1
|
Sheth TS, Acharya F. Optimization and evaluation of modified release solid dosage forms using artificial neural network. Sci Rep 2024; 14:16358. [PMID: 39014107 PMCID: PMC11252257 DOI: 10.1038/s41598-024-67274-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/09/2024] [Indexed: 07/18/2024] Open
Abstract
This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.
Collapse
Affiliation(s)
- Tulsi Sagar Sheth
- Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India
- Parul Institute of Applied Sciences, Parul University, Vadodara, Gujarat, 391760, India
| | - Falguni Acharya
- Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India.
| |
Collapse
|
2
|
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] [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.
Collapse
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
| |
Collapse
|
3
|
Taseva AR, Persoons T, D'Arcy DM. Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus. Eur J Pharm Biopharm 2023; 189:36-47. [PMID: 37120067 DOI: 10.1016/j.ejpb.2023.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/19/2023] [Accepted: 04/22/2023] [Indexed: 05/01/2023]
Abstract
Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.
Collapse
Affiliation(s)
- Alexandra R Taseva
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Tim Persoons
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Deirdre M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| |
Collapse
|
4
|
Shahiwala AF, Qawoogha SS, Faruqui N. Designing Optimum Drug Delivery Systems Using Machine Learning Approaches: a Prototype Study of Niosomes. AAPS PharmSciTech 2023; 24:94. [PMID: 37012582 DOI: 10.1208/s12249-023-02547-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/28/2023] [Indexed: 04/05/2023] Open
Abstract
This study demonstrates a machine learning approach in designing optimized drug formulations. Preferred Reporting Items for Systematic Reviews and Meta-Analyses system was adopted to screen literature resulting in 114 niosome formulations. Eleven properties (input parameters) related to drugs and niosomes affecting particle size and drug entrapment (output variables) were precisely identified and used for the network training. The hyperbolic tangent sigmoid transfer function with Levenberg-Marquardt backpropagation was used to train the model. The network showed the highest prediction accuracy of 93.76% and 91.79% for % drug entrapment and particle size prediction. Sensitivity analysis identified drug/lipid ratio and cholesterol/surfactant ratio as the most significant factors affecting % drug entrapment and particle size of niosomes. Accordingly, nine Donepezil hydrochloride noisome batches were prepared using a 3 × 3 factorial design with drug/lipid ratio and cholesterol/surfactant ratio as factors to validate the developed model. The model reached a prediction accuracy of more than 97% for experimental batches. Finally, the superiority of global artificial neural network was demonstrated compared to the local response surface methodology for Donepezil niosome formulations. Even though the ANN successfully predicted the parameters of Donepezil niosomes, several drugs with different physicochemical properties must be tested to confirm the validity of the model and its usefulness for designing new drug niosomal formulations.
Collapse
Affiliation(s)
- Aliasgar F Shahiwala
- Department of Pharmaceutics, Dubai Pharmacy College for Girls, Dubai, United Arab Emirates.
| | - Samar Salam Qawoogha
- Department of Pharmaceutics, Dubai Pharmacy College for Girls, Dubai, United Arab Emirates
| | - Nuruzzaman Faruqui
- Department of Software Engineering, Daffodil International University, Birulia, Bangladesh
| |
Collapse
|
5
|
A hybrid framework of artificial intelligence-based neural network model (ANN) and central composite design (CCD) in quality by design formulation development of orodispersible moxifloxacin tablets: Physicochemical evaluation, compaction analysis, and its in-silico PBPK modeling. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
|
6
|
Rebollo R, Oyoun F, Corvis Y, El-Hammadi MM, Saubamea B, Andrieux K, Mignet N, Alhareth K. Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2022; 14:39736-39745. [PMID: 36001743 DOI: 10.1021/acsami.2c06627] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.
