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

A Europe-based large pharma company engaged in development of novel antibody therapeutics against Rheumatoid Arthritis (RA), was analyzing the data to demonstrate the advantage of longitudinal meta-analysis over conventional meta-analysis that uses end-of-study (EOS) data, toward facilitating more effective Model Informed Drug Development (MIDD) decisions.

Client’s requirement

A prominent pharmaceutical company based in Switzerland, specializing in the development of groundbreaking antibody therapeutics for rheumatoid arthritis (RA), sought to enhance their understanding of the competitive landscape in early clinical Phase II. Focused on advancing model-informed drug development (MIDD) decisions, the company aimed to distinguish the advantages of longitudinal meta-analysis over conventional end-of-study (EOS) approaches. Specifically, their objective was to assess the competitive position of their novel antibody against all approved biologics for RA. Given the complexity of RA as an autoimmune disease, the client recognized the importance of considering patient characteristics, prior therapies, and the speed of onset in drug effects. The company required a model-based meta-analysis (MBMA)-ready dataset that meticulously curates existing scientific evidence on the efficacy of marketed biologics for RA.

Our approach

In line with the specified requirements, Excelra’s Clinical Pharmacology team meticulously followed specified requirements, employing a robust scientific curation methodology and systematic literature review (SLR) to conduct model-based meta-analysis (MBMA). The process involved defining the project scope using PICOS methodology for a Systematic Literature Review in PubMed. The dataset included a comprehensive summary of time-course responses from late-stage clinical trials, incorporating information on prior and concomitant medications, response status, baseline patient characteristics, and sample sizes for statistical analysis. They developed a screened and labeled database, incorporating 119 additional sources from FDA drug labeling and traditional meta-analysis publications. A customized clinical outcomes database captured key information, including time versus response, patient population details, interventions, comparators, and study design. A stringent 3-level Quality Control (QC) process ensured database precision. This collaboration aimed to provide valuable insights to drive strategic decisions in the development of their novel antibody therapy for rheumatoid arthritis.

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