SUPPORT RELATED DIFFERENTIAL IMPACT OF SUBSTITUENTS ON PERFORMANCE OF (ALKOXY-PHENYL)BENZAMIDES IN NORMAL PHASE TLC

• Published in: Journal of liquid chromatography & related technologies Vol. 36; no. 17; pp. 2363 - 2377
• Main Authors: Oros, G., Cserháti, T.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 21.10.2013; Taylor & Francis Ltd
• Subjects: adsorption; Aluminum oxide; benzanilide; canonic correlation; Chromatography; Computation; Correlation analysis; Derivatives; Impact analysis; Mathematical analysis; Mathematical models; QSSR; reduce variables; Silicon dioxide; Studies; thin layer chromatography
• DOI: 10.1080/10826076.2013.790762
• ISSN: 1082-6076

The performance of 14 (alkoxy-phenyl)benzamide derivatives was studied on silica and alumina surfaces by normal phase TLC. The impact of substituent of benzanilide moiety on retention of analytes was determined by Free Wilson Analysis. It was found that the introduction of the methyl group in ortho position on benzyl submoiety, as well as alkoxy group in ortho position on anilide submoiety, altered the retention of analytes by a significantly different manner on two stationary phases; meanwhile, this influence in meta position depended on the size of the alkoxy group. The substitution in para position on anilide submoiety influenced the retention on silica and alumina surfaces by a similar manner. Weighting the impact of the substituent on retention of benzanilides, the canonical correlation analysis was successfully applied for reduction of the number of variables without losing relevant information. The relationships between measured retention parameters of analytes and computed molecular descriptors were calculated by linear regression analysis. It was established that the computed molecular descriptors in 78 of 3346 of cases correlated with measured retention parameters (P = 0.1-10%). This finding indicates that further developments are requested for the use of in silico descriptors in Quantitative Structure Retention Relationship (QSRR) studies to predict expected physicochemical properties of this type of molecules.