In silico comparison between the mutated and wild-type androgen receptors and their influence on the selection of optimum androgenic receptor blockers for the treatment of prostate cancer

Introduction

Prostate cancer (PCa) is a malignant growth that occurs in men over the age of 50 years and consists of an increase in prostate size due to an increase in the number of cells. In Iraq, this disease has low incidence and mortality according to previous studies, as 7.6% of all types of cancers are prostate cancer.^1,2 In addition, the most likely age for development is between 50 and 74 years. Furthermore, the progression of PCa is related to excess androgen stimulation; however, current treatments include surgery and/or hormones that completely block androgenic receptors.^3,4

When the disease reaches a stage of resistance to treatments, it is called Castration-Resistant Prostate Cancer (CRPC).⁵ Although new treatment strategies have been developed for CRPC, they are very few and quite inefficient. Many types of CRPC rely on decreasing the activity of the androgen receptor (AR), the signaling pathway for survival. Due to this, the androgen receptor is key to the design of new therapeutic agents.⁵ To date, more than 600 different mutations have been found in the androgen receptors where the repercussions of these mutations on their (receptors) structure, signaling, and resistance to PCa treatments are analyzed. For this reason, the development of strategies for the identification of effective drugs acting on androgen receptors is of great importance in obtaining new therapeutic agents against PCa.⁶ This methodology is known as Intensive Structure-Based Virtual Screening (SBVS).^5,6 With the SBVS you can identify new bioactive compounds, make modifications in the structure of molecules, and look for the conformation and optimal position of a ligand with its target molecule to adjust it for the purpose of the drug.^6–9 In addition, the methodology of drug repositioning, which accelerates the process of discovering new uses of existing therapeutic agents, is both cost- and time-effective. Therefore, the use of computational technologies based on protein structure (SBVS) for the design or repositioning of drugs (new uses of existing drugs) is an alternative method that will favor the investigation of new therapeutic agents against CRPC.¹⁰

Methods

The in-silico experiments were carried out at the Center for Research in Iraqi Medical and Pharmaceutical Research Center (IMRC). Subsequently, background analysis will be carried out with services provided by protein Data bank-EMBL-EBI (The European Bioinformatics Institute). The Current work is a preliminary trial for the selection of pharmacological molecules with inhibitory potential of the normal and mutated androgen receptor (T877A).^12–18

Results

The first result obtained from the analysis was carried out with the help of the optimal parameters of the AR binding site which were determined with the use of AutoDock Vina and Chimera. The parameters are described below (Table 1).

With the server of AutoDock Vina de Chimera and AutoDock Vina (direct program), the calculation of the theoretical value for the affinity of the coupling of the natural ligand or inhibitors with androgen receptors, both wild and mutated. The following Tables 2 and 3 showed the analyses of affinities existed in the interactions between wild AR with the natural ligands.

The results showed that Dihydrotestosterone (DHT) ligand has a higher affinity for the AR than testosterone, which is the precursor of DHT. Pang et al. (2021) reported this affinity and mentioned that DHT has a significant effect on the receptor and is active in the functions in which AR is involved, such as transcription of genes for cell survival and growth.¹⁷ The validation of the established parameters of the normal androgen receptor binding site, with the use of receptor inhibitors, reflects that the ligand binding site corresponds to the binding site of the natural ligands (testosterone and DHT); however, the drug Enzalutamide and Apalutamide showed high affinity for another receptor site. Also, Chen et al. (2019) mentioned that Enzalutamide and Apalutamide are second-generation antiandrogens that inhibit AR’s activity during cancer development. In contrast, first-generation drugs included Bicalutamide, Flutamide, and Nilutamide.¹⁸ The results obtained were presented in Table 4.

The results obtained with Enzalutamide (0.3) and Apalutamide (0.4) showed a low affinity for the common binding site of the androgen receptor, so it was analyzed that in which area of the receptor do these molecules bind and what is their affinity under a cube size of 40×40×40Å (Table 5).

Discussion

The change in Enzalutamide and Apalutamide was due to the interaction of these drugs with another area different from the normal, expected, binding site of AR, giving an increase of 0.3 (Enzalutamide) and 0.4 (Apalutamide) to -7.6 and -7.7, respectively (Figure 1). To visualize the interactions of the complexes, the PyMOL program was used, with which the amino acid residue involved in the interactions between AR with natural ligands were determined, as with inhibitory ligands. For the wild-type AR and the natural ligand, testosterone, complex, it was observed that they are determined by links between the residues threonine 877 with H (2.4Å) and arginine 752 with O (2.3Å) (Figure 1). In the case of the wild-type AR and dihydrotestosterone complex, the affinity is greater than that observed with Testosterone, and the residues involved in the formation of said complex are given by arginine 752 with the terminal nitrogen of DHT (2.4Å), followed by the association between threonine 877 with the opposite oxygen at a distance of 1.9Å, and finally the third interaction occurs between asparagine with the terminal O of DHT, this union is given at a distance of 2.4Å (Figure 2). Moreover, Table 6 showed the interactions between AR and inhibitory ligands, the residues involved are described as the distance between the molecules (Figure 3).

Figure 1. The binding site of androgen receptor inhibitor drugs for both first generation (nilutamide) and second generation (Enzalutamide) drugs.

(a) showed uncommon androgen receptors and site of enzalutamide binding (b) showed wild-type androgen receptor binding to a common site.

Figure 2. Dihydrotestosterone-wild androgen receptor interaction.

Figure 3. Inhibitor-wild androgen receptor interaction.

a) Nilutamide, b) Bicalutamide, c) Flutamide, d) Enzalutamide and e) Apalutamide.

The results of the interