In Silico Analysis of Pinostrobin Derivatives from Boesenbergia pandurata on ErbB4 Kinase Target and QSPR Linear Models to Predict Drug Clearance for Searching Anti-Breast Cancer Drug Candidates

Background: ErbB4 is a member of ErbB family of receptor tyrosine kinases (RTKs) and plays an important role in resistance to ErbB2 inhibitors. Objective: This study aimed to design a pinostrobin derivative with activity as an ErbB4 inhibitor and to establish a quantitative structure-property relationship (QSPR) of pinostrobin and its derivatives to predict drug clearance. Materials and Methods: In this research, an in silico study was conducted on pinostrobin and its derivatives by predicting the prediction of activity spectra for substances (PASS) with PASS online, followed by molecular docking using the AutoDockTools 4.2.6 program on ErbB4 protein kinase and visualizing the docking results using the Discovery Studio Visualizer software. While the study of QSPR pinostrobin and its derivatives was determined using physicochemical parameters with clearance (CL_tot) using SPSS. Results: From the data obtained, 5-O-2- phenylacetylpinostrobin has a high affinity for ErbB4 protein with a free energy of binding (ΔG) -10.37 kcal/mol and an inhibition constant (Ki) of 26.06 nM. Conclusion: Probability “to be active” (Pa) 5-O-2- phenylacetylpinostrobin of 0.595 for kinase inhibitors and 0.666 for apoptosis agonists, thus becoming candidates for breast cancer drugs. The QSPR model can be used to predict the properties of molecules such as CL_tot, this will be useful in the drug design process. The best QSPR regression equation for pinostrobin and its derivatives is Log (1/CL_tot) = 0.705 Log S + 0.035 MR + 0.375. This equation can be used as a reference in predicting CL_tot.