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Wang Y, He L, Wang M, Yuan J, Wu S, Li X, Lin T, Huang Z, Li A, Yang Y, Liu X, He Y. The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning. Int J Pharm 2024; 656:124128. [PMID: 38621612 DOI: 10.1016/j.ijpharm.2024.124128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/24/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024]
Abstract
Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.
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Affiliation(s)
- Yang Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Liqiang He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Meijing Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Jiongpeng Yuan
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Siwei Wu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xiaojing Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Tong Lin
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Zihui Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Andi Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Yuhang Yang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xujie Liu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
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Biswas AA, Dhondale MR, Singh M, Agrawal AK, Muthudoss P, Mishra B, Kumar D. Development and comparison of machine learning models for in-vitro drug permeation prediction from microneedle patch. Eur J Pharm Biopharm 2024:114311. [PMID: 38710374 DOI: 10.1016/j.ejpb.2024.114311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024]
Abstract
The field of machine learning (ML) is advancing to a larger extent and finding its applications across numerous fields. ML has the potential to optimize the development process of microneedle patch by predicting the drug release pattern prior to its fabrication and production. The early predictions could not only assist the in-vitro and in-vivo experimentation of drug release but also conserve materials, reduce cost, and save time. In this work, we have used a dataset gleaned from the literature to train and evaluate different ML models, such as stacking regressor, artificial neural network (ANN) model, and voting regressor model. In this study, models were developed to improve prediction accuracy of the in-vitro drug release amount from the hydrogel-type microneedle patch and the in-vitro drug permeation amount through the micropores created by solid microneedles on the skin. We compared the performance of these models using various metrics, including R-squared score (R2 score), root mean squared error (RMSE), and mean absolute error (MAE). Voting regressor model performed better with drug permeation percentage as an outcome feature having RMSE value of 3.24. In comparison, stacking regressor have a RMSE value of 16.54, and ANN model has shown a RMSE value of 14. The value of permeation amount calculated from the predicted percentage is found to be more accurate with RMSE of 654.94 than direct amount prediction, having a RMSE of 669.69. All our models have performed far better than the previously developed model before this research, which had a RMSE of 4447.23. We then optimized voting regressor model's hyperparameter and cross validated its performance. Furthermore, it was deployed in a webapp using Flask framework, showing a way to develop an application to allow other users to easily predict drug permeation amount from the microneedle patch at a particular time period. This project demonstrates the potential of ML to facilitate the development of microneedle patch and other drug delivery systems.
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Affiliation(s)
- Anuj A Biswas
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
| | - Madhukiran R Dhondale
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
| | - Maan Singh
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
| | - Ashish K Agrawal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
| | - Prakash Muthudoss
- A2Z4.0 Research and Analytics Private Limited, Chennai 600062, Tamilnadu, India; NuAxon Bioscience Inc., Bloomington, IN 47401-6301, USA; School of Pharmaceutical Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Velan Nagar P.V. Vaithiyalingam Road Pallavaram, 600117 Chennai, Tamilnadu, India.
| | - Brahmeshwar Mishra
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
| | - Dinesh Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
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Qureshi S, Alavi SE, Mohammed Y. Microsponges: Development, Characterization, and Key Physicochemical Properties. Assay Drug Dev Technol 2024. [PMID: 38661260 DOI: 10.1089/adt.2023.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Abstract
Microsponges are promising drug delivery carriers with versatile characteristics and controlled release properties for the delivery of a wide range of drugs. The microsponges will provide an optimized therapeutic effect, when delivered at the site of action without rupturing, then releasing the cargo at the predetermined time and area. The ability of the microsponges to effectively deliver the drug in a controlled manner depends on the material composition. This comprehensive review entails knowledge on the design parameters of an optimized microsponge drug delivery system and the controlled release properties of microsponges that reduces the side effects of drugs. Furthermore, the review delves into the fabrication techniques of microsponges, the mechanism of drug release from the microsponges, and the regulatory requirements of the U.S. Food and Drug Administration (FDA) for the successful marketing of microsponge formulation. The review also examines the patented formulations of microsponges. The prospects of these sophisticated drug delivery systems for improved clinical outcomes are highlighted.
