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Nano-particles, characterized by ultrafine dimensions measured in nanometers (nm; 1 nm = 10-9 meters), are ubiquitous in nature due to human activities. These ultramicroscopic entities exhibit unique materialistic properties, leading to their widespread applications in various fields, including medicine, engineering, catalysis, and environmental remediation.
Nano-particles are exceptionally diverse, with variations in size, shape, and materialistic properties. They can be categorized into organic and inorganic nano-particles, with the former group encompassing dendrimers, liposomes, and polymeric nano-particles, while the latter includes fullerenes, quantum dots, and gold nano-particles.
Additionally, nano-particles can be classified based on their composition, such as carbon-based, ceramic, semi-conducting, or polymeric nano-particles. Classification criteria often depend on their intended applications, whether in diagnostics, therapy, or other fields, as well as the methods used for their synthesis.
Optimization is a fundamental process used in computer science and physics to identify the most suitable value for a given function within a predefined domain. In the context of nanosuspensions of antidiabetic drugs, optimization plays a crucial role in achieving desirable outcomes.
Consider a function f(x) defined within a domain of real numbers set A.
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The goal of optimization is to find the maximum or minimum optimal solution within set A. There are three general methods for optimizing a function:
Design of experiments (DOE) is a vital branch of applied statistics that encompasses the planning, execution, analysis, and interpretation of controlled experiments. DOE is instrumental in assessing the factors that influence the value of a parameter or a group of parameters.
It offers a powerful approach for data collection and analysis in a wide range of experimental scenarios.
DOE enables the manipulation of multiple input factors simultaneously, allowing for the exploration of their combined effects on a desired output or response. This is particularly valuable as it can reveal important interactions that may be overlooked when studying one factor at a time. DOE can be conducted using a full factorial approach, where all possible combinations are investigated, or a fractional factorial approach, where only a portion of possible combinations is explored.
Key concepts in designing experiments include blocking, randomization, and replication:
Quality by Design (QbD) is a systematic and science-based approach to drug development that prioritizes predefined objectives, emphasizing the understanding and control of both product and process. This approach, rooted in quality risk management and sound scientific principles, aims to ensure the supply of safe and effective drugs to consumers while enhancing manufacturing quality performance.
QbD offers several notable advantages:
In this study, a comprehensive evaluation of nanosuspensions of various antidiabetic drugs was conducted using different experimental designs. The table below summarizes the details of each drug, the design employed, and the independent and dependent variables studied:
| Drug Used | Design Employed | Independent Variables | Dependent Variables | Reference |
|---|---|---|---|---|
| Furosemide | Full factorial experimental design (4 Factors and 2 Levels) | Stirring time, Injection rate, antisolvent: solvent ratio & stabilizer: drug ratio | Particle size, polydispersity index | Mohammad H. Shariare et al., Saudi Pharmaceutical Journal, Volume 27, Issue 1, January 2019, Pages 96-105 |
| Ibuprofen | 2^2 full factorial design | Milling time, solvent to antisolvent ratio | Mean particle size, polydispersity index | A.R. Fernandes et al., Saudi Pharmaceutical Journal, Volume 25, Issue 8, December 2017, Pages 1117-1124 |
| Febuxostat | Central composite design | Bead volume, milling time, polymer and surfactant concentration | Particle size, polydispersity index (PDI), zeta potential | Bhupesh K. Ahuja et al., International Journal of Pharmaceutics, Volume 478, Issue 2, 30 January 2015, Pages 540-552 |
| Dihydroartemisinin | Central composite rotatable design | Various factors | Particle size, polydispersity index | Xiaoyun Zhang et al., Powder Technology, Volume 197, Issues 1–2, 10 January 2010, Pages 120-128 |
| Diacerein | 3^2 full factorial design | Various factors | Particle size | Khyati Kamleshkumar Parekh et al., International Journal of Pharmaceutical Sciences and Research, 2017 |
| Olmesartan medoxomil | Media milling technique | Various factors | Particle size, zeta potential, saturation solubility, dissolution rate | Hetal Paresh Thakkar et al., J Pharm Bioallied Sci. 2011 Jul-Sep; 3(3): 426–434 |
| Glimepiride | Full factorial design | Various factors | Maximum plasma concentration, particle size distribution, zeta potential, polydispersity index, entrapment efficiency | Sarita Kumari et al., AAPS PharmSciTech, December 2012, Volume 13, Issue 4, pp 1031–1044 |
| Sitagliptin | Various factors | Eudragit RL100 concentration (%), Tween 80 concentration (%) and sonication time | Particle size (nm), drug loading (%) and in-vitro drug release (%) | Mohammed Asadullah Jahangir et al., An International Journal, Volume 46, 2018 |
| Polypeptide-k | Box Behnken design (Quality by Design) | Various factors | Moisture content, solubility, product yield, angle of repose | Puneet Kaur Sachin Kumar Singh et al., Powder Technology, Volume 284, November 2015, Pages 1-11 |
| Lacidipine | Box Behneken Design | Various factors | Dissolution rate, particle size reduction, decreased crystallinity | Mohamed A. A. Kassem et al., AAPS PharmSciTech, May 2017, Volume 18, Issue 4, pp 983–996 |
| Glycyrrhizin | 3^2 factorial method | Various factors | Particle size, encapsulation efficiency, stability, chemical interactions | Rum Rania Shakti Dahiya et al., European Journal of Pharmaceutical Sciences, Volume 106, 30 August 2017, Pages 220-230 |
| Ficus religiosa | Central composite design | Various factors | Particle size, polydispersity index (PDI) and zeta potential, entrapment efficiency, surface morphology | Karunanidhi Priyanka et al., Journal of Drug Delivery Science and Technology, Volume 43, February 2018, Pages 94-102 |
| Losartan Potassium | Box Behnken 3^3 experimental design | Various factors | Encapsulation efficiency, drug release | Pritam Patil et al., Chemical Engineering Research and Design, Volume 104, December 2015, Pages 98-109 |
In conclusion, the evaluation and preparation of nanosuspensions for antidiabetic drugs represent a vital area of research with significant implications for improving drug formulations and enhancing bioavailability. Through systematic optimization, rigorous design of experiments, and adherence to Quality by Design principles, researchers can unlock the potential of nanotechnology in the field of medicine. The diverse range of antidiabetic drugs and experimental approaches showcased in this report highlights the importance of tailored strategies to achieve optimal results in nanosuspension development.
Evaluation and Preparation of Nanosuspensions of Antidiabetic Drugs. (2024, Jan 12). Retrieved from https://studymoose.com/document/evaluation-and-preparation-of-nanosuspensions-of-antidiabetic-drugs
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