ReviewAAPS PharmSciTech2026
Process Analytical Technology Integration in 3D Printing Processes: Benefits and Challenges.
Review in AAPS PharmSciTech, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional printing (3DP) manufacturing technologies will transform pharmaceutical industry, offering the ability to produce personalized medicine and complex dosage forms with diverse geometries, with the advantage of on-demand dispensing. Maintaining consistent product quality and regulatory compliance pose major challenges in the current landscape due to the inherent variability and multivariate complexity of 3DP. Employing Process Analytical Technology (PAT) with Quality by Design (QbD) and continuous manufacturing (CM) principles provides the necessary framework for real-time monitoring (RTM), control, and understanding of critical process parameters and quality attributes. As a result, key PAT tools are now applied to various 3DP processes such as extrusion-based printing, powder bed fusion, and inkjet printing, utilizing spectroscopic, thermal, imaging, and emerging sensing technologies. Data generated from these tools are further processed using chemometric models, machine learning (ML), and multivariate data analysis for real-time decision-making and closed-loop control, thus enabling smart manufacturing. Although progress has been made, certain challenges remain regarding PAT, model transferability, validation, scalability, data management, and regulatory issues. This promotes a continuously evolving landscape for implementing PAT in 3DP that supports standardization, real-time release testing (RTRT), and lifecycle management. Emerging technologies focus on combining PAT with artificial intelligence (AI), digital twins, and automated feedback systems to develop self-regulating, adaptive 3DP platforms. This review highlights PAT's role as a key element in advancing pharmaceutical 3DP from experimental applications to robust, scalable, and regulatory-compliant manufacturing, offering a comprehensive overview of current technologies, data analytics, regulatory issues, challenges, and future directions in the field.
Indexed as
Identifiers
42717181What OpenQuestion 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 OpenQuestion graph.