ReviewTherapeutic innovation & regulatory science2025
Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.
Review in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Intelligent information management enables quality-by-design in pharmaceutical production.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Formulation strategies are vital in drug development, but trial-and-error optimization of critical parameters is inefficient. Artificial Intelligence (AI) addresses this by enabling data-driven decision-making. AI has emerged as a pivotal tool in drug discovery, formulation, and development. This manuscript explores the significant applications of AI in various aspects of liquid dosage form manufacturing, aligned with Industry 5.0 principles. This integration seeks to enhance productivity, monitor and modify development processes, enable human-centric automation, improve product quality, and optimize industry workflows. Essential steps in developmental design include solvent selection, drug-excipient compatibility, crystallinity, salt formation, permeability, and stability. This technological approach facilitates rapid decision-making and ensures batch-to-batch consistency by investigating major attrition rates in manufacturing and discovery. We highlight the utilization of AI tools and techniques alongside Process Analytical Technology (PAT) for real-time applications in the production of parenteral solutions, suspensions, emulsions, and other liquid forms. A comprehensive review of the current state of AI in common manufacturing steps involved in liquid dosage formulation development is provided, identifying potential research gaps. We discuss ongoing challenges, propose realistic strategies to address these challenges and outline future research directions.
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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.