Target Assessment Platform
Accelerating Drug Discovery through GWAS, PheWAS & Intelligent Annotation

Overview

The biopharmaceutical industry faces a persistent challenge: the vast majority of potential drug targets fail to translate into safe, efficacious therapies — largely because target selection is not sufficiently grounded in human genetic evidence. Our Target Assessment Platform was built to address this gap head-on.

By integrating Genome-Wide Association Studies (GWAS), Phenome-Wide Association Studies (PheWAS), and deep locus-level reporting with a unified annotation engine, the platform empowers research teams to evaluate, prioritise, and document drug targets with unprecedented speed and scientific rigour. Related capability Bioinformatics

Key Outcome: Research teams reduced early-stage target shortlisting time by over 60%, while simultaneously increasing the proportion of targets supported by multi-trait human genetic evidence.

Our client

Drug discovery organisations managing large therapeutic portfolios.

Client’s challenge

Drug discovery organisations managing large therapeutic portfolios struggle with three interconnected problems when selecting and assessing targets:

• Fragmented genetic evidence: GWAS and PheWAS signals are held in disparate databases and require expert bioinformaticians to query, interpret, and reconcile — creating bottlenecks and inconsistency.
• Shallow locus characterisation: Identifying a lead SNP is only the first step. Understanding the full genomic context — fine-mapped variants, co-localisation with expression QTLs, nearby genes — requires laborious manual work.
• Lack of portfolio-level annotation: Organisations cannot easily compare how strongly each asset in their portfolio is supported by genetic evidence, nor track annotation completeness over time.
• Slow target identification for new projects: When a new research project is initiated, scientists spend weeks compiling evidence that could be surfaced automatically.

Read Supporting insight:Drug target dossier: target intelligence for data-driven drug discovery

Client’s goals

• Build a scalable platform that integrates GWAS and PheWAS queries against major biobanks and consortia.
• Automate locus-level reporting — covering fine-mapping, LD structure, co-localisation, and gene prioritisation.
• Develop an annotation layer that tracks evidence quality across the portfolio in a standardised, auditable manner.
• Enable rapid target identification for new disease areas with minimal manual curation effort.

Our Approach

The Target Assessment Platform was designed as a modular, end-to-end solution, with four tightly integrated components:

Capability | Description

GWAS Integration

Automated ingestion and harmonisation of GWAS summary statistics from public consortia (UKBB, FinnGen, GWAS Catalog) and proprietary datasets. Users can query by gene, phenotype, or genomic region, with results normalised to a common schema.

PheWAS Engine

Cross-trait scanning of genetic variants across hundreds of phenotypes simultaneously. The engine highlights pleiotropy, flags safety signals, and surfaces novel therapeutic indications — all within a single interface.

Locus Reports

On-demand, publication-quality locus reports generated automatically for any lead variant or gene. Reports include LD plots, fine-mapped credible sets, co-localisation with eQTL data, and nearest gene prioritisation with evidence scoring.

Portfolio Annotation

A structured annotation layer that links genetic evidence to each programme in the research portfolio. Tracks evidence tier, data source provenance, curator sign-off, and completeness score — enabling portfolio-wide evidence dashboards.

Target Identification Module

For new research projects, a guided workflow aggregates genetic, transcriptomic, and phenotypic evidence to rank candidate targets. Outputs a scored, ranked target list ready for scientific review within hours.
Read Similar case study: Re-analysing database for novel targets

Technology Stack & Integration

• Cloud-native architecture supporting real-time and batch query modes across large-scale genomic datasets.
• API-first design enabling seamless integration with existing research informatics environments (ELNs, data lakes, BI tools).
• Role-based access control with audit trail — compliant with enterprise data governance standards.
• Automated pipeline orchestration ensuring GWAS and PheWAS indices are refreshed as new data releases become available.

Design Principle

Every feature was built around the scientist’s workflow — minimising the number of steps between a research question and a defensible, evidence-backed answer.

Research Productivity

• Target shortlisting for a new disease area — previously a 3–4 week manual exercise — is now completed within 2–3 days using the Target Identification module.
• Locus reports that previously required a dedicated bioinformatician 1–2 days to produce are generated on-demand in under 4 hours.
• Scientists across biology, translational medicine, and precision medicine teams can access and interpret genetic evidence without requiring specialist bioinformatics support.

Evidence Quality

• PheWAS integration surfaced previously unrecognised phenotypic associations for several portfolio targets, informing indication expansion strategies.
• Automated fine-mapping and co-localisation within locus reports elevated the quality of target validation packages submitted for project go/no-go decisions.
• Standardised annotation tiers introduced consistent, reproducible evidence grading across all projects — replacing ad hoc, scientist-by-scientist approaches.

Portfolio Oversight

• Leadership teams gained a real-time, portfolio-wide view of genetic evidence strength — enabling more informed prioritisation of resources and investment decisions.
• Annotation completeness tracking identified evidence gaps early, allowing teams to proactively commission additional analyses before key decision milestones.
• Audit-ready provenance records reduced time spent preparing evidence summaries for governance reviews by an estimated 40%.

Scientific Impact: By grounding target decisions in robust, multi-trait human genetic evidence from the earliest stages of research, the platform directly supports a higher-confidence, genetically validated drug pipeline.