Data-driven competitive landscape analysis to facilitate go/no-go decision in clinical development

Overview

In early clinical development, pharmaceutical companies must evaluate whether a new therapy can compete with existing treatments before committing further resources. Data-driven competitive landscape analysis provides a quantitative approach for assessing therapeutic performance against marketed drugs.

Excelra collaborated with a Swiss pharmaceutical company developing a novel antibody therapy for rheumatoid arthritis (RA). The objective was to assess the antibody’s competitive position against approved biologics using model-based meta-analysis (MBMA) and longitudinal clinical trial data. Leveraging advanced Data Curation Services, Scientific Informatics, and Clinical Data Services, Excelra built a high-quality dataset to support evidence-based decision-making in clinical development.

Our client

The client is a large pharmaceutical company based in Switzerland engaged in developing novel antibody therapeutics for rheumatoid arthritis (RA). Their research team aimed to determine whether their investigational antibody had a competitive advantage compared with existing biologic therapies before progressing further in clinical trials.

Client’s challenge

Evaluating the competitiveness of a novel biologic therapy requires detailed comparison against existing treatments across multiple clinical trials.

The client faced several challenges:

• Limited visibility into longitudinal clinical efficacy data across existing therapies
• Difficulty comparing early-stage antibody data with marketed biologics
• Need for model-based meta-analysis (MBMA) rather than traditional end-of-study analysis
• Requirement for structured datasets capturing patient characteristics, dosing regimens, and clinical responses
• Need for robust evidence to support a go/no-go decision in clinical development

The complexity of these analyses required specialized expertise in clinical pharmacology and meta-analysis methodologies.

Client’s goals

The project aimed to:

• Assess the competitive position of the client’s antibody therapy in Phase II B development
• Compare efficacy against approved biologic drugs for rheumatoid arthritis
• Evaluate the longitudinal time course of clinical responses
• Build an MBMA-ready clinical outcomes database
• Support strategic go/no-go decisions for further clinical development

Our Approach

Systematic Literature Review (SLR)

Excelra’s Clinical Pharmacology team conducted a systematic literature review using the PICOS methodology to identify relevant studies in PubMed. The literature search focused on clinical trials evaluating marketed biologics for rheumatoid arthritis.

Additional references were obtained from:

• FDA drug labeling information
• Traditional meta-analysis publications

This comprehensive search identified 119 relevant sources for analysis.

Clinical outcomes database development

Excelra curated a customized clinical outcomes database containing summary-level data for each clinical study.

The database captured key parameters including:

• Clinical outcomes (time vs response)
• Patient demographics and baseline characteristics
• Dose regimens and treatment interventions
• Comparator therapies
• Study design and sample size
• Prior and concomitant medications with response status

A three-level quality control process ensured the accuracy and reliability of the curated dataset.

Longitudinal Model-Based Meta-Analysis

Using the curated dataset, Excelra performed longitudinal MBMA to evaluate clinical efficacy over time.

The dataset included:

• 37 Phase II and III studies
• 13,474 patients
• 75 treatment arms
• 502 summary response data points

These data points enabled time-course comparisons of treatment responses across multiple biologic therapies for RA.

Comparative analysis of biologic therapies

Excelra’s analysis compared the client’s antibody with existing RA biologics such as:

• Etanercept
• Adalimumab
• Infliximab
• Rituximab
• Tocilizumab

Clinical response was evaluated using ACR20 endpoints, measuring the percentage of patients achieving a 20% improvement in RA symptoms.

Our Solution

• We were able to fully automate and refactor all the custom Perl workflows into the form of a functional Nextflow pipeline containing 18 modules.
  One of those modules was a GSNAP alignment module with:
  • Automated workload partitioning for high-throughput parallel execution.
  • Failure-aware retry logic that isolates and reprocesses only problematic subsets.
  • Configurable parameters for chunk size, retry thresholds, and quality score cutoffs.
• By automatically partitioning the fastq files into chunks and parallelizing the GSNAP alignment, we were able to cut the GSNAP alignment time in half.
• The resulting bulk RNA-seq Nextflow pipeline finished with a run-time of under 3 hours of wall time.