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Leane M, Pitt K, Reynolds G, Tantuccio A, Moreton C, Crean A, Kleinebudde P, Carlin B, Gamble J, Gamlen M, Stone E, Kuentz M, Gururajan B, Khimyak YZ, Van Snick B, Andersen S, Misic Z, Peter S, Sheehan S. Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture. Pharm Dev Technol 2024; 29:395-414. [PMID: 38618690 DOI: 10.1080/10837450.2024.2342953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.
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Affiliation(s)
- Michael Leane
- Drug Product Development, Bristol Myers Squibb, Moreton, UK
| | - Kendal Pitt
- Leicester School of Pharmacy, De Montfort University, Leicester, UK
| | - Gavin Reynolds
- Oral Product Development, Pharmaceutical Technology & Development, AstraZeneca, Macclesfield, UK
| | - Anthony Tantuccio
- Technology Intensification, Hovione LLC, East Windsor, New Jersey, USA
| | | | - Abina Crean
- SSPC, the SFI Centre for Pharmaceutical Research, School of Pharmacy, University College Cork, Cork, Ireland
| | - Peter Kleinebudde
- Faculty of Mathematics and Natural Sciences, Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Brian Carlin
- Owner, Carlin Pharma Consulting, Lawrenceville, New Jersey, USA
| | - John Gamble
- Drug Product Development, Bristol Myers Squibb, Moreton, UK
| | - Michael Gamlen
- Chief Scientific Officer, Gamlen Tableting Ltd, Heanor, UK
| | - Elaine Stone
- Consultant, Stonepharma Ltd. ATIC, Loughborough, UK
| | - Martin Kuentz
- Institute for Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences FHNW, Muttenz, Switzerland
| | - Bindhu Gururajan
- Pharmaceutical Development, Novartis Pharma AG, Basel, Switzerland
| | - Yaroslav Z Khimyak
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Bernd Van Snick
- Oral Solids Development, Drug Product Development, JnJ Innovative Medicine, Beerse, Belgium
| | - Sune Andersen
- Oral Solids Development, Drug Product Development, JnJ Innovative Medicine, Beerse, Belgium
| | - Zdravka Misic
- Innovation Research and Development, dsm-firmenich, Kaiseraugst, Switzerland
| | - Stefanie Peter
- Research and Development Division, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Stephen Sheehan
- External Development and Manufacturing, Alkermes Pharma Ireland Limited, Dublin 4, Ireland
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Bagasariya D, Charankumar K, Shah S, Famta P, Fernandes V, Shahrukh S, Khatri DK, Singh SB, Srivastava S. Quality by design endorsed atorvastatin-loaded nanostructured lipid carriers embedded in pH-responsive gel for melanoma. J Microencapsul 2024; 41:27-44. [PMID: 37982590 DOI: 10.1080/02652048.2023.2282971] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/08/2023] [Indexed: 11/21/2023]
Abstract
AIM Our aim was to repurpose atorvastatin for melanoma by encapsulating in a nanostructured lipid carrier matrix to promote tumour cell internalisation and skin permeation. pH-responsive chitosan gel was employed to restrict At-NLCs in upper dermal layers. METHODS We utilised a quality by design approach for encapsulating At within the NLC matrix. Further, cellular uptake and cytotoxicity was evaluated along with pH-responsive release and ex vivo skin permeation. RESULTS Cytotoxicity assay showed 3.13-fold enhanced cytotoxicity on melanoma cells compared to plain drug with nuclear staining showing apoptotic markers. In vitro, release studies showed 5.9-fold rapid release in chitosan gel matrix at pH 5.5 compared to neutral pH. CONCLUSIONS At-NLCs prevented precipitation, promoted skin permeation, and SK-MEL 28 cell internalisation. The localisation of NLCs on the upper dermal layer due to electrostatic interactions of skin with chitosan gel diminished the incidence of untoward systemic effects.
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Affiliation(s)
- Deepkumar Bagasariya
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Kondasingh Charankumar
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Saurabh Shah
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Paras Famta
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Valencia Fernandes
- Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Syed Shahrukh
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Dharmendra Kumar Khatri
- Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Shashi Bala Singh
- Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Saurabh Srivastava
- Department of Pharmaceutics, Pharmaceutical Innovation and Translational Research Lab (PITRL), National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
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Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022; 182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.
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Lou H, Lian B, Hageman MJ. Applications of Machine Learning in Solid Oral Dosage Form Development. J Pharm Sci 2021; 110:3150-3165. [PMID: 33951418 DOI: 10.1016/j.xphs.2021.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.
