhood exposures, there has been little effort to incorporate newly developed epigenetic biomarkers into our understanding of neighborhood characteristics and health outcomes. Methods: Using 157 participants of the Detroit Neighborhood Health Study with detailed assessments of neighborhood characteristics and genome-wide DNA methylation profiling via the Illumina 450K methylation array, we assessed the relationship between objective neighborhood characteristics and a validated DNA methylationbased epigenetic mortality risk score (eMRS). Associations were adjusted for age, race, sex, ever smoking, ever alcohol usage, education, years spent in neighborhood, and employment. A secondary model additionally adjusted for personal neighborhood perception. We summarized 19 neighborhood quality indicators assessed for participants into 9 principal components which explained over 90% of the variance in the data and served as metrics of objective neighborhood quality exposures. Results: Of the nine principal components utilized for this study, one was strongly associated with the eMRS (β = 0.15; 95% confidence interval = 0.06-0.24; P = 0.002). This principal component (PC7) was most strongly driven by the presence of abandoned cars, poor streets, and non-art graffiti. Models including both PC7 and individual indicators of neighborhood perception indicated that only PC7 and not neighborhood perception impacted the eMRS. When stratified on neighborhood indicators of greenspace, we observed a potentially protective effect of large mature trees as this feature substantially attenuated the observed association.
Personalized medicine is an emerging approach for disease management. Individuals can vary dramatically in their response to cardiovascular drugs. Personalization of medication dosing is necessary for increasing efficacy and reduction of side-effects based on the patient's race, weight, renal function, liver function or other relevant factors. However, adjustments for oral medications are not easily made due to limited formulations by pharmaceutical companies. We hypothesized it is feasible to produce personalized pills tailored to each individual patient’s clinical and biological characteristics through automated 3D-printing.
Methods:
A prototype computer algorithm was developed in our laboratory. The software includes two databases: 3D volume and dosage-adjustment factors. After inputting clinical and biological factors specific to the patients (age, race, weight, GFR, etc.), the software automatically calculates a personalized dose. Once the appropriate dose is determined, 3 volume datasets are generated and transferred to a STL file of a 4x4 pill board for 3D-printing to test the accuracy and variability of 3D-printed “pills”. Five different doses (80 pills) were created with dosing increments of 25%.
Results:
All pills were successfully printed using a 3D-printer (Figure). Doses of printed pills ranged from 124 mg to 373 mg. There was high reproducibility with standard deviations ranging from 3-5 mg. There were differences of 0.5-6% between the printed pills and computer-generated volumes in 5 different dose ranges indicating pills can be replicated accurately using a volume-concentration equation by the software.
Conclusion:
The study demonstrates that personalized pills based on each individual's clinical and biological characteristics can be produced with high precision through 3D-printing. Further studies are needed to develop 1) a standard adjustment formula for each individual drugs 2) cost-effective 3D drug printing techniques.
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