Collapse
Affiliation(s)
- René Rebollo
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Feras Oyoun
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Yohann Corvis
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Mazen M El-Hammadi
- Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, University of Seville, c/Prof. García González n◦2, 41012Seville, Spain
| | - Bruno Saubamea
- Université Paris Cité, US25 INSERM, UMS3612 CNRS, Plateforme Imagerie Cellulaire et Moléculaire, 75006Paris, France
| | - Karine Andrieux
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Nathalie Mignet
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Khair Alhareth
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| |
Collapse
|
7
|
Yuan Y, Yan H, Cui Z, Liu Z, Su W, Zhang R. Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose. J Chem Inf Model 2022; 62:4122-4133. [PMID: 36036609 DOI: 10.1021/acs.jcim.2c00747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments. Atom-atom electrostatic interaction energies are subsequently computed using the predicted atomic multipole moments in the pilot system pentose of RNA. Here, the performance of the five methods is compared in terms of both the multipole moment prediction errors and the electrostatic energy prediction errors. For the predicted high-rank multipole moments of the four elements (O, C, N, and H) in capped pentose, ARDGPR and Kriging consistently outperform the other three methods. Therefore, the multipole moments predicted by the two best methods of ARDGPR and Kriging are then used to predict electrostatic interaction energy of each pentose. Finally, the absolute average energy errors of ARDGPR and Kriging are 1.83 and 4.33 kJ mol-1, respectively. Compared to Kriging, the ARDGPR method achieves a 58% decrease in the absolute average energy error. These satisfactory results demonstrated that the ARDGPR method with the strong feature extraction ability can predict the electrostatic interaction energy of pentose in RNA correctly and reliably.
Collapse
Affiliation(s)
- Yongna Yuan
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 730000
| | - Haoqiu Yan
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 730000
| | - Zeyang Cui
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 730000
| | - Zhenyu Liu
- School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, China, 730070
| | - Wei Su
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 730000
| | - Ruisheng Zhang
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 730000
| |
Collapse
|
8
|
Nagy B, Galata DL, Farkas A, Nagy ZK. Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review. AAPS J 2022; 24:74. [PMID: 35697951 DOI: 10.1208/s12248-022-00706-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/06/2022] [Indexed: 01/22/2023] Open
Abstract
Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance.
Collapse
Affiliation(s)
- Brigitta Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary.
| |
Collapse
|
9
|
State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics 2022; 14:pharmaceutics14010183. [PMID: 35057076 PMCID: PMC8779224 DOI: 10.3390/pharmaceutics14010183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 11/30/2022] Open
Abstract
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
Collapse
|
10
|
Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release 2021; 338:119-136. [PMID: 34418520 DOI: 10.1016/j.jconrel.2021.08.030] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/18/2023]
Abstract
In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.
Collapse
Affiliation(s)
- 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
| | - Hanlu Gao
- 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.
| |
Collapse
|
11
|
Prediction of Drug Stability Using Deep Learning Approach: Case Study of Esomeprazole 40 mg Freeze-Dried Powder for Solution. Pharmaceutics 2021; 13:pharmaceutics13060829. [PMID: 34204912 PMCID: PMC8230350 DOI: 10.3390/pharmaceutics13060829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 11/16/2022] Open
Abstract
A critical step in the production of Esomeprazole powder for solution is a period between the filling process and lyophilization, where all vials, partially closed, are completely exposed to environmental influences. Excessive instability reflects in pH value variations caused by oxygen's impact. In order to provide pH control, which consequently affects drug stability, Esomeprazole batches, produced in the same way, were kept in partially closed vials for 3 h at temperatures of 20 °C and -30 °C, after which they were lyophilized and stored for long-term stability for 36 months. The aim of the presented study was to apply a deep-learning algorithm for the prediction of the Esomeprazole stability profile and to determine the pH limit for the reconstituted solution of the final freeze-dried product that would assure a quality product profile over a storage period of 36 months. Multilayer perceptron (MLP) as a deep learning tool, with four layers, was used. The pH value of Esomeprazole solution and time of storage (months) were inputs for the network, while Esomeprazole assay and four main impurities were outputs of the network. In order to keep all related substances and Esomeprazole assay in accordance with specifications for the whole shelf life, the pH value for the reconstituted finish product should be set in the range of 10.4-10.6.
Collapse
|
12
|
Djuris J, Cirin-Varadjan S, Aleksic I, Djuris M, Cvijic S, Ibric S. Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients. Pharmaceutics 2021; 13:pharmaceutics13050663. [PMID: 34063158 PMCID: PMC8148097 DOI: 10.3390/pharmaceutics13050663] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 11/25/2022] Open
Abstract
Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients.
