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  ["project_title"]=>
  string(146) "Development and Validation of Multi-Scale AI Foundation Models for Personalized Drug Response Prediction in Type 2 Diabetes and Ulcerative Colitis"
  ["project_narrative_summary"]=>
  string(764) "This research proposes developing and validating Omniclin, an integrated AI foundation model predicting drug responses from molecular to patient level. By integrating 200M single-cell profiles, 200K clinical trials, 500K electronic health records, and patient-level data from YODA trials, we will create the first multi-scale model spanning genes to personalized outcomes. Using graph-based transformers, our models will predict drug efficacy and safety at an individual level with mechanistic insights. We focus on Type 2 Diabetes Mellitus (T2DM) and Ulcerative Colitis (UC) as proof-of-concept diseases due to prevalence, treatment diversity, and data availability. This work addresses the critical need to de-risk drug development and enable precision medicine."
  ["project_learn_source"]=>
  string(10) "web_search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(4) "Omar"
    ["last_name"]=>
    string(9) "Abudayyeh"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(28) "Brigham and Women's Hospital"
    ["email"]=>
    string(26) "oabudayyeh@bwh.harvard.edu"
    ["state_or_province"]=>
    string(2) "MA"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  bool(false)
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1629) "Background: Drug development faces high failure rates and escalating costs, largely due to the inability to predict individual patient responses and accurately translate molecular insights into clinical outcomes. Existing models fail to integrate molecular mechanisms with patient-level heterogeneity, limiting both precision medicine and drug development efficiency.

Objective: To develop and validate OmniClin, an AI foundation model that integrates multi-scale biological data, clinical trial outcomes, and electronic health records to predict individualized drug efficacy and safety. This platform directly enables both precision therapeutic selection for patients and early identification of likely responders and non-responders to de-risk clinical development.

Study Design: Meta-analysis of individual participant data from YODA T2DM and UC trials, integrated with external molecular datasets, clinical trial databases, and EHR data.

Participants: All patients enrolled in YODA-accessible T2DM and UC trials with complete baseline characteristics and outcome data.

Primary and Secondary Outcome Measures:

Primary: Model accuracy in predicting individual patient treatment response (HbA1c reduction ≥0.5% for T2DM; clinical remission for UC).

Secondary: Prediction accuracy for adverse events, mechanistic pathway discovery, and cross-disease generalization to support broader drug development applications.

Statistical Analysis: Graph transformer models trained on integrated multi-scale data. Validation performed using held-out trials using AUROC and precision-recall. "
  ["project_brief_bg"]=>
  string(2018) "The pharmaceutical industry faces a productivity crisis, with drug development costs exceeding $2.6 billion per approved therapy and 90% of candidates failing clinical trials [1]. This inefficiency stems from our inability to predict how individual patients will respond to treatments based on their unique molecular and clinical characteristics.

Recent advances have produced clinical prediction models using graph-based approaches. GRAM uses medical ontologies to predict disease risk from EHR data [2], while G-BERT applies graph neural networks to medication recommendation [3]. The Clinical Knowledge Graph (CKG) integrates multi-omics with clinical data for biomarker discovery [4]. However, these models have critical limitations: they either operate only at the clinical level without molecular mechanisms, or when they do incorporate molecular data, they cannot make individualized patient predictions.

Even state-of-the-art efforts like PlaNet, which integrates clinical trials with biological networks to predict drug efficacy and adverse events, only achieves population-level predictions rather than personalized treatment recommendations [5]. Similarly, molecular-focused models like GraphDTA predict drug-target interactions [6] but cannot translate findings to individual patient outcomes.

Our project transcends these limitations by developing OmniClin, the first AI model to span from genes to individualized patient outcomes. By training on 200 million single-cell profiles and integrating 200,000 clinical trials with 500,000 MIMIC-IV EHRs [7] and YODA patient-level data, we enable truly personalized predictions. Unlike existing approaches, our graph transformer architecture connects molecular mechanisms directly to individual clinical outcomes, providing interpretable pathways explaining why specific treatments work for certain patients. This breakthrough enables precision medicine where clinicians can confidently prescribe the right drug to the right patient.

"
  ["project_specific_aims"]=>
  string(1598) "Aim 1: Integrate YODA patient-level data into OmniClin's knowledge graph infrastructure  

Hypothesis: Integration of individual patient data from YODA trials with existing molecular, clinical trial, and EHR data will create the most comprehensive multi-scale biomedical knowledge graph to date. 