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Affiliation(s)
- Sundus Qureshi
- Department of Pharmacy, The University of Lahore, Lahore, Pakistan
| | - Seyed Ebrahim Alavi
- Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Yousuf Mohammed
- Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Pharmacy, The University of Queensland, Brisbane, Australia
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Pisay M, Padya S, Mutalik S, Koteshwara KB. Stability Challenges of Amorphous Solid Dispersions of Drugs: A Critical Review on Mechanistic Aspects. Crit Rev Ther Drug Carrier Syst 2024; 41:45-94. [PMID: 38037820 DOI: 10.1615/critrevtherdrugcarriersyst.2023039877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
The most common drawback of the existing and novel drug molecules is their low bioavailability because of their low solubility. One of the most important approaches to enhance the bioavailability in the enteral route for poorly hydrophilic molecules is amorphous solid dispersion (ASD). The solubility of compounds in amorphous form is comparatively high because of the availability of free energy produced during formulation. This free energy results in the change of crystalline nature of the prepared ASD to the stable crystalline form leading to the reduced solubility of the product. Due to the intrinsic chemical and physical uncertainty and the restricted knowledge about the interactions of active molecules with the carriers making, this ASD is a challenging task. This review focused on strategies to stabilize ASD by considering the various theories explaining the free-energy concept, physical interactions, and thermal properties. This review also highlighted molecular modeling and machine learning computational advancement to stabilize ASD.
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Affiliation(s)
- Muralidhar Pisay
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka State, India
| | - Singh Padya
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka State, India
| | - Srinivas Mutalik
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka State, India
| | - Kunnatur B Koteshwara
- Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, Karnataka State, India
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Arsiccio A, Stratta L, Menzen T. Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study. J Mol Model 2023; 29:377. [PMID: 37968495 DOI: 10.1007/s00894-023-05777-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/31/2023] [Indexed: 11/17/2023]
Abstract
CONTEXT Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. METHODS We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.
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Affiliation(s)
- Andrea Arsiccio
- Coriolis Pharma, Fraunhoferstrasse 18 b, 82152, Martinsried, Germany.
| | - Lorenzo Stratta
- Molecular Engineering Laboratory (molE), Department of Applied Science and Technology, Politecnico di Torino, 24 corso Duca degli Abruzzi, IT-10129, Torino, Italy
| | - Tim Menzen
- Coriolis Pharma, Fraunhoferstrasse 18 b, 82152, Martinsried, Germany
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Dawoud MHS, Mannaa IS, Abdel-Daim A, Sweed NM. Integrating Artificial Intelligence with Quality by Design in the Formulation of Lecithin/Chitosan Nanoparticles of a Poorly Water-Soluble Drug. AAPS PharmSciTech 2023; 24:169. [PMID: 37552427 DOI: 10.1208/s12249-023-02609-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/25/2023] [Indexed: 08/09/2023] Open
Abstract
The aim of the current study is to explore the potential of artificial intelligence (AI) when integrated with Quality by Design (QbD) approach in the formulation of a poorly water-soluble drug, for its potential use in carcinoma. Silymarin is used as a model drug for its potential effectiveness in liver cancer. A detailed QbD approach was applied. The effect of the critical process parameters was studied on each of the particle size, size distribution, and entrapment efficiency. Response surface designs were applied in the screening and optimization of lecithin/chitosan nanoparticles, to obtain an optimized formula. The release rate was tested, where artificial neural network models were used to predict the % release of the drug from the optimized formula at different time intervals. The optimized formula was tested for its cytotoxicity. A design space was established, with an optimized formula having a molar ratio of 18.33:1 lecithin:chitosan and 38.35 mg silymarin. This resulted in nanoparticles with a size of 161 nm, a polydispersity index of 0.2, and an entrapment efficiency of 97%. The optimized formula showed a zeta potential of +38 mV, with well-developed spherical particles. AI successfully showed high prediction ability of the drug's release rate. The optimized formula showed an enhancement in the cytotoxic effect of silymarin with a decreased IC50 compared to standard silymarin. Lecithin/chitosan nanoparticles were successfully formulated, with deep process and product understanding. Several tools were used as AI which could shift pharmaceutical formulations from experience-dependent studies to data-driven methodologies in the future.
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Affiliation(s)
- Marwa H S Dawoud
- Department of Pharmaceutics, Faculty of Pharmacy, October University for Modern Sciences and Arts, intersection of 26th of July road and Elwahat road, 6th of October city, Giza, Egypt.
| | - Islam S Mannaa
- Department of Pharmaceutics, Faculty of Pharmacy, October University for Modern Sciences and Arts, intersection of 26th of July road and Elwahat road, 6th of October city, Giza, Egypt
| | - Amira Abdel-Daim
- Department of Biochemistry, Faculty of Pharmacy, October University for Modern Sciences and Arts, Giza, Egypt
| | - Nabila M Sweed
- Department of Pharmaceutics, Faculty of Pharmacy, October University for Modern Sciences and Arts, intersection of 26th of July road and Elwahat road, 6th of October city, Giza, Egypt
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Momeni M, Rakhshani S, Abbaspour M, Alizadeh F, Sheikhi N, GhorbanZadeh F, Habibi Z, Tabesh H. Dataset development of pre-formulation tests on fast disintegrating tablets (FDT): data aggregation. BMC Res Notes 2023; 16:131. [PMID: 37400854 DOI: 10.1186/s13104-023-06416-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023] Open
Abstract
OBJECTIVES Tablet manufacturing development is costly, laborious, and time-consuming. Technologies related to artificial intelligence like ,predictive model ,can be used in the control process to facilitate and accelerate the tablet manufacturing process. predictive models have become popular recently. However, predictive models need a comprehensive dataset of related data in the field, due to the lack of a dataset of tablet formulations, the aim of this study is to aggregate and integrate fast disintegration tablet's formulation into a comprehensive dataset. DATA DESCRIPTION The search strategy has been prepared between the years of 2010 to 2020, consisting of the keyword's 'formulation' ,'disintegrating' and 'Tablet', as well as their synonyms. By searching four databases, 1503 articles were retrieved, from these articles only 232 articles met all of the study's criteria. By reviewing 232 articles, 1982 formulations have been extracted, afterward pre-processing and cleaning data, contain steps of unifying the name and units, removing inappropriate formulations by an expert, and finally, data tidying was done on data. The developed dataset contains valuable information from various FDT's formulations, which can be used in pharmaceutical studies that are critical to the discovery and development of new drugs. this method can be applied to aggregate datasets from the other dosage forms.
