In addition, auditable systems must show which databases were queried, what results were returned, and whether the process succeeded. Such systems are nearing adoption for real-world decisions and already support research direction, data gap identification, and testing prioritization.
Insilica’s Dr Luechtefeld Discusses Moving Computational Toxicology Beyond Predictive Models
At PharmSci 360, Insilica CEO Thomas Luechtefeld will present on auditable agentic AI systems and how they are expanding computational toxicology beyond single predictive models.
In an interview with BioPharm International® ahead of the American Association of Pharmaceutical Scientists’s (AAPS) 2026
Dr Luechtefeld explains that
Dr Luechtefeld says
"It's going to be more and more important that people can trace information, trace decision making, and ground everything to truth," Dr Luechtefeld asserts.
Why are single predictive models no longer enough for toxicology decisions?
Dr Luechtefeld points out that the computational models that dominated the past decade are becoming a smaller part of a larger ecosystem. Quantitative structure-activity relationship (QSAR) models and molecular simulation retain value, he explains, but the decision context must broaden. When extensive experimental data exist for a chemical, he adds, those data should inform the decision rather than a QSAR prediction.
In addition, auditable systems must show which databases were queried, what results were returned, and whether the process succeeded, Dr Luechtefeld says. Such systems are nearing adoption for real-world decisions and already support research direction, data gap identification, and testing prioritization, he emphasizes. He expects significant changes within 1 to 2 years in how toxicology teams work.
About the speaker
Thomas Luechtefeld, PhD, Founder and CEO, Insilica
In addition to his role at Insilica, Dr Luechtefeld, is also the creator of ToxIndex, an AI platform for chemical and product safety assessment. ToxIndex coordinates specialized agents across thousands of databases, more than 100 million regulatory and scientific documents, and hundreds of predictive models. The platform assembles this evidence into structured risk assessments. Each claim retains its source, version, and audit history, allowing an assessor to inspect the evidence behind a conclusion and evaluate its relevance to a specific context of use. Dr Luechtefeld holds a PhD in Environmental Health Engineering from the Johns Hopkins Bloomberg School of Public Health. His research on chemical safety data, predictive toxicology, and integrated testing strategies has appeared in Toxicological Sciences, ALTEX, and Regulatory Toxicology and Pharmacology. He also contributes to the European Union Horizon ONTOX project and the National Science Foundation-funded BioBricks open knowledge graph for chemical safety.
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