Please login first
Obtaining QSPR models for the prediction of physicochemical properties of topical antimicrobials
* , ,
1  Institute of Pharmacy and Foods, University of Havana
Academic Editor: Ihosvany Camps


The traditional form of development and investigation of the antimicrobials has been resulting inefficient according to the delay of the new candidates discovery in the last years. Several limitations have been demonstrated, such as the long time invested, the expensive experimental trials or the errors in the manipulation of the researcher. From bottom of the problem, it is already necessary change for another form that would be more convenient and efficient satisfying the high demands of humans. Thanks to the evolution of technology at the final of XX century, the application of the computational methods in the design of drugs raised as a promised alternative. Specifically, the structure-property relationship studies are oriented to determent the function capable to predict a particular property of a compound, using the information contained in their molecular descriptors. This strategy allowed us to analyze a great quantity of molecules in a minor time and with less resources. Five specific models were defined in the present work in order to predict the interested physicochemical properties (aqueous solubility (S), coefficient of partition (P), constant of distribution (D), constant of acid dissociation (Ka) and superficial tension (σ)) for the external use only of a series of 400 antimicrobial compounds, with simplified representations, physicochemical properties and molecular descriptors were obtained through the softwares ACD-Labs and MODESLAB. After an exhaustive validation, the specific models of log P and log D demonstrated a better prediction capacity with the standard errors of estimate for the specific functions were inferior or close to the logarithmic unit. Also, the prediction coefficients were 0.849 and 0.737 respectively. The results suggest the employment of them in the design and development of antimicrobials for topical use.

Keywords: QSPR, models, prediction, antimicrobials, topical