While conferences often showcase emerging tools and technologies, NBC 2026 felt different. The themes of artificial intelligence (AI) and new approach methodologies (NAMs) were not peripheral topics, they were central to many discussions, from early discovery through clinical development and regulatory strategy.
Taken together, these signals suggest that we are entering a foundational shift in how drug development science is conducted, integrated, and ultimately evaluated.
AI and NAMs: From Innovation to Infrastructure
The messaging at NBC was very clear around NAMs and AI in terms of a convergence of the two. AI is rapidly becoming a core infrastructure for predictive toxicology, translational modelling (e.g., PBPK, QSP) and integration of complex, multimodal datasets. NAMs, including organoids, spheroids, organ-on-a-chip and in silico models are evolving into true alternatives to animal models and driving regulatory-relevant approaches to safety and efficacy determinations. Together, AI and NAMs are enabling an actionable framework centered on human biology and predictive drug development.
Progressing Translational Science
A consistent theme was the need to improve the progression from preclinical models to human outcomes. Integral to this are the combination of in vitro assays with computational modelling, biomarker (and omics) data and again, using AI to bridge datasets across drug development. Bioanalytically, this challenges us to ensure our assays support predictive models and leverage fit-for-purpose method performance that supports the context of use.
Data-Centric Drug Development
While it can be argued drug development “has always been data dependent (as it should be),” an increasing emphasis is emerging on how data can be easily integrated across platforms, how it supports predictive models and how it enables earlier go/no-go decision making. For laboratories, this does have further practical implications. Bioanalytical data must support downstream modelling, clinical interpretation and regulatory submission narratives. Of course, new tools are enabling this with a digital infrastructure (LIMS, ELN, AI-tools) becoming the strategic enabler and not just for operational support.
Implications for the Bioanalytical Lab and Operations
Like what I came away with from WRIB, ongoing transformations of bioanalysis are driven by the bigger holistic picture. From NBC the key takeaway points were:
Greater integration across disciplines (bioanalysis, clinical pharmacology, modeling, biomarkers)
Evolution of fit-for-purpose validation aligned to scientific intent rather than rigid frameworks
AI-augmented workflows (QC, data review, report generation, anomaly detection)
Support for emerging modalities and NAMs requiring new analytical approaches and novel biomarkers
For bioanalytical scientists and lab leaders, this represents a pivotal moment. The opportunity is not just to adapt, but to help shape how these emerging frameworks are implemented in real-world, regulated environments.