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Affiliation(s)
- Mehri Momeni
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saleh Rakhshani
- Department of pharmaceutics, school of pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Abbaspour
- Department of pharmaceutics, school of pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Faezeh Alizadeh
- Student research committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nafiseh Sheikhi
- Student research committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Faezeh GhorbanZadeh
- Student research committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Habibi
- Student research committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamed Tabesh
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Han R, Ye Z, Zhang Y, Cheng Y, Zheng Y, Ouyang D. Predicting liposome formulations by the integrated machine learning and molecular modeling approaches. Asian J Pharm Sci 2023; 18:100811. [PMID: 37274923 PMCID: PMC10232664 DOI: 10.1016/j.ajps.2023.100811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/20/2023] [Accepted: 03/22/2023] [Indexed: 06/07/2023] Open
Abstract
Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future.
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Affiliation(s)
- Run Han
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
| | - Yaxin Cheng
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
| | - Ying Zheng
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
- Faculty of Health Sciences, University of Macau, Macao 999078, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macao 999078, China
- Faculty of Health Sciences, University of Macau, Macao 999078, China
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Deng J, Ye Z, Zheng W, Chen J, Gao H, Wu Z, Chan G, Wang Y, Cao D, Wang Y, Lee SMY, Ouyang D. Machine learning in accelerating microsphere formulation development. Drug Deliv Transl Res 2023; 13:966-982. [PMID: 36454434 DOI: 10.1007/s13346-022-01253-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2022] [Indexed: 12/03/2022]
Abstract
Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R2 = 0.880 vs. R2 = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.
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Affiliation(s)
- Jiayin Deng
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wenwen Zheng
- Department of Clinical Laboratory, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Jian Chen
- Zhuhai Livzon Microsphere Technology Co., Ltd, Zhuhai, China
| | - Haoshi Gao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
- Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China
| | - Zheng Wu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Yongjun Wang
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Yanqing Wang
- Zhuhai Livzon Microsphere Technology Co., Ltd, Zhuhai, China.
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
- Faculty of Health Sciences, University of Macau, Macau, China.
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
- Faculty of Health Sciences, University of Macau, Macau, China.
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Abstract
INTRODUCTION Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines. AREAS COVERED the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods. EXPERT OPINION The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.
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Affiliation(s)
- Jonathan Zaslavsky
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
| | - Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, M5S 3M2, Toronto, ON, Canada
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12
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Mazur H, Erbrich L, Quodbach J. Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines. Pharm Dev Technol 2023; 28:219-231. [PMID: 36715438 DOI: 10.1080/10837450.2023.2173778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of their variable geometry design. The geometry design can be used to determine the surface area to volume (SA/V) ratio, which affects drug release from the dosage forms. This study investigated artificial neural networks (ANN) to predict suitable geometries for the desired dose and release profile. Filaments with 5% API load and polyvinyl alcohol were 3D printed using Fused Deposition Modeling to provide a wide variety of geometries with different dosages and SA/V ratios. These were dissolved in vitro, and the API release profiles were described mathematically. Using these data, ANN architectures were designed with the goal of predicting a suitable dosage form geometry. Poor accuracies of 68.5% in the training and 44.4% in the test settings were achieved with a classification architecture. However, the SA/V ratio could be predicted accurately with a mean squared error loss of only 0.05. This study shows that the prediction of the SA/V ratio using AI works, but not of the exact geometry. For this purpose, a global database could be built with a range of geometries to simplify the prescription process.
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Affiliation(s)
- Hellen Mazur
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany
| | - Leon Erbrich
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany
| | - Julian Quodbach
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University, Düsseldorf, Germany.,Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
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13
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Ali H, Muthudoss P, Ramalingam M, Kanakaraj L, Paudel A, Ramasamy G. Machine Learning-Enabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data. AAPS PharmSciTech 2023; 24:34. [PMID: 36627410 DOI: 10.1208/s12249-022-02493-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/14/2022] [Indexed: 01/11/2023] Open
Abstract
An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al.. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset ( http://www.models.life.ku.dk/Tablets ) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. HIGHLIGHTS: • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated.
