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Valaee M, Sohrabi MR, Motiee F. Rapid simultaneous analysis of anti human immunodeficiency virus drugs in pharmaceutical formulation by smart spectrophotometry based on multivariate calibration and least squares support vector machine methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122292. [PMID: 36608513 DOI: 10.1016/j.saa.2022.122292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
In this study, two chemometrics methods, including partial least squares regression (PLS) and least squares support vector machine (LS-SVM) were applied for the simultaneous determination of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. These approaches along with the spectrophotometric method were used to solve spectral overlapping problems between mentioned components. The results of PLS showed that the number of components for ZDV and LMV were 10 and 10 with mean square prediction error (MSPE) of 0.4045 and 2.1189, respectively. This method revealed recoveries ranging from 99.48% to 100.40% and 99.55% to 101.25% for ZDV and LMV, respectively. By applying leave-one-out cross-validation (LOO-CV), γ (regularization parameter) and σ2 (width of the function) values were found to be 50, 1500 and 210, 20 with root mean square error (RMSE) of 0.6156 and 0.3163 for ZDV and LMV, respectively. The mean recoveries obtained by the LS-SVM were 100.82% and 98.93% for ZDV and LMV, respectively. A comparison between the suggested methods and high-performance liquid chromatography (HPLC) as a reference technique was implemented, which did not show a significant difference. The results obtained in this research revealed that the chemometrics approaches can be efficient, simple, inexpensive, and precise for routine analysis and quality control of the drug.
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Affiliation(s)
- Masoumeh Valaee
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahmoud Reza Sohrabi
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Fereshteh Motiee
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
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Lanthanum doped zirconium oxide-nanocomposite as sensitive electrochemical platforms for Tenofovir detection. Microchem J 2022. [DOI: 10.1016/j.microc.2022.108053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Kim JY, Choi DH. Control Strategy for Excipient Variability in the Quality by Design Approach Using Statistical Analysis and Predictive Model: Effect of Microcrystalline Cellulose Variability on Design Space. Pharmaceutics 2022; 14:2416. [PMID: 36365234 PMCID: PMC9696966 DOI: 10.3390/pharmaceutics14112416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 09/24/2023] Open
Abstract
Although various quality by design (QbD) approaches have been used to establish a design space to obtain robust drug formulation and process parameters, the effect of excipient variability on the design space and drug product quality is unclear. In this study, the effect of microcrystalline cellulose (MCC) variability on drug product quality was examined using a design space for immediate-release tablets of amlodipine besylate. MCC variability was assessed by altering the manufacturer and grade. The formulation was developed by employing the QbD approach, which was optimized using a D-optimal mixture design. Using 36 different MCCs, the effect of MCC variability on the design space was assessed. The design space was shifted by different manufacturers and grades of MCC, which resulted in associations between the physicochemical properties of MCC and critical quality attributes (CQAs). The correlation between the physicochemical properties of MCCs and CQAs was assessed through a statistical analysis. A predictive model correlating the physicochemical properties of MCCs with dissolution was established using an artificial neural network (ANN). The ANN model accurately predicted dissolution with low absolute and relative errors. The present study described a comprehensive QbD approach, statistical analysis, and ANN to comprehend and manage the effect of excipient variability on the design space.
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Affiliation(s)
| | - Du Hyung Choi
- Department of Pharmaceutical Engineering, Inje University, Gimhae-si 621-749, Gyeongnam, Korea
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State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics 2022; 14:pharmaceutics14010183. [PMID: 35057076 PMCID: PMC8779224 DOI: 10.3390/pharmaceutics14010183] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 11/30/2022] Open
Abstract
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
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Quality-by-design in pharmaceutical development: From current perspectives to practical applications. ACTA PHARMACEUTICA (ZAGREB, CROATIA) 2021; 71:497-526. [PMID: 36651549 DOI: 10.2478/acph-2021-0039] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/23/2020] [Indexed: 01/19/2023]
Abstract
Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.
