Computationally accelerated identification of P-glycoprotein inhibitors

Abstract

Overexpression of the polyspecific efflux transporter, P-glycoprotein (P-gp, MDR1, ABCB1), is a major mechanism by which cancer cells acquire multidrug resistance (MDR), the resistance to diverse chemotherapeutic drugs. Inhibiting drug transport by P-gp can resensitize cancer cells to chemotherapy. However, there are no P-gp inhibitors available to patients. Clinically unsuccessful P-gp inhibitors tend to bind at the pump's transmembrane drug binding domains and are often P-gp transport substrates, resulting in lowered intracellular concentration of the drug and altered pharmacokinetics. In prior work, we used virtual-assisted drug discovery to identify novel P-gp inhibitors that target the pump's cytoplasmic nucleotide binding domains and showed that these domains are viable drug discovery targets. Here we develop an enhanced virtual-assisted pipeline that expands upon prior work by iteratively screening compounds against multiple regions and conformations of P-gp. This increased computational complexity is offset by custom Tanimoto chemical datasets which compress the initial docking library while maximizing chemical diversity. Molecules that were similar to resultant hits were subsequently retrieved and screened. We identified nine novel P-gp inhibitors that reverse MDR in two types of P-gp overexpressing human cancer cell lines, reflecting a 14% hit rate. Of these inhibitors, all were non-toxic to non-cancerous human cells, and six were not transport substrates of P-gp. Our novel P-gp inhibitors are chemically diverse and are good candidates for lead optimization. Our results demonstrate that the nucleotide binding domains of P-gp are an underused target in the effort to reverse P-gp-mediated multidrug resistance.