Table 1:

Future research priorities in precision medicine

Improved disease classification and understanding of natural history:

• Natural history: Patient stratification into complicated and indolent disease courses

• Exposome: Understand which environmental factors are linked to IBD pathogenesis, what influences susceptibility to these factors and at what time in an individual’s life these environmental factors have greatest impact.

• Patient heterogeneity: Improve IBD stratification by multi-omics approaches. Create more effective molecular sub classifications in IBD. Link stratifications to clinically meaningful outcomes.

Optimal design of cohort studies and clinical trials:

• Design of short-term studies: Factors governing treatment response and (primary and secondary) non-response.

• Design of long-term studies: Factors leading to long term consequences of IBD including malignancy, fibrosis and surgery risk.

• Definition of key clinical outcomes, surrogate endpoints and treatment targets: Short term (response and remission, mucosal healing, early drug side effects) and long term (hospitalisation, fibrosis, surgery, neoplasia, de-escalation of therapy, long term drug side effects).

• Phenotyping: Use of multi-omic technologies on relevant clinical material including blood, intestinal tissue, stool, saliva and urine.

• Inclusivity: Address inequalities in research methodologies including access across ethnic groups, ages and countries.

• Head to head studies: Better understand comparative efficacy of therapeutic interventions.

• Biomarker stratification: Discovery and validation of predictive or prognostic biomarkers embedded from outset of clinical development programmes.

• Tissue sampling: Defining mechanisms to support multicentre collection, shipping, cryopreservation and storage of samples. Development of governance to access, protocols and technologies to profile readily available clinical archival material (e.g. formalin fixed paraffin embedded (FFPE) tissue).

Computational integration:

• Digital health: Collection, interpretation and visualisation of healthcare data from electronic healthcare records for research.

• Collaboration: Encouragement of multicentre industry-academic-informatic partnerships.

• Bioinformatic training: Availability of training and open access sharing of bioinformatic pipelines including emerging artificial intelligence and machine learning approaches.

• Data integration/systems biology: Development of multi-omic integrative approaches to incorporate clinical, microbiome, metabolome, transcriptome, genome with environmental exposure.

Mechanistic biological validation:

• Functional experiments: Testing of biological implications of immune and microbial associations identified by -omic studies.

Incorporation of precision medicine strategies into clinical practice:

• Health economics: Delineation of models relevant to diverse healthcare settings, financial structures and populations globally.

• Accessibility: Availability of high throughput, accessible and cost-effective technology to introduce biomarkers in clinical practice.

• Clinical guidelines: Incorporation of validated biomarkers in treatment pathways.