An In Silico Study for Expanding the Utility of Cannabidiol in Alzheimer’s Disease Therapeutic Development
1Heerae Co., Ltd., Seoul 06253, Republic of Korea; kch38896@naver.com
2Department of Software, Sejong University, Seoul 05006, Republic of Korea; yurimlee714@gmail.com
*Correspondence: wikim@sejong.ac.krAbstract
Cannabidiol (CBD), a major non-psychoactive component of the cannabis plant, has shown therapeutic potential in Alzheimer’s disease (AD). In this study, we identified potential CBD targets associated with AD using a drug-target binding affinity prediction model and generated CBD analogs using a genetic algorithm combined with a molecular docking system. As a result, we identified six targets associated with AD: Endothelial NOS (ENOS), Myeloperoxidase (MPO), Apolipoprotein E (APOE), Amyloid-beta precursor protein (APP), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), and Presenilin-1 (PSEN1). Furthermore, we generated CBD analogs for each target that optimize for all desired drug-likeness properties and physicochemical property filters, resulting in improved values and docking scores compared to CBD. Molecular dynamics (MD) simulations were applied to analyze each target’s CBD and highest-scoring CBD analogs. The MD simulations revealed that the complexes of ENOS, MPO, and ADAM10 with CBD exhibited high conformational stability, and the APP and PSEN1 complexes with CBD analogs demonstrated even higher conformational stability and lower interaction energy compared to APP and PSEN1 complexes with CBD. These findings demonstrated the capable binding of the six identified targets with CBD and the enhanced binding stability achieved with the developed CBD analogs for each target.
1. Introduction
Cannabis, which contains more than 700 chemical compounds, has traditionally been used in various industries, including food, pharmaceuticals, and textiles. Two major compounds in cannabis are tetrahydrocannabinol (THC) and cannabidiol (CBD), which are known as cannabinoids [1]. THC is a major psychoactive component of the cannabis plant and is classified as a narcotic. On the contrary, CBD, a major non-psychoactive component of the cannabis plant, has polypharmacological properties and has been extensively evaluated for various disease indications. Clinical studies on CBD have shown its efficacy in treating multiple clinical conditions, such as chronic pain, depression, anxiety disorders, sleep disorders, and psychosis [2,3]. Additionally, CBD has been recognized as a typical therapeutic and preventive agent for neurological diseases, including anxiety, depression, and epilepsy [4,5,6,7], and neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease (AD), and amyotrophic lateral sclerosis [5,8,9].
AD is one of the most common neurodegenerative diseases, with no treatment or prevention measures but only limited symptom relief [10]. AD majorly has two biomarkers, which are senile plaques made of -amyloid protein and hyperphosphorylated tau-induced neurofibrillary tangles [10,11]. Recently, several works have indicated CBD’s role in treating and preventing AD. CBD has been shown to reduce tau protein hyperphosphorylation [12] and accumulation of -amyloid protein [13] and to have anti-inflammatory and antioxidant properties [14,15,16].
Previous works have been introduced in two approaches to expand CBD’s role in treating and preventing AD. The first approach was identifying targets associated with AD by which CBD was activated. Especially, several works have shown that CBD has a low affinity for classic targets, such as cannabinoid receptor type 1, 2 (CB1, CB2), despite acting as an antagonist/inverse agonist in them [17,18,19,20], so they identify potential targets, such as non-endocannabinoid G protein-coupled receptor 3, 55, 6 (GPR3, GPR55, GPR6), transient receptor potential vanilloid type 1 (TRPV1), and peroxisome proliferator-activated receptors (PPARs), and indicate the pharmacological role of CBD on targets in vitro, and in vivo [13,21,22,23,24]. The second approach was generating CBD analogs that increase affinities on targets, have a wide range of anti-inflammatory activities, and optimize their properties [25,26,27]. CBD analogs were generated experimentally, and most of their biological properties and activities are indicated in vitro. Therefore, existing CBD targets and analogs have demonstrated their utility, but their generation steps are relatively time-consuming and costly compared in silico.
