Evidence map›Paper›PMID 40696093›Full record

ReviewAAPS PharmSciTech2025

Hot Melt Extrusion Technology in Taste Masking.

Geetha Nayak, Usha Y Nayak

Abstract readReview
PubMed Publisher
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

2 authors.

Geetha NayakDepartment of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Usha Y NayakDepartment of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India. usha.nayak@manipal.edu.ORCID http://orcid.org/0000-0002-1995-3114

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hot melt extrusion (HME) is a widely used method of taste masking in medication formulations, notably for bitter Active Pharmaceutical Ingredients (APIs). This continuous, solvent-free technique successfully wraps APIs within a polymer matrix, forming a barrier that considerably decreases bitterness perception. These advantageous properties of HME in taste masking are especially important for pediatric, geriatric, and veterinary formulations, where palatability is critical. The technique entails melting a polymer matrix and combining it with the medicine, followed by extrusion to make granules or dosage forms. The efficacy of taste masking is dependent on selecting acceptable excipients and ensuring their compatibility with the medicine. Temperature, screw speed, and drug-to-polymer ratio are critical process parameters that must be tuned. Various screw configurations can influence the physical condition of the API during extrusion, increasing taste-masking efficacy. Future advances in HME technology are expected, with a focus on enhancing taste masking for a broader range of APIs. Innovations in the study of polymers and the combination of process analytical technology (PAT) will significantly enhance the effectiveness and reliability of flavor-masked formulations, resolving the important need for patient-friendly drugs that are taken orally.

Indexed as

Flavoring AgentsHot Melt Extrusion TechnologyTasteTechnology, PharmaceuticalChemistry, PharmaceuticalDrug CompoundingExcipientsHot TemperatureHumansPharmaceutical PreparationsPolymersExcipientsFlavoring AgentsPharmaceutical PreparationsPolymersBitter APIsHot Melt Extrusion (HME)Palatability enhancementPediatric formulationTaste masking

Identifiers

What OpenQuestion holds

Textmetadata
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.