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  string(627) "The objective of this study is to develop a Target Trial Emulation (TTE) framework to systematically evaluate key prognostic and predictive factors influencing the efficacy and safety profiles of biologic therapies in Crohn’s disease. By leveraging this framework, the study aims to identify clinical contexts in which non–TNF biologics, such as ustekinumab, may offer superior effectiveness or improved safety as initial therapy for biologic-naïve patients. Ultimately, this approach seeks to inform more personalized, evidence-based biologic selection strategies that move beyond the current empiric sequencing paradigm."
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  array(7) {
    ["first_name"]=>
    string(9) "Yanguang "
    ["last_name"]=>
    string(3) "Cao"
    ["degree"]=>
    string(5) "Ph.D."
    ["primary_affiliation"]=>
    string(43) "University of North Carolina at Chapel Hill"
    ["email"]=>
    string(16) "yanguang@unc.edu"
    ["state_or_province"]=>
    string(2) "NC"
    ["country"]=>
    string(13) "United States"
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      ["p_pers_l_name"]=>
      string(5) "Zhang"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
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      ["requires_data_access"]=>
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    ["label"]=>
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  string(53) "ARPA-H (Advanced Research Projects Agency for Health)"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1599) "Background: Anti–TNF-α biologics (e.g., infliximab) are first-line for moderate-to-severe Crohn’s disease, while non-TNF agents (e.g., ustekinumab) are often used after anti-TNF failure d. This empiric sequencing leaves uncertainty about optimal initial biologic choice and which patients may benefit from alternative mechanisms with different safety profiles.

Objective: Develop a Target Trial Emulation (TTE) framework to identify prognostic and predictive factors that differentiate biologic efficacy and safety, and to define contexts where non-TNF biologics may be preferable as initial therapy in biologic-naïve patients.

Study Design: Assemble trial-derived clinical data for infliximab, adalimumab, and ustekinumab and apply an AI-enabled causal inference TTE that models treatment assignment, confounding, and time-varying covariates .

Participants: Adults with Crohn’s disease from NCT03464136, NCT00265122, NCT01369342, NCT01369329, NCT01369355, NCT00771667, NCT00207766, and NCT00207662.

Primary and Secondary Outcome Measure(s): Week-52 remission (CDAI <150) or sustained fistula closure; serious infections and/or SAEs through Week 52. Secondary efficacy and safety: CDAI response (≥70/≥100) and endoscopic remission; TEAEs and AE-related discontinuation.

Statistical Analysis: (1) Develop/validate the TTE for fidelity, interpretability, and bias assessment; (2) run counterfactual simulations across clinically relevant scenarios; (3) estimate comparative effects and subgroup heterogeneity to inform personalized biologic selection."
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  string(2945) "Anti–tumor necrosis factor-α (anti-TNF-α) biologics, such as infliximab, have long been established as foundational therapies for patients with moderate-to-severe Crohn’s disease (CD). Their widespread use is largely driven by an extensive clinical experience base and demonstrated efficacy across multiple disease phenotypes, with particularly strong evidence in luminal disease, fistulizing CD, and patients with a high inflammatory burden. As a result, anti-TNF-α agents have historically served as first-line biologic therapy in many clinical settings.

    In recent years, newer biologic therapies with distinct mechanisms of action, such as the anti-IL-12/23 antibody ustekinumab, have emerged and shown promising efficacy in specific clinical contexts. Importantly, these biologics exhibit different safety and tolerability profiles. Infliximab and other anti-TNF-α agents have been associated with higher immunogenicity and broader systemic immunosuppression, raising concerns regarding serious infections and other immune-related adverse events. In contrast, newer biologics may offer improved safety profiles in certain patient populations, although their comparative effectiveness and long-term safety remain incompletely characterized.

    A major challenge in optimizing biologic selection is the lack of robust head-to-head randomized clinical trials comparing these agents. In current practice, treatment decisions are largely driven by accumulated clinical experience rather than direct comparative evidence. Anti-TNF-α therapies are frequently prescribed as first-line biologics, while alternative agents are often introduced only after loss of response or intolerance to TNF-α inhibitors. This raises critical unanswered questions: how should biologics be selected for biologic-naïve patients? Are there identifiable subpopulations for whom non-TNF biologics may be more effective or safer as initial therapy? What clinical characteristics define these patients?

    Given the mechanistic differences among biologic classes, variation in safety profiles is expected, making appropriate patient–drug matching clinically meaningful. Identifying patient subgroups that are better suited to specific biologic therapies could reduce unnecessary toxicities while preserving or improving therapeutic efficacy.

