Development of Predictive Quantitative Structure-Activity Relationship Models of Epipodophyllotoxin Derivatives

Abstract

Epipodophyllotoxins are the most important anticancer drugs used in chemotherapy for various types of cancers. To further, improve their clinical efficacy a large number of epipodophyllotoxin derivatives have been synthesized and tested over the years. In this study, a quantitative structure-activity relationship (QSAR) model has been developed between percentage of cellular protein-DNA complex formation and structural properties by considering a data set of 130 epipodophyllotoxin analogues. A systematic stepwise searching approach of zero tests, missing value test, simple correlation test, multicollinearity test, and genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r( train)2 = 0.721; q cv2 = 0.678) was obtained with descriptors such as solvent-accessible surface area, heat of formation, Balaban index, number of atom classes, and sum of E-state index of atoms. The robustness of the QSAR models was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r( test)2 for the test set. The root mean square error between the experimental and predicted percentage of cellular protein–DNA complex formation (PCPDCF) was 0.194 and r( test)2 = 0.689, revealing good predictability of the QSAR model.

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“…The Q 2 value of all the hypothesis are greater than 0.55, F-test values are very high. High is necessary, the best QSAR model should be chosen based on its predictive ability, so the best model should have high Q 2 value also [24].…”

“…A large F indicates that the model fit is not a chance occurrence. It has been shown that a high value of statistical characteristics is not necessary for the proof of a highly predictive model [24]. Hence, in order to evaluate the predictive ability of our QSAR model the values of the of the correlation coefficient of predicted and actual activities and the correlation coefficient for regressions through the origin (predicted vs. actual activities and vice versa) were calculated and a score value of 0.76 for the test set and a score value of 0.86 for the training set were obtained, which is similar to [23].…”

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“…The multi-target model based on Balaban index (J), a LUMO parameter, and second order valence connectivity index ( 2 χ v ) was very useful to describe the antimicrobial activity of synthesized compounds. Naik, Dubey, and Kumar [31], developed new predictive models for epipodophyllotoxin derivatives. Epipodophyllotoxins are important anticancer drugs used in chemotherapy for various types of cancers.…”