[Modeling and simulation of pharmacokinetic drug-drug interaction caused by induction of metabolic enzymes]

Verification of a cocktail approach for quantitative drug-drug interaction assessment: a comparative analysis between the results of a single drug and a cocktail drug

A cocktail approach is a method to comprehensively evaluate the activity of cytochrome P450 enzymes (CYPs) by co-administering multiple CYP substrates. This is the first report that compares the results from a cocktail study to a single substrate separate administration study (single study) with concomitant administration of CYP inducers/inhibitors. The validity of a cocktail study for use as a quantitative drug-drug interactions (DDIs) assessment was evaluated.We administered a cocktail drug (caffeine, losartan, omeprazole, dextromethorphan, midazolam) with rifampicin, cimetidine or fluvoxamine. A comparative analysis was performed between the results of a cocktail study and single studies. The results of single studies were obtained from a literature review and the trials of single substrate separate administration.A strong positive correlation of the AUC ratio of all drugs between single studies and the cocktail study was obtained. The ratio of AUC change of 12 combinations converged to 0.82-1.09, and 2 combinations ranged between 0.74-1.32.The differences in the degree of interaction between the single studies and cocktail study are acceptable to evaluate DDIs for almost all combinations. Our results indicate that a cocktail study is an adequate and quantitative evaluation method for DDIs.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

BACKGROUND

Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).

METHODS

The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.

RESULTS

Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).

CONCLUSIONS

Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.