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Affiliation(s)
- Hussain Ali
- Christ (Deemed to Be University), Bangalore, 560029, Karnataka, India
| | - Prakash Muthudoss
- A2Z4.0 Research and Analytics Private Limited, Chennai, 600062, Tamilnadu, India
| | - Manikandan Ramalingam
- Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, 603103, Chennai, Tamilnadu, India
| | - Lakshmi Kanakaraj
- Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, 603103, Chennai, Tamilnadu, India
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria. .,Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010, Graz, Austria.
| | - Gobi Ramasamy
- Christ (Deemed to Be University), Bangalore, 560029, Karnataka, India.
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14
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Hassan M, Ali S, Kim JY, Saadia A, Sanaullah M, Alquhayz H, Safdar K. Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images. Interdiscip Sci 2023. [PMID: 36611082 DOI: 10.1007/s12539-022-00545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 01/09/2023]
Abstract
Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.
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15
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Ong JJ, Castro BM, Gaisford S, Cabalar P, Basit AW, Pérez G, Goyanes A. Accelerating 3D printing of pharmaceutical products using machine learning. Int J Pharm X 2022; 4:100120. [PMID: 35755603 PMCID: PMC9218223 DOI: 10.1016/j.ijpx.2022.100120] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/26/2022] [Accepted: 05/29/2022] [Indexed: 12/11/2022] Open
Abstract
Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.
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Affiliation(s)
- Jun Jie Ong
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Brais Muñiz Castro
- IRLab, CITIC Research Center, Department of Computer Science, University of A Coruña, Spain
| | - Simon Gaisford
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK
| | - Pedro Cabalar
- IRLab, Department of Computer Science, University of A Coruña, Spain
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK
| | - Gilberto Pérez
- IRLab, CITIC Research Center, Department of Computer Science, University of A Coruña, Spain
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.,FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK.,Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, iMATUS and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782, Spain
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16
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Sousa AS, Serra J, Estevens C, Costa R, Ribeiro AJ. A quality by design approach in oral extended release drug delivery systems: where we are and where we are going? J Pharm Investig 2022. [DOI: 10.1007/s40005-022-00603-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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17
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Wang J, Heshmati Aghda N, Jiang J, Mridula Habib A, Ouyang D, Maniruzzaman M. 3D bioprinted microparticles: Optimizing loading efficiency using advanced DoE technique and machine learning modeling. Int J Pharm 2022; 628:122302. [DOI: 10.1016/j.ijpharm.2022.122302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 11/15/2022]
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18
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Muthudoss P, Tewari I, Chi RLR, Young KJ, Ann EYC, Hui DNS, Khai OY, Allada R, Rao M, Shahane S, Das S, Babla I, Mhetre S, Paudel A. Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts. AAPS PharmSciTech 2022; 23:277. [PMID: 36229571 DOI: 10.1208/s12249-022-02403-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.
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Affiliation(s)
- Prakash Muthudoss
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia.,A2Z4.0 Research and Analytics Private Limited, Old No:810, New No:62, CTH Road, Behind Lenskart, Thirumullaivoil, Chennai, Tamilnadu, India
| | - Ishan Tewari
- The Machine Learning Company, Beed, Maharashtra, India.,Institute of Technology, Nirma University, Ahmedabad, Gujarat, India
| | - Rayce Lim Rui Chi
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Kwok Jia Young
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Eddy Yii Chung Ann
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Doreen Ng Sean Hui
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Ooi Yee Khai
- Perkin Elmer Sdn Bhd, L2, 2-01, Wisma Academy, Jalan 19/1, Seksyen 19, 46300, Petaling Jaya, Selangor, Malaysia
| | - Ravikiran Allada
- Novugen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Manohar Rao
- PerkinElmer (India) Private Limited, Vayudooth Chambers, 12th floor, Trinity Circle, Mahatma Gandhi Rd, Bengaluru, Karnataka, 560001, India
| | | | - Samir Das
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Irfan Babla
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Sandeep Mhetre
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria. .,Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010, Graz, Austria.
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19
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Lu A, Zhang J, Jiang J, Zhang Y, Giri BR, Kulkarni VR, Aghda NH, Wang J, Maniruzzaman M. Novel 3D Printed Modular Tablets Containing Multiple Anti-Viral Drugs: a Case of High Precision Drop-on-Demand Drug Deposition. Pharm Res 2022; 39:2905-2918. [PMID: 36109460 PMCID: PMC9483370 DOI: 10.1007/s11095-022-03378-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/18/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Anqi Lu
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Jiaxiang Zhang
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Junhuang Jiang
- Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yu Zhang
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Bhupendra R Giri
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Vineet R Kulkarni
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Niloofar Heshmati Aghda
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Jiawei Wang
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Mohammed Maniruzzaman
- Pharmaceutical Engineering and 3D Printing (PharmE3D) Labs, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA.