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Affiliation(s)
- Hao Lou
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States.
| | - Bo Lian
- College of Pharmacy, University of Arizona, Tucson, AZ 85721, United States
| | - Michael J Hageman
- Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS 66047, United States; Biopharmaceutical Innovation and Optimization Center, University of Kansas, Lawrence, KS 66047, United States
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Yao Y, Zhang S, Liang Y. iORI-ENST: identifying origin of replication sites based on elastic net and stacking learning. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:317-331. [PMID: 33730950 DOI: 10.1080/1062936x.2021.1895884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/23/2021] [Indexed: 06/12/2023]
Abstract
DNA replication is not only the basis of biological inheritance but also the most fundamental process in all living organisms. It plays a crucial role in the cell-division cycle and gene expression regulation. Hence, the accurate identification of the origin of replication sites (ORIs) has a great meaning for further understanding the regulatory mechanism of gene expression and treating genic diseases. In this paper, a novel, feasible and powerful model, namely, iORI-ENST is designed for identifying ORIs. Firstly, we extract the different features by incorporating mono-nucleotide binary encoding and dinucleotide-based spatial autocorrelation. Subsequently, elastic net is utilized as the feature selection method to select the optimal feature set. And then stacking learning is employed to predict ORIs and non-ORIs, which contains random forest, adaboost, gradient boosting decision tree, extra trees and support vector machine. Finally, the ORI sites are identified on the benchmark datasets S1 and S2 with their accuracies of 91.41% and 95.07%, respectively. Meanwhile, an independent dataset S3 is employed to verify the validation and transferability of our model and its accuracy reaches 91.10%. Comparing with state-of-the-art methods, our model achieves more remarkable performance. The results show our model is a feasible, effective and powerful tool for identifying ORIs. The source code and datasets are available at https://github.com/YingyingYao/iORI-ENST.
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Affiliation(s)
- Y Yao
- School of Mathematics and Statistics, Xidian University, Xi'an, P. R. China
| | - S Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, P. R. China
| | - Y Liang
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
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Thomas S, Palahnuk H, Amini H, Akseli I. Data-smart machine learning methods for predicting composition-dependent Young's modulus of pharmaceutical compacts. Int J Pharm 2021; 592:120049. [PMID: 33171260 DOI: 10.1016/j.ijpharm.2020.120049] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/24/2020] [Accepted: 11/01/2020] [Indexed: 11/27/2022]
Abstract
The ability to predict mechanical properties of compacted powder blends of Active Pharmaceutical Ingredients (API) and excipients solely from component properties can reduce the amount of 'trial-and-error' involved in formulation design. Machine Learning (ML) can reduce model development time and effort with the imperative of adequate historical data. This work describes the utility of linear and nonlinear ML models for predicting Young's modulus (YM) of directly compressed blends of known excipients and unknown API mixed at arbitrary compositions given only the true density of the API. The models were trained with data from compacts of three BCS Class I APIs and two excipients blended at four drug loadings, three excipient compositions, and compacted to five nominal solid fractions. The prediction accuracy of the models was measured using three cross-validation (CV) schemes. Finally, we demonstrate an application of the model to enable Quality-by-Design in formulation design. Limitations of the models and future work have also been discussed.
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Affiliation(s)
- Stephen Thomas
- Engineering Technologies, Bristol Myers Squibb, 556 Morris Ave., Summit, NJ 07901, USA
| | - Hannah Palahnuk
- The College of New Jersey, 2000 Pennington Rd., Ewing, NJ 08628, USA
| | - Hossein Amini
- Engineering Technologies, Bristol Myers Squibb, 556 Morris Ave., Summit, NJ 07901, USA
| | - Ilgaz Akseli
- Engineering Technologies, Bristol Myers Squibb, 556 Morris Ave., Summit, NJ 07901, USA
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7
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Kapourani A, Valkanioti V, Kontogiannopoulos KN, Barmpalexis P. Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy. INTERNATIONAL JOURNAL OF PHARMACEUTICS-X 2020; 2:100064. [PMID: 33354666 PMCID: PMC7744708 DOI: 10.1016/j.ijpx.2020.100064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 12/11/2022]
Abstract
The objective of the present study was to evaluate the use of artificial neural networks (ANNs) in the development of a new chemometric model that will be able to simultaneously distinguish and quantify the percentage of the crystalline and the neat amorphous drug located within the drug-rich amorphous zones formed in an amorphous solid dispersion (ASD) system. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy was used, while Rivaroxaban (RIV, drug) and Soluplus® (SOL, matrix-carrier) were selected for the preparation of a suitable ASD model system. Adequate calibration and test sets were prepared by spiking different percentages of the crystalline and the amorphous drug in the ASDs (prepared by the melting - quench cooling approach), while a 24 full factorial experimental design was employed for the screening of ANN's structure and training parameters as well as spectra region selection and data preprocessing. Results showed increased prediction performance, measured based on the root mean squared error of prediction (RMSEp) for the test sample, for both the crystalline (RMSEp (crystal) = 0.86) and the amorphous (RMSEp (amorphous) = 2.14) drug. Comparison with traditional regression techniques, such as partial least square and principle component regressions, revealed the superiority of ANNs, indicating that in cases of high structural similarity between the investigated compounds (i.e., the crystalline and the amorphous forms of the same compound) the implementation of more powerful/sophisticated regression techniques, such as ANNs, is mandatory.
