Multi-Omics Target Discovery for Rheumatoid Arthritis

Autoimmune

Multi-Omics Target Discovery for Rheumatoid Arthritis

Pharma discovery group needed a ranked, validated target list to prioritize a new autoimmune portfolio before Q4 gate review.

Company

Mid-size pharma discovery group (internal RA franchise)

Timeline

October 2025

Engagement

Target Discovery and Disease Mapping Pipeline

Target Discovery and Disease Mapping

3 wks: Computational delivery
847: Targets evaluated
12: Priority targets delivered
9/12: Supported by prior clinical evidence

The Challenge

A pharma discovery group was launching a new rheumatoid arthritis program and needed to identify druggable targets with strong genetic and pathway evidence. Internal bioinformatics had produced a list of 200+ candidates from GWAS hits but lacked druggability scoring, pathway context, and competitive landscape analysis. Leadership required a ranked, decision-ready target list before Q4 portfolio gate review in 4 weeks.

Business Constraints

Budget: $125K (exploratory target ID budget)
Timeline: Ranked target list in 3 weeks
Must include druggability, safety, and competitive landscape for each target

HelixForge Approach

Week 1: Multi-Omics Integration and Disease Mapping

Input

Public RA datasets: GWAS (Okada et al.), synovial tissue RNA-seq (12 cohorts)
Single-cell RNA-seq from RA synovium (6 published atlases)
Known RA drug targets and clinical trial landscape (ClinicalTrials.gov)

Methods

Cross-cohort differential expression meta-analysis (847 unique genes)
Pathway enrichment (Reactome, KEGG) with disease-specific weighting
Genetic colocalization analysis (eQTL + GWAS integration)

Output

847-gene ranked list by disease relevance score
Pathway map linking top 50 genes to RA inflammatory cascade

Week 2: Druggability and Safety Scoring

Druggability scoring (PDB structure availability, binding pocket analysis, known ligand classes) filtered to 94 targets. Safety assessment (essential gene databases, knockout phenotypes, tissue expression breadth) removed 31 high-risk targets. Competitive landscape analysis (patents, clinical trials, approved drugs) flagged 18 targets with crowded IP. Final scored pool: 63 targets across 4 priority tiers.

Week 3: Validation Evidence and Decision Package

Output

Top 12 priority targets with genetic evidence, druggability score, and safety tier
2 to 3 backup targets per priority tier (36 total in expanded list)
Recommended validation experiments per target (CRISPR knockdown, biomarker assay)
Competitive landscape brief for each priority target
Portfolio gate presentation deck with decision framework

Priority Target List

Top 12 targets delivered with full evidence dossiers; 36 targets in expanded tier list.

Ranked RA targets by disease relevance, druggability, and safety
Rank	Target	Druggability	Genetic Evidence	Safety Tier	Status
1	TYK2 (JAK family)	0.92	Strong (GWAS + eQTL)	Moderate	Priority A
2	GPR65	0.78	Strong (GWAS)	Low risk	Priority A
3	CD19 (B-cell)	0.95	Moderate (pathway)	Moderate	Priority A
4	IL6ST (gp130)	0.88	Strong (expression)	Moderate	Priority A
5	PTPN22	0.71	Strong (GWAS)	Low risk	Priority B
6–12	Mixed	0.65–0.85	Moderate to strong	Low to moderate	Priority B/C

Results and impact

Speed, validation, and business outcomes

Speed vs. Internal Bioinformatics Approach

Metric	Internal Bioinformatics	HelixForge	Improvement
Timeline	4 to 6 months	3 weeks	6× faster
Cost	$400K+ (FTE time)	$125K	69% savings
Targets Evaluated	200 (GWAS only)	847 (multi-omics)	4× broader evaluation
Decision-Ready Output	Gene list only	12 targets + dossiers + gate deck	Portfolio-ready deliverable

Validation Evidence Review (4 weeks post-delivery)

Literature and prior clinical data cross-check on top 12 targets.

Target	Druggability Score	Clinical Evidence	Validated?	Notes
TYK2	0.92	Deucravacitinib approved (PsA)	Yes	Strong precedent; RA indication expanding
GPR65	0.78	Preclinical only	Partial	Novel target; genetic evidence strong
CD19	0.95	Multiple CAR-T trials	Yes	Crowded IP; differentiated approach needed
IL6ST	0.88	Tocilizumab approved (RA)	Yes	Validated pathway; novel modality opportunity
PTPN22	0.71	GWAS only	Partial	Undrugged; high genetic confidence
Rank 6–12	0.65–0.85	Mixed	4/7 supported	3 novel targets flagged for exploratory work

9/12: Targets with prior clinical evidence
3: Novel undrugged targets identified
100%: Gate review targets approved by committee
4 wks: Timeline to portfolio decision

Immediate Wins

Portfolio gate approved: all 12 priority targets accepted by review committee for 2026 budget allocation
Competitive advantage: 3 novel targets (GPR65, PTPN22, and 1 backup) with no competing clinical programs identified
Team alignment: shared evidence dossiers eliminated 6 weeks of internal debate on target prioritization

Strategic Advantages

Multi-omics integration surfaced GPR65, missed by GWAS-only internal analysis
Safety pre-filter prevented pursuit of 31 targets with essential gene or broad tissue expression liability
Competitive landscape brief identified white-space opportunities for 4 targets with strong biology but no active trials

Follow-on engagement
Q1 2026: small molecule discovery engagement on TYK2 and GPR65. Estimated combined cost: $650K. Target: ranked chemical matter within 4 weeks per target.

Model validation

Lessons and recommendations

What Worked

Single-cell RNA-seq weighting improved target ranking vs. bulk tissue analysis alone
Genetic colocalization (eQTL + GWAS) provided strongest predictor of portfolio committee approval
Competitive landscape integration prevented duplicate investment in crowded targets (CD19, IL6ST flagged early)

Challenges and Mitigations

GPR65 scored lower on druggability (0.78) due to limited structural data for this GPCR class.

Mitigation: Flagged as 'structure-enabled' target with AlphaFold model quality assessment; recommended cryo-EM partnership for lead series.

Two targets (Rank 8 and Rank 11) had conflicting evidence between GWAS and expression data.

Mitigation: Added evidence confidence tier (concordant/discordant/single-source) to dossier template.

When to use HelixForge for target discovery

New franchise or indication expansion requiring ranked target lists
Internal bioinformatics producing gene lists without druggability or safety context
Portfolio gate or investment committee deadlines
Need for competitive landscape and white-space analysis alongside biology

ROI: approximately 8:1 (FTE cost avoidance + 6× faster timeline + avoided pursuit of 31 unsafe targets).

Next steps: advance top 2 to 3 targets into small molecule or biologics discovery pipelines; iterate with proprietary patient cohort data if available.

About This Engagement

Client profile: Mid-size pharma, internal RA franchise, 12-person discovery team
Project duration: 3 weeks (computational delivery) + 4 weeks (evidence review)
Total cost: $125K
Date: October 2025

This case study is anonymized at client request. Target names are published biology; program-specific strategy details have been redacted. Full dossiers available under NDA.

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