Parkinson’s disease (PD) is a complex and multifactorial neurodegenerative disease (Ontology: DOID:14330; OMIM: 168600). ¹ The underlying aspects of the neurodegenerative process have been unraveled and are becoming known, while treatments that can modify this process are still in the experimental stage. Currently, PD has a symptomatic treatment derived from the gradual loss of neurons in the substantia nigra, which contributes to motor and non-motor symptoms. At the onset of the disease, motor symptoms respond predominantly to levodopa treatment. However, the symptoms of chronic PD tend not to respond satisfactorily to levodopa treatment, which is partly explained by the fact that PD is considered a complex disease combining genetic and environmental factors, in which genes can either generate the disease on their own or be part of the risk factors. Parkinson Disease are associated with five targets, one of this is an immuno-relevant target. In Figure 1 shows the protein targets that have been associated with various stages of PD. It should be noted that in the Synuclein Alpha (SNCA) pathway siRNAs have been described as negative regulators. Likewise, some small molecules such as Nilotibib are immunoregulators. ²

The PD patients have an insidious quality of life, which is stressful due to treatments based solely on symptomatic features. As the world’s population continues to age, the prevalence of PD is expected to double in some age groups, placing a considerable burden on healthcare systems. Fortunately, the rise of rational polypharmacological approaches is increasing, in particular for the treatment of multifactorial and complex diseases, such as neurodegenerative diseases. PD is an ideal model for a polypharmacological approach due to its clinical heterogeneity. Indeed, the search for specific targets has been the basis of pharmacology for many years. With the advent of systems pharmacology, a quantitative area based on computational methods it has become possible to develop compounds directed to more than one target. In this sense, it has been possible to take data from fields such as clinical neurophysiology and neuropharmacology, and organize them with high-throughput screening methods, such as ligand-based virtual screening, or structure-based virtual screening. Thus, PD compounds represent a promising approach to shed light on the etiology and treatment of complex neurodegenerative diseases such as PD. There are few candidate polypharmacological compounds targeting dopaminergic neurons, ^{3 – 5} although none of them are in clinical development yet. For this reason, we are interested in contributing to show polypharmacological opportunities in PD.

Chemoinformatics allows systematic studies of chemical structure discernment and comparison of chemical structures present in compound databases. The relationships that can be established through informatics strategies reveal pharmacological or polypharmacological compounds with PD-related endpoints as a common factor. Polypharmacology is the binding of drugs to multiple targets, the main one being the search for lead structures that interact with several biological targets related to a specific pathological entity; not others that may produce unwanted adverse effects. ⁶ Therefore, the goal of this paper is to identify compounds with over one receptor target related to PD.

In our study we selected 1,562 active compounds (IC50 equal to or lower than 10 μM) reported against almost one target related to PD, shown in Figure 2. It is relevant that 36 of the compounds have a polypharmacological activity against over two different target families (Table S1). Of these multi-target compounds, only two exhibit activities against different target families related to PD (e.g., muscarinic acetylcholine receptors, norepinephrine transporter, and dopamine receptors). Additionally, strategic search reveals promising chemical dual-entities. For example, a compound with activity against adenosine A2a and dopamine D1 receptors, six compounds with activity against norepinephrine transporter and dopamine receptors (D1-D5), and two compounds with dual activity against norepinephrine transporter and muscarinic acetylcholine receptors (M1-M5). Interestingly, 25 compounds with dual activity against dopamine receptors and muscarinic acetylcholine receptors has been identified. The chemical structures and activity profile of representative polypharmacological compounds were shown in Figures 3 and 4.

As discussed in the Introduction section, multi-target therapy offers new perspectives that could resolve different issues in relationship with single-target drug design. At same time, polypharmacology therapy offers the possibility to generate novel and refined approaches to address to complex diseases such as neurological diseases. In this context, the generation and optimization of polypharmacological agents, represents a new challenge in drug design.

