ReviewAAPS PharmSciTech2025
Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.
Review in AAPS PharmSciTech, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data.BMC medical informatics and decision making · 2026Pooled it
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- The Application of Machine Learning in the Development of Co-Amorphous Dry Powder Inhalation.AAPS PharmSciTech · 2026Article
- Nanoemulgels as Advanced Topical Drug Delivery Systems: Mechanistic Insights and Therapeutic Applications in Skin Disorders, Infections, Wound Healing, and Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
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
Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex and time-consuming process of drug development, many firms, including big pharma and medium-scale industries, are constantly looking for ways to reduce the time for providing lifesaving medications to patients in need without compromising the safety and efficacy of the product. Formulation of novel drug products in a pharmaceutical R&D and scaling up the process to a large-scale production involves a huge investment and an eye for detail in the intricacies of the processes. Intervention of AI and machine learning (ML) can solve many problems in this aspect. With the rise of Industry 4.0, the relative shift of industry towards process automation, accelerated development has become vital in all domains. The investments in R&D by the large pharmaceutical companies reached up to $190 bn in 2024, according to a report by IQVIA. There is a noted upsurge in investments in the domains interlinking AI and ML with pharmaceutical research. Pharmaceutical formulation development can excel in the early stages, and the productivity can witness a steady growth if AI and ML tools are utilized. Most of the research in this domain remains in the budding stages, and its adoption in the industry needs further refinement by delineating structured guidance from the experts and regulatory agencies. The current review speaks about the current studies reported in the arena of formulation development and also sheds light on some of the areas where the pharmaceutical product development on a larger scale can benefit from AI and ML.
Indexed as
Identifiers
41299184What 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.