Collapse
Affiliation(s)
- Jelena Djuris
- Department of Pharmaceutical Technology and Cosmetology, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia; (I.A.); (S.C.); (S.I.)
- Correspondence:
| | | | - Ivana Aleksic
- Department of Pharmaceutical Technology and Cosmetology, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia; (I.A.); (S.C.); (S.I.)
| | - Mihal Djuris
- Department of Catalysis and Chemical Engineering, Institute of Chemistry, Technology and Metallurgy—National Institute of the Republic of Serbia, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia;
| | - Sandra Cvijic
- Department of Pharmaceutical Technology and Cosmetology, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia; (I.A.); (S.C.); (S.I.)
| | - Svetlana Ibric
- Department of Pharmaceutical Technology and Cosmetology, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia; (I.A.); (S.C.); (S.I.)
| |
Collapse
|
13
|
González-Fernández FM, Bianchera A, Gasco P, Nicoli S, Pescina S. Lipid-Based Nanocarriers for Ophthalmic Administration: Towards Experimental Design Implementation. Pharmaceutics 2021; 13:pharmaceutics13040447. [PMID: 33810399 PMCID: PMC8067198 DOI: 10.3390/pharmaceutics13040447] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 02/07/2023] Open
Abstract
Nanotherapeutics based on biocompatible lipid matrices allow for enhanced solubility of poorly soluble compounds in the treatment of ophthalmic diseases, overcoming the anatomical and physiological barriers present in the eye, which, despite the ease of access, remains strongly protected. Micro-/nanoemulsions, solid lipid nanoparticles (SLN) or nanostructured lipid carriers (NLC) combine liquid and/or solid lipids with surfactants, improving drug stability and ocular bioavailability. Current research and development approaches based on try-and-error methodologies are unable to easily fine-tune nanoparticle populations in order to overcome the numerous constraints of ocular administration routes, which is believed to hamper easy approval from regulatory agencies for these systems. The predictable quality and specifications of the product can be achieved through quality-by-design (QbD) implementation in both research and industrial environments, in contrast to the current quality-by-testing (QbT) framework. Mathematical modelling of the expected final nanoparticle characteristics by variation of operator-controllable variables of the process can be achieved through adequate statistical design-of-experiments (DoE) application. This multivariate approach allows for optimisation of drug delivery platforms, reducing research costs and time, while maximising the understanding of the production process. This review aims to highlight the latest efforts in implementing the design of experiments to produce optimised lipid-based nanocarriers intended for ophthalmic administration. A useful background and an overview of the different possible approaches are presented, serving as a starting point to introduce the design of experiments in current nanoparticle research.
Collapse
Affiliation(s)
- Felipe M. González-Fernández
- Department of Food and Drug, University of Parma, Viale Parco Area delle Scienze, 27/a, 43124 Parma, Italy; (A.B.); (S.N.)
- Nanovector S.r.l., Via Livorno, 60, 10144 Torino, Italy;
- Correspondence: (F.M.G.-F.); (S.P.)
| | - Annalisa Bianchera
- Department of Food and Drug, University of Parma, Viale Parco Area delle Scienze, 27/a, 43124 Parma, Italy; (A.B.); (S.N.)
| | - Paolo Gasco
- Nanovector S.r.l., Via Livorno, 60, 10144 Torino, Italy;
| | - Sara Nicoli
- Department of Food and Drug, University of Parma, Viale Parco Area delle Scienze, 27/a, 43124 Parma, Italy; (A.B.); (S.N.)
| | - Silvia Pescina
- Department of Food and Drug, University of Parma, Viale Parco Area delle Scienze, 27/a, 43124 Parma, Italy; (A.B.); (S.N.)
