Automated design of ligands to polypharmacological profiles

Abstract

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Figure 1. Adaptive drug design

(a) Closed loop of automated ligand design algorithm by multi-objective evolutionary optimisation. (b) Multi-objective prioritization by vector scalarisation. The multi-target objectives are defined as the co-ordinates of the ideal achievement point, O, (gold cross) and the predicted values of each generated compound (coloured circles) are also defined as a co-ordinates in a multi-dimensional space. The Pareto frontier is displayed as a red dotted line. The multi-objective prioritisation is inverse to the magnitude of vectors (||a|| < ||b|| < ||c||). Compound A is prioritised the highest. Compounds C and D have the same vector length (||c||=||d||) and thus prioritised equally and above the Pareto optimal compounds E and F. (c) Evolution of donepezil (1) (19% inhibition of D2 receptor @ 10μM; dopamine D2 Bayesian score = 25) into dopamine D2 inverse agonist 3 (96% inhibition of D2 receptor @ 10μM; dopamine D2 Bayesian score = 92). The Tanimoto similarity between donepezil and compound 3 is only 0.35.

Figure 2. Polypharmacology profiles of designed ligands

Comparison of the predicted Bayesian and observered polypharmacology profiles for (a) donepezil (1); (b) the isoindole analogues (2-8) – of the 160 ligand-target associations, 100 were correctly predicted by the Bayesian models (p=0.001; probability of success of 0.63 with 95% CI of 0.55 – 0.70); (c) the benzolactam analogues (9a-11b) – of the 160 ligand-target associations, 107 are correctly predicted (p=1.1e⁻⁵); (d) the 2,3-dihydro-indol-1-yl analogues (12 and 13); (e) and the morpholino analogues (14-29) – of the 540 ligand-target associations, 437 are correctly predicted (p<2.2e⁻¹⁶). The figure is composed of data in Supplementary Tables 1 and 3. In total, of the 800 predictions in the matrix, on novel, prospectively designed ligands, 599 were experimentally confirmed correct (p<2.2e⁻¹⁶), with a probability of success of 0.75 (95% confidence interval: 0.72 – 0.78).

Figure 3. Reducing> α₁ anti-target activity by evolutionary design

(a) Summary of the evolution of the prioritized benzolactam analogues (compound 11a and 11b) from a parent isoindole analogue (5). The full evolutionary pathway from compound 5 to 11b is show in Supplementary Figure 7. (b) Comparison of polypharmacology profiles for the Bayesian model score and experimental binding affinities (k_i) of compound 5 and benzolactam analogues (9a-11b) for the seven target objectives (α_1A, α_1B, α_1D vs 5-HT_1A, D2, D3, D4). On average the selectivity ratios for the D2, D3, D4 and 5-HT_1A receptors for the synthesized benzolactams over the α₁ receptors are 2.8-, 59-, 58- and 312-fold respectively. In comparison, the average selectivity ratios for D2, D3, D4 and 5-HT_1A for the isoindole analogues over the α₁ receptors are 0.27-, 2.1-, 1- and 14-fold, respectively. With respect to the algorithm prioritization for the benzolactam analogues (Supplementary Table 8) against the multi-target objectives, the order of prioritization matches the experimentally determined order for the analogues (dichloro phenylpiperazine > 2-methoxy phenylpiperazine >2-pyridine piperazine).

Figure 4. Evolution of dopamine D4 ligands from donepezil

(a) Summary of the evolution of donepezil (1) (dopamine D4 Bayesian score = 26, D4 k_i = 614nM) into dopamine D4 inverse agonist 13 (dopamine D4 Bayesian score = 112, D4 k_i = 8.9nM). The Tanimoto similarity between donepezil and compound 13 is only 0.26. The full evolutionary pathway from donepezil to 13 is show in Supplementary Figure 9 (b) Summary of the evolution of selective novel dopamine D4 ligands. Compound 13 is further evolvSed by selection for novelty and D4 selectivity into the morpholino analogues 18, 20 (r and s), 21 (r and s) and 27 (r and s). The full evolutionary pathway from 13 to 27 is show in Supplementary Figure 11 (c-f) Behavioral analysis of a novel dopamine D4 ligand in D4 receptor (D4R) knockout (KO) mice. (c) Distance travelled in the open field over 60 min by D4R animals. Mice were given (i.p.) vehicle or 0.7 or 1 mg/kg compound 13 and tested immediately over 60 min. (d) Time spent in the centre zone in the open field by D4R animals. (e) The numbers of head-pokes in the hole-board test in D4R mice. Animals were injected with vehicle or 0.7 or 1 mg/kg compound 13 and were tested 30 min later over 10 min. (f) Percent time in the open areas in the zero maze in D4R (h) mice. Animals were administered vehicle or compound 13 and tested 30 min later for 5 min. N=8-14 D4R mice/treatment-condition. *p<0.05, WT versus D4R-KO mice; +p<0.05, comparisons within genotype to the vehicle; ^p<0.05, comparisons between 0.7 and 1 mg/kg compound 13; ^p<0.05, compared to the 0-20 min time-point.