    To address these gaps, we propose leveraging advanced modeling approaches, including artificial intelligence and machine learning, to develop a target trial emulation framework using clinical trial data. This framework will enable causal deconvolution of factors driving treatment response and toxicity across biologic therapies, supporting evidence-based identification of patient subpopulations that are optimally matched to specific biologic agents. Ultimately, this work aims to inform more precise, individualized biologic selection strategies for Crohn’s disease.

"
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  string(984) "The overall goal of this study is to analyze clinical trial data to develop a target trial emulation (TTE) framework that enables systematic comparison of the efficacy and safety profiles of biologic therapies across diverse clinical scenarios. Through this framework, we aim to address a central clinical question: whether distinct patient subpopulations can be identified that are optimally matched to specific biologic agents based on efficacy and toxicity considerations.

Aim 1: To develop and train an AI-enabled target trial emulation model using available clinical trial data, and to rigorously evaluate the model’s ability to reproduce observed clinical trial outcomes.

Aim 2: To apply the validated target trial emulation model to simulate counterfactual treatment scenarios and compare the efficacy and safety profiles of biologic therapies across clinically relevant conditions, thereby addressing key comparative effectiveness and safety questions.

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  string(484) "Adult patients receiving biologic agents for the treatment of Crohn’s disease from studies NCT03464136, NCT00265122, NCT01369342, NCT01369329, NCT01369355, NCT00771667, NCT00207766, NCT00207662.

Inclusion criteria: (1) Patients must be 18 Years and older (Adult, Older Adult); (2) Patients have been diagnosed with Crohn’s disease; (3) Patients have received biologics or comparator drugs for treatment of Crohn’s disease. 

No exclusion criteria.  



"
  ["project_main_outcome_measure"]=>
  string(2050) "Primary Outcome Measure

Clinical remission is the most used primary efficacy endpoint in Crohn’s disease trials and is typically defined using validated clinical disease activity indices.

•	Crohn’s Disease Activity Index (CDAI)

Clinical remission is defined as CDAI <150, measured at prespecified time points (e.g., Week 12, Week 26, Week 52).

o	CDAI integrates patient-reported symptoms (stool frequency, abdominal pain, general well-being), medication use, physical findings, and laboratory values.

o	Data are collected prospectively using standardized patient diaries and investigator assessments over a 7-day window prior to each study visit.

o	CDAI has been widely accepted by regulatory agencies and remains a standard endpoint in pivotal trials of biologics.

Fistula-related outcomes are primary endpoints in studies enrolling patients with fistulizing Crohn’s disease.

•	Complete fistula closure is typically defined as:

o	Absence of draining fistulas on gentle compression

o	Confirmed on ≥2 consecutive visits, often separated by ≥4 weeks

•	Fistula assessments are conducted by trained investigators using standardized physical examinations and, when applicable, supported by imaging (e.g., MRI).

Primary Safety Outcomes

Safety endpoints are assessed throughout the study period and follow standardized regulatory definitions.

•	Serious Adverse Events (SAEs)

Defined according to ICH guidelines, including events that result in death, are life-threatening, require hospitalization, result in disability, or are medically significant.

•	Serious infections

Particular attention is paid to infections requiring hospitalization, intravenous antibiotics, or leading to treatment interruption or discontinuation, given the immunosuppressive nature of biologics.

All adverse events are coded using MedDRA terminology, and severity and relatedness to study drug are assessed by investigators.

"
  ["project_main_predictor_indep"]=>
  string(1908) "Patient Baseline Characteristics

Baseline characteristics typically include a comprehensive set of demographics, diagnostic, disease-related, and treatment-related variables that define patient heterogeneity at study entry and serve as key prognostic and predictive factors in Crohn’s disease.

Demographic variables

•	Age at enrollment and age at diagnosis

•	Sex

•	Race and ethnicity

•	Body weight, body mass index (BMI)

•	Geographic region or study site

•	Smoking status (current, former, never)

Diagnostic and disease-related variables

•	Disease duration since Crohn’s disease diagnosis

•	Disease location and behavior (e.g., ileal, colonic, ileocolonic; inflammatory, stricturing, penetrating), typically classified by Montreal classification

•	Presence of perianal or fistulizing disease

•	Baseline disease activity indices (e.g., Crohn’s Disease Activity Index [CDAI])

•	Baseline endoscopic disease severity (when available)

•	Biomarkers of inflammation (e.g., C-reactive protein [CRP], fecal calprotectin)

•	History of disease complications (e.g., strictures, abscesses, prior surgery)

Prior and concomitant treatment history

•	Prior exposure to biologics (biologic-naïve vs biologic-experienced)

•	Prior failure or intolerance to anti-TNF agents

•	Use of immunomodulators (e.g., azathioprine, methotrexate)

