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Pätzmann N, O'Dwyer PJ, Beránek J, Kuentz M, Griffin BT. Predictive computational models for assessing the impact of co-milling on drug dissolution. Eur J Pharm Sci 2024; 198:106780. [PMID: 38697312 DOI: 10.1016/j.ejps.2024.106780] [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: 02/10/2024] [Revised: 04/12/2024] [Accepted: 04/27/2024] [Indexed: 05/04/2024]
Abstract
Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.
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Affiliation(s)
- Nicolas Pätzmann
- School of Pharmacy, University College Cork, Cork, Ireland; Department Preformulation and Biopharmacy, Zentiva, k.s., Prague, Czechia
| | | | - Josef Beránek
- Department Preformulation and Biopharmacy, Zentiva, k.s., Prague, Czechia
| | - Martin Kuentz
- Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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2
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Lange JJ, Anelli A, Alsenz J, Kuentz M, O'Dwyer PJ, Saal W, Wyttenbach N, Griffin BT. Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions. Mol Pharm 2024; 21:3343-3355. [PMID: 38780534 DOI: 10.1021/acs.molpharmaceut.4c00080] [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] [Indexed: 05/25/2024]
Abstract
This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.
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Affiliation(s)
- Justus Johann Lange
- School of Pharmacy, University College Cork, College Road, Cork T12 R229, Cork County, Ireland
| | - Andrea Anelli
- Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Limited, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Jochem Alsenz
- Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Limited, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Martin Kuentz
- Insitute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, Muttenz CH-4231, Basel City, Switzerland
| | - Patrick J O'Dwyer
- School of Pharmacy, University College Cork, College Road, Cork T12 R229, Cork County, Ireland
| | - Wiebke Saal
- Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Limited, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Nicole Wyttenbach
- Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Limited, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Brendan T Griffin
- School of Pharmacy, University College Cork, College Road, Cork T12 R229, Cork County, Ireland
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3
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Zaslavsky J, Allen C. A dataset of formulation compositions for self-emulsifying drug delivery systems. Sci Data 2023; 10:914. [PMID: 38123567 PMCID: PMC10733404 DOI: 10.1038/s41597-023-02812-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Self-emulsifying drug delivery systems (SEDDS) are a well-established formulation strategy for improving the oral bioavailability of poorly water-soluble drugs. Traditional development of these formulations relies heavily on empirical observation to assess drug and excipient compatibility, as well as to select and optimize the formulation compositions. The aim of this work was to leverage previously developed SEDDS in the literature to construct a comprehensive SEDDS dataset that can be used to gain insights and advance data-driven approaches to formulation development. A dataset comprised of 668 unique SEDDS formulations encompassing 20 poorly water-soluble drugs was curated. While there are still opportunities to enhance the quality and quantity of data on SEDDS, this research lays the groundwork to potentially simplify the SEDDS formulation development process.
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Affiliation(s)
- Jonathan Zaslavsky
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada.
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada.
- Acceleration Consortium, Toronto, ON, M5S 3H6, Canada.
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4
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Murray JD, Lange JJ, Bennett-Lenane H, Holm R, Kuentz M, O'Dwyer PJ, Griffin BT. Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation. Eur J Pharm Sci 2023; 191:106562. [PMID: 37562550 DOI: 10.1016/j.ejps.2023.106562] [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: 05/15/2023] [Revised: 07/09/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.