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20
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Dibal P, Onwuka E, Suleiman Z, Salihu B, Nwankwo E, Okoh S. An Overview of IoT Solutions in Climate Smart Agriculture for Food Security in Sub Saharan Africa: Challenges and Prospects. EAI Endorsed Trans IoT 2022. [DOI: 10.4108/eetiot.v8i3.2696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION: Climate smart agriculture (CSA) which involves the integration of IoT and cloud computing is an emerging agricultural paradigm that is foreseen to be the main driver of agriculture as the 21st century progresses. Sub-Saharan Africa lags in this regard and therefore deserves a special focus.
OBJECTIVES: This paper presents an overview of Internet-of-Things (IoT) solutions in CSA in the context of food security in sub-Saharan Africa (SSA)
METHODS: An overview of the status of food insecurity in SSA and associated factors is presented. The paper then focused on IoT as a technology and how it can be used for CSA in SSA through use cases; possible challenges were also examined.
RESULTS: The paper showed that with CSA, SSA can become a net exporter of food.
CONCLUSION: The paper concludes with open issues like the funding of research and development which must be addressed if SSA is to leverage IoT technology to attain food security.
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Ma L, Zhang J, Lin L, Wang T, Ma C, Wang X, Li M, Qiao Y, Wang Y, Zhang G, Wu Z. Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing. Acta Pharm Sin B 2022; 13:2188-2201. [DOI: 10.1016/j.apsb.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 11/01/2022] Open
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22
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Xiouras C, Cameli F, Quilló GL, Kavousanakis ME, Vlachos DG, Stefanidis GD. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization. Chem Rev 2022; 122:13006-13042. [PMID: 35759465 DOI: 10.1021/acs.chemrev.2c00141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.
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Affiliation(s)
- Christos Xiouras
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Fabio Cameli
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Gustavo Lunardon Quilló
- Chemical Process R&D, Crystallization Technology Unit, Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium.,Chemical and BioProcess Technology and Control, Department of Chemical Engineering, Faculty of Engineering Technology, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium
| | - Mihail E Kavousanakis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Georgios D Stefanidis
- School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece.,Laboratory for Chemical Technology, Ghent University; Tech Lane Ghent Science Park 125, B-9052 Ghent, Belgium
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23
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Lee H, Kim J, Kim S, Yoo J, Choi GJ, Jeong Y, Zalesny R. Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions. J CHEM-NY 2022; 2022:1-11. [DOI: 10.1155/2022/4148443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes.
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Abstract
In recent research on the formulation prediction of oral dissolving drugs, deep learning models with significantly improved performance compared to machine learning models were proposed. However, the performance degradation due to limitations of an imbalanced dataset with a small size and inefficient neural network structure has still not been resolved. Therefore, we propose new deep learning-based prediction models that maximize the prediction performance for disintegration time of oral fast disintegrating films (OFDF) and cumulative dissolution profiles of sustained-release matrix tablets (SRMT). In the case of OFDF, we use principal component analysis (PCA) to reduce the dimensionality of the dataset, thereby improving the prediction performance and reducing the training time. In the case of SRMT, the Wasserstein generative adversarial network (WGAN), a neural network-based generative model, is used to overcome the limitation of performance improvement due to the lack of experimental data. To the best of our knowledge, this is the first work that utilizes WGAN for pharmaceutical formulation prediction. Experimental results show that the proposed methods have superior performance than the existing methods for all the performance metrics considered.
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Wang S, Di J, Wang D, Dai X, Hua Y, Gao X, Zheng A, Gao J. State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics 2022; 14:183. [PMID: 35057076 DOI: 10.3390/pharmaceutics14010183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Ye Z, Ouyang D. Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms. J Cheminform 2021; 13:98. [PMID: 34895323 PMCID: PMC8665485 DOI: 10.1186/s13321-021-00575-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022] Open
Abstract
Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compound solubility in organic solvents. A dataset containing 5081 experimental temperature and solubility data of compounds in organic solvents was extracted and standardized. Molecular fingerprints were selected to characterize structural features. lightGBM was compared with deep learning and traditional machine learning (PLS, Ridge regression, kNN, DT, ET, RF, SVM) to develop models for predicting solubility in organic solvents at different temperatures. Compared to other models, lightGBM exhibited significantly better overall generalization (logS ± 0.20). For unseen solutes, our model gave a prediction accuracy (logS ± 0.59) close to the expected noise level of experimental solubility data. lightGBM revealed the physicochemical relationship between solubility and structural features. Our method enables rapid solvent screening in chemistry and may be applied to solubility prediction in other solvents.
