Data-Driven Approaches Used for Compound Library Design for the Treatment of Parkinson's Disease

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015-2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.

Keywords: Parkinson’s disease; chemoinformatics; computational drug design; data-driven approach.

Figure 1

Selection of the most representative compounds for the treatment of PD. (A) Estimation of the optimal number of clusters. (B) Dendrogram−hierarchical clustering (hclust) of the compounds for PD treatment using Ward’s method (*, ward D2).

Figure 2

Structural network of the drugs used for the PD treatment and their pharmacological targets and their representative components. The upper structural network organizes the compounds (green nodes) and their pharmacological targets (yellow nodes). The structural network below derived from the Cytohubba plug-in shows the most connected nodes (rosmarinic acid, chrysin, naringenin, and cordycepin, and MAO-A and MAO-B). Nodes represent the compounds and pharmacological targets, and edges are the reported interactions between them.

Figure 3

Molecular leaders and evolutionary library. (A) Molecule leaders for the library: rosmarinic acid, chrysin, naringenin, and cordycepin. (B) Bemis–Murcko fragments (strings) from rosmarinic acid, chrysin, naringenin (I–IV), and cordycepin (V). (C) Bemis–Murcko fragments (frameworks) from rosmarinic acid, chrysin, naringenin (VI), and cordycepin compounds (VII–XIII).

Figure 4

Selected compounds with more conformational stability and biological activity on MAO-B. (A) Cordycepin-derived compound (21 NP); (B) rosmarinic acid-derived compound (14 NP); (C) rosmarinic acid-derived compound (6 PP); (D) naringenin-derived compound (20 NP) and (E) chrysin derived compound (22 NP).

Figure 5

2D representation of the best-coupled interactions. (A) Interaction of CYP450 with rosmarinic acid-derived compound (14 NP, ΔG = −8.617973). (B) Interaction of NO with rosmarinic acid-derived compound (6 APP, ΔG = −8.725966).

Figure 6

PBPK comparison of different model simulations across different dosages of 21NP molecule modeled in a healthy patient. (A) Five different compartments (organs) dosed with 10 mg/kg in a healthy individual. (B) Five different compartments (organs) dosed with 100 mg/kg in a healthy individual. (C) Different brain compartments dosed with 10 mg/kg in a healthy individual. (D) Different brain compartments dosed 100 mg/kg in a healthy individual. The heart, liver, and kidney are the organs with the longest elimination time of molecule 21 NP, whereas in the Brain (C,D), there is an increased concentration of molecule 21 NP in tissue and intracellular compartments.

Figure 7

PBPK Model comparison of different model simulations across different dosages of 6 APP molecules modeled in a healthy individual. (A) Five different compartments (organs) dosed with 10 mg/kg in a healthy individual. (B) Five different compartments dosed with 100 mg/kg in a healthy individual. (C) Different brain compartments dosed with 10 mg/kg in a healthy individual. (D) Different brain compartments dosed 100 mg/kg in a healthy individual. The heart, liver, and kidney are the organs that have the longest elimination time of molecule 6 APP, whereas (C,D) (Brain) have an increase in the concentration of molecule 6 APP in tissue and intracellular compartments.