Medera Announces Publication of Study Utilizing Machine Learning to Enhance Next-Generation Drug Screening with Human mini-Heart Technology

In This Article:

  • Traditional methods for evaluating therapeutic efficacy and cardiotoxicity often lead to high failure rates during clinical trials, resulting in significant development costs

  • Human-specific diseases cannot be accurately modeled by animals, leading to limited medical options or advancements

  • This new study leverages the capabilities of artificial intelligence (AI) and machine learning (ML) to address the challenge of achieving automated and comprehensive "smart" drug screening using Medera's mini-Heart technology platform

  • Innovative AI/ML-based model combines data from multiple human mini-Heart screening assays and takes advantage of the complementary strengths to achieve superior next-generation drug classification capabilities

  • The unique combination of AI/ML and human mini-Hearts can accelerate drug discovery, clinical translation and precision medicine by improving screening efficiency, reducing costs, enhancing safety and creating new opportunities for patient benefits

SUMMIT, N.J. and BOSTON, Mass., Oct. 31, 2024 /PRNewswire/ -- Medera Inc. ("Medera"), a clinical-stage biotechnology company focused on targeting difficult-to-treat cardiovascular diseases using a range of next-generation gene- and cell-based approaches, announced today the publication of a new study in the peer-reviewed journal Pharmacological Research (volume 209). This study, entitled "Enhanced Drug Classification Using Machine Learning with Multiplexed Cardiac Contractility Assays," demonstrates how Novoheart, Medera's wholly-owned preclinical subsidiary focused on human cardiovascular disease modeling for drug discovery, is leveraging AI and ML to improve next-generation drug screening processes.

The present work aims to address a long-standing challenge in the pharmaceutical industry: accurately screening and classifying drug candidates for their effects on human heart function. By applying AI-driven automation, the objective is to enhance the success rates of future clinical trials and ultimately improve patient benefits by modernizing the drug development process.

Traditional methods for evaluating therapeutic efficacy and cardiotoxicity often lead to high failure rates (over 90%) during clinical trials, resulting in development costs that can exceed $2 billion per drug on average. Furthermore, human-specific diseases cannot be accurately modeled by animals, leading to limited medical options or advancements. Novoheart's present study demonstrates a novel approach utilizing AI/ML combined with comprehensive functional data from its various human mini-Heart assays, engineered from human pluripotent stem cell-derived cardiomyocytes, to create a more predictive and automated preclinical model of human cardiac responses.  This innovative approach improves the accuracy of drug screening, promising to increase efficiency, reduce costs, and enhance safety of developing new drugs, thereby creating opportunities for drug developers and improving outcomes for patients.