Replacing Traditional TQT Studies with Model-Based Concentration-QTc (C-QTc) Analysis for Cardiovascular Safety in Drug Development

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.”

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. 

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.

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. 

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. 

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