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Res."],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Three-dimensional printing (3DP) holds significant potential for developing personalized pharmaceutical oral dosage forms (printlets). 3D printing has the advantage of fabricating complex geometric structures for versatile drug release profiles, enhancing patient preference, palatability and swallowability, reducing the pill burden, and increasing dose accuracy. Optimizing printing parameters is crucial in determining the quality of the printlets during dosage development. The integration of machine learning (ML) can reduce production costs and time through parameter optimization based on trained datasets. This research is focused on optimizing parameters for fused deposition modeling (FDM) based batch and continuous printing methods. The algorithm was trained using a three-level full factorial design, which generated data in the form of printlets with different parameters. Both defect and defect-free printlets were analyzed using image segmentation. Machine learning tools including Gaussian Process Regressor (GPR) and Efficient Global Optimization (EGO) were used to predict and select processing parameters for a targeted percentage surface defect. The final trained algorithm predicted new parameter sets for both batch (R\n                    <jats:sup>2<\/jats:sup>\n                    -0.8783) and continuous (R\n                    <jats:sup>2<\/jats:sup>\n                    -0.9364) printing methods to achieve zero defects, and the same was confirmed through printing and characterization of printlets which showed no defects. The algorithm was later adapted successfully to a variety of materials within the temperature range of 190\u2013220 \u2103 and predicted zero-defect printlets. Scanning electron microscopy (SEM) revealed the absence of defects on the surfaces of the materials. Results showed that flow rate (110 and 120 mm\n                    <jats:sup>3<\/jats:sup>\n                    \/s) had a significant impact on printlet quality withwithout defects for both batch and continuous printing, compared to print speed, print temperature, and infill density. This research provides new insights into the development of optimized FDM printlets using batch and continuous printing with adaptive machine learning for pharmaceutical dosage manufacturing.\n                  <\/jats:p>","DOI":"10.1007\/s13346-025-02006-4","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T17:29:34Z","timestamp":1762277374000},"page":"2073-2088","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Kshitij","family":"Chitnis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Rhoads","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leela Raghava Jaidev","family":"Chakka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samrat","family":"Choudhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3373-5586","authenticated-orcid":false,"given":"Mohammed","family":"Maniruzzaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"2006_CR1","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.addr.2021.04.019","volume":"174","author":"J Wang","year":"2021","unstructured":"Wang J, Zhang Y, Aghda NH, Pillai AR, Thakkar R, Nokhodchi A, Maniruzzaman M. 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M.M., an author of this manuscript, holds stock in, serves on a scientific advisory board for, or is a consultant for CoM3D Ltd. (Surrey, UK), DosePlus Therapeutics, Inc. (Princeton, NJ, USA), and Septum Solutions LLC (Houston, TX, USA). M.M and L.C. also hold stock in SmarGic Tech LLC (Oxford, MS, USA). The terms of this arrangement have been reviewed and approved by the University of Texas at Austin and the University of Mississippi (Ole Miss) in accordance with their policies on objectivity in research. The companies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}