The modern analytical instruments furnish an enormous volume of high-quality measurements, which in turn generates huge data volumes. Powerful mathematical and statistical methods are consequently being called upon to extract just the useful information from this wide amount of data. In recent years, chemometric techniques are playing a very important role in the analysis concerning the pharmaceutical field. Multivariate methods allow to select the most useful analytical information allowing to give rapid analytical responses with reasonable accuracy and precision, minimizing at the same time the sample preparation. By chemometric tools it is possible to develop mathematical models to be applied in design, analysis or control of drugs or pharmaceutical formulations. Design of Experiments (DoE), Principal Component Analysis (PCA), Partial Least Squares (PLS) and Artificial Neuron Network (ANN) have in fact become routine applications in research or development of new drugs. The relevant results obtained in pharmaceutical analysis are above all due to the ability of these techniques in detecting and quantifying all the variable interactions and therefore in increasing the overall system knowledge. DoE has imposed itself as a powerful tool to quantify the effect of one or more variables on a set of measured responses. It provides a methodological framework for changing simultaneously operating parameters by involving a number of experiments as low as possible so to provide maximum information. PCA, PLS and ANN have been moreover combined each other or to other calibration methods to efficiently reduce and interpret complex data systems. In our laboratory, multivariate analysis is applied on spectrophotometric data from very complex mixtures. In contrast with the conventional methods, based on the use of univariate variables, the multivariate techniques show a very powerful resolution, using absorbances from the full spectrum or large wavelength regions1-3. In another research line, the Multivariate Curve Resolution (MCR) technique is applied on UV data to clarify the degradation kinetics of photolabile drugs4. The chemometric methods can be used in drug design, when it is very important to predict the properties of new molecules. For example, Quantitative Structure Property Relationships (QSPR) techniques help to establish correlations between the molecular structure and chemical or chemical-physics properties of a series of compounds. We are trying to define a QSPR model to correlate the oxidation rate of the 1,4-dihydropyridine antihypertensives with their chemical structure and then to apply this model to design new structures with a high photostability. 1. M.De Luca, F.Oliverio, G.Ioele, G.Ragno, Chemom. Intell. Lab. Syst. 2008 ( in press) 2. E.Dinc¸ D.Baleanu, G.Ioele, M.De Luca, G.Ragno, J. Pharm. Biomed. Anal. 2008 ( in press) 3. M.De Luca, G.Ioele, A.Risoli, G.Ragno, Microchemical J. 83 (2006) 24-34 4. A.de Juan, R.Tauler, Crit. Rev. Anal. Chem. 36 (2006) 163-176

Application of the multivariate analytical techniques in pharmaceutical chemistry

DE LUCA M;IOELE, Giuseppina;RAGNO, Gaetano
2009

Abstract

The modern analytical instruments furnish an enormous volume of high-quality measurements, which in turn generates huge data volumes. Powerful mathematical and statistical methods are consequently being called upon to extract just the useful information from this wide amount of data. In recent years, chemometric techniques are playing a very important role in the analysis concerning the pharmaceutical field. Multivariate methods allow to select the most useful analytical information allowing to give rapid analytical responses with reasonable accuracy and precision, minimizing at the same time the sample preparation. By chemometric tools it is possible to develop mathematical models to be applied in design, analysis or control of drugs or pharmaceutical formulations. Design of Experiments (DoE), Principal Component Analysis (PCA), Partial Least Squares (PLS) and Artificial Neuron Network (ANN) have in fact become routine applications in research or development of new drugs. The relevant results obtained in pharmaceutical analysis are above all due to the ability of these techniques in detecting and quantifying all the variable interactions and therefore in increasing the overall system knowledge. DoE has imposed itself as a powerful tool to quantify the effect of one or more variables on a set of measured responses. It provides a methodological framework for changing simultaneously operating parameters by involving a number of experiments as low as possible so to provide maximum information. PCA, PLS and ANN have been moreover combined each other or to other calibration methods to efficiently reduce and interpret complex data systems. In our laboratory, multivariate analysis is applied on spectrophotometric data from very complex mixtures. In contrast with the conventional methods, based on the use of univariate variables, the multivariate techniques show a very powerful resolution, using absorbances from the full spectrum or large wavelength regions1-3. In another research line, the Multivariate Curve Resolution (MCR) technique is applied on UV data to clarify the degradation kinetics of photolabile drugs4. The chemometric methods can be used in drug design, when it is very important to predict the properties of new molecules. For example, Quantitative Structure Property Relationships (QSPR) techniques help to establish correlations between the molecular structure and chemical or chemical-physics properties of a series of compounds. We are trying to define a QSPR model to correlate the oxidation rate of the 1,4-dihydropyridine antihypertensives with their chemical structure and then to apply this model to design new structures with a high photostability. 1. M.De Luca, F.Oliverio, G.Ioele, G.Ragno, Chemom. Intell. Lab. Syst. 2008 ( in press) 2. E.Dinc¸ D.Baleanu, G.Ioele, M.De Luca, G.Ragno, J. Pharm. Biomed. Anal. 2008 ( in press) 3. M.De Luca, G.Ioele, A.Risoli, G.Ragno, Microchemical J. 83 (2006) 24-34 4. A.de Juan, R.Tauler, Crit. Rev. Anal. Chem. 36 (2006) 163-176
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/161048
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