QSAR of Antiproliferative Activity of N‐Substituted 2‐Amino‐5‐(2,4‐dihydroxyphenyl)‐1,3,4‐thiadiazoles in Various Human Cancer Cells

Abstract

Antiproliferative activities in vitro against some human cancer cell lines of N-substituted 2-amino-5-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles were correlated with their structuredependent properties. Phase affinity: lipophilicity and phospholipophilicity were determined by Reversed-Phase Overpressured Layer Chromatography (RP-8 OPLC) and Immobilized Artificial Membrane (IAM) High-Performance Liquid Chromatography (HPLC), respectively. Electronic and steric descriptors were calculated by computational methods. The results indicate that phospholipophilicity and electronic properties expressed by partial charge of atoms of 2-amino-1,3,4-thiadiazole moiety are the main factors that influence the antiproliferative effects of studied compounds. The Quantitative Structure -Activity Relationship (QSAR) equations obtained by the Multiple Linear Regression (MLR) analysis predict activity of compounds of high and average effects precisely and can be used in screening of new potential antiproliferative analogues of 2-amino-5-(2,4-dihydroxyphenyl)-1,3,4-thiadiazole set. At the same time, these dependences as well as Quantitative Activity -Activity Relationship (QAAR) equations point to some differences in the individual human cancer cell lines studied.

Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization

“…In the specific problem of QSAR analysis for cancer drugs, multivariate linear regression, PLS, PCR, kernel methods, and neural network type methods have been widely applied. Linear approaches are most prominent in QSAR analysis. , Multivariate linear regression has been used in numerous studies − including analysis of antiproliferative activity of compounds in human cancer cells . A major challenge in the analysis is that structural descriptors can be highly correlated, and the number of descriptors is large, often exceeding the number of chemical compounds available for training.…”

“…The method goes beyond the existing work in this application field in two main aspects (Figure ). First, multiple cancer cell lines are integrated into one model, instead of building separate models for individual cell types , or averaging across the cancer cell lines . This results in an integrative QSAR prediction, i.e., the columns of the drug response matrix in Figure are predicted together (not one by one independently), making it possible to utilize their commonalities.…”

“…6,8 Multivariate linear regression has been used in numerous studies 10−12 including analysis of antiproliferative activity of compounds in human cancer cells. 13 A major challenge in the analysis is that structural descriptors can be highly correlated, and the number of descriptors is large, often exceeding the number of chemical compounds available for Figure 1. Two complementary approaches of exploiting high-throughput drug screening data from cancer cell lines.…”

NMR QSAR Model for the Analysis of 4‐(5‐Arylamino‐1,3,4‐thiadiazol‐2‐yl)benzene‐1,3‐diols

“…ÃÃ level also showed an important role of the atomic net charge of the amine-nitrogen atom and carbon atoms of the 1,3,4-thiadiazole ring [22] but in this case, better cross-validated correlations were obtained. The presented models confirm a key role of heteroatoms in the interaction with the molecular target.…”

Synthesis of Novel 2-Amino-(2-thioxo-3-alkyl-4-methyl-3H-thiazol-5-yl)-[1,3,4]thiadiazole Derivatives and Their Growth Stimulant Activity

“…Among the amino, alkylamino and acetylamino thiazole, and thiadiazole derivatives, the new substances with antitubercular , antidepressant and sedative , anticonvulsant , antitumor, teratogenic , anesthetic , antihistaminic and spasmolytic , antimicrobial and anti‐inflammatory , as well as antifungal , herbicidal , and acaricidal activities are discovered.…”