Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors

Abstract

Systematic identification of protein-drug interaction networks is crucial to correlate complex modes of drug action to clinical indications. We introduce a novel computational strategy to identify protein-ligand binding profiles on a genome-wide scale and apply it to elucidating the molecular mechanisms associated with the adverse drug effects of Cholesteryl Ester Transfer Protein (CETP) inhibitors. CETP inhibitors are a new class of preventive therapies for the treatment of cardiovascular disease. However, clinical studies indicated that one CETP inhibitor, Torcetrapib, has deadly off-target effects as a result of hypertension, and hence it has been withdrawn from phase III clinical trials. We have identified a panel of off-targets for Torcetrapib and other CETP inhibitors from the human structural genome and map those targets to biological pathways via the literature. The predicted protein-ligand network is consistent with experimental results from multiple sources and reveals that the side-effect of CETP inhibitors is modulated through the combinatorial control of multiple interconnected pathways. Given that combinatorial control is a common phenomenon observed in many biological processes, our findings suggest that adverse drug effects might be minimized by fine-tuning multiple off-target interactions using single or multiple therapies. This work extends the scope of chemogenomics approaches and exemplifies the role that systems biology has in the future of drug discovery.

Figure 1. The three largest clusters of the off-target network formed from their global structural similarities.

Each node in the graph represents one off-target as found in supplemental material Table S1. Two nodes are connected by an edge if their global structures are similar (measured by a CE z-score larger than 4.0).

Figure 2. Effects of Torcetrapib, Anacetrapib and JTT-705 in regulating the RAAS system through the combinational control of nuclear hormone receptors.

The red, purple, and blue lines between inhibitors and off-targets indicate strong, relatively strong, and weak binding affinities, respectively. The brown and black lines between off-targets and pathways or clinical indications represent positive and negative regulation, respectively. A. Regulation control of nuclear hormone receptors on RAAS system. B. Binding profile of Torcetrapib on nuclear hormone receptors. C. Binding profile of Anacetrapib on nuclear hormone receptors. D. Binding profile of JTT-705 on nuclear hormone receptors.

Figure 3. Effects of Torcetrapib, Anacetrapib and JTT-705 on inflammation through combinational control of nuclear hormone receptors.

The color and line schema are the same as those in Fig. 2. A. Regulation control of nuclear hormone receptors on inflammatory system. B. Binding profiles of Torcetrapib on nuclear hormone receptors. C. Binding profile of Anacetrapib on nuclear hormone receptors. D. Binding profile of JTT-705 on nuclear hormone receptors.

Figure 4. Effects of Torcetrapib, Anacetrapib and JTT-705 on cancer through combinational control of nuclear hormone receptors.

The color and line schema are the same as those in Fig. 2. A. Regulation control of nuclear hormone receptors on cancer system. B. Binding profiles of Torcetrapib on nuclear hormone receptors. C. Binding profile of Anacetrapib on nuclear hormone receptors. D. Binding profile of JTT-705 on nuclear hormone receptors.

Figure 5. The anti-proliferation effect of JTT-705 through activation of PPARα/γ and the p38 MAPK pathway.

Solid lines show relationships established with experimental evidence. Red dash lines show our hypothesis for how JTT-705 induces the activation of the p38 MAPK pathway. Color and line schema are the same as those in Fig. 2.