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Affiliation(s)
- Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
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27
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Wang X, Sun Y, Ling L, Ren X, Liu X, Wang Y, Dong Y, Ma J, Song R, Yu A, Wei J, Fan Q, Guo M, Zhao T, Dao R, She G. Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis-An Assessment Combining Machine Learning-Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment. Front Pharmacol 2021; 12:704040. [PMID: 34671253 PMCID: PMC8520986 DOI: 10.3389/fphar.2021.704040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 08/18/2021] [Indexed: 12/26/2022] Open
Abstract
Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti-rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date. Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations. Study design and methods: The IL-1β-induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure-activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro. Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β-induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA . Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation-immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.
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Affiliation(s)
- Xiuhuan Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Youyi Sun
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Ling Ling
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Xueyang Ren
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Xiaoyun Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Yu Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Ying Dong
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Jiamu Ma
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Ruolan Song
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Axiang Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Jing Wei
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Qiqi Fan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
| | - Miaoxian Guo
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Tiantian Zhao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Rina Dao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Gaimei She
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.,Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing, China
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Matsuzaka Y, Totoki S, Handa K, Shiota T, Kurosaki K, Uesawa Y. Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System. Int J Mol Sci 2021; 22:10821. [PMID: 34639159 PMCID: PMC8509615 DOI: 10.3390/ijms221910821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/11/2022] Open
Abstract
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
- Center for Gene and Cell Therapy, Division of Molecular and Medical Genetics, The Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | - Shin Totoki
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kentaro Handa
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Tetsuyoshi Shiota
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kota Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
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Abstract
We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.
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Munir N, Nugent M, Whitaker D, McAfee M. Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions. Pharmaceutics 2021; 13:1432. [PMID: 34575508 DOI: 10.3390/pharmaceutics13091432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 08/26/2021] [Accepted: 09/06/2021] [Indexed: 01/11/2023] Open
Abstract
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.
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31
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Bennett-Lenane H, O'Shea JP, Murray JD, Ilie AR, Holm R, Kuentz M, Griffin BT. Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study. Pharmaceutics 2021; 13:1398. [PMID: 34575483 DOI: 10.3390/pharmaceutics13091398] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 11/16/2022] Open
Abstract
In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.
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32
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Bannigan P, Aldeghi M, Bao Z, Häse F, Aspuru-Guzik A, Allen C. Machine learning directed drug formulation development. Adv Drug Deliv Rev 2021; 175:113806. [PMID: 34019959 DOI: 10.1016/j.addr.2021.05.016] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/31/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.
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Affiliation(s)
- Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON M5S 1M1, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
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Elbadawi M, McCoubrey LE, Gavins FKH, Ong JJ, Goyanes A, Gaisford S, Basit AW. Harnessing artificial intelligence for the next generation of 3D printed medicines. Adv Drug Deliv Rev 2021; 175:113805. [PMID: 34019957 DOI: 10.1016/j.addr.2021.05.015] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/02/2021] [Accepted: 05/13/2021] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.
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Mackay BS, Marshall K, Grant-Jacob JA, Kanczler J, Eason RW, Oreffo ROC, Mills B. The future of bone regeneration: integrating AI into tissue engineering. Biomed Phys Eng Express 2021; 7. [PMID: 34271556 DOI: 10.1088/2057-1976/ac154f] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/16/2021] [Indexed: 01/16/2023]
Abstract
Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.
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Affiliation(s)
- Benita S Mackay
- Optoelectronics Research Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Karen Marshall
- Bone and Joint Research Group, Centre for Human Development, Stem Cells and Regeneration, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6HW, United Kingdom
| | - James A Grant-Jacob
- Optoelectronics Research Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Janos Kanczler
- Bone and Joint Research Group, Centre for Human Development, Stem Cells and Regeneration, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6HW, United Kingdom
| | - Robert W Eason
- Optoelectronics Research Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom.,Institute of Developmental Sciences, Faculty of Life Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Richard O C Oreffo
- Bone and Joint Research Group, Centre for Human Development, Stem Cells and Regeneration, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6HW, United Kingdom.,Institute of Developmental Sciences, Faculty of Life Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Ben Mills
- Optoelectronics Research Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom
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Zhang P, Guo Z, Ullah S, Melagraki G, Afantitis A, Lynch I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nat Plants 2021; 7:864-876. [PMID: 34168318 DOI: 10.1038/s41477-021-00946-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.
