Evidence map›Paper›PMID 36171513›Full record

ArticleThe AAPS journal2022

NIR Spectroscopy as an Online PAT Tool for a Narrow Therapeutic Index Drug: Toward a Platform Approach Across Lab and Pilot Scales for Development of a Powder Blending Monitoring Method and Endpoint Determination.

Sameer Talwar, Pallavi Pawar, Huiquan Wu, Koushik Sowrirajan, Suyang Wu, Benoît Igne, Richard Friedman, Fernando J Muzzio, James K Drennen

Abstract read
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In one paragraph

Article in The AAPS journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 30% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. A case study on decentralized manufacturing of 3D printed medicines.International journal of pharmaceutics: X · 2023
    Article
  3. 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

9 authors at 4 institutions in 1 country.

Sameer TalwarDuquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA.
Pallavi PawarDepartment of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA.
Huiquan WuOffice of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA. Huiquan.wu@fda.hhs.gov.
Koushik SowrirajanOffice of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Suyang WuOffice of Pharmaceutical Quality, CDER, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Benoît IgneDuquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA.
Richard FriedmanOffice of Manufacturing Quality, Office of Compliance, CDER, FDA, Silver Spring, MD, 20993, USA.
Fernando J MuzzioDepartment of Chemical and Biochemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA.
James K DrennenDuquesne University Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA, 15282, USA. Drennen@duq.edu.
Center for Drug Evaluation and Research · USDuquesne University · USGilead Sciences (United States) · USRutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

Indexed as

ExcipientsSpectroscopy, Near-InfraredCalibrationChemistry, PharmaceuticalDrug CompoundingEndpoint DeterminationLeast-Squares AnalysisPhenytoinPowdersResearch DesignTechnology, PharmaceuticalExcipientsPhenytoinPowdersalgorithmsendpoint determinationengineering platform systemnarrow therapeutic index (NTI) drugpartial least squares (PLS)powder blendingprincipal component analysis (PCA)process analytical technology (PAT)process monitoringscale-up

Identifiers

PMID36171513
OpenAlexW4297518751

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.