In this paper, we identified six potential CBD targets associated with AD and generated CBD analogs that can interact with each target through in in silico methods. Subsequently, we explored the potential of these CBD targets and analogs using extensive analyses. Potential targets were identified using our target identification method, which consisted of molecular similarity and a drug–target binding affinity prediction model called DeepPurpose [28]. These targets demonstrated interaction with CBD based on drug–target binding affinity and molecular docking. To generate CBD analogs, we employed our molecular-constrained optimization method [29], which combined a genetic algorithm (GA)-based method with molecular docking. This method automatically modified the molecular substructure while maintaining the scaffold, aiming to achieve a lower docking score to the protein target and optimize desired drug-likeness properties and physicochemical filters. Finally, the structural stability of CBD-identified target complexes and CBD analog-identified target complexes were analyzed through molecular dynamics (MD) simulations.
2. Results
2.2. Generation of CBD Analogs for Each Identified Potential Target
Utilizing a molecular-constrained optimization method, we systematically generated CBD analogs for each identified potential target with a dual focus: minimizing docking scores while optimizing drug-likeness properties and adhering to physicochemical property filters. As a result, we generated three CBD analogs for ENOS and APP, one CBD analog for MPO and APOE, five CBD analogs for ADAM10, and seven CBD analogs for PSEN1, as shown in Figure 5. The value of CBD analogs, calculated by DeepPurpose, increased overall compared to CBD. The No.2 molecule for ENOS, the No.1 molecule for MPO, the No.2 and 3 molecules for APP, and the No.1 molecule for ADAM10 had the highest values, and values are increased by 1.02, 0.80, 1.04, and 0.8 compared to CBD, respectively.
2.2.1. Optimization of Molecular Properties
The molecular-constrained optimization method generated CBD analogs that conform to desired drug-likeness properties and physicochemical property filters. The generated CBD analogs satisfied criteria such as a quantitative estimate of drug-likeness (QED) ≥ 0.5, synthetic accessibility (SAScore) ≤ 6.0, logP ≤ 5.0, and physicochemical property filters, such as pan-assay interference compounds (PAINS) filter [40], BRENK filter [41], NIH filter [42], and ZINC filter. The QED cutoff was based on a study in which oral drugs approved by the FDA have an average of 0.539 QED [43]. The SAScore cutoff was based on a study in which synthesis is difficult if SAS > 6.0 [44]. The cutoff for logP was based on a study in which 90% of oral drugs that have achieved phase II clinical status had logP < 5.0 [45]. We optimized penalized logP (plogP) [46], which was used to prevent abnormally many ring structures and was calculated as follows:
Additionally, we measured the blood–brain barrier (BBB) permeant of generated CBD analogs using SwissADME [47] due to our target disease being AD. Most of the CBD analogs exhibited a BBB permeant, except for the No.1 molecule for MPO and the No.4 and No.5 molecules for ADAM10.
Table 4 outlines the optimized drug-likeness properties, physicochemical property filters, and BBB permeability of the CBD analogs for each identified potential target. These results collectively showed the performance of the molecular-constrained optimization method in yielding CBD analogs that satisfied the desired properties while retaining the potential for BBB permeation, and it explained the possibility of the drug for AD.
2.2.2. Molecular Docking Analysis for CBD Analog–Protein Complex
CBD analogs had lower or similar docking scores than CBD. Table 5 demonstrated docking scores between CBD analogs and each identified a potential target, and docking scores are observed to be in the range of −8.8 to −6.1. Notably, multiple analogs demonstrated superior docking scores compared to CBD, with three analogs for ENOS, two for ADAM10, and one for MPO and APOE. This result demonstrated the potential of the molecular-constrained optimization method in generating CBD analogs that exhibit enhanced interactions with the targets.
Additionally, given the recognized significance of the endocannabinoid system in AD [48,49], we employed the CIRCE [50] to validate the interaction between CBD analogs and this system. This software predicts whether a query compound acts as a ligand in the endocannabinoid system by comparing its similarity to known ligands interacting with CB1, CB2, and GPRs. The probability prediction of the endocannabinoid system class is expressed as a value ranging from 0 to 1. As shown in Table 5, all CBD analogs exhibited the potential to interact with the endocannabinoid system, having a score of 0.2 or higher. Key substructures of CBD analogs for CB1 and CB2 interactions are illustrated in Figure S1. This result demonstrated that CBD analogs exhibited enhanced interactions with the targets and exhibited interactions with the endocannabinoid system.
