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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.

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combinationto aid meaningful interpretation and analysis of such data.The eTRANSAFE Consortium's effort to drive integrative drug safetyresearch at a large scale hereby present the lessons learnt and theproposal of solution at the guidelines in practice at this InnovativeMedicines Initiative (IMI) project. Recommendations in theseguidelines were compiled from feedback received from keystakeholders in regulatory agencies, EFPIA companies, and academicpartners. The research reproducibility guidelines presented in thisstudy lay the foundation for a comprehensive data sharing andknowledge management plans accounting for research datamanagement in the drug safety space - FAIR data sharing guidelines,and the model verification guidelines as generic deliverables that bestpractices that can be reused by other scientific community members at large.FAIR data sharing is a dynamic landscape that rapidly evolves withfast-paced technology advancements. The research reproducibility indrug safety guidelines introduced in this study provides a reusableframework that can be adopted by other research communities thataim to integrate public and private data in biomedical research space