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Piccione PM, Lang MN, Amado Becker F, Hofstetter A, Marchal S, Ly K, Legras V, Ewert A, Kohler D, Maurer R, Willecke N, Burwood R, Kroll P. Computer-Aided formulation design for pharmaceutical drug product development, part 01: Materials exploration through a visualization tool. Int J Pharm 2024:124891. [PMID: 39481812 DOI: 10.1016/j.ijpharm.2024.124891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 10/27/2024] [Accepted: 10/28/2024] [Indexed: 11/03/2024]
Abstract
An interactive tool has been developed to help design oral solid dosage form formulations. The tool enables quantitative explorations and comparisons of physical, bulk, and mechanical properties, and takes into account functional characteristics as well. In this manner, comparisons and clustering of both excipients and APIs can be carried out. These comparisons enable the generation of alternatives as well as surrogate identification, so as to spare resources and material. Multiple data sources were merged to create a "joint" data table with all relevant properties. Four main workflow activities are supported: Explore Materials, Search Similar APIs, Search Similar Excipients and Search Material Clusters. Multi-dimensional filtering can be superimposed to each functionality. Suggested visualizations are made particularly accessible by providing them as "standard plots". The underlying philosophy is to empower formulation scientists to explore options, rather than prescribe decisions on exclusively mathematical grounds. The tool described here is the first step towards a holistic optimization incorporating predictions of mixture properties. Methodology of use is illustrated through three material selection application examples.
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Affiliation(s)
- Patrick M Piccione
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland; dsm-firmenich, Route de la Plaine 125, 1283 La Plaine, Switzerland.
| | - Moritz N Lang
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland; Roche Diagnostics GmbH, 82377 Penzberg, Germany
| | | | | | | | - Kevin Ly
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | - David Kohler
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland
| | - Reto Maurer
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland
| | | | - Ryan Burwood
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland
| | - Paul Kroll
- F. Hoffmann-La Roche AG, 4070 Basel, Switzerland; Muvon Therapeutics AG, 8092 Zürich, Switzerland
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Mendis NP, Wang J, Lakerveld R. Simultaneous Solvent Selection and Process Design for Continuous Reaction–Extraction–Crystallization Systems. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c05012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Nethrue Pramuditha Mendis
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Jiayuan Wang
- School of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Richard Lakerveld
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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Fujinami M, Maekawara H, Isshiki R, Seino J, Yamaguchi J, Nakai H. Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2020. [DOI: 10.1246/bcsj.20200045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Hiroki Maekawara
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Ryota Isshiki
- Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Junichiro Yamaguchi
- Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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Tsichla A, Severins C, Gottfried M, Marquardt W. An Experimental Assessment of Model-Based Solvent Selection for Enhancing Reaction Kinetics. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Angeliki Tsichla
- Aachener Verfahrenstechnik−Process Systems Engineering, RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany
- Bayer Technology Services GmbH, 51368 Leverkusen, Germany
| | | | | | - Wolfgang Marquardt
- Aachener Verfahrenstechnik−Process Systems Engineering, RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany
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Piccione PM, Baumeister J, Salvesen T, Grosjean C, Flores Y, Groelly E, Murudi V, Shyadligeri A, Lobanova O, Lothschütz C. Solvent Selection Methods and Tool. Org Process Res Dev 2019. [DOI: 10.1021/acs.oprd.9b00065] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Xu R, Zhao Y, Han Q, Liu X, Cao H, Wen H. On the database-based strategy of candidate extractant generation for de-phenol process in coking wastewater treatment. Chin J Chem Eng 2018. [DOI: 10.1016/j.cjche.2018.01.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Struebing H, Obermeier S, Siougkrou E, Adjiman CS, Galindo A. A QM-CAMD approach to solvent design for optimal reaction rates. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.09.032] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Reif MM, Hünenberger PH. Origin of Asymmetric Solvation Effects for Ions in Water and Organic Solvents Investigated Using Molecular Dynamics Simulations: The Swain Acity-Basity Scale Revisited. J Phys Chem B 2016; 120:8485-517. [PMID: 27173101 DOI: 10.1021/acs.jpcb.6b02156] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The asymmetric solvation of ions can be defined as the tendency of a solvent to preferentially solvate anions over cations or cations over anions, at identical ionic charge magnitudes and effective sizes. Taking water as a reference, these effects are quantified experimentally for many solvents by the relative acity (A) and basity (B) parameters of the Swain scale. The goal of the present study is to investigate the asymmetric solvation of ions using molecular dynamics simulations, and to connect the results to this empirical scale. To this purpose, the charging free energies of alkali and halide ions, and of their hypothetical oppositely charged counterparts, are calculated in a variety of solvents. In a first set of calculations, artificial solvent models are considered that present either a charge or a shape asymmetry at the molecular level. The solvation asymmetry, probed by the difference in charging free energy between the two oppositely charged ions, is found to encompass a term quadratic in the ion charge, related to the different solvation structures around the anion and cation, and a term linear in the ion charge, related to the solvation structure around the uncharged ion-sized cavity. For these simple solvent models, the two terms are systematically counteracting each other, and it is argued that only the quadratic term should be retained when comparing the results of simulations involving physical solvents to experimental data. In a second set of calculations, 16 physical solvents are considered. The theoretical estimates for the acity A are found to correlate very well with the Swain parameters, whereas the correlation for B is very poor. Based on this observation, the Swain scale is reformulated into a new scale involving an asymmetry parameter Σ, positive for acitic solvents and negative for basitic ones, and a polarity parameter Π. This revised scale has the same predictive power as the original scale, but it characterizes asymmetry in an absolute sense, the atomistic simulations playing the role of an extra-thermodynamic assumption, and is optimally compatible with the simulation results. Considering the 55 solvents in the Swain set, it is observed that a moderate basity (Σ between -0.9 and -0.3, related to electronic polarization) represents the baseline for most solvents, while a highly variable acity (Σ between 0.0 and 3.0, related to hydrogen-bond donor capacity modulated by inductive effects) represents a landmark of protic solvents.
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Affiliation(s)
- Maria M Reif
- Physics Department (T38), Technische Universität München , D-85748 Garching, Germany
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