Objective: Successfully harmonize and integrate T2DM and UC patient-level trial data with molecular and clinical knowledge, establishing graph connections between individual patients, their outcomes, cellular responses, and biomedical pathways. 



Aim 2: Develop and validate next-generation graph foundation models for multi-scale prediction. 

Hypothesis: Novel graph transformer architectures designed to handle heterogeneous multi-scale data will outperform existing approaches for drug response prediction. 

Objective: Train and validate new graph foundation models that effectively learn from integrated molecular-to-clinical data, achieving >0.80 AUROC for treatment response prediction.



Aim 3: Demonstrate personalized safety and efficacy prediction with mechanistic interpretability 

Hypothesis: Graph-based models will accurately predict individual patient treatment responses while providing interpretable mechanistic pathways explaining differential drug effects.

Objective: Validate model predictions for both efficacy and safety at the individual patient level, and extract mechanistic insights through graph attention analysis to identify biomarkers and pathways underlying personalized treatment responses.

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(6) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
    [4]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [5]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(899) "YODA Trials Inclusion: - T2DM trials and UC trials. All the available data. 



Patient-level Inclusion Criteria: - Age ≥18 years at enrollment - Confirmed diagnosis (T2DM: HbA1c ≥6.5% or on diabetes medication; UC: endoscopic/histologic confirmation) - Complete baseline demographics, medical history, and laboratory values - At least one post-baseline efficacy assessment  Exclusion Criteria: - Missing primary outcome data - Protocol violations affecting treatment exposure - Concurrent participation in other interventional trials  



External Data Sources: - Single-cell data: 200M profiles from CELLxGENE, Human Cell Atlas - Clinical trials: ClinicalTrials.gov database (200K trials, 66K with results) - EHR data: MIMIC-IV (500K patients) - Biomedical knowledge: PubMed abstracts, KEGG, Reactome pathways. Specifically primeKG and iKraph knowledge graphs. 

"
  ["project_main_outcome_measure"]=>
  string(888) "Primary Outcomes: - T2DM: Binary treatment response (HbA1c reduction ≥0.5% from baseline at primary endpoint) - UC: Clinical remission (Mayo score ≤2 with no subscore >1, or equivalent validated score) – 



Model performance: AUROC for predicting individual patient treatment response  



Secondary Outcomes: 

- Continuous efficacy measures: Actual HbA1c change (T2DM), Mayo score change (UC) 



- Safety: Any serious adverse event, treatment discontinuation due to adverse events 



- Time to treatment failure or dose escalation 



- Biomarker changes: CRP, fecal calprotectin (UC); insulin sensitivity indices (T2DM)



 - Patient-reported outcomes where available 



- Model calibration and decision curve analysis 



- Mechanistic pathway enrichment scores

"
  ["project_main_predictor_indep"]=>
  string(573) "Primary Independent Variables: - Treatment assignment (specific drug and dose) 

- OmniClin cellular response predictions (multi-dimensional embedding representing predicted transcriptional changes) 

- Integrated molecular-clinical risk score from OmniClin  

Key Features: 

- Drug mechanism of action classification 

- Predicted on-target and off-target cellular effects 

- Patient-specific molecular signatures derived from baseline characteristics mapped through the model 

- Graph-derived pathway activation scores

"
  ["project_other_variables_interest"]=>
  string(1064) "Patient Demographics: - Age, sex, race/ethnicity, BMI, smoking status  

Disease Characteristics: 

- Disease duration, severity scores at baseline 

- T2DM: Baseline HbA1c, fasting glucose, insulin use, diabetes complications 

- UC: Disease extent, prior biologic exposure, extraintestinal manifestations, baseline Mayo score components  

Clinical History: 

- Comorbidities (cardiovascular, renal, hepatic) 

- Concomitant medications 

- Prior treatment failures 

- Laboratory values: Complete blood count, comprehensive metabolic panel, inflammatory markers  



Molecular Predictions: 

- Cell-type-specific response scores from OmniCell 

- Pathway activation predictions 

- Drug-drug interaction scores 

- Genetic risk scores where available (polygenic and pharmacogenomic)

- Protein-protein interaction network centrality scores

Trial Characteristics: - Study phase, year, duration - Inclusion/exclusion stringency - Run-in period presence

"
  ["project_stat_analysis_plan"]=>
  string(1935) "1. Data Harmonization and Quality Control: 