This study is an overview of multitarget compounds reported in the literature with potential application against PD. The data was obtained from ChEMBL V.30 (the most recent version, at the time or writing). These data do not represent all the compounds reported in the public domain against the different related targets on PD. For example, there are other primary resources with reported activity data such as PubChem or Binding DB, as well as those of private companies, ²⁶ that have not been included in this study. For example, a recent case remarks the not exhaustive data exploration on the literature (related with PD), is the case of 9-deazaxanthine derivatives, that has been dual against A _2A (antagonists) and MAO-B (inhibitors), reported with nanomolar activity. ²⁷ Furthermore, this study is limited by the targets selected ( Figure 2) to explore. This is a key point in the design and optimization of new chemical entities against PD.

This study uncovers the possibility of optimizing the tested compounds (e.g., shown in Figures 3- 6) to identify new chemical structures and features related to a polypharmacological or selective profile against different PD targets. This offers a possibility to identify compounds that act on different molecular levels (i.e., changing the signaling, gene expression, or physiological effects) on PD. In fact, the parallel modulation of different endpoints could contribute to reducing the necessary doses to generate a therapeutic effect, that at the same time contributes to reducing the associated side effects on in vivo models.

Currently, there are 15 original and 3 repurposing compounds that have been approved for clinical use and other 19 compounds continue to be tested in clinical trials phase 3 with a single drug target. ^{28 , 29} This data remarks a key opportunity to change the paradigm of “one compound one target” in drug discovery, and that the “promiscuity” concept should not be associated (directly) with serious side effects.

This new drug design approach has been guided by computational methods that have contributed to reducing the gap in information related to the development of new poly-active compounds. For example, now machine and deep learning techniques contribute to predicting and identifying optimized compounds against two or more endpoints. ³⁰ However, this model has been constructed using classification, clustering, or regression models that have not been possible to identify small chemical changes related to unexpected activity changes (i.e., activity cliff, AC). Accordingly, Figure 5 shows a new “proof of concept” to explore and use the reported dual-activity data to identify D-AC, and using D-SALI values to identify the most prominent selective and dual compounds on a data set. In fact, a perspective is an adaptation of the D-SALI value to identify triple (or quadruple, quintuple, etc.) activity cliffs.

This study has illustrated the potent (<10 μM) poly-active compounds against different targets related to PD existence. However, same compounds have been associated with promiscuity against other central nervous system (CNS) targets. For example, CHEMBL461571 and CHEMBL115280 ( Figure 3-A). They have also been associated with opioid and adrenergic receptor and dopamine transporter uptake, to name a few of these targets. ^{11 , 12} CHEMBL250699 ( Figure 3-B) has been associated with the irruption of serotonin receptors, ^{13 , 14} CHEMBL257991 are other examples with associated activity against adrenergic receptors and sirtuins (epigenetic targets), ^{15 , 16} and CHEMBL1949930 with activity against serotonin receptor and dopamine transporter. ¹⁷ Interestingly, these results reveal the promiscuity of some compounds interacting with different targets in the CNS.

Despite the high CHEMBL115280 and CHEMBL250699 promiscuity, these compoundshave been associated with a promising bioavailability profile on in vivo models. ¹⁸ Another excellent case, reflecting these compounds potential, is CHEMBL1949930, which exhibits low toxicity, brain permeability, and a good bioavailability profile. ¹⁹ These examples show that not in all cases poly-activity is associated with toxicological or bioavailability issues.

In the last decade, techniques and tools have emerged to design dual compounds; this facilitates the design of multi-target polyactive drug candidates. It is now possible to optimize compounds for two end-targets at the same time, provided the data exist to process them. In the next section, we present a study case of 25 compounds, with dual activity against dopamine receptors (D1-D5) and muscarinic acetylcholine receptors (M1-M5), illustrated In Figure 3.

In this paper, identified 36 polypharmacological chemical scaffolds related to Parkinson’s disease. In this manner we demonstrate that design of polypharmacological drugs is an opportunity in Parkinson’s Disease treatment.

In recent years, the old concept that drug selectivity meant having a single drug target has been left behind. Mainly, in multifactorial diseases, which involve interactions between molecular, cellular, and physiological pathways. Thus, in neurodegenerative disorders, including PD, the polypharmacological design approach is a fertile field for the development of new therapeutic strategies.

Figshare. Supplementary material, DOI: https://doi.org/10.6084/m9.figshare.21096919.v1. ³¹

This project contains the following data: ‐

Table S1. Datasets related to the development of PD reported in the ChEMBL V.30 Database.