What the FDA’s new QTc guidance means for exposure-QT modeling and TQT alternatives, and how A2-Ai can help apply it in practice.

In December 2025, the FDA released new guidance on how heart rate corrected QT (QTc) information should be presented in prescription drug and biological product labeling. Earlier documents such as ICH E14 and S7B focused on how QTc effects should be evaluated, while this new guidance centers upon how those findings should be communicated so prescribers and patients can interpret them more easily. Although framed as a labeling document, it also signals the FDA’s expectations for how QT data should be developed, especially when sponsors rely on Thorough QT (TQT) alternatives of exposure-QT modeling. At A2-Ai we are prepared to help sponsors adjust their QT strategies in response to this shift.

Why This Guidance Matters

The QT interval remains one of the main ways to assess proarrhythmic risk. Any small drug-related changes can raise concern about ventricular arrhythmias, including torsades de pointes. Historically, labels varied widely in how they described QT findings, which often left clinicians to interpret study designs and risk levels on their own. The new guidance’s main objective aims to bring consistency and clarity to this information for patients and clinicians. Consequently, the key impact to sponsors is a deepened need to approach QT evaluation with more thoughtful planning and execution.

Key Elements of the Updated Labeling Guidance

1. Clear Expectations for exposure-QT Information When concentration-QTc (C-QTc) analyses are available, the results should appear in the Clinical Pharmacology section under a Cardiac Electrophysiology heading. The FDA continues to view ER modeling as a reliable approach when supported by solid data and adequate exposure coverage.

2. Defined Locations for QTc Information throughout the Label Depending on the strength and nature of the findings, QTc information may appear in several sections. These include Clinical Pharmacology, Drug Interactions, Warnings and Precautions, Adverse Reactions, Contraindications, and Patient Counseling Information. Example language is provided that covers scenarios ranging from no detectable effect to incomplete information.

Nuances and What to Know Up Front

A few details can help shape how this guidance should be interpreted:

• The guidance applies to both new and already approved products. Sponsors should update labeling if new QTc findings emerge.
• Some QTc findings may need to be communicated to patients. The scope is not limited to prescriber-focused labeling.
• The recommendations only apply to non-antiarrhythmic drugs and biologics.
• Study methods remain flexible. The FDA does not dictate electrocardiogram (ECG) collection methods, correction formulas, or specific exposure response (ER) modeling approaches. Sponsors must be clear about assumptions, data quality, and uncertainty.

Together, these points show that the guidance does more than organize labeling. It provides a clearer framework for how QT risk should be developed, interpreted, and ultimately communicated.

What This Means for Sponsors and How A2-Ai Supports These Efforts

Early planning becomes even more important under this guidance. High-quality ECG collection and strong exposure-QT modeling helps teams understand QT risk and make it easier to craft accurate statements for the label. The work done during early development directly shapes how transparent and defensible the labeling will be later.

A2-Ai has deep experience with all aspects of QTc. This includes designing the studies, running QTc pharmacometrics, and successfully obtaining TQT waivers to skip TQT studies altogether. To take it a step further, we developed an open-source tool, CQT Toolkit, which offers a structured workflow for preparing C-QTc analyses. The toolkit guides users through data assembly, visualization, model development, diagnostics, and documentation, helping to produce clear, reproducible analyses that align with regulatory expectations. The idea is this continues to put the power in sponsor’s hands to make the best decisions as efficiently as possible!

Using tools like CQT Toolkit makes it easier for sponsors to present QT findings in a consistent, interpretable way. It simplifies internal review, supports regulatory discussions, and reduces the risk of unclear or incomplete labeling language. A2-Ai works with clients to integrate QT planning into development and to build models that support both scientific understanding and clear communication.

Looking Ahead

The FDA’s 2025 QTc labeling guidance reinforces the need for transparency and consistency in how cardiac electrophysiology findings are shared. QT strategy is no longer only about generating the right data. It also involves planning for how those data will be interpreted and communicated across the lifecycle of the product.

Learn more about A2-Ai’s CQT Toolkit Here: https://a2-ai.github.io/cqtkit-docs/versions/1.0.2/