Real-World Applications of InSilicon™
How InSilicon™ Is Applied in Practice
InSilicon™ is applied where teams need a defensible way to reduce animal studies without weakening scientific or regulatory decision standards. Each use case reflects common real-world contexts in which organizations evaluate where in silico evidence can meaningfully inform planning, prioritization, and execution.
Preclinical Portfolio Optimization
The challenge
Preclinical portfolios often include programs with uneven signal strength, overlapping study designs, and varying levels of data readiness. Identifying low-probability programs early is difficult, leading to continued investment in studies with limited translational value.
How InSilicon™ helps
Evaluates portfolio-wide data to compare program readiness, predicted biological response, and replaceability potential. Supports earlier prioritization decisions and clearer allocation of in vivo resources.
The Analysis
Program-level readiness scoring, comparative signal assessment across endpoints, prioritization matrices, and scenario modeling across time, cost, and animal use.
The impact
Earlier identification of lower-signal programs, reduced redundant studies, and reallocation of effort toward candidates with stronger evidence profiles—using assumptions that are stated and traceabl.
Regulatory-Ready Evidence Development
How InSilicon™ helps
Generates auditable analytic outputs with documented inputs, assumptions, performance context, and stated limits, aligned to emerging expectations for in silico evidence.
The Analysis
Traceable evidence packages, sensitivity checks, applicability notes, performance summaries, and regulator-facing reporting structures.
The impact
Improved readiness for regulatory engagement, clearer submission support materials, and more structured discussions grounded in reviewable evidence rather than exploratory analysis.
The challenge
Regulatory discussions require evidence that is transparent, traceable, and clearly bounded. Many analytics outputs are difficult to review or defend due to undocumented assumptions or unclear limitations.
Animal Study Reduction
The challenge
Teams are under increasing pressure to reduce animal use while maintaining scientific rigor and confidence in decision-making. Determining where in vivo studies are unnecessary or can be refined requires structured, evidence-based assessment.
How InSilicon™ helps
Models biological outcomes in silico to assess study replaceability, compare scenarios, and identify where animal studies can be eliminated, reduced, or refined without compromising decision standards.
The Analysis
Replaceability distributions by study type, risk-scoring by endpoint, non-inferiority comparisons, and scenario tables outlining tradeoffs across animal use, timelines, and uncertainty.
The impact
Scenario-based estimates of reduced animal use, earlier go/no-go decisions, and clearer justification for study design choices—documented for internal and external review.
Government-Funded Research Programs
How InSilicon™ helps
Supports ethically aligned research planning through reproducible workflows, documented assumptions, and scenario-based evaluation of reduction and replacement strategies.
The Analysis
Program-level modeling frameworks, pilot evaluation criteria, sensitivity analyses, and governance-ready summaries suitable for public and institutional review.
The impact
Measured progress toward ethical research goals, with clearly articulated tradeoffs and results that support oversight, replication, and broader adoption.
The challenge
Publicly funded research initiatives, including NIH-supported programs, require scalable approaches to reduce animal testing while maintaining transparency, reproducibility, and public accountability.
