The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Future of Pharmaceutical Manufacturing Sciences
Open accessAbstract
The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.
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Cites
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
Overcoming the challenges in administering biopharmaceuticals: Formulation and delivery strategies
Mitragotri, S., Burke, P. A., & Langer, R. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4363 · Cited by 1,702
Some problems in particle technology
Hulburt, H. M., & Katz, S. (1964) · Chemical Engineering Science · doi:10.1016/0009-2509(64)85047-8 · Cited by 1,670
De Beer, T., Burggraeve, A., Fonteyne, M., Saerens, L., Remon, J. P., & Vervaet, C. (2011) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2010.12.012 · Cited by 559
Dynamic simulation of dispersed gas-liquid two-phase flow using a discrete bubble model
Delnoij, E., Lammers, F. A., Kuipers, J. A. M., & van Swaaij, W. P. M. (1997) · Chemical Engineering Science · doi:10.1016/s0009-2509(96)00515-5 · Cited by 314
Cited by
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032026 · Cited by 347
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
Therapeutic RNA-silencing oligonucleotides in metabolic diseases
Goga, A., & Stoffel, M. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00407-5 · Cited by 58
Tahara, K. (2020) · Advanced Powder Technology · doi:10.1016/j.apt.2019.10.031 · Cited by 31
Metadata
- Authors
- Rantanen, J., & Khinast, J.
- Venue
- Journal of Pharmaceutical Sciences
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 104
- Issue
- 11
- Pages
- 3612-3638
- ISSN
- 0022-3549 (ISSN-L)
- OpenAlex
- W1786404767
- Cited by
- 442
- References
- 5
- FWCI
- 32.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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