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Aljohani K. Mathematical modeling and numerical simulation of supercritical processing of drug nanoparticles optimization for green processing: AI analysis. PLoS One 2024; 19:e0309242. [PMID: 39231157 PMCID: PMC11373824 DOI: 10.1371/journal.pone.0309242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 08/07/2024] [Indexed: 09/06/2024] Open
Abstract
In recent decades, unfavorable solubility of novel therapeutic agents is considered as an important challenge in pharmaceutical industry. Supercritical carbon dioxide (SCCO2) is known as a green, cost-effective, high-performance, and promising solvent to develop the low solubility of drugs with the aim of enhancing their therapeutic effects. The prominent objective of this study is to improve and modify disparate predictive models through artificial intelligence (AI) to estimate the optimized value of the Oxaprozin solubility in SCCO2 system. In this paper, three different models were selected to develop models on a solubility dataset. Pressure (bar) and temperature (K) are the two inputs for each vector, and each vector has one output (solubility). Selected models include NU-SVM, Linear-SVM, and Decision Tree (DT). Models were optimized through hyper-parameters and assessed applying standard metrics. Considering R-squared metric, NU-SVM, Linear-SVM, and DT have scores of 0.994, 0.854, and 0.950, respectively. Also, they have RMSE error rates of 3.0982E-05, 1.5024E-04, and 1.1680E-04, respectively. Based on the evaluations made, NU-SVM was considered as the most precise method, and optimal values can be summarized as (T = 336.05 K, P = 400.0 bar, solubility = 0.00127) employing this model. Fig 4.
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Affiliation(s)
- Khalid Aljohani
- Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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2
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Alamoudi JA. Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO 2: a machine learning perspective. Front Med (Lausanne) 2024; 11:1467289. [PMID: 39286644 PMCID: PMC11402729 DOI: 10.3389/fmed.2024.1467289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 08/20/2024] [Indexed: 09/19/2024] Open
Abstract
Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.
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Affiliation(s)
- Jawaher Abdullah Alamoudi
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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3
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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4
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Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models. Sci Rep 2023; 13:1313. [PMID: 36693828 PMCID: PMC9873658 DOI: 10.1038/s41598-022-25562-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 12/01/2022] [Indexed: 01/25/2023] Open
Abstract
Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).
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Development of machine learning model and analysis study of drug solubility in supercritical solvent for green technology development. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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6
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Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent. Sci Rep 2022; 12:18875. [PMID: 36344531 PMCID: PMC9640585 DOI: 10.1038/s41598-022-21233-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).
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Alshahrani SM, Saqr AA, Alfadhel MM, Alshetaili AS, Almutairy BK, Alsubaiyel AM, Almari AH, Alamoudi JA, Abourehab MAS. Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models. Molecules 2022; 27:molecules27185762. [PMID: 36144490 PMCID: PMC9506598 DOI: 10.3390/molecules27185762] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 11/26/2022] Open
Abstract
Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10−8, 2.173 × 10−9, and 1.372 × 10−8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10−3 as an output.
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Affiliation(s)
- Saad M. Alshahrani
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
- Correspondence: (S.M.A.); (A.M.A.); (M.A.S.A.)
| | - Ahmed Al Saqr
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | - Munerah M. Alfadhel
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | - Abdullah S. Alshetaili
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | - Bjad K. Almutairy
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | - Amal M. Alsubaiyel
- Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah 52571, Saudi Arabia
- Correspondence: (S.M.A.); (A.M.A.); (M.A.S.A.)
| | - Ali H. Almari
- Department of Pharmaceutics, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia
| | - Jawaher Abdullah Alamoudi
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh 145111, Saudi Arabia
| | - Mohammed A. S. Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia 61519, Egypt
- Correspondence: (S.M.A.); (A.M.A.); (M.A.S.A.)
