Evidence map›Paper›PMID 38314247›Full record

ArticleComputer applications in engineering education2023

Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing.

Daniel J Laky, Daniel Casas-Orozco, Mesfin Abdi, Xin Feng, Erin Wood, Gintaras V Reklaitis, Zoltan K Nagy

Abstract read
In one paragraph

Article in Computer applications in engineering education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Daniel J LakyDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA.ORCID 0000-0002-3917-8039
Daniel Casas-OrozcoDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA.
Mesfin AbdiOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA.
Xin FengOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA.
Erin WoodOffice of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA.
Gintaras V ReklaitisDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA.
Zoltan K NagyDavidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA.

Funding

Risk-based process synthesis and Industry 4.0 framework for pharmaceutical manufacturing processesU01FD006738 · FDA · PURDUE UNIVERSITY · PI NAGY, ZOLTAN KALMAN · 2019 to 2021
$3.0M
FDA HHS U01 FD006738
6 · The paper itself

Abstract

The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.

Indexed as

digital designengineering educationJupyter Notebookpharmaceutical manufacturingPythonsmart manufacturing

Identifiers

PMID38314247
PMCPMC10838379

What OpenQuestion holds

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Registered trials

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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.