Aim and Objective:
In this study, chemometric methods as correlation analysis, cluster
analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to
reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D)
structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11
cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of
them.
Material and Methods:
All chemometric analyses have been carried out, graphically presented and
also discussed for each group of drugs. At first, compounds’ structural and chromatographic
parameters were correlated. The best results of correlation analysis were as follows: correlation
coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36
“other” compounds, respectively. Next, part of molecular and HPLC experimental data from each
group of drugs were submitted to FA/PCA and CA techniques.
Results:
Almost all results obtained by FA or PCA, and total data variance, from all analyzed
parameters (experimental and calculated) were explained by first two/three factors: 84.28%,
76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds,
respectively. Compounds clustering by CA method had similar characteristic as those obtained by
FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely
characterized and discussed in case of their molecular structure and pharmacological activity.
Conclusion:
Proposed QSAR strategy of reduced number of parameters could be useful starting
point for further statistical analysis as well as support for designing new drugs and predicting their
possible activity.
Auto-correlation analysis of ocean surface wind vectors