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Abdelbasset WK, Elkholi SM, Ahmed Ismail K, A.A.M. Alalwani T, Hachem K, Mohamed A, Agustiono Kurniawan T, Andreevna Rushchitc A. Modeling and computational study on prediction of pharmaceutical solubility in supercritical CO2 for manufacture of nanomedicine for enhanced bioavailability. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.119306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Alshehri S, Alqarni M, Namazi NI, Naguib IA, Venkatesan K, Mosaad YO, Pishnamazi M, Alsubaiyel AM, Abourehab MAS. Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug. Sci Rep 2022; 12:13106. [PMID: 35907929 PMCID: PMC9338975 DOI: 10.1038/s41598-022-17350-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/25/2022] [Indexed: 11/09/2022] Open
Abstract
These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.
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Affiliation(s)
- Sameer Alshehri
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Mohammed Alqarni
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nader Ibrahim Namazi
- Pharmaceutics and Pharmaceutical Technology Department, College of Pharmacy, Taibah University, Al Madinah Al Munawarah, 30001, Saudi Arabia
| | - Ibrahim A Naguib
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Kumar Venkatesan
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, 62529, Kingdom of Saudi Arabia
| | - Yasser O Mosaad
- Department of Pharmacy Practice and Clinical Pharmacy, Faculty Pharmacy, Future Unibversity in Egypt, New Cairo, 11835, Egypt
| | - Mahboubeh Pishnamazi
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam. .,The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam.
| | - Amal M Alsubaiyel
- Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah, 52571, Saudi Arabia
| | - Mohammed A S Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.,Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia, 61519, Egypt
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Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning. Sci Rep 2022; 12:13138. [PMID: 35908085 PMCID: PMC9338996 DOI: 10.1038/s41598-022-17440-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).
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11
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Computational prediction of drug solubility in supercritical carbon dioxide: Thermodynamic and artificial intelligence modeling. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118888] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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12
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Optimization of Heterogeneous Catalyst-assisted Fatty Acid Methyl Esters Biodiesel Production from Soybean Oil with Different Machine Learning Methods. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103915] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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13
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Tianhao Z, Sh. Majdi H, Olegovich Bokov D, Abdelbasset WK, Thangavelu L, Su CH, Chinh Nguyen H, Alashwal M, Ghazali S. Prediction of busulfan solubility in supercritical CO2 using tree-based and neural network-based methods. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118630] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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14
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Medium Gaussian SVM, Wide Neural Network and stepwise linear method in estimation of Lornoxicam pharmaceutical solubility in supercritical solvent. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118120] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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15
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Pishnamazi M, Zabihi S, Jamshidian S, Borousan F, Hezave AZ, Marjani A, Shirazian S. Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide. Sci Rep 2021; 11:1075. [PMID: 33441880 PMCID: PMC7807078 DOI: 10.1038/s41598-020-80399-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 12/21/2020] [Indexed: 11/22/2022] Open
Abstract
Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10–05 and 1.07 × 10–03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308–338 K and 12–40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.
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Affiliation(s)
- Mahboubeh Pishnamazi
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.,The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam
| | - Samyar Zabihi
- Department of Process Engineering, Research and Development Department, Shazand-Arak Oil Refinery Company, Arak, Iran
| | - Sahar Jamshidian
- Environment, Development and Sustainability Department, Shadram Company, Arak, Iran
| | - Fatemeh Borousan
- Department of Chemistry, Yasouj University, Yasouj, 75914-353, Iran.,Incubation Centre of Science and Technology Park, Fanavari Atiyeh Pouyandegan Exir Company, Arak, 381314-3553, Iran.,Incubation Centre of Science and Technology Park, Fanavari Arena Exir Sabz Company, Arak, 381314-3553, Iran
| | - Ali Zeinolabedini Hezave
- Incubation Centre of Science and Technology Park, Fanavari Atiyeh Pouyandegan Exir Company, Arak, 381314-3553, Iran.,Incubation Centre of Science and Technology Park, Fanavari Arena Exir Sabz Company, Arak, 381314-3553, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, Chelyabinsk, Russia, 454080
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