We used the No.1 CBD analog in each target for visualization and MD simulation. The binding sites involving the CBD analog and each of the identified potential targets are shown in Figure 6 and Figure 7, with further details provided in Table 6. As shown in Figure 7 and summarized in Table 6, the number of stabilized hydrophobic interactions in the complexes was as follows: 9 for CBD analog in complex with ENOS, 7 for MPO, 7 for APOE, 7 for APP, 10 for ADAM10, and 11 for PSEN. The number of stabilized hydrogen bonds in the complexes was as follows: four for CBD analog in complex with MPO, three for APP, and two for ADAM10.
The structural analysis revealed distinct interactions between CBD analogs and the identified targets compared to the CBD complex. The CBD analog-ENOS complex shared the same four residues as the CBD complex: Gly355, Cys184, Ser354, and Phe353. CBD analog-MPO complex shared the same six residues as the CBD complex: Leu415, Phe407, Leu417, Pro145, Phe147, and Arg424. In these residues, Phe147 and Arg424 changed hydrophobic interaction to the hydrogen bond. The CBD analog-APOE complex shared all residues identically with the CBD complex. The CBD analog-APP complex shared all residues identically with the CBD complex. In these residues, Ala9(B), Asp24(A), and Gln8(B) shifted hydrophobic interaction to the hydrogen bonds. The CBD analog-ADAM10 complex shared the same nine residues: Thr422, Pro373, Asn556, Gln560, Pro631, Ser630, His371, Val372, and Gly629. In these residues, Thr422 and Asn556 shifted hydrophobic interaction to the hydrogen bond. The CBD analog-PSEN1 complex shared the same nine residues: Gln116, Ile690, Tyr119, Phe173, Phe229, Val176, Ala232, Phe698, and Arg115. Overall, this result demonstrated the potential of CBD analogs to foster distinct binding interactions, potentially influencing the affinity of the compounds for these targets.
2.2.3. Molecular Dynamics Analysis for CBD Analog–Target Complex
We measured the binding stability of the CBD analog–protein complex using MD simulation during a 100 ns simulation. As shown in Figure 8, the range of RMSD values was 0–0.4 nm, 0–1.2 nm, 0–1.4 nm, 0–0.4 nm, 0–0.7 nm, and 0–0.8 nm for the CBD analog in complex with ENOS, MPO, AOPE, APP, ADAM10, and PSEN, respectively. The range of Rg values was 5.7–5.9 nm, 6.7–6.9 nm, 4.1–4.3 nm, 3.1–3.3 nm, 6.7–6.8 nm, and 7.1–7.4 nm for the CBD analog in complex with ENOS, MPO, AOPE, APP, ADAM10, and PSEN, respectively. The range of SASA values was 36.8–53.1 nm2, 65.2–84.6 nm2, 12.9–24 nm2, 3.4–11 nm2, 63–81.9 nm2, and 82.1–111.9 nm2 for the CBD analog in complex with ENOS, MPO, AOPE, APP, ADAM10, and PSEN, respectively. As shown in Figure 9, the range of RMSF values was 0–0.7 nm, 0.1–1.4 nm, 0–0.6 nm, 0–0.6 nm, 0.1–2.9 nm, and 0–0.8 nm for the CBD analog in complex with ENOS, MPO, AOPE, APP, ADAM10, and PSEN, respectively.
Similar to the CBD-target complexes, the majority of CBD analog–target complexes showed good stability during MD simulations, as evidenced by RMSD values consistently below 1 nm, low degrees of fluctuation in Rg and SASA, and RMSF values generally within the range of 3 nm or lower. However, there were some notable differences compared to the CBD-target complexes. In the case of the CBD analog-ADAM10 complex, the RMSF value was higher than the CBD complex, indicating that this complex exhibited slightly more flexibility in specific residues. The RMSF values of the CBD complex with APOE, APP, and PSEN1 showed a sharp change at the end of the residue, which may indicate instability. In contrast, the CBD analog with these targets exhibited stability, indicating that the analogs might have improved the binding interactions compared to CBD. The CBD analog-APP complex demonstrated better stability when compared to the CBD-APP complex.