- Correspondence: (F.M.G.-F.); (S.P.)
| |
Collapse
|
14
|
Arboretti R, Ceccato R, Pegoraro L, Salmaso L, Housmekerides C, Spadoni L, Pierangelo E, Quaggia S, Tveit C, Vianello S. Machine learning and design of experiments with an application to product innovation in the chemical industry. J Appl Stat 2021; 49:2674-2699. [DOI: 10.1080/02664763.2021.1907840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Rosa Arboretti
- Department of Civil, Environmental and Architectural Engineering, Università degli Studi di Padova, Padua, Italy
| | - Riccardo Ceccato
- Department of Management and Engineering, Università degli Studi di Padova, Vicenza, Italy
| | - Luca Pegoraro
- Department of Management and Engineering, Università degli Studi di Padova, Vicenza, Italy
| | - Luigi Salmaso
- Department of Management and Engineering, Università degli Studi di Padova, Vicenza, Italy
| | | | | | | | | | | | | |
Collapse
|
15
|
Obeid S, Madžarević M, Krkobabić M, Ibrić S. Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio. Int J Pharm 2021; 601:120507. [PMID: 33766640 DOI: 10.1016/j.ijpharm.2021.120507] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/24/2022]
Abstract
The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diazepam release from fused deposition modeling (FDM) printed tablets. Diazepam printed tablets of various shapes were created by a computer-aided design (CAD) program and prepared by fused deposition modeling using previously prepared polyvinyl alcohol/diazepam filaments via hot-melt extrusion. The surface to volume ratio (SA/V) for each shape was calculated. Printing parameters were varied including infill density (20%, 70% and 100%) and infill pattern (line and zigzag). Influence of tablet SA/V ratio and printing parameters (infill density and infill pattern) on the release of diazepam from printed tablets were modeled using self-organizing maps (SOM) and multi-layer perceptron (MLP). SOM as an unsupervised neural network was used for visualizing interrelation among the data, whereas MLP was used for the prediction of drug release properties. MLP had three layers (with structure 2-3-5) and was trained using back propagation algorithm. Input parameters for the modeling were: infill density and SA/V ratio; while output parameters were percent of drug release in five time points. The data set for network training was divided into training, validation and test sets. The dissolution rate increased with higher SA/V ratio, lower infill density (less than 50%) and zigzag infill pattern. The established ANN model was tested; calculated f 2 factors for two tested formulations (70.24 and 77.44) showed similarity between experimentally observed and predicted drug release profiles. Trained MLP network was able to predict drug release behavior as a function of infill density and SA/Vol ratio, as established design space for formulated 3D printed diazepam tablets.
Collapse
Affiliation(s)
- Samiha Obeid
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Marijana Madžarević
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Mirjana Krkobabić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Svetlana Ibrić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| |
Collapse
|
16
|
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] [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.
Collapse
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
| |
Collapse
|
17
|
Stanojević G, Medarević D, Adamov I, Pešić N, Kovačević J, Ibrić S. Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading. Molecules 2020; 26:molecules26010111. [PMID: 33383691 PMCID: PMC7795907 DOI: 10.3390/molecules26010111] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 11/16/2022] Open
Abstract
Various three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this technology in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.
Collapse
Affiliation(s)
- Gordana Stanojević
- Institute for Medicines and Medical Devices of Montenegro, Ivana Crnojevića 64a, 81000 Podgorica, Montenegro;
| | - Djordje Medarević
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (D.M.); (I.A.); (N.P.); (J.K.)
| | - Ivana Adamov
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (D.M.); (I.A.); (N.P.); (J.K.)
| | - Nikola Pešić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (D.M.); (I.A.); (N.P.); (J.K.)
| | - Jovana Kovačević
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (D.M.); (I.A.); (N.P.); (J.K.)
| | - Svetlana Ibrić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia; (D.M.); (I.A.); (N.P.); (J.K.)
- Correspondence: ; Tel.: +381-11-3951-371
| |
Collapse
|
18
|
Gao H, Wang W, Dong J, Ye Z, Ouyang D. An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design. Eur J Pharm Biopharm 2020; 158:336-346. [PMID: 33301864 DOI: 10.1016/j.ejpb.2020.12.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 01/05/2023]
Abstract
Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low efficiency. Current research aims to integrate various computational tools, including machine learning (ML), molecular dynamic (MD) simulation and physiologically based pharmacokinetic (PBPK) modeling, to accelerate the development of SD formulations. Firstly, based on a dataset consisting of 674 dissolution profiles of SD, the random forest algorithm was used to construct a classification model to distinguish two types of dissolution profiles: "spring-and-parachute" and "maintain supersaturation", and a regression model to predict the time-dependent dissolution profiles. Both of the two prediction models showed good prediction performance. Moreover, feature importance was performed to help understand the key information that contributes to the model. After that, the vemurafenib (VEM) SD formulation in previous report was used as an example to validate the models. MD simulation was used to investigate the dissolution behavior of two SD formulations with two polymers (HPMCAS and Eudragit) at the molecular level. The results showed that the HPMCAS-based formulation resulted in faster dissolution than the Eudragit formulation, which agreed with the reported experimental results. Finally, a PBPK model was constructed to accurately predict the human pharmacokinetic profile of the VEM-HPMCAS SD formulation. In conclusion, combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations. The integrated computational methodology will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.
