ReviewThe AAPS journal2026
New Frontiers of Drug Development Through the Use of New Approach Methodologies.
Review in The AAPS journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
On November 2025, AAPS PharmSci 360 convened a symposium that included experts in the application of various types of New Approach Methodologies (NAMs). They shared their experiences and insights on topics that included microphysiological systems (MPS), the use of 3D organoids, and in silico tools. MPS systems were explored from the perspective of their testing and qualification through an academia-industry-government tissue chip testing consortium whose mission is to perform context-of-use-based testing of MPS and conduct comparative evaluations to support the use of MPS systems in safety assessments. 3D organoids were described and insights shared on how and when they may provide an appropriate tool for evaluating drug safety and effectiveness, and their use in supporting personalized medicine. Regarding in silico tools, examples were provided to describe their growing utility in predicting drug toxicity, population variability, and its potential benefits over traditional evidence-based toxicology. These tools include in silico models (e.g., physiologically based pharmacokinetic models), Artificial Intelligence (AI), and Machine Learning (ML) which, unlike other aspects of model informed drug development, reduce reliance on predefined models and allows for the integration of diverse sources of data. Computationally, these tools can generate predictions in hours where it would otherwise have taken weeks or months (and extensive experimentation). Within this meeting report, we highlight the key issues discussed during that symposium and share additional aspects to consider when developing a NAMs-based roadmap for specific contexts of use.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.