Traditional safety pharmacology and toxicology studies often rely on a limited set of endpoints, potentially overlooking subtle physiological changes that could inform compound safety profiles.
This session explores how integrating emkaPACK non-invasive and easyTEL+ implantable telemetry technologies expand the scope of safety assessments.
Leveraging Novel Telemetry for Advanced Hemodynamic Characterization in Murine Models – Validating Wireless Pressure Monitoring for Pre-Clinical Safety Pharmacology
Session Outline: This session will explore the transition toward high-fidelity, continuous telemetry in murine models, focusing on the interplay between autonomic plasticity and the biological limits of cardiac performance. By moving beyond simple heart rate and blood pressure observations, we explore the “Kinetic Ceiling” of naïve mice: The point at which chronotropy begins to compromise ventricular filling and stroke volume efficiency. Through a series of multi-factorial challenges, including the mapping of baroreflex sensitivity and the assessment of circadian dipping architecture, we also demonstrate how telemetry data acquisition, in unrestrained subjects, can expose subtle warning signals that are typically masked by traditional measurement methods or anesthesia. This presentation aims to evaluate how refined tools and methodology can offer a more rigorous framework for de-risking molecules early in the development process of new compounds.
About Will : Will completed his studies at Bishop’s University and Sherbrooke University, studying cardiac physiology in the context of various pathologies. Joining the IPS Therapeutique team in 2020, he’s been heavily involved in all studies involving telemetry data. Now lead study director of IPST’s cardiac team, Will coordinates a group focused on the advancement surgical and analytical methodologies, and their implementation in preclinical efficacy and safety studies.
From Zero to Data, our Experience with the Rodent easyTEL+ Telemetry: human and machine learnings
Session Outline: Kardigan is a precision therapeutics company developing medicines that target the root cause of specific cardiovascular diseases where no treatments exist. They are modernizing both clinical trials and research paradigms with the use of AI, machine learning, and tech to improve efficiency, timelines, and outcomes for patients. In this session, Beth Geist and Daniel Edelberg will present a brief tale of their initial experiences leveraging the rodent easyTEL+ system, from set-up to data generation and smart analysis.
About Beth : Beth is an in vivo pharmacologist with a career focus on cardiovascular disease models. She holds board certifications from the Safety Pharmacology Society, the American Board of Toxicology, and the American College of Laboratory Animal Medicine. With over 10 years of experience using emka hardware and software, she has utilized telemetry data collection across a wide range of species and study designs.
About Daniel : Daniel Edelberg, PhD, is a Machine Learning Scientist at Kardigan. He earned his PhD in Applied Mathematics from Yale University. Currently, his research focuses on clinical and preclinical predictive modeling using advanced mathematical systems and machine learning technology.