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Shanbhogue H M, Thirumaleshwar S, Kumar Tm P, Kumar S H. Artificial Intelligence in Pharmaceutical Field - A Critical Review. Curr Drug Deliv 2021; 18:1456-1466. [PMID: 34139981 DOI: 10.2174/1567201818666210617100613] [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: 10/07/2020] [Revised: 04/09/2021] [Accepted: 04/17/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mimic the human thought process. As drug development involves high R & D costs and uncertainty in time consumption, artificial intelligence can serve as one of the promising solutions to overcome all these demerits. Due to the availability of enormous data, there are chances of missing out on some crucial details. For solving these issues, algorithms like machine learning, deep learning, and other expert systems are being used. On successful implementation of AI in the pharmaceutical field, the delays in drug development, and failure at the clinical and marketing level can be reduced. This review comprises information regarding the development of AI, its subfields, its overall implementation, and its application in the pharmaceutical sector and provides insights on challenges and limitations concerning AI.
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Affiliation(s)
- Maithri Shanbhogue H
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Shailesh Thirumaleshwar
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Pramod Kumar Tm
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Hemanth Kumar S
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
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Kapourani A, Valkanioti V, Kontogiannopoulos KN, Barmpalexis P. Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy. INTERNATIONAL JOURNAL OF PHARMACEUTICS-X 2020; 2:100064. [PMID: 33354666 PMCID: PMC7744708 DOI: 10.1016/j.ijpx.2020.100064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 12/11/2022]
Abstract
The objective of the present study was to evaluate the use of artificial neural networks (ANNs) in the development of a new chemometric model that will be able to simultaneously distinguish and quantify the percentage of the crystalline and the neat amorphous drug located within the drug-rich amorphous zones formed in an amorphous solid dispersion (ASD) system. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy was used, while Rivaroxaban (RIV, drug) and Soluplus® (SOL, matrix-carrier) were selected for the preparation of a suitable ASD model system. Adequate calibration and test sets were prepared by spiking different percentages of the crystalline and the amorphous drug in the ASDs (prepared by the melting - quench cooling approach), while a 24 full factorial experimental design was employed for the screening of ANN's structure and training parameters as well as spectra region selection and data preprocessing. Results showed increased prediction performance, measured based on the root mean squared error of prediction (RMSEp) for the test sample, for both the crystalline (RMSEp (crystal) = 0.86) and the amorphous (RMSEp (amorphous) = 2.14) drug. Comparison with traditional regression techniques, such as partial least square and principle component regressions, revealed the superiority of ANNs, indicating that in cases of high structural similarity between the investigated compounds (i.e., the crystalline and the amorphous forms of the same compound) the implementation of more powerful/sophisticated regression techniques, such as ANNs, is mandatory.
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Affiliation(s)
- Afroditi Kapourani
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Vasiliki Valkanioti
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Konstantinos N Kontogiannopoulos
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.,Ecoresources P.C., 15-17 Giannitson-Santaroza Str., Thessaloniki 54627, Greece
| | - Panagiotis Barmpalexis
- Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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Valizadeh M, Sohrabi MR, Motiee F. The application of continuous wavelet transform based on spectrophotometric method and high-performance liquid chromatography for simultaneous determination of anti-glaucoma drugs in eye drop. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 242:118777. [PMID: 32801022 DOI: 10.1016/j.saa.2020.118777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/22/2020] [Accepted: 07/29/2020] [Indexed: 06/11/2023]
Abstract
In this study, a fast, low-cost, accurate, and precise spectrophotometric method based on the continuous wavelet transform (CWT) was assayed to determine dorzolamide (DOR) and timolol (TIM) in an eye drop sample simultaneously. Different wavelet families were investigated to select the best family for analyzing the DOR and TIM. The Mexican hat wavelet (MHW) family with the wavelength of 281 nm and Gaussian wavelet family (gaus2) in the wavelength of 267 nm were found for the simultaneous analysis of DOR and TIM, respectively. Mean recovery values of synthetic mixtures were found 97.44%±2.63 and 99.18%±4.00 for DOR and TIM, respectively. The root mean square errors (RMSE) of DOR and TIM were achieved 0.5550 and 0.3306, respectively. Eye drop as a real sample was analyzed by spectrophotometry coupled with the CWT technique, as well as high-performance liquid chromatography (HPLC) as a reference method. The obtained results were compared with each other by the one-way analysis of variance (ANOVA) test and there was no significant difference between them.
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Affiliation(s)
- Maryam Valizadeh
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahmoud Reza Sohrabi
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Fereshte Motiee
- Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
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Roberto de Alvarenga Junior B, Lajarim Carneiro R. Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs. Molecules 2019; 24:E3804. [PMID: 31652589 PMCID: PMC6833076 DOI: 10.3390/molecules24203804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 02/03/2023] Open
Abstract
Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.
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