3. Discussion
This study found that CBD and CBD analogs are promising in AD treatment. Our analysis showed potential protein targets associated with AD, and the computational generation of CBD analogs has shown potential for enhancing the stability and binding interactions with these potential targets. However, we noted a few limitations that need to be considered. First, we acknowledged the variability in complex stability, as indicated by high RMSF values at the end of binding site residues for three CBD-target complexes and one CBD analog–target complex. These variations might indicate potential challenges in achieving consistent binding stability with all targets, and suggest that future MD simulations might benefit from longer durations than the commonly employed 100 ns to capture more comprehensive insights into stability. Additionally, we recognized that our binding affinity calculations depended on the DeepPurpose model, which may not be the optimal tool for precisely measuring binding affinity. Further refinement in binding affinity prediction models could enhance the accuracy of our findings.
Nevertheless, these findings underscore the potential for CBD to play a more expansive role in the development of AD treatments and excited possibilities for CBD analogs as drug candidates to improve binding interactions and stability with AD-associated protein targets through various analyses. These results will require further validation through experimental investigations to ascertain their real-world therapeutic applications and safety profiles. While CBD analogs have shown promise by satisfying critical drug-likeness properties, additional considerations should be given to optimizing ADME properties and bioavailability. In future steps, conducting in vitro and in vivo experiments to validate the observed effects of CBD and CBD analogs on the identified targets is essential. Moreover, exploring potential therapeutic effects with other AD treatments and investigating long-term effects are avenues for further research. Therefore, while this study has provided valuable insights into the potential of CBD and CBD analogs for AD treatment, it represents just the beginning. Addressing the challenges in optimizing ADME properties and bioavailability and experimental validation in vitro and in vivo are essential to translating these findings into practical and effective AD treatments.
4. Materials and Methods
4.2. CBD Analog Generation Using Our Molecular-Constrained Optimization Method
We generated CBD analogs that optimized each identified potential target found by our target identification method. The workflow for generating CBD analogs was shown in Figure 10. It consisted of two steps. First, we generated novel molecules, called CBD analogs, which preserved the Murcko scaffold of CBD [59] and optimized multiple drug-likeness properties, such as the QED, SAScore, plogP, and logP, using our molecular-constrained optimization method. In this work, we additionally optimized physicochemical property filters, such as PAINS, BRENK, NIH, and ZINC filters, to prevent the generation of toxic and synthetically infeasible subgroups in small molecules [60]. Second, we calculated the docking score between generated CBD analogs and the target, and then gave the docking score as feedback from the first step.
4.3. The Results Analysis Method
Molecular Docking
To simulate molecular docking, we prepared small molecules and target proteins using OpenBabel [61] and Biovia discovery studio visualizer (version 21.20298). The OpenBabel had converted SMILES of molecules to 3D structures. The Pymol software (version 2.5.7) removed ligand residues from proteins. To delineate the binding sites of our identified targets, we employed PrankWeb [62] to predict potential pockets within these target proteins. Subsequently, we retrieved ligand structures of known inhibitors for these targets from the Protein Data Bank (PDB) website (https://www.rcsb.org/ (accessed on 23 July 2023)) and referenced previous docking papers. We specifically defined the binding sites from the multiple pockets generated by PrankWeb by considering those containing binding residues highlighted in the literature or known inhibitors cataloged in the PDB. We used the Qvina-W to reveal the interaction between active small molecules and target proteins. After docking simulation, we visualized the small molecules–protein complex to Biovia discovery studio visualizer and the interaction site between molecules and ligands to LIGPLOT (version 2.2) [63].
