Abstract Title
Using Computational Methods to Design Pyrazolopyrimidines as Potential Inhibitors of an Inflammatory Cytokine
Additional Funding Sources
The project described was supported by the Pacific Northwest Louis Stokes Alliance for Minority Participation through the National Science Foundation under Award No. HRD-1410465.
Abstract
Inflammatory cytokines (ICs) are signaling proteins and certain ICs have been shown to worsen inflammatory conditions, especially in breast cancer, where IC signaling has been shown to promote metastasis of primary tumors. This has led to significant interest in targeting ICs with small molecule drugs for the prevention and treatment of metastatic breast cancer. Classical drug discovery is a costly and time-consuming effort, often relying on inefficient screening methods and the synthesis of hundreds of compounds to identify drug candidates. Using a rational design approach and knowledge of inhibitors previously investigated in our lab, a series of pyrazolo[3,4-d]pyrimidine compounds have been designed as potential inhibitors of an IC. The goal of this work is to utilize computational chemistry to optimize the designed compounds for increased potency and desirable drug-like properties. Using a docking approach enabled by AutoDock Vina and automated ADME calculations using SwissADME, binding affinity and druglike properties can be predicted, circumventing the need for more costly methods. Thus far, a focused library of such compounds have been investigated, one of them with predicted binding energy as low as -8.0 kcal/mol and good druglikeness. These and other results are described herein.
Using Computational Methods to Design Pyrazolopyrimidines as Potential Inhibitors of an Inflammatory Cytokine
Inflammatory cytokines (ICs) are signaling proteins and certain ICs have been shown to worsen inflammatory conditions, especially in breast cancer, where IC signaling has been shown to promote metastasis of primary tumors. This has led to significant interest in targeting ICs with small molecule drugs for the prevention and treatment of metastatic breast cancer. Classical drug discovery is a costly and time-consuming effort, often relying on inefficient screening methods and the synthesis of hundreds of compounds to identify drug candidates. Using a rational design approach and knowledge of inhibitors previously investigated in our lab, a series of pyrazolo[3,4-d]pyrimidine compounds have been designed as potential inhibitors of an IC. The goal of this work is to utilize computational chemistry to optimize the designed compounds for increased potency and desirable drug-like properties. Using a docking approach enabled by AutoDock Vina and automated ADME calculations using SwissADME, binding affinity and druglike properties can be predicted, circumventing the need for more costly methods. Thus far, a focused library of such compounds have been investigated, one of them with predicted binding energy as low as -8.0 kcal/mol and good druglikeness. These and other results are described herein.