Innovation in QT Strategies: Leveraging Early-stage Safety Methodologies and AI to Maximize Your Opportunity for a TQT Waiver

Session information:

Overview:

Only about 10-15 percent of new drugs make it from discovery to market, with 70 percent failing during Phase I due to either unmanageable toxicity or poor pharmacokinetics. This highlights the importance of robust and reliable Phase I data in determining the future of a drug candidate. Early and accurate cardiac safety assessments, particularly those focusing on the QT interval, play a key role in assessing the potential for toxicity and guiding informed decisions regarding drug development.

Traditionally, QT assessments have been conducted in later stages of clinical development in Thorough QT (TQT) studies. However, a more proactive approach is available: early phase QT assessments performed in healthy volunteers during Phase I studies. These assessments, typically conducted during single ascending dose (SAD) or multiple ascending dose (MAD) studies, provide valuable cardiac safety data earlier in the development process. In many cases, early-phase findings can be used to support waivers for TQT studies, reducing the need for costly and time-consuming trials during later development stages. However, relying on the principal investigator’s read of 12-lead ECGs will not typically be able to detect QT interval changes at the level of precision achieved with a TQT study or meet the requirements for a TQT waiver.

To support rigorous, high-quality measurements of changes from baseline of the QT and other ECG intervals during Phase I studies, the Expert Precision QT (EPQT) methodology was developed in collaboration with the FDA. Relying on iCOMPAS technology, which has benefited from years of development and validation using data from individuals with drug-induced or inherited QT prolongation, high-precision measurement of the QT interval is ensured. With these data and analyses being available at an early phase, more informed decisions around the safety of a drug and the viability of the candidate to move to later phase studies are enabled. Moreover, this high-quality data can be used to support a TQT waiver later in development, alongside other data.

Given the requirement for precise data and analysis for early phase assessments, quality checks of the ECG data that can be quickly conducted after data acquisition enables recollection of data should the quality be lacking and opportunity for site-retraining as necessary. Artificial intelligence (AI) is an ideal solution for these analyses to automatically screen continuous ECG recordings for a quality assessment without the need for human engagement.

By combining high-precision, early-phase QT assessments from EPQT with AI-enabled ECG early quality checks, drug developers can obtain actionable safety data that informs critical decisions throughout the development process. Early identification of cardiac safety risks can help mitigate potential toxicity issues before they escalate, improving the likelihood of successful drug development and reducing the risk of late-stage failures. Moreover, the semi-automation of ECG analysis enhances operational efficiency, reduces manual labor and minimizes the potential for human error.