QSRR models for potential local anaesthetic drugs using high performance liquid chromatography

Abstract

Quantitative structure-retention relationships (QSRR) were proposed for Separon SGX C18 and Separon SGX Phenyl columns using physico-chemical molecular descriptors for the compounds, which are potential local anaesthetic drugs. Chemometrical methods were used for the QSRR studies of the HPLC retention factor k of 59 esters of alkoxyphenylcarbamic acid, which exhibit surface and/or infiltration anaesthetic activity. Four separation systems were used: phenyl column and acetonitrile/water mobile phase, phenyl column and methanol/water mobile phase, C18 column and acetonitrile/water mobile phase, and C18 column and methanol/water mobile phase. The values of logP and logS and ¹³C and ¹H NMR chemical shifts were simulated and utilized in calculating the corresponding QSRR models and predicting the retention factors by artificial neural networks (ANN). In addition, principal component analysis and cluster analysis were used for a closer characterization of alkoxyphenylcarbamic acid esters. The proposed ANN models, based on optimally selected species descriptors, showed a high degree of correlation between k predicted and k measured. The intercepts and the slopes of the obtained dependences were close to the theoretically expected values of 0 and 1, respectively.