Evidence map›Paper›PMID 36056225›Full record

ReviewAAPS PharmSciTech2022

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions.

Amritha G Nambiar, Maan Singh, Abhishek R Mali, Dolores R Serrano, Rajnish Kumar, Anne Marie Healy, Ashish Kumar Agrawal, Dinesh Kumar

Abstract readReview
PubMed Publisher
In one paragraph

Review in AAPS PharmSciTech, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Review
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

8 authors.

Amritha G NambiarDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Maan SinghDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Abhishek R MaliDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Dolores R SerranoSchool of Pharmacy, Complutense University, Madrid, Spain.
Rajnish KumarDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Anne Marie HealySchool of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland.
Ashish Kumar AgrawalDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Dinesh KumarDepartment of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, 221005, India. dinesh.phe@iitbhu.ac.in.ORCID http://orcid.org/0000-0001-9785-0500

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Indexed as

Artificial IntelligenceChemistry, PharmaceuticalBiological AvailabilityDrug CompoundingExcipientsSolubilityWaterExcipientsWateramorphous solid dispersionsbioavailabilitycontinuous manufacturingdissolutionmelt extrusionmolecular modelingsolubilityspray drying

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.