4.4. Molecular Dynamics Simulation
The MD simulation described the conformational changes and structural safety of the ligand–protein complex during the binding process. Moreover, they further provided evidence for the binding mode of small molecules and receptor proteins [64]. We used CBD and each CBD analog, the top-predicted docking pose with the best docking score among ten times docking simulations, as the starting point for an MD simulation. We generated the initial protein complex structure using CHARMM-GUI [65] and performed MD simulation using GROMACS (version 22.4) [66]. MD simulations were performed with periodic boundary conditions. The van der Waals forces and Lennard-Jones interactions involved a cut-off distance of 1.2 Å. The first stage optimized each system structure using 5000 iterations (5 ps) with the steepest descent algorithm. After minimizing the energy, the system equilibrated the NVT (constant particle number, volume, and temperature) and the NPT (constant particle number, pressure, and temperature). NVT and NPT were ensembled for 125 ps. Temperature coupling was set to 1ps and pressure coupling to 5 ps. Finally, the MD simulation was run for 100 ns under constant pressure and temperature. We analyzed RMSD, Rg, SASA, and RMSF for complex structures obtained from the 100 ns MD simulations using GROMACS (version 22.4).
Appendix Group
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The protein structures of identified targets were acquired online from RCSB Protein Data Bank, including ENOS (PDB ID: 3NOS), MPO (PDB ID: 1DNW), APOE (PDB ID: 1B68), APP (PDB ID: 1AAP), ADAM10 (PDB ID: 6BE6), and PSEN1 (PDB ID: 5A63).
Conflicts of Interest
The authors declare no conflict of interest.
| Uniprot ID | Target Name | Binding Affinity () |
|---|---|---|
| P29474 | ENOS | 5.42 |
| P05164 | MPO | 7.00 |
| P02649 | APOE | 5.85 |
| P05067 | APP | 5.86 |
| O14672 | ADAM10 | 4.81 |
| P49768 | PSEN1 | 6.78 |
| PDB ID | Target Name | Docking Score (kcal/mol) |
|---|---|---|
| 3NOS | ENOS | −8.0 |
| 1DNW | MPO | −6.6 |
| 1B68 | APOE | −5.8 |
| 1AAP | APP | −6.4 |
| 6BE6 | ADAM10 | −7.0 |
| 5A63 | PSEN1 | −7.9 |
| Complex Name | Residues of Hydrophobic Interaction | Residues of ydrogen Bond (Å) |
|---|---|---|
| CBD-ENOS | Gly355, Ser354, Phe473, Phe353, Trp178, Leu193, Ala181, Val336, Mel339, Arg183, Cys184 | - |
| CBD-MPO | Pro145, Phe146, Met411, Arg424, Leu420, Leu415, Phe147, Leu417, Leu406, Phe407, Glu242 | Glu102 (2.9 Å) |
| CBD-APOE | Leu63, Arg32, Tyr36, Asp35, Glu50, Trp39 | - |
| CBD-APP | Thr26 (A), Ala9 (A), Tyr22 (A), Gln8 (A), Asp24 (A), Gln8 (B), Ala9 (B), Tyr22 (B), Asp24 (B), Thr26 (B) | - |
| CBD-ADAM10 | Glu579, Gln560, Phe635, Ser630, Asp425, Pro631, Ser423, Val376, Gly629, Val372, Asn556, Pro373, Ser350, Thr422, His371 | - |
| CBD-PSEN1 | Phe229, Phe173, Ile690, Leu20, Tyr119, Arg115, Phe698, Thr172, Val176, Ala232, Gln116 | - |
