The pharmaceutical industry is confronting a critical problem: as artificial intelligence accelerates drug discovery, the reliability of the research itself is cracking. The American Association of Pharmaceutical Scientists (AAPS) is launching two major conference tracks in October to address research integrity in the AI era and emerging alternatives to animal testing, signaling that the industry recognizes it must rebuild trust in its own findings before AI can truly transform medicine.

Why Is Research Integrity Suddenly a Priority for Drug Makers?

The stakes are enormous. When AI systems help identify promising drug candidates from millions of possibilities, the entire pipeline depends on the underlying research being trustworthy. Yet reproducibility failures and fraudulent papers have plagued preclinical research for years. The AAPS PharmSci 360 conference, scheduled for October 25 through 28 in New Orleans, will dedicate an entire track to this problem, with sessions examining how to identify problematic papers, verify citations, and establish ethical practices for using generative AI in research.

The conference arrives as federal agencies are taking concrete action. The FDA issued draft guidance in March outlining four principles for new approach methodologies (NAMs), which are laboratory systems and computational models designed to reduce or replace animal testing. Meanwhile, the National Institutes of Health (NIH) has committed $150 million over five years to develop and standardize human-based research technologies through its Complement-ARIE program.

What Are the New Technologies Replacing Animal Testing?

The second major conference track will explore emerging drug development technologies that could transform how researchers test safety and efficacy. These alternatives include human-derived laboratory systems, AI applications, organ-on-chip models, neural organoids, and computational models. Johns Hopkins professor Thomas Hartung will lead this full-day curated track, bringing credibility from his recent research demonstrating that human neural organoids can exhibit synaptic plasticity and other building blocks associated with learning and memory.

The practical implications are significant. Instead of relying solely on animal models, researchers can now test drug candidates on human tissue systems grown in laboratories. These systems are faster, more ethically sound, and often more predictive of how drugs will behave in human patients. The morning symposium will cover neural microphysiological systems, regulatory data packages for organ chips, AI-assisted chemical read-across, and organoid intelligence.

How to Navigate the Shift Toward AI-Driven, Post-Animal Drug Development

• Establish Research Integrity Standards: Organizations must implement methods for identifying problematic papers, verifying citations, and establishing ethical practices for using generative AI in research. The Center for Open Science is leading this effort, with researcher Maryam Zaringhalam moderating sessions on open-science policies intended to accelerate research translation.
• Invest in New Approach Methodologies: Pharmaceutical companies should explore human-derived laboratory systems, organ-on-chip models, and neural organoids as alternatives to animal testing. Federal agencies are providing funding and validation standards to support this transition, making it a strategic priority for drug developers.
• Build Trust With Regulators: The FDA and NIH are establishing clear principles and funding mechanisms for non-animal safety science. Companies that align their development processes with these emerging standards will have a competitive advantage in getting drugs approved faster.
• Combine AI With Human Expertise: AI can accelerate the screening and analysis of drug candidates, but human researchers must verify findings and maintain ethical oversight. The conference will showcase how startups, model developers, and regulators are approaching this collaboration.

The research integrity track will be anchored by Brian Nosek, co-founder and executive director of the Center for Open Science. Nosek will deliver a keynote on promoting openness, integrity, and trustworthiness in research findings. A morning symposium will examine organizations that enable scientific fraud, publishers' ability to catch problematic manuscripts, lessons from large-scale replication of preclinical cancer experiments, and NIH efforts to build a more reliable evidence ecosystem.

"The program arrives as federal agencies transition toward validation standards, targeted funding, and specific regulatory applications," noted the AAPS in announcing the conference tracks.

American Association of Pharmaceutical Scientists

The timing reflects a broader shift in how the industry views AI's role in drug discovery. Rather than treating AI as a silver bullet that will automatically produce better drugs faster, pharma companies are recognizing that AI is only as good as the data and research practices that feed it. If the underlying science is flawed or fraudulent, AI will amplify those errors at scale.

NIH Deputy Director Nicole Kleinstreuer will deliver an afternoon keynote on building trust in non-animal safety science, followed by a session examining how startups, model developers, and regulators are approaching post-animal drug development. This session directly reflects recent Johns Hopkins work; Hartung and colleague Lena Smirnova coauthored a 2025 study reporting synaptic plasticity and other building blocks associated with learning and memory in human neural organoids.

The conference represents a maturation of the AI drug discovery field. Early enthusiasm focused on speed and scale; now the industry is grappling with the harder questions of reliability, reproducibility, and ethical oversight. For patients waiting for new treatments, this shift toward trustworthy AI-driven research could ultimately mean safer, more effective drugs reaching the market sooner.