Exploring a structural protein-drug interactome for new therapeutics in lung cancer

Abstract

The pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy.

Figure 1

Physico-chemical properties of binding pockets on cancer targets and non-cancer targets. (A, G) Solvent-accessible surface area (SASA). (D, J) Volume of the cavities identified in targets. (B, H) Distribution of aromatic; (E, K) aliphatic; (C, I) hydrogen-bond acceptors; and (F, L) hydrogen bond donors within binding pockets.

Figure 2

Physico-chemical properties of the cancer drugs, non-cancer drugs and non-drug compounds using 0.1 μM binding threshold. (A) Compound rotatable bonds versus number of targets. (B) Distribution of rotatable bonds among NCI compounds, non-cancer and cancer drugs. (C) Logarithm of the partition coefficient (cLogP) vs. number of cavities. (D) Distribution of cLogP among NCI compounds, non-cancer and cancer drugs. (E) Ratio of cancer to non-cancer targets of cancer drugs and non-cancer drugs. (F) Distribution of ratio of targets over non-targets among for NCI compounds, non-cancer drugs, and cancer drugs.

Figure 3

Drug-target networks for cancer drugs and non-cancer drugs. (A) Overview of drug-target networks. Each node represents a drug and two nodes are linked if they share a target. (B) Degree and betweenness of the drugs. Degree of a node is defined as the number of edges connected to the node. Betweenness of a node is defined as the number of non-redundant shortest pathways going through each node.

Figure 4

Kaplan-Meier time to cancer occurrence curves of patients with (green curve) or without (red curve) losartan (A) and ergotamine (B). Time to cancer occurrence was measured from occurrence of 1st hypertension diagnosis to 1st diagnosis of lung cancer (A) and from occurrence of 1st migraine pain diagnosis to 1st diagnosis of any of the major cancer types (B).

Figure 5

Effects of losartan and astemizole on tumor growth in an H460 NSCLC xenograft mouse model.