- Standardize variable definitions across trials using CDISC standards and medical ontologies 

- Implement multiple imputation for missing baseline covariates (<10% missingness threshold) 

- Assess cross-trial heterogeneity using I² statistics 

 2. Model Development: 

- OmniClin Validation: 

Compare predicted cellular responses with observed biomarker changes using Spearman correlation and canonical correlation analysis

 - OmniClin Architecture: 

Graph transformer with attention mechanisms integrating:  	

 * Node features: Patient characteristics, genetic risk scores, cellular predictions, drug properties  

 * Edge features: Molecular interactions, clinical relationships   

* Training using stratified k-fold cross-validation at trial level  



3. Primary Analysis: 

- Individual patient data meta-analysis using hierarchical models accounting for trial-level clustering 

- Performance metrics: AUROC, AUPRC, sensitivity/specificity at clinically relevant thresholds 

- Calibration assessment using Hosmer-Lemeshow tests and calibration plots  

4. Subgroup and Sensitivity Analyses: 

- Stratified analysis by drug class, disease severity, prior treatment exposure 

- Leave-one-trial-out validation to assess generalizability

- Temporal validation using trials by enrollment period 



5. Mechanistic Interpretation: 

- Extract attention weights to identify influential pathways 

- Pathway enrichment analysis using Gene Set Enrichment Analysis 

- Network propagation to trace decision paths  

6. Comparative Effectiveness: 

- Propensity score matching to compare predicted vs actual treatment assignments 

- Simulate optimized treatment allocation and project potential benefit

"
  ["project_software_used"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(6) "python"
      ["label"]=>
      string(6) "Python"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(695) "Project Start Date: July 1, 2025 

Data Access and Harmonization Complete: September 30, 2025 

Model Development and Initial Validation: December 31, 2025 

Comprehensive Analysis Completion: March 31, 2026 

Manuscript Draft Completed: May 15, 2026 

First Journal Submission: June 1, 2026 

Results Reported to YODA: June 30, 2026  

Key Deliverables Timeline: 

- Month 1-3: Data integration pipeline and quality control

- Month 4-6: Model training and validation 

- Month 7-9: Subgroup analyses and mechanistic interpretation 

- Month 10-11: Manuscript preparation 

- Month 12: Submission and dissemination

"
  ["project_dissemination_plan"]=>
  string(1561) "Response: Primary Manuscript: Focus on the development and validation of multi-scale AI models for personalized drug response prediction using integrated molecular and clinical data from T2DM and UC trials.

•	Target Journals: Nature, Science, Nature Medicine, Lancet Digital Health, Science Translational Medicine

•	Audience: Clinical researchers, drug developers, regulatory scientists

Secondary Manuscripts:

1.	Mechanistic interpretation of differential drug response patterns identified through integrated molecular-clinical modeling

2.	Clinical implementation framework for AI-driven precision medicine in chronic inflammatory and metabolic diseases

3.	Methodological advances in graph-based transformer architectures for multi-scale biological data integration

Additional Products:

•	Technical documentation demonstrating feasibility of individualized patient prediction (noting that models cannot be released due to proprietary training data)

•	Conference presentations at major computational biology and clinical meetings

•	Open-source code for data harmonization pipelines and evaluation metrics

Target Audiences:

•	Pharmaceutical companies: Improve trial design and patient stratification

•	Clinicians: Evidence for personalized treatment selection approaches

•	Regulatory agencies: Framework for evaluating AI-based precision medicine tools

•	Patients: Improved treatment matching and outcomes through precision medicine

"
  ["project_bibliography"]=>
  string(973) "
[1] DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 47, 20-33 (2016).
[2] Choi, E. et al. GRAM: Graph-based Attention Model for Healthcare Representation Learning. KDD 787-795 (2017).
[3] Shang, J. et al. Pre-training of Graph Augmented Transformers for Medication Recommendation. IJCAI 5953-5959 (2019).
[4] Santos, A. et al. A knowledge graph to interpret clinical proteomics data. Nat. Biotechnol. 40, 692-702 (2022).
[5] Brbić, M. et al. Predicting drug outcome of population via clinical knowledge graph. medRxiv doi:10.1101/2024.03.06.24303800v2 (2024).
[6] Nguyen, T. et al. GraphDTA: Predicting drug-target binding affinity with graph neural networks. Bioinformatics 37, 1140-1147 (2021).
[7] Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).
 
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