Collapse
Affiliation(s)
- Hanlu Gao
- 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
| | - Jie Dong
- 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
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China.
| |
Collapse
|
19
|
Saravanakumar A, Rajeshkumar L, Balaji D, Jithin Karunan MP. Prediction of Wear Characteristics of AA2219-Gr Matrix Composites Using GRNN and Taguchi-Based Approach. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04817-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
20
|
Simões MF, Silva G, Pinto AC, Fonseca M, Silva NE, Pinto RM, Simões S. Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome. Eur J Pharm Biopharm 2020; 152:282-295. [DOI: 10.1016/j.ejpb.2020.05.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/21/2020] [Accepted: 05/14/2020] [Indexed: 12/30/2022]
|
21
|
Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 2019; 151-152:169-190. [PMID: 31071378 DOI: 10.1016/j.addr.2019.05.001] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/14/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.
Collapse
Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Fatemeh Atyabi
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Rassoul Dinarvand
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| |
Collapse
|
22
|
Optimization and Prediction of Ibuprofen Release from 3D DLP Printlets Using Artificial Neural Networks. Pharmaceutics 2019; 11:pharmaceutics11100544. [PMID: 31635414 PMCID: PMC6835658 DOI: 10.3390/pharmaceutics11100544] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/11/2019] [Accepted: 10/12/2019] [Indexed: 11/28/2022] Open
Abstract
The aim of this work was to investigate effects of the formulation factors on tablet printability as well as to optimize and predict extended drug release from cross-linked polymeric ibuprofen printlets using an artificial neural network (ANN). Printlets were printed using digital light processing (DLP) technology from formulations containing polyethylene glycol diacrylate, polyethylene glycol, and water in concentrations according to D-optimal mixture design and 0.1% w/w riboflavin and 5% w/w ibuprofen. It was observed that with higher water content longer exposure time was required for successful printing. For understanding the effects of excipients and printing parameters on drug dissolution rate in DLP printlets two different neural networks were developed with using two commercially available softwares. After comparison of experimental and predicted values of in vitro dissolution at the corresponding time points for optimized formulation, the R2 experimental vs. predicted value was 0.9811 (neural network 1) and 0.9960 (neural network 2). According to difference f1 and similarity factor f2 (f1 = 14.30 and f2 = 52.15) neural network 1 with supervised multilayer perceptron, backpropagation algorithm, and linear activation function gave a similar dissolution profile to obtained experimental results, indicating that adequate ANN is able to set out an input–output relationship in DLP printing of pharmaceutics.
Collapse
|
23
|
McKinley D, Patel SK, Regev G, Rohan LC, Akil A. Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm. Int J Pharm 2019; 571:118715. [PMID: 31560958 DOI: 10.1016/j.ijpharm.2019.118715] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 08/05/2019] [Accepted: 08/12/2019] [Indexed: 12/26/2022]
Abstract
The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization.
Collapse
Affiliation(s)
- DeAngelo McKinley
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA, 30341, USA
| | - Sravan Kumar Patel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Galit Regev
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Lisa C Rohan
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Department of Obstetrics, Gynecology & Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Magee-Womens Research Institute, Pittsburgh, PA, 15213, USA
| | - Ayman Akil
- Department of Pharmaceutical Sciences, College of Pharmacy, Mercer University, Atlanta, GA, 30341, USA.
| |
Collapse
|
24
|
Galata DL, Farkas A, Könyves Z, Mészáros LA, Szabó E, Csontos I, Pálos A, Marosi G, Nagy ZK, Nagy B. Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks. Pharmaceutics 2019; 11:E400. [PMID: 31405029 PMCID: PMC6723897 DOI: 10.3390/pharmaceutics11080400] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 12/22/2022] Open
Abstract
The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.