| Target Name | No. | QED | SAScore | plogP | logP | Filters (PAINS, BRENK, NIH, ZINC) | BBB Permeant |
|---|---|---|---|---|---|---|---|
| ENOS | No.1 | 0.73 | 3.28 | 0.12 | 3.41 | Pass | Yes |
| No.2 | 0.80 | 3.58 | 0.86 | 4.44 | Pass | Yes | |
| No.3 | 0.83 | 3.65 | 0.64 | 4.30 | Pass | Yes | |
| MPO | No.1 | 0.65 | 3.80 | −0.86 | 2.94 | Pass | No |
| APOE | No.1 | 0.82 | 3.50 | 1.18 | 4.69 | Pass | Yes |
| APP | No.1 | 0.68 | 3.69 | −0.57 | 3.12 | Pass | No |
| No.2 | 0.85 | 3.80 | −0.02 | 3.78 | Pass | Yes | |
| No.3 | 0.73 | 3.67 | 1.11 | 4.78 | Pass | Yes | |
| ADAM10 | No.1 | 0.69 | 3.77 | −0.41 | 3.36 | Pass | Yes |
| No.2 | 0.74 | 3.76 | −0.01 | 3.75 | Pass | Yes | |
| No.3 | 0.76 | 3.73 | 0.23 | 3.97 | Pass | Yes | |
| No.4 | 0.67 | 3.82 | −0.45 | 3.36 | Pass | No | |
| No.5 | 0.67 | 3.82 | −0.89 | 2.93 | Pass | No | |
| PSEN1 | No.1 | 0.79 | 3.58 | 0.92 | 4.51 | Pass | Yes |
| No.2 | 0.82 | 3.85 | 0.95 | 4.81 | Pass | Yes | |
| No.3 | 0.73 | 3.79 | 0.43 | 4.22 | Pass | Yes | |
| No.4 | 0.81 | 3.72 | 0.18 | 3.91 | Pass | Yes | |
| No.5 | 0.76 | 3.73 | 0.22 | 3.96 | Pass | Yes | |
| No.6 | 0.75 | 3.60 | 0.05 | 3.65 | Pass | Yes | |
| No.7 | 0.87 | 3.79 | 0.29 | 0.40 | Pass | Yes |
| Target Name | No. | Docking Score (kcal/mol) | Potential1 | Potential2 |
|---|---|---|---|---|
| ENOS | No.1 | −8.8 | 0.33 | 0.4 |
| No.2 | −8.7 | 0.3 | 0.68 | |
| No.3 | −8.2 | 0.3 | 0.46 | |
| MPO | No.1 | −7.4 | 0.32 | 0.54 |
| APOE | No.1 | −6.1 | 0.61 | 0.82 |
| APP | No.1 | −6.5 | 0.63 | 0.83 |
| No.2 | −6.1 | 0.21 | 0.44 | |
| No.3 | −6.1 | 0.63 | 0.83 | |
| ADAM10 | No.1 | −7.0 | 0.35 | 0.54 |
| No.2 | −7.6 | 0.37 | 0.57 | |
| No.3 | −7.2 | 0.32 | 0.54 | |
| No.4 | −6.9 | 0.34 | 0.62 | |
| No.5 | −6.7 | 0.36 | 0.59 | |
| PSEN1 | No.1 | −8.0 | 0.22 | 0.48 |
| No.2 | −7.9 | 0.42 | 0.77 | |
| No.3 | −7.9 | 0.32 | 0.54 | |
| No.4 | −7.8 | 0.23 | 0.43 | |
| No.5 | −7.7 | 0.43 | 0.56 | |
| No.6 | −7.7 | 0.32 | 0.47 | |
| No.7 | −7.5 | 0.33 | 0.53 |
| Complex Name | Residues of Hydrophobic Interaction | Residues of Hydrogen Bond (Å) |
|---|---|---|
| CBD analog | Trp178, Gly355, Pre334, | - |
| -ENOS | Cys184, Trp356, Mel358, | |
| Gly186, Ser354, Phe353 | ||
| CBD analog | Glu102, Leu415, | Phe147 (2.57 Å), Arg424 (3.33 Å), |
| -MPO | Phe146, Leu420, Phe407, | Arg333 (3.25 Å), |
| Leu417, Pro145, Phe147 | His336 (2.94 Å) | |
| CBD analog | Glu59, Tyr36, Leu63 | |
| -APOE | Arg32, Asp35, Trp39, Glu50 | - |
| CBD analog | Ala9 (A), Thr26 (B), Tyr22, | la9 (B) (3.21 Å) |
| -APP | Asp24 (B), Ty422, Gln8 (A), Thr26 (A) | Asp24 (A) (2.81 Å), Gln8 (B) (2.79 Å) |
| CBD analog | Gly629, Try638, Ser370, | |
| -ADAM10 | Val372, Ser630, His371, | Thr422 (2.71 Å), Asn556 (3.05 Å) |
| Pro631, Gln560, Pro373, Val376 | ||
| CBD analog | Gln116, Ile690, Tyr119, | |
| -PSEN1 | Phe173, Ala228, Phe229, Val176, | - |
| Ala232, Phe698, Arg115, Thr24 |