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Integration of genomics, proteomics, and phenotypic data using machine learning to predict drug efficacy and toxicity in personalized therapeutic contexts.
Current multiomics approaches capture static snapshots of drug-induced toxicity, but temporal integration of proteomics, metabolomics, and transcriptomics data remains unexplored for early toxicity prediction. This research would establish time-series multiomics signatures that precede clinical manifestation of organ toxicity.
The species-dependent variability in drug responses remains a critical barrier in translating preclinical findings to human outcomes. This research addresses the integration of multiomics data across different species to build predictive models that accurately forecast human drug efficacy and safety.
Existing systems pharmacology models operate at bulk tissue or population levels, missing critical cellular heterogeneity that drives individual drug responses. This research integrates single-cell transcriptomics, proteomics, and metabolomics with population pharmacokinetics to enable true cellular-resolution personalized pharmacotherapy.
Current drug-drug interaction (DDI) prediction relies on isolated hepatic metabolism studies, but multiomics profiling of integrated organ-on-chip systems could capture systemic DDI mechanisms across kidney, liver, and intestinal tissues. This research develops comprehensive multiomics frameworks for predicting clinically relevant DDIs.
The microbiome's role in drug metabolism and efficacy is increasingly recognized, but systematic integration of metagenomic, metaproteomic, and metabolomic microbiome data with host genomics and phenomics remains absent. This research develops machine learning models linking microbiome-derived metabolite signatures to individualized drug responses.
Current PDX studies use endpoint multiomics analysis, missing dynamic treatment response signatures that could inform adaptive dosing or therapy switching. This research establishes frameworks for longitudinal multiomics monitoring in PDX models to guide real-time clinical trial design and personalized treatment modification.
Many approved drugs produce unexplained clinical effects or adverse events due to unknown off-target interactions. This research integrates phosphoproteomics, ubiquitinomics, and mechanistic metabolomics to systematically identify and characterize cryptic drug targets and their downstream functional consequences.
Immunotherapy resistance emerges through complex epigenetic reprogramming that current single-omics approaches fail to capture. This research integrates ATAC-seq, ChIP-seq, multi-parameter transcriptomics, and immune cell proteomics to predict durable versus transient immunotherapy responses and identify resistance mechanisms.