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August 6, 2025

Overview

Concentration-QTc (C-QTc) analysis, also referred to as concentration-QT analysis, is reshaping how biopharma teams assess cardiovascular safety in drug development. Once reliant on expensive and time-consuming TQT studies, developers now use model-based QT assessments to deliver faster, more predictive, and more cost-efficient results. This blog explores how Certara experts leverage C-QTc analysis—also known as model-based QT analysis—to meet regulatory expectations, avoid unnecessary trials, and accelerate development timelines. Through real-world examples, we demonstrate how this approach reduces risk, supports TQT waivers, and improves decision-making across multiple therapeutic areas.

Why Cardiovascular Safety Matters

Cardiovascular safety remains one of the most critical—and challenging—requirements in drug development. Failing to properly assess QT interval prolongation can lead to serious consequences: costly delays, restrictive labeling, or even termination of a promising program late in development. When done right, however, QT evaluation not only safeguards patients—it helps pave a clear path to regulatory approval. 

“Every drug program, regardless of therapeutic area or modality, must clear the cardiovascular safety hurdle as mandated in the ICH E14 guidance,” explained Patrick Smith, Senior Vice President of Translational Science at Certara. “A prolonged QT interval can lead to life-threatening arrhythmias, and as drug developers, ensuring patient safety is a fundamental responsibility.”

Moving Beyond the TQT Study

For many years, the traditional approach to QT risk was the standalone thorough QT (TQT) study—a complex, expensive clinical trial designed to prove a negative. Often requiring at least nine months and millions of dollars, the TQT study became a development bottleneck.

Fortunately, updated ICH E14 guidance has ushered in a smarter approach: concentration-QTc modeling using data already collected in early-phase clinical studies. By integrating pharmacokinetic (PK) and electrocardiogram (ECG) data, drug developers can now build pharmacometric models that assess the relationship between drug concentration and QT interval changes.

“When executed properly, this approach often eliminates the need for a dedicated TQT study altogether,” says Smith. 

 

Multiple regulatory pathways now enable TQT waivers: 

  • 5.1  Pathway (2015 revision): Used when the data can demonstrate no clinically relevant QT effect—with 2x the high clinical exposure
  • 5.1 Nonclincial Integrated Risk Assessment Pathway: Used when the data can demonstrate no clinically relevant QT effect at the the high clinical exposure and the nonclincial data can be used as supplementary evidence
  • 6.1 Substitution with Nonclincial Integrated Risk Assessment Pathway: Used when sufficiently high doses cannot be safely given to healthy subjects (e.g., in oncology); supported by robust modeling and nonclinical data. 

The Power of Exploratory Analysis

To maximize efficiency, many development teams are incorporating exploratory concentration-QTc analyses before committing to full model development. This early assessment helps determine if the data supports a linear modeling approach—and if any additional ECG or PK data is needed.

“One of the biggest advantages of the concentration-QTc approach is that you can integrate ECG and PK sampling into studies you’re already planning,” notes Adekemi Taylor, VP and Regional USA Lead for Quantitative Science Services at Certara. “It’s far more cost-effective—and it allows you to evaluate QT risk in scenarios that weren’t directly tested.”

Key considerations for success include:

  • Ensuring ECGs are collected in triplicate to meet regulatory requirements
  • Capturing ECG and PK at Tmax for both parent drug and relevant metabolites
  • Controlling for confounding variables (e.g., meals, co-medications)
  • Assessing assumptions like linearity, heart rate correction, and time delays in the drug effect on QT (hysteresis)

Exploratory analysis also provides the opportunity to refine model selection—whether that’s a standard linear model or something more complex like an Emax or PK/PD model—based on the totality of evidence.

Model-Based QT Strategies: Real-World Successes Using C-QT Analysis to Replace TQT Studies

Example 1: Managing Multiple Active Metabolites

A sponsor developing a compound with five metabolites that potentially required QT risk characterization in the context of CYP3A4-mediated drug-drug interactions. 

Rather than building every possible model combination, exploratory analysis was used to focus on the parent and one key metabolite. The team identified a threefold safety margin above the high clinical exposure, supporting a strong case for a TQT waiver. 

