Evidence map›Paper›PMID 40560305›Full record

ReviewTherapeutic innovation & regulatory science2025

Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.

D Nithyanantham, Akhil Nair, Usha Y Nayak

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

D NithyananthamCenter for Drug Delivery Technologies, Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Akhil NairCenter for Drug Delivery Technologies, Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Usha Y NayakCenter for Drug Delivery Technologies, Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. usha.nayak@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceDrug CompoundingDrug IndustryChemistry, PharmaceuticalExcipientsHumansExcipientsArtificial intelligenceFluid flowManufacturingPharma 5.0Process analytical technology

Identifiers

PMID40560305
PMCPMC12446089

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.