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Affiliation(s)
- Jack D Murray
- School of Pharmacy, University College Cork, Cork, Ireland
| | - Justus J Lange
- School of Pharmacy, University College Cork, Cork, Ireland; Roche Pharmaceutical Research & Early Development, Pre-Clinical CMC, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, Switzerland
| | | | - René Holm
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Martin Kuentz
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland
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5
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Al-Japairai K, Hamed Almurisi S, Mahmood S, Madheswaran T, Chatterjee B, Sri P, Azra Binti Ahmad Mazlan N, Al Hagbani T, Alheibshy F. Strategies to improve the stability of amorphous solid dispersions in view of the hot melt extrusion (HME) method. Int J Pharm 2023; 647:123536. [PMID: 37865133 DOI: 10.1016/j.ijpharm.2023.123536] [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: 06/08/2023] [Revised: 09/24/2023] [Accepted: 10/18/2023] [Indexed: 10/23/2023]
Abstract
Oral administration of drugs is preferred over other routes for several reasons: it is non-invasive, easy to administer, and easy to store. However, drug formulation for oral administration is often hindered by the drug's poor solubility, which limits its bioavailability and reduces its commercial value. As a solution, amorphous solid dispersion (ASD) was introduced as a drug formulation method that improves drug solubility by changing the molecular structure of the drugs from crystalline to amorphous. The hot melt extrusion (HME) method is emerging in the pharmaceutical industry as an alternative to manufacture ASD. However, despite solving solubility issues, ASD also exposes the drug to a high risk of crystallisation, either during processing or storage. Formulating a successful oral administration drug using ASD requires optimisation of the formulation, polymers, and HME manufacturing processes applied. This review presents some important considerations in ASD formulation, including strategies to improve the stability of the final product using HME to allow more new drugs to be formulated using this method.
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Affiliation(s)
- Khater Al-Japairai
- Department of Pharmaceutical Engineering, Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Gambang 26300, Malaysia.
| | - Samah Hamed Almurisi
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia.
| | - Syed Mahmood
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Thiagarajan Madheswaran
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia.
| | - Bappaditya Chatterjee
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, V.L.Mehta Road, Mumbai 400055, India.
| | - Prasanthi Sri
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University, Kuala Lumpur 57000, Malaysia.
| | | | - Turki Al Hagbani
- Department of Pharmaceutics, College of Pharmacy, University of Ha'il, Ha'il 81442, Saudi Arabia.
| | - Fawaz Alheibshy
- Department of Pharmaceutics, College of Pharmacy, University of Ha'il, Ha'il 81442, Saudi Arabia; Department of Pharmaceutics, College of Pharmacy, Aden University, Aden 6075, Yemen.
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6
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Bao Z, Bufton J, Hickman RJ, Aspuru-Guzik A, Bannigan P, Allen C. Revolutionizing drug formulation development: The increasing impact of machine learning. Adv Drug Deliv Rev 2023; 202:115108. [PMID: 37774977 DOI: 10.1016/j.addr.2023.115108] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023]
Abstract
Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.
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Affiliation(s)
- Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Jack Bufton
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Riley J Hickman
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5S 1M1, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Department of Materials Science & Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada; CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON M5S 1M1, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada.
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7
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Holm R, Kuentz M, Ilie-Spiridon AR, Griffin BT. Lipid based formulations as supersaturating oral delivery systems: From current to future industrial applications. Eur J Pharm Sci 2023; 189:106556. [PMID: 37543063 DOI: 10.1016/j.ejps.2023.106556] [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: 03/21/2023] [Revised: 06/30/2023] [Accepted: 08/03/2023] [Indexed: 08/07/2023]
Abstract
Lipid-based formulations, in particular supersaturated lipid-based formulations, are important delivery approaches when formulating challenging compounds, as especially low water-soluble compounds profit from delivery in a pre-dissolved state. In this article, the classification of lipid-based formulation is described, followed by a detailed discussion of different supersaturated lipid-based formulations and the recent advances reported in the literature. The supersaturated lipid-based formulations discussed include both the in situ forming supersaturated systems as well as the thermally induced supersaturated lipid-based formulations. The in situ forming drug supersaturation by lipid-based formulations has been widely employed and numerous clinically available products are on the market. There are some scientific gaps in the field, but in general there is a good understanding of the mechanisms driving the success of these systems. For thermally induced supersaturation, the technology is not yet fully understood and developed, hence more research is required in this field to explore the formulations beyond preclinical studies and initial clinical trials.
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Affiliation(s)
- René Holm
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Denmark.
| | - Martin Kuentz
- University of Applied Sciences and Arts Northwestern Switzerland, Institute of Pharmaceutical Technology, Hofackerstr. 30, CH-4132 Muttenz, Switzerland
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State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics 2022; 14:pharmaceutics14010183. [PMID: 35057076 PMCID: PMC8779224 DOI: 10.3390/pharmaceutics14010183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 11/30/2022] Open
Abstract
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
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