The Future of Drug Discovery & Development: A Conversation with Dr. Thomas Hartung

Featured session at Bethesda MPS Day, which took place on 11/9/2023.

This concluding session featured a discussion between Ben Swenor of Emulate and Dr. Thomas Hartung, Director of the Johns Hopkins Center for Alternatives to Animal Testing. Dr. Hartung outlined the long-running shift away from conventional animal models toward more human-relevant in vitro systems, including organoids and Organ-on-a-Chip (microphysiological) models. He emphasized the transformative role of stem cell technologies, human iPSC-derived models, and advanced sensor platforms (such as microelectrode arrays) that allow for measurements of function rather than just cell viability. This progress not only enriches our understanding of diseases like autism and Alzheimer’s but also holds promise for creating better predictive models for drug efficacy, safety, and human relevance.

The conversation also covered how artificial intelligence (AI) can integrate diverse datasets, including MPS outputs, to further improve the predictive power of preclinical testing. The ultimate goals are to streamline drug development, reduce costs, improve patient safety, and ensure that novel therapies align closely with human biology. While regulatory acceptance of such methodologies is still evolving, recent policy changes and an increasingly proactive stance by agencies like the FDA signal a growing willingness to adopt these advanced models. Ethical considerations, particularly around complex organoid systems with emerging cognitive-like properties, also came to the fore, underscoring the need for careful governance and responsible innovation.

Key learnings from this presentation include:

  • Human-relevant models: Organoids and microphysiological systems offer more biologically accurate and function-focused endpoints than traditional animal models, potentially reducing late-stage drug failures.
  • Complex functionality: Advances such as brain organoids with neural circuitry and electrophysiological outputs allow researchers to assess cognition-like functions and disease phenotypes, moving beyond simple toxicity assays.
  • Integration with AI: Combining data from organoids, chips, and other in vitro systems with artificial intelligence can identify which tests best predict human outcomes and optimize the overall discovery pipeline.
  • Regulatory shifts: Regulatory agencies, notably the FDA, are increasingly open to non-animal test methods. The FDA Modernization Act and active engagement with alternative models signify a policy environment supportive of innovation.
  • Ethical and societal considerations: As organoid complexity grows, so do questions about consciousness, ethics, and consent. Embedded ethics frameworks and public engagement will be critical as organoid intelligence research advances.
  • Path to adoption: Although initial use cases may focus on lead optimization and complex disease modeling, standardization, quality control, and technological maturation will eventually enhance throughput, affordability, and broader adoption across biomedical research.