Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes.

PURPOSE: While the beneficial effects of medications are numerous, drug-drug interactions may lead to adverse drug reactions that are preventable causes of morbidity and mortality. Our goal was to quantify the prevalence of potential drug-drug interactions in drug prescriptions at Danish hospitals, estimate the risk of adverse outcomes associated with discouraged drug combinations, and highlight the patient types (defined by the primary diagnosis of the admission) that appear to be more affected. METHODS: This cross-sectional (descriptive part) and cohort study (adverse outcomes part) used hospital electronic health records from two Danish regions (~2.5 million people) from January 2008 through June 2016. We included all inpatients receiving two or more medications during their admission and considered concomitant prescriptions of potentially interacting drugs as per the Danish Drug Interaction Database. We measured the prevalence of potential drug-drug interactions in general and discouraged drug pairs in particular during admissions and associations with adverse outcomes: post-discharge all-cause mortality rate, readmission rate and length-of-stay. RESULTS: Among 2 886 227 hospital admissions (945 475 patients; median age 62 years [IQR: 41-74]; 54% female; median number of drugs 7 [IQR: 4-11]), patients in 1 836 170 admissions were exposed to at least one potential drug-drug interaction (659 525 patients; median age 65 years [IQR: 49-77]; 54% female; median number of drugs 9 [IQR: 6-13]) and in 27 605 admissions to a discouraged drug pair (18 192 patients; median age 68 years [IQR: 58-77]; female 46%; median number of drugs 16 [IQR: 11-22]). Meropenem-valproic acid (HR: 1.5, 95% CI: 1.1-1.9), domperidone-fluconazole (HR: 2.5, 95% CI: 2.1-3.1), imipramine-terbinafine (HR: 3.8, 95% CI: 1.2-12), agomelatine-ciprofloxacin (HR: 2.6, 95% CI: 1.3-5.5), clarithromycin-quetiapine (HR: 1.7, 95% CI: 1.1-2.7) and piroxicam-warfarin (HR: 3.4, 95% CI: 1-11.4) were associated with elevated mortality. Confidence interval bounds of pairs associated with readmission were close to 1; length-of-stay results were inconclusive. CONCLUSIONS: Well-described potential drug-drug interactions are still missed and alerts at point of prescription may reduce the risk of harming patients; prescribing clinicians should be alert when using strong inhibitor/inducer drugs (i.e. clarithromycin, valproic acid, terbinafine) and prevalent anticoagulants (i.e. warfarin and non-steroidal anti-inflammatory drugs - NSAIDs) due to their great potential for dangerous interactions. The most prominent CYP isoenzyme involved in mortality and readmission rates was 3A4.