Collapse
Affiliation(s)
- Dorián László Galata
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Zsófia Könyves
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Lilla Alexandra Mészáros
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Edina Szabó
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - István Csontos
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Andrea Pálos
- Directorate General for Medicine Authorization and Methodology, Strategy, Development and Methodology Division, National Institute of Pharmacy and Nutrition, Zrínyi u. 3, H-1051 Budapest, Hungary
| | - György Marosi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary.
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary
| |
Collapse
|
25
|
McKinley D, Kumar Patel S, Regev G, Rohan LC, Akil A. WITHDRAWN: Delineating the Effects of Hot-Melt Extrusion on the Performance of a Polymeric Film using Artificial Neural Networks and an Evolutionary Algorithm. Int J Pharm X 2019. [DOI: 10.1016/j.ijpx.2019.100031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|
26
|
Nagy B, Petra D, Galata DL, Démuth B, Borbás E, Marosi G, Nagy ZK, Farkas A. Application of artificial neural networks for Process Analytical Technology-based dissolution testing. Int J Pharm 2019; 567:118464. [DOI: 10.1016/j.ijpharm.2019.118464] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/03/2019] [Accepted: 06/24/2019] [Indexed: 10/26/2022]
|
27
|
Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters. J Pharm Innov 2019. [DOI: 10.1007/s12247-019-09398-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
28
|
Huang K, Lv F, Wu D, Wang Z. Optimization of Process Conditions for Styrene Epoxidation Based on the Artificial Intelligence Method. Chem Eng Technol 2019. [DOI: 10.1002/ceat.201800018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Kai Huang
- Southeast UniversitySchool of Chemistry and Chemical Engineering Jiulong Lake, Nanjing 211189 Jiangsu China
| | - Fei Lv
- Southeast UniversitySchool of Chemistry and Chemical Engineering Jiulong Lake, Nanjing 211189 Jiangsu China
| | - Dongfang Wu
- Southeast UniversitySchool of Chemistry and Chemical Engineering Jiulong Lake, Nanjing 211189 Jiangsu China
| | - Zhili Wang
- Southeast UniversitySchool of Chemistry and Chemical Engineering Jiulong Lake, Nanjing 211189 Jiangsu China
| |
Collapse
|
29
|
Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters. J Pharmacol Sci 2019; 140:20-25. [PMID: 31105026 DOI: 10.1016/j.jphs.2019.03.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/23/2019] [Accepted: 03/25/2019] [Indexed: 12/25/2022] Open
Abstract
Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient.
Collapse
|
30
|
Takayama K, Kawai S, Obata Y, Todo H, Sugibayashi K. Prediction of Dissolution Data Integrated in Tablet Database Using Four-Layered Artificial Neural Networks. Chem Pharm Bull (Tokyo) 2017; 65:967-972. [PMID: 28966281 DOI: 10.1248/cpb.c17-00539] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
A large number of dissolution data were measured and integrated into a previously constructed tablet database composed of 14 kinds of compounds as model active pharmaceutical ingredients (APIs) with contents ranging from 10 to 80%. The database has contained physicochemical and powder properties of APIs, together with basic physical attributes of tablets such as the tensile strength and the disintegration time. In order to enhance the value of this database, drug dissolution data are essential to improving key information for designing tablet formulations. A four-layered artificial neural network (4LNN), newly implemented in commercially available software, was employed to predict dissolution data from physicochemical and powder properties of APIs. Our results showed that an excellent model for the prediction of dissolution data was achieved with 4LNN method. The function of 4LNN was appreciably better than that of conventional three-layered model, despite both models adopting the same number of nodes and algorithms for activation functions. Furthermore, linear regression models resulted in poor prediction of dissolution data.
Collapse
Affiliation(s)
- Kozo Takayama
- Department of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Josai University
| | - Shota Kawai
- Department of Pharmaceutical Sciences, Hoshi University
| | - Yasuko Obata
- Department of Pharmaceutical Sciences, Hoshi University
| | - Hiroaki Todo
- Department of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Josai University
| | - Kenji Sugibayashi
- Department of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Josai University
| |
Collapse
|
31
|
Kazemi P, Khalid MH, Pérez Gago A, Kleinebudde P, Jachowicz R, Szlęk J, Mendyk A. Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures: computational intelligence modeling and parametric analysis. DRUG DESIGN DEVELOPMENT AND THERAPY 2017; 11:241-251. [PMID: 28176905 PMCID: PMC5261554 DOI: 10.2147/dddt.s124670] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.
