Structure-Guided Design of G-Protein-Coupled Receptor Polypharmacology

Abstract

Many diseases are polygenic and can only be treated efficiently with drugs that modulate multiple targets. However, rational design of compounds with multi-target profiles is rarely pursued because it is considered too difficult, in particular if the drug must enter the central nervous system. Here, a structure-based strategy to identify dual-target ligands of G-protein-coupled receptors is presented. We use this approach to design compounds that both antagonize the A_2A adenosine receptor and activate the D₂ dopamine receptor, which have excellent potential as antiparkinson drugs. Atomic resolution models of the receptors guided generation of a chemical library with compounds designed to occupy orthosteric and secondary binding pockets in both targets. Structure-based virtual screens identified ten compounds, of which three had affinity for both targets. One of these scaffolds was optimized to nanomolar dual-target activity and showed the predicted pharmacodynamic effect in a rat model of Parkinsonism.

Keywords: Parkinson's disease; drug design; polypharmacology; receptors; virtual screening.

Figure 1

Design of virtual chemical library. a) Sequence alignment of the orthosteric binding sites of the A_2AAR and D₂R. Only one out of 12 residues (marked yellow) is the same in both pockets. b) A virtual chemical library was constructed guided by the receptor binding sites. Compounds were designed to target both the orthosteric binding pocket (OBP) and a secondary binding pocket (SBP). Key binding site residues are shown as circles (negatively charged Glu169 and Asp114 in red, and Asn253 in yellow). c) An N‐methyl‐2‐aminoindane (1) scaffold was used as the core fragment of the virtual library, which was connected to building blocks using two reactions (amide coupling and Buchwald‐Hartwig amination).

Scheme 1

Synthesis of compounds 2–11. Reagents and conditions: a) N‐aryl‐5‐bromopentanamides, K₂CO₃, DMF, RT, overnight, 6–65 % (HPLC); b) ethyl 5‐bromopentanoate, K₂CO₃, DMF, RT, overnight, 70 %; c) KOH, MeOH, 85 %; d) 1H‐imidazo[4,5‐b]pyridin‐2‐amine, HATU, DIEA, DMF/DCM, RT, overnight, 22 % (HPLC); e) 4‐bromobutanenitrile, K₂CO₃, CH₃CN, RT, overnight, 24 %; f) LiAlH₄, Et₂O, 1 h, 82 % (HPLC); g) aryl chlorides, K₂CO₃, CH₃CN, 70–160 °C, 1–4 h, 40–51 % (HPLC) for 3′, 4, and 9; h) aryl chloride or bromide, CuI, 1,10‐phenantroline, K₂CO₃, DMF, 120 °C, 48 h, 2–28 % (HPLC) for 10 and 11; i) phenyl boronic acid, Pd(PPh₃)₄, K₂CO₃, dioxane/H₂O, 100 °C, overnight, 20 % (HPLC, over 2 steps).

Figure 2

Binding modes of dual‐target ligands. Predicted binding poses of compounds a,d) 2, b,e) 3, and c,f) 4. The A_2AAR (PDB code: 3PWH^[32]) and D₂R (homology model) are shown as blue and grey cartoons, respectively. Key binding site residues and ligands are shown as sticks.

Figure 3

Predicted binding modes of dual‐target ligands. Experimental data and predicted binding modes of compounds a) 30 and b) 37. The A_2AAR (PDB code: 5OLO^[33], MD‐refined binding modes) and D₂R (homology model) are shown as blue and grey cartoons, respectively. Key binding site residues and the ligands are shown as sticks.

Figure 4

Blood‐brain barrier penetration and evaluation in rodent model of Parkinsonism. a) Brain‐to‐plasma ratio of compound 30 determined in rats (24 mg kg⁻¹). Data represent mean±SD. b) Bar graph of the number of net rotations (contralateral‐ipsilateral rotation count, 30 min) induced by DMSO (17 %)/saline (n=6) or compound 30 (n=7, 24 mg kg⁻¹, IP) dissolved in DMSO (17 %)/saline. **P<0.01 accordingly to Mann‐Whitney test. c) Bar graph of the number of net rotations (30 min) induced by DMSO (17 %)/saline (n=6), raclopride (n=7, 2 mg kg⁻¹, IP), compound 30 (n=7, 24 mg kg⁻¹, IP) or the combination of raclopride and compound 30 (n=7) dissolved in DMSO (17 %)/saline. *P<0.05 accordingly to One‐way ANOVA followed by Tukey's multiple comparisons test. Data represent mean±SEM.