The process of assessment of drug efficacy produces multivariate data which are difficult to interpret. The interpretation and extraction of relevant data requires application of chemometric algorithms for multivariate data analysis. The aim of our study was evaluation of the efficacy of local treatment with chlorhexidine (CHX) in patients suffering from periodontal disease by chemometric algorithms for multivariate data analysis. Several algorithms were used: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). The PCA models identified the examined variables as suitable for monitoring the periodontal disease progression at the same time revealing mutual relationship among them. The developed PLS-DA model successfully distinguished patients treated with CHX from non-treated patients. The OPLS-DA model revealed differences in the mechanism of action of the two widely applied treatments in periodontal disease, local administration of CHX and local administration of doxycycline (DOX). The approach presented in this study opens the possibility of application of chemometric algorithms for multivariate data analysis for assessment of treatment efficacy.
Keywords: ALP, alkaline phosphatase; AST, aspartate aminotransferase; CAL, clinical attachment loss; CHX, chlorhexidine; Chemometric algorithms; Chlorhexidine; DOX, doxycycline; GCF, gingival crevicular fluid; GI, index of gingival inflammation; IL-1β, interleukin -1 beta; LDH, lactate dehydrogenase; Multivariate data analysis; OPLS-DA, orthogonal projection to latent structures discriminant analysis; PCA, principal component analysis; PD, pocket depth; PLS-DA, partial least square discriminant analysis; Periodontal disease; TNF, α- tumor necrosis factor-alpha; Treatment efficacy; VIP, variable influence on projection.