Targeting the off-targets: a computational bioinformatics approach to understanding the polypharmacology of nelfinavir.

In recent years, the identification of new pharmacological effects of already established or abandoned drugs has become a valuable tool for drug repositioning purposes. The HIV drug nelfinavir belongs to those drugs for which empirical data indicate additional pharmacological applications for various diseases, including cancer. To identify and confirm binding partners of nelfinavir other than HIV-1 protease, Xie et al. performed a systematic computational analysis to identify possible structural similarities between the nelfinavir-binding pocket of HIV-1 protease and 5985 protein database entries. Of 126 possible binding partners to nelfinavir, a remarkably high percentage of protein kinases were identified. Further in-depth computational ligand-binding studies indicated the EGF receptor and cytosolic protein kinase B as the most likely off-targets of nelfinavir. Astonishingly, these in silico data are in accordance with previous data obtained by experimental in vitro analysis, indicating a high predictive value of the computer-based approach developed and applied by Xie et al. The computational approach and the authors' results, with respect to their integration in systems biology, are presented and discussed.