“This approach allowed us to streamline the modeling and avoid unnecessary complexity while still addressing regulatory concerns,” says Kara Schmelzer.

 

Example 2: Oncology Program with Limited Data

In an oncology program, high exposures couldn’t be reached in healthy volunteers due to dose-limiting toxicities, and ECGs in early studies did not meet modeling criteria. A phase 3 sub study was designed to collect ECGs and PK data from over 130 patients, allowing the team to build a QTcP and QTcF model that addressed the drug effect on QT using appropriate heart rate correction.

The analysis showed no clinically meaningful QT prolongation, supporting drug approval. 

“This was a great example of adapting the modeling approach to the data we could reasonably collect—while still delivering confidence to regulators,” says Schmelzer. 

 

Example 3: Overcoming Saturation with a DDI Strategy

A novel BTK inhibitor showed saturable absorption at high doses, making it difficult to achieve super-therapeutic exposures. To overcome this, ritonavir was used to inhibit CYP3A4 metabolism and increase drug exposure. Unlike other CYP3A4 inhibitors, ritonavir at sub therapeutic doses does not cause an increase in the QT interval. 

The resulting model demonstrated a clean QT profile at significantly higher concentrations, eliminating the need for a dedicated TQT study. 

“We combined a DDI study with QT analysis in one efficient design, avoiding unnecessary separate trials while still covering all regulatory bases,” adds Schmelzer. 

A More Predictive, Efficient Path Forward

Assessing QT risk no longer needs to be a costly bottleneck. With the right strategy, drug developers can leverage existing data, reduce unnecessary trials, and deliver a more precise evaluation of cardiovascular safety. 

“This model-informed approach is one of the real success stories of modern drug development,” says Smith. “It’s faster, cheaper, and more scientifically rigorous than the traditional TQT paradigm.” 

By applying exploratory analysis early and aligning modeling with regulatory expectations, development teams can move forward with greater clarity, confidence—and speed. 

Learn more

Explore Certara’s pharmacometrics offerings, including our ModelBased QT Analysis services, through the dedicated resources available at Certara.com. 

Frequently Asked Questions

What is C-QTc analysis?

C-QTc analysis, short for concentration-QTc analysis, is a model-based approach that evaluates the relationship between a drug’s plasma concentration and its effect on the QT interval of the heart. It integrates pharmacokinetic (PK) and electrocardiogram (ECG) data to assess cardiac safety without requiring a standalone TQT study.

What’s the difference between QT and QTc?

The QT interval measures the time it takes for the heart to repolarize after each beat. QTc refers to the QT interval corrected for heart rate, as heart rate fluctuations can affect raw QT values. QTc is the regulatory standard for evaluating drug-induced cardiac risk.

What is a TQT study and why is it being replaced?

A Thorough QT (TQT) study is a dedicated clinical trial historically used to assess a drug’s QT prolongation potential. These trials are expensive and time-consuming. Regulatory agencies now accept model-based concentration-QTc analysis using data from existing studies, which can replace the need for a dedicated TQT study under the ICH E14 guidance.

How does C-QTc analysis help replace TQT studies?

C-QTc analysis uses data from early-phase clinical trials to model the QT effect of a drug, including at therapeutic and supratherapeutic concentrations. If the model demonstrates no significant QTc prolongation, regulators may grant a TQT waiver, eliminating the need for a standalone TQT study.

What regulatory guidance supports the use of C-QTc analysis?

The ICH E14 guidance (and its Q&A updates) supports the use of model-based approaches—including concentration-QTc modeling—for QT assessment. Pathways like the 5.1 Waiver and 6.1 Substitution provide clear mechanisms for using this approach to satisfy regulatory requirements.

Who benefits most from C-QTc analysis?

Any drug developer looking to reduce costs, shorten timelines, and manage cardiovascular safety risks benefits from this approach. It is particularly valuable in oncology, rare diseases, or when high exposures cannot be safely achieved in healthy volunteers.

Erika Brooks

Marketing Director, Quantitative Science Services

With over 22 years of experience in hospitals, health systems, associations, life sciences, physician practices, and suppliers, Erika is an experienced marketing strategist and supports the Quantitative Science Services offering with Go-to market planning and execution.

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