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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\u2010source 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.<\/jats:p>","DOI":"10.1002\/cae.22660","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T11:54:09Z","timestamp":1690458849000},"page":"1662-1677","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing"],"prefix":"10.1002","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3917-8039","authenticated-orcid":false,"given":"Daniel J.","family":"Laky","sequence":"first","affiliation":[{"name":"Davidson School of Chemical Engineering Purdue University West Lafayette Indiana USA"},{"name":"Department of Chemical and Biological Engineering University of Wisconsin\u2010Madison Madison Wisconsin USA"}]},{"given":"Daniel","family":"Casas\u2010Orozco","sequence":"additional","affiliation":[{"name":"Davidson School of Chemical Engineering Purdue University West Lafayette Indiana USA"}]},{"given":"Mesfin","family":"Abdi","sequence":"additional","affiliation":[{"name":"Office of Pharmaceutical Quality, Center for Drug Evaluation and Research Food & Drug Administration Silver Spring Maryland USA"}]},{"given":"Xin","family":"Feng","sequence":"additional","affiliation":[{"name":"Office of Pharmaceutical Quality, Center for Drug Evaluation and Research Food & Drug Administration Silver Spring Maryland USA"}]},{"given":"Erin","family":"Wood","sequence":"additional","affiliation":[{"name":"Office of Pharmaceutical Quality, Center for Drug Evaluation and Research Food & Drug Administration Silver Spring Maryland USA"}]},{"given":"Gintaras V.","family":"Reklaitis","sequence":"additional","affiliation":[{"name":"Davidson School of Chemical Engineering Purdue University West Lafayette Indiana USA"}]},{"given":"Zoltan K.","family":"Nagy","sequence":"additional","affiliation":[{"name":"Davidson School of Chemical Engineering Purdue University West Lafayette Indiana USA"}]}],"member":"311","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/700\/1\/012063"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2015.04.007"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpharm.2021.120554"},{"key":"e_1_2_11_5_1","doi-asserted-by":"crossref","unstructured":"R.Biehler Y.Fleischer L.Budde D.Frischemeier D.Gerstenberger S.Podworny andC.Schulte Data science education in secondary schools: Teaching and learning decision trees with codap and Jupyter Notebooks as an example of integrating machine learning into statistics education. 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