Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases

Abstract

Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested off-targets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels.Electronic supplementary materialThe online version of this article (10.1186/s13321-017-0249-4) contains supplementary material, which is available to authorized users.

Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds

“…Such models can be built using neural networks or support vector machines or random forest models/decision trees. ,, QSAR models require a considerably large data set of actives and inactives on the targets of interest for their training but then can be used to score unknown compounds for their probability to be active. Successful examples have used QSAR models to identify dual modulators for defined target pairs with high retrieval and low false positive rates. , …”

“…Successful examples have used QSAR models to identify dual modulators for defined target pairs with high retrieval and low false positive rates. 182,183 Furthermore, generative artificial intelligence is continuously gaining relevance in early drug discovery and might also hold enormous potential in multitarget compound design. Recently, a generative artificial intelligence model trained to capture the constitution of ChEMBL annotated compounds represented as SMILES strings and fine-tuned on a set of ligands for two specific target families was successfully employed to discover novel dual ligands.…”

The current status of pharmacotherapy for the treatment of Parkinson’s disease: transition from single-target to multitarget therapy

“…Among the predicted compounds, derivative 44 showed the best profile toward the targets of interest (hA 1 K i = 34 nM; hA 2A K i = 41 nM; hPDE-10A IC 50 = 3.2 mM) (Fig. 7) [101].…”

Selection of protein conformations for structure-based polypharmacology studies

“…However, designing drugs with a polypharmacological profile represents a challenging task [1,3,4] and the few examples of intended polypharmacology are usually within related protein families. However, recent efforts in predicting polypharmacology for distantly related targets have started to yield promising results [5][6][7]. Indeed, computational approaches have certainly proved to play a key role in exploiting the available structural information, and to perform de novo multi-target drug design and in silico profiling [8].…”