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Case Study:Exposure Response Modeling in Hematology


Exposure-response data gained from clinical studies can provide a basis for model-based analysis and simulation, helping to predict the expected relationships between exposure and response.  Using this approach, it may be possible to optimize dosage regimens and to individualize treatment in specific patient subsets for which there are limited data.  In this blog, we examine a case study of an exposure response modeling project conducted by our Quantitative Pharmacology and Pharmacometrics team. 


Sponsor seeks to gain exposure response information for its Phase 3 hematology product. 

Cytel Solution

An expert team of pharmacometricians, biostatisticians and statistical programmers were assigned to project.  The first step was to create a pooled analysis dataset, combinding episodes and populations of interest across 3 studies. Cytel's pharmacometrics group then conducted direct effect exposure-response analysis to characterize relationship between exposure to product and endpoint of interest.  The indirect effect of cumulative exposure to the product was quantified and analyzed, finding predictions of positive effect with accumulating exposure. Time-to-event analyses, (using Cox proportional hazards models) were utilized to quantify the relative risk associated with changes in specified exposure metrics. 

Value added:

The sponsor was able to support the protective effects of exposure levels above a new threshold value, previously not used for this indication, and obtain orphan drug status from the European Medicines Association.  The sponsor was also able to quantitatively support their claim that cumulative exposure to their product added additional protective benefits for patients. 

 Cytel's Quantitative Pharmacology and Pharmacometrics group have experience using a variety of models for the analysis of exposure-response data. To find out more about these services click below.



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Further Reading 

Blog: It's time to bridge the gap between pharmacometrics and biostatistics

Blog: Biomarker driven clinical strategy

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