•	Baseline corticosteroid use and dose

•	Prior history of serious infections or malignancy

Comorbidity and risk-related variables

•	Relevant comorbid conditions (e.g., autoimmune diseases, metabolic disorders)

•	Infection risk factors (e.g., latent tuberculosis status, hepatitis status)

•	Baseline laboratory safety parameters (e.g., liver enzymes, blood counts)"
  ["project_other_variables_interest"]=>
  string(3) "N/A"
  ["project_stat_analysis_plan"]=>
  string(1321) "The analysis will proceed in three sequential steps. First, we will construct a target trial emulation model that integrates baseline covariates, time-varying longitudinal factors, and treatment-specific efficacy and toxicity profiles for each biologic therapy. Model performance will be rigorously evaluated with respect to fidelity to observed data, clinical interpretability and utility, and potential sources of bias, including confounding and model misspecification.

     Second, the validated target trial emulation framework will be used to generate counterfactual simulations under alternative treatment strategies. These simulations will enable systematic evaluation of the comparative efficacy and safety of each biologic across clinically relevant scenarios, including differences in disease severity, concomitant steroid use, comorbidity burden, and prior infection history.

     Third, formal comparative analyses will be conducted to address the prespecified clinical questions, quantifying treatment effects on key efficacy and safety outcomes across patient subgroups. Results will be summarized using clinically interpretable estimands, supporting evidence-based identification of patient populations most likely to benefit from each biologic therapy while minimizing toxicity risk.

"
  ["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"
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  ["project_timeline"]=>
  string(1041) "Months 0–6: TTE Specification, Data Curation, and Model Development (Aim 1)

Finalize the target trial emulation (TTE) protocol, including eligibility criteria, treatment strategies, estimands, and causal assumptions

Curate, harmonize, and quality-check patient-level clinical trial data

Define baseline and time-varying covariates, outcomes, and confounding structure

Develop and train the AI-enabled TTE model

Conduct initial diagnostics for covariate balance, positivity, and model stability



Months 7–12: Model Validation and Counterfactual Simulation (Aims 1 and 2)

Validate model fidelity by comparing emulated outcomes with observed clinical trial results

Perform sensitivity analyses to assess robustness to modeling assumptions and unmeasured confounding

Finalize the validated TTE model

Simulate counterfactual treatment strategies across biologics and clinical contexts

Conduct comparative efficacy and safety analyses across patient subgroups"
  ["project_dissemination_plan"]=>
  string(249) "Manuscripts will be drafted and submitted to Clinical Pharmacology and Therapeutics or autoimmune disease-related journals after completion of the proposed project. Results will be reported back to YODA following manuscript revisions and acceptance."
  ["project_bibliography"]=>
  string(1666) "
Best WR, Becktel JM, Singleton JW, Kern F Jr. Development of a Crohn’s disease activity index: National Cooperative Crohn’s Disease Study. Gastroenterology. 1976;70(3):439–444.
Sandborn WJ, Feagan BG, Rutgeerts P, et al.Ustekinumab as induction and maintenance therapy for Crohn’s disease. N Engl J Med. 2012;367(16):1519–1528. doi:10.1056/NEJMoa1203572.
Hanauer SB, Feagan BG, Lichtenstein GR, et al. Maintenance infliximab for Crohn’s disease: the ACCENT I randomized trial. Lancet. 2002;359(9317):1541–1549. doi:10.1016/S0140-6736(02)08512-4.
Present DH, Rutgeerts P, Targan S, et al. Infliximab for the treatment of fistulas in patients with Crohn’s disease. N Engl J Med. 1999;340(18):1398–1405. doi:10.1056/NEJM199905063401804.
Daperno M, D’Haens G, Van Assche G, et al. Development and validation of a new, simplified endoscopic activity score for Crohn’s disease: the SES-CD. Gastrointest Endosc. 2004;60(4):505–512. doi:10.1016/S0016-5107(04)01878-4.
Mosli MH, Zou G, Garg SK, et al. C-reactive protein, fecal calprotectin, and stool lactoferrin for detection of endoscopic activity in Crohn’s disease. Am J Gastroenterol. 2015;110(6):802–819. doi:10.1038/ajg.2015.120.
U.S. Food and Drug Administration. Crohn’s Disease: Developing Drugs for Treatment—Guidance for Industry. Silver Spring, MD: U.S. Food and Drug Administration; 2016.
International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Clinical Safety Data Management: Definitions and Standards for Expedited Reporting (E2A/E2D). Geneva, Switzerland: ICH; 1995.
"
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data partner
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pi country
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pi affil
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products
array(2) {
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num of trials
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res
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