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Affiliation(s)
- Peng Zhang
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK.
| | - Zhiling Guo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Sami Ullah
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
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36
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Affiliation(s)
- Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
| | - Wenmian Yang
- State Key Laboratory of Internet of Things for Smart City University of Macau Macau China
| | - Yilong Yang
- School of Software Beihang University Beijing China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
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37
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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38
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Thakral NK, Meister E, Jankovsky C, Li L, Schwabe R, Luo L, Chen S. Prediction of in vivo supersaturation and precipitation of poorly water-soluble drugs: Achievements and aspirations. Int J Pharm 2021; 600:120505. [PMID: 33753162 DOI: 10.1016/j.ijpharm.2021.120505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/10/2021] [Accepted: 03/13/2021] [Indexed: 12/14/2022]
Abstract
This review focuses on options available to a pharmaceutical scientist to predict in vivo supersaturation and precipitation of poorly water-soluble drugs. As no single device or system can simulate the complex gastrointestinal environment, a combination of appropriate in vitro tools may be utilized to get optimal predictive information. To address the empirical issues encountered during small-scale and full-scale in vitro predictive testing, theoretical background and relevant case studies are discussed. The practical considerations for selection of appropriate tools at various stages of drug development are recommended. Upcoming technologies that have potential to further reduce in vivo studies and expedite the drug development process are also discussed.
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Affiliation(s)
- Naveen K Thakral
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
| | - Eva Meister
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States
| | - Corinne Jankovsky
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States
| | - Li Li
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States; Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, 4849 Calhoun Road, Houston, TX 77204, United States
| | - Robert Schwabe
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States
| | - Laibin Luo
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States
| | - Shirlynn Chen
- Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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Narayanan H, Dingfelder F, Butté A, Lorenzen N, Sokolov M, Arosio P. Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation. Trends Pharmacol Sci 2021; 42:151-165. [DOI: 10.1016/j.tips.2020.12.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/10/2020] [Accepted: 12/16/2020] [Indexed: 12/19/2022]
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Shi G, Lin L, Liu Y, Chen G, Luo Y, Wu Y, Li H. Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets. RSC Adv 2021; 11:8323-8345. [PMID: 35423324 PMCID: PMC8695199 DOI: 10.1039/d0ra08030f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/26/2021] [Indexed: 11/21/2022] Open
Abstract
The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.
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Affiliation(s)
- Guolin Shi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Longfei Lin
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuling Liu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Gongsen Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yuting Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yanqiu Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Hui Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
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42
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Kuentz M, Holm R, Kronseder C, Saal C, Griffin BT. Rational Selection of Bio-Enabling Oral Drug Formulations - A PEARRL Commentary. J Pharm Sci 2021; 110:1921-1930. [PMID: 33609523 DOI: 10.1016/j.xphs.2021.02.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/19/2022]
Abstract
New drug candidates often require bio-enabling formation technologies such as lipid-based formulations, solid dispersions, or nanosized drug formulations. Development of such more sophisticated delivery systems generally requires higher resource investment compared to a conventional oral dosage form, which might slow down clinical development. To achieve the biopharmaceutical objectives while enabling rapid cost effective development, it is imperative to identify a suitable formulation technique for a given drug candidate as early as possible. Hence many companies have developed internal decision trees based mostly on prior organizational experience, though they also contain some arbitrary elements. As part of the EU funded PEARRL project, a number of new decision trees are here proposed that reflect both the current scientific state of the art and a consensus among the industrial project partners. This commentary presents and discusses these, while also going beyond this classical expert approach with a pilot study using emerging machine learning, where the computer suggests formulation strategy based on the physicochemical and biopharmaceutical properties of a molecule. Current limitations are discussed and an outlook is provided for likely future developments in this emerging field of pharmaceutics.
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Affiliation(s)
- Martin Kuentz
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, CH 4132 Muttenz, Switzerland.
| | - René Holm
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340 Beerse, Belgium; Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark
| | - Christian Kronseder
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, CH 4132 Muttenz, Switzerland
| | - Christoph Saal
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Brendan T Griffin
- School of Pharmacy, University College Cork, College Road, Cork, T12 YN60, Ireland
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43
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Salma H, Melha YM, Sonia L, Hamza H, Salim N. Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks. J Pharm Sci 2021; 110:2531-43. [PMID: 33548245 DOI: 10.1016/j.xphs.2021.01.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 12/12/2022]
Abstract
The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm2 was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f2 values equal to 99.99 for all the formulations.
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Gao M, Liu S, Chen J, Gordon KC, Tian F, McGoverin CM. Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective. Int J Pharm 2021; 597:120334. [PMID: 33540015 DOI: 10.1016/j.ijpharm.2021.120334] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 01/17/2023]
Abstract
Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.
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Affiliation(s)
- Ming Gao
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Sibo Liu
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Jianan Chen
- Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Research Tower, MaRS Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Keith C Gordon
- Dodd-Walls Centre, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Fang Tian
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China
| | - Cushla M McGoverin
- Nycrist Pharmtech Limited, 2/2D, A3, Science and Technology Park, 3009 Guanguang Rd, Guangming, Shenzhen, Guangdong 518107, China.