Collapse
Affiliation(s)
- Pezhman Kazemi
- Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Mohammad Hassan Khalid
- Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Ana Pérez Gago
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine-University, Düsseldorf, Germany
| | - Peter Kleinebudde
- Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine-University, Düsseldorf, Germany
| | - Renata Jachowicz
- Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Jakub Szlęk
- Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| | - Aleksander Mendyk
- Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland
| |
Collapse
|
32
|
Khalid MH, Tuszyński PK, Kazemi P, Szlek J, Jachowicz R, Mendyk A. Transparent computational intelligence models for pharmaceutical tableting process. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40294-016-0019-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Purpose
Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process.
Methods
This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail.
Results
CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model.
Conclusions
CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.
Collapse
|
33
|
Li Y, Abbaspour MR, Grootendorst PV, Rauth AM, Wu XY. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology. Eur J Pharm Biopharm 2015; 94:170-9. [DOI: 10.1016/j.ejpb.2015.04.028] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 04/17/2015] [Accepted: 04/27/2015] [Indexed: 12/20/2022]
|
34
|
Medarević DP, Kleinebudde P, Djuriš J, Djurić Z, Ibrić S. Combined application of mixture experimental design and artificial neural networks in the solid dispersion development. Drug Dev Ind Pharm 2015; 42:389-402. [PMID: 26065534 DOI: 10.3109/03639045.2015.1054831] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.
Collapse
Affiliation(s)
- Djordje P Medarević
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia and
| | - Peter Kleinebudde
- b Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine-University Duesseldorf , Duesseldorf , Germany
| | - Jelena Djuriš
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia and
| | - Zorica Djurić
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia and
| | - Svetlana Ibrić
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia and
| |
Collapse
|
35
|
Iwaniak A, Minkiewicz P, Darewicz M, Protasiewicz M, Mogut D. Chemometrics and cheminformatics in the analysis of biologically active peptides from food sources. J Funct Foods 2015. [DOI: 10.1016/j.jff.2015.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|
36
|
Azmir J, Zaidul I, Sharif K, Uddin M, Jahurul M, Jinap S, Hajeb P, Mohamed A. Supercritical carbon dioxide extraction of highly unsaturated oil from Phaleria macrocarpa seed. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.06.049] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
37
|
Aleksić I, Duriš J, Ilić I, Ibrić S, Parojčić J, Srčič S. In silico modeling of in situ fluidized bed melt granulation. Int J Pharm 2014; 466:21-30. [PMID: 24607215 DOI: 10.1016/j.ijpharm.2014.02.045] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 02/23/2014] [Accepted: 02/28/2014] [Indexed: 11/25/2022]
Abstract
Fluidized bed melt granulation has recently been recognized as a promising technique with numerous advantages over conventional granulation techniques. The aim of this study was to evaluate the possibility of using response surface methodology and artificial neural networks for optimizing in situ fluidized bed melt granulation and to compare them with regard to modeling ability and predictability. The experiments were organized in line with the Box-Behnken design. The influence of binder content, binder particle size, and granulation time on granule properties was evaluated. In addition to the response surface analysis, a multilayer perceptron neural network was applied for data modeling. It was found that in situ fluidized bed melt granulation can be used for production of spherical granules with good flowability. Binder particle size had the most pronounced influence on granule size and shape, suggesting the importance of this parameter in achieving desired granule properties. It was found that binder content can be a critical factor for the width of granule size distribution and yield when immersion and layering is the dominant agglomeration mechanism. The results obtained indicate that both in silico techniques can be useful tools in defining the design space and optimization of in situ fluidized bed melt granulation.
Collapse
Affiliation(s)
- Ivana Aleksić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Jelena Duriš
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Ilija Ilić
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000 Ljubljana, Slovenia
| | - Svetlana Ibrić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Jelena Parojčić
- Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Stanko Srčič
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000 Ljubljana, Slovenia
| |
Collapse
|