Bottlenecks to Breakthroughs: Tackling Challenges in QSP Modeling

Quantitative Systems Pharmacology (QSP) has emerged as a powerful discipline to meet this need. By combining systems biology, pharmacokinetics (PK), and pharmacodynamics (PD), QSP models simulate how drugs interact with complex biological networks in virtual patient populations.

QSP enables researchers to:

• Identify and prioritize therapeutic targets
• Evaluate drug design properties early in development
• Anticipate first-in-human (FIH) dosing, therapeutic index, and recommended Phase 2 dose (RP2D)
• Optimize dose regimens and predict efficacy outcomes
• Flag safety concerns before clinical trials begin
• Support regulatory submissions with mechanistic, evidence-based predictions

QSP has been proven to reduce development risk, support regulatory decision-making, and accelerate timelines. Yet despite its power, adoption across pharma and biotech has been limited, and scalability remains a challenge.

While QSP holds transformative potential, it has historically struggled with scalability, usability, and accessibility. Key challenges include:

1. Complexity and Specialist Dependence
   QSP models often require writing and debugging large amounts of code, accessible only to a handful of expert modelers who originally created the model. This creates bottlenecks and makes scaling difficult.
2. Slow Simulation Speeds
   Running large, data-rich simulations across virtual patient populations can take days, precluding certain analyses from taking place and delaying critical decisions.
3. Knowledge Silos
   Expertise and models frequently remain confined to individuals or small teams. This makes knowledge transfer difficult as organizations grow and evolve.
4. Lack of Standardization
   QSP models vary widely in complexity, structure, and toolsets. This creates inefficiencies, hinders reuse, and complicates communication with regulators and cross-functional teams.
5. Fragmented Workflows
   Model diagrams, underlying equations, and simulation code are often disconnected. This makes models harder to interpret, validate, and integrate into decision-making pipelines.

Because of these barriers, QSP modeling remains underutilized in drug development. While the benefits are proven, it has not yet been fully accessible or scalable.

Recognizing these limitations, Certara developed Certara IQ™—an AI-enabled platform designed by leveraging decades of QSP modeling experience. Certara IQ is built to bridge the critical gaps that have long hindered QSP adoption, making the discipline more scalable, efficient, and accessible across the life sciences industry.

• Pre-Validated Model Libraries
  Certara IQ includes a repository of validated, well-documented models across therapeutic areas. These models provide powerful starting points for new projects, reducing duplication and accelerating timelines.
• AI-Enhanced, Intuitive Interface
  With AI-driven visualizations and declarative programming, users can interact with models through intuitive diagrams directly linked to underlying equations and references. This improves transparency and usability across teams.
• Cloud-Based Computational Power
  Large-scale simulations that once took days now complete in minutes, enabling rapid iteration and faster decision-making.
• Declarative Programming for Reusability
  Instead of complex code, models can be built using declarative statements, making them reusable and accessible even for non-coding scientists.
• Integrated End-to-End Workflows
  Certara IQ unifies model creation, simulation, analysis, and reporting, streamlining regulatory submissions and internal decision-making.
• Collaboration Tools
  Teams share, edit, and compare scenarios seamlessly—preserving institutional knowledge and fostering innovation.
• GxP Compliant Tools
• Consultant Support

Built by incorporating the decades of experience that Certara’s global team of QSP experts brings, and used by Certara’s QSP consulting team, Certara IQ is the trusted QSP modeling tool for projects of any size.

• Days to run simulations
• Specialist-driven coding bottlenecks
• Limited knowledge sharing and reuse
• Fragmented, inefficient workflows
• Lack of standardization challenging for modeling groups and regulators

Quantitative Systems Pharmacology has long promised to transform drug development, but its full impact has been limited by complexity, slow computation, fragmented workflows, and reliance on a small pool of experts. These challenges have kept QSP from scaling across organizations, despite its proven ability to improve decision-making and reduce costly failures. The motivation behind Certara IQ is to ease these barriers—streamlining model creation, accelerating simulations, unifying workflows, and opening access to more scientists. Using pre-built workflows and models, users can significantly reduce the likelihood of errors. By addressing the core pain points that have held QSP back, Certara IQ enables teams to leverage advanced modeling at scale, bringing more effective therapies to patients faster.

By automating complexity and leveraging AI, Certara IQ enables researchers and developers to:

• Make earlier, more confident go/no-go decisions
• Optimize dosing and safety predictions before clinical trials
• De-risk late-stage failures
• Shorten the time to market for innovative therapies

By democratizing QSP, Certara IQ empowers companies to harness QSP modeling at scale, using it across portfolios and pipelines.