Case Study
Early Termination Decision for a Nonviable Treatment Mechanism in Osteoporosis
Case Study Summary
A key challenge facing biopharma innovators is the assessment of the probability of success for a novel mechanism of action (MOA). Quantitative Systems Pharmacology (QSP) modeling techniques play a crucial role in guiding drug development decision-making and are well suited to questions about MOA. This case study highlights a strategic and scientific collaboration between scientists at Daiichi-Sankyo and Metrum Research Group (MetrumRG) early in the translational development of an oral calcium sensing receptor blocker, a novel MOA for treating osteoporosis. The team applied QSP modeling methods to assess the expected efficacy and safety profiles of this novel MOA and inform an early termination decision.

Challenge
The primary challenge was navigating the complexities of osteoporosis treatment and accurately assessing the potential of the novel MOA without conducting extensive clinical trials. Daiichi-Sankyo faced pressure to make timely decisions and avoid unnecessary investment in a nonviable program.

Solution
MetrumRG devised a comprehensive strategy, leveraging bone health expertise and QSP modeling capabilities. A mechanistic, multiscale QSP model was developed to simulate the dynamics of the novel MOA. The model was built and simulated using the Metworx platform, facilitating efficient computations and analyses.

Results
The QSP model-based approach provided valuable insights, enabling Daiichi-Sankyo to confidently terminate the development of assets focused on the nonviable MOA following a single dose first-in-human trial. Meanwhile, competitors who proceeded with lengthy Phase 2 studies on similar mechanisms encountered failure.

Impact
Identifying and terminating the nonviable MOA early in drug development had significant positive impacts for Daiichi-Sankyo. It led to substantial cost savings (estimated at $25 million) by avoiding further investment in later stage clinical development and allowed the rapid reprioritization of research and development efforts to other programs. Additionally, it saved precious time and demonstrated the value of QSP modeling methods for efficient, accurate, and strategic decision-making.
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