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Affiliation(s)
- Afroditi Kapourani
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Vasiliki Valkanioti
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Konstantinos N Kontogiannopoulos
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.,Ecoresources P.C., 15-17 Giannitson-Santaroza Str., Thessaloniki 54627, Greece
| | - Panagiotis Barmpalexis
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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8
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Ismail HY, Singh M, Albadarin AB, Walker GM. Complete two dimensional population balance modelling of wet granulation in twin screw. Int J Pharm 2020; 591:120018. [PMID: 33122111 DOI: 10.1016/j.ijpharm.2020.120018] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 10/18/2020] [Accepted: 10/21/2020] [Indexed: 11/29/2022]
Abstract
In this study, a complete two dimensional (internal coordinates) population balance model (2D-PBM) is developed, calibrated and validated as a predictive tool for predicting the particle size and the liquid content distribution of the granules produced from twin screw granulation (TSG). The model is calibrated and validated using experimental distributions for the two internal coordinates that are captured using image processing. Granulation runs are conducted at multiple liquid to solid (L/S) ratios and liquid binder viscosities, and then used to calibrate and validate the 2D-PBM. The mathematical model accounts for aggregation and breakage of the particles occurring in three zones of the TSG with inhomogeneous screw configurations (2 conveying zones and 1 kneading zone). A Madec aggregation kernel, and a linear breakage selection function are used in the 2D-PBM and finite volume numerical approximation is used for solving the model. The calibrated model shows that the aggregation rate in the conveying elements is higher than in the kneading elements while the breakage rate in the kneading elements is much higher than in the conveying elements. Also, the increase in L/S ratio and liquid viscosity leads to higher aggregation rates and lower breakage rates.
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Affiliation(s)
- Hamza Y Ismail
- Pharmaceutical Manufacturing Technology Centre, Bernal Institute, University of Limerick, Limerick, Ireland; Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.
| | - Mehakpreet Singh
- Pharmaceutical Manufacturing Technology Centre, Bernal Institute, University of Limerick, Limerick, Ireland; Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland
| | - Ahmad B Albadarin
- Pharmaceutical Manufacturing Technology Centre, Bernal Institute, University of Limerick, Limerick, Ireland; Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland
| | - Gavin M Walker
- Pharmaceutical Manufacturing Technology Centre, Bernal Institute, University of Limerick, Limerick, Ireland; Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland
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Kosugi A, Leong KH, Urata E, Hayashi Y, Kumada S, Okada K, Onuki Y. Effect of Different Direct Compaction Grades of Mannitol on the Storage Stability of Tablet Properties Investigated Using a Kohonen Self-Organizing Map and Elastic Net Regression Model. Pharmaceutics 2020; 12:pharmaceutics12090886. [PMID: 32961856 PMCID: PMC7559487 DOI: 10.3390/pharmaceutics12090886] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/10/2020] [Accepted: 09/17/2020] [Indexed: 11/16/2022] Open
Abstract
This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25 °C, 75% relative humidity for 1 week. Then, measurement of the tablet properties was conducted on both pre- and post-storage tablets. The tablet properties were tensile strength (TS), friability, and disintegration time (DT). The experimental data were analyzed using a Kohonen self-organizing map (SOM). The SOM analysis successfully classified the test grades into three distinct clusters having different changes in the behavior of the tablet properties accompanying storage. Cluster 1 showed an obvious rise in DT induced by storage, while cluster 3 showed a substantial change in mechanical strength of the tablet including a reduction in the TS and a rise in friability. Furthermore, the data were analyzed using an Elastic net regression technique to investigate the general relationships between the powder properties of mannitol and the change behavior of the tablet properties. Consequently, we succeeded in identifying the crucial powder properties for the storage stability of the resulting tablets. This study provides advanced technical knowledge to characterize the effect of different direct compaction grades of mannitol on the storage stability of tablet properties.
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Affiliation(s)
- Atsushi Kosugi
- Formulation Development Department, Development & Planning Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan; (A.K.); (Y.H.); (S.K.)
| | - Kok Hoong Leong
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Eri Urata
- Laboratory of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama; 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan; (E.U.); (K.O.)
| | - Yoshihiro Hayashi
- Formulation Development Department, Development & Planning Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan; (A.K.); (Y.H.); (S.K.)
| | - Shungo Kumada
- Formulation Development Department, Development & Planning Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan; (A.K.); (Y.H.); (S.K.)
| | - Kotaro Okada
- Laboratory of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama; 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan; (E.U.); (K.O.)
| | - Yoshinori Onuki
- Laboratory of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama; 2630 Sugitani, Toyama-shi, Toyama 930-0194, Japan; (E.U.); (K.O.)
- Correspondence: ; Tel.: +81-76-415-8827
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