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Mswahili ME, Lee M, Martin GL, Kim J, Kim P, Choi GJ, Jeong Y. Cocrystal Prediction Using Machine Learning Models and Descriptors. Applied Sciences 2021; 11:1323. [DOI: 10.3390/app11031323] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.
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Thakur A, Mishra AP, Panda B, Rodríguez DCS, Gaurav I, Majhi B. Application of Artificial Intelligence in Pharmaceutical and Biomedical Studies. Curr Pharm Des 2021; 26:3569-3578. [PMID: 32410553 DOI: 10.2174/1381612826666200515131245] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 02/01/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Artificial intelligence (AI) is the way to model human intelligence to accomplish certain tasks without much intervention of human beings. The term AI was first used in 1956 with The Logic Theorist program, which was designed to simulate problem-solving ability of human beings. There have been a significant amount of research works using AI in order to determine the advantages and disadvantages of its applicabication and, future perspectives that impact different areas of society. Even the remarkable impact of AI can be transferred to the field of healthcare with its use in pharmaceutical and biomedical studies crucial for the socioeconomic development of the population in general within different studies, we can highlight those that have been conducted with the objective of treating diseases, such as cancer, neurodegenerative diseases, among others. In parallel, the long process of drug development also requires the application of AI to accelerate research in medical care. METHODS This review is based on research material obtained from PubMed up to Jan 2020. The search terms include "artificial intelligence", "machine learning" in the context of research on pharmaceutical and biomedical applications. RESULTS This study aimed to highlight the importance of AI in the biomedical research and also recent studies that support the use of AI to generate tools using patient data to improve outcomes. Other studies have demonstrated the use of AI to create prediction models to determine response to cancer treatment. CONCLUSION The application of AI in the field of pharmaceutical and biomedical studies has been extensive, including cancer research, for diagnosis as well as prognosis of the disease state. It has become a tool for researchers in the management of complex data, ranging from obtaining complementary results to conventional statistical analyses. AI increases the precision in the estimation of treatment effect in cancer patients and determines prediction outcomes.
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Affiliation(s)
- Abhimanyu Thakur
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Ambika P Mishra
- Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Bhubaneswar, Orissa, India
| | - Bishnupriya Panda
- Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Bhubaneswar, Orissa, India
| | - Diana C S Rodríguez
- Foundation for Clinical and Applied Cancer Research-FICMAC, Bogota, Colombia
| | - Isha Gaurav
- Patna Women's College (Autonmous), Patna, Bihar, India
| | - Babita Majhi
- Department of Computer Science and Information Technology, Guru Ghashidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India
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Tang Y, Li Z, Yang D, Fang Y, Gao S, Liang S, Liu T. Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning. Chin Med 2021; 16:2. [PMID: 33407711 PMCID: PMC7789502 DOI: 10.1186/s13020-020-00409-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022] Open
Abstract
Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
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Affiliation(s)
- Yuqi Tang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Zechen Li
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Dongdong Yang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China.
| | - Yu Fang
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shanshan Gao
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, China
| | - Shan Liang
- School of Automation, Chongqing University, Chongqing, 400044, China
| | - Tao Liu
- Electronic Engineering College, Chengdu University of Information Technology, Chengdu, 610225, China
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Abstract
Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.
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Abstract
The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.
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Affiliation(s)
| | - Moe Elbadawi
- UCL School of Pharmacy, University College London, London, UK
| | - Mine Orlu
- UCL School of Pharmacy, University College London, London, UK
| | - Simon Gaisford
- UCL School of Pharmacy, University College London, London, UK
- FabRx Ltd., Ashford, Kent, UK
| | - Abdul W. Basit
- UCL School of Pharmacy, University College London, London, UK
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Kamerzell TJ, Middaugh CR. Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development. J Pharm Sci 2020; 110:665-681. [PMID: 33278409 DOI: 10.1016/j.xphs.2020.11.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 12/11/2022]
Abstract
The rapid growth in technological advances and quantity of scientific data over the past decade has led to several challenges including data storage and analysis. Accurate models of complex datasets were previously difficult to develop and interpret. However, improvements in machine learning algorithms have since enabled unparalleled classification and prediction capabilities. The application of machine learning can be seen throughout diverse industries due to their ease of use and interpretability. In this review, we describe popular machine learning algorithms and highlight their application in pharmaceutical protein development. Machine learning models have now been applied to better understand the nonlinear concentration dependent viscosity of protein solutions, predict protein oxidation and deamidation rates, classify sub-visible particles and compare the physical stability of proteins. We also applied several machine learning algorithms using previously published data and describe models with improved predictions and classification. The authors hope that this review can be used as a resource to others and encourage continued application of machine learning algorithms to problems in pharmaceutical protein development.
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Affiliation(s)
- Tim J Kamerzell
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA; Division of Internal Medicine, HCA MidWest Health, Overland Park, KS, USA.
| | - C Russell Middaugh
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA
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