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®SCS Capabilities — Treatment Optimization in Phase 2-3 Development
Competition keeps raising the bar for new drugs, so it becomes increasingly important to get the dose and regimen right — and to do so as early and efficiently as possible. Pharsight® SCS uses integrated, probabilistic, data-driven modeling and simulation to test alternative trial designs and strategies that optimize Phase 2-3 treatments.
With Pharsight Trial Simulator™, we quickly build and link simulation models for subject adherence, PK, PD, and clinical trial endpoints. The parameters for the PK and PD models are estimated using a population mixed effects modeling approach that combines your proprietary data from past trials with literature data. We model and estimate the uncertainty in the parameter estimates and demonstrate how this uncertainty results in the uncertainty in clinical endpoints. These results can be visualized in an interactive, user-friendly form with Drug Model Explorer® (DMX®).
Benefits of optimized treatments selected for Phase 2-3 trials include decreased trial time and associated cost and improved learning – perhaps even saving a good drug that would have failed in Phase 3 due to inadequate information in Phase 2.
Strategic objectives and SCS approaches on an engagement to optimize Phase 2-3 treatment include:
Objective |
Approach/Methods |
Find the dose or dose range that provides the best balance between efficacy and safety for the target populations. |
Model dose-response and adverse events. Constrain population-based simulations within given limits or weigh in a clinical utility index (CUI) to optimize risk-benefit tradeoffs. |
Find the best dose frequency. |
Simulate and compare PK/PD of alternative regimens and formulations (e.g., sustained vs. immediate release) against market and competitor data. Determine optimal trade-offs of efficacy vs. convenience in dose and dose frequency. |
Find the most promising combinations for co-administered drugs. |
Model PK and PD interactions within alternative combination regimens. |
Account for realistic subject behavior, including adherence. |
Simulate different subject behaviors within the target population to determine effects on response. |
Optimize learning in Phase 2 to strengthen confirmation in Phase 3. |
Quantify uncertainty about dose-response (e.g., test optimal doses that fall outside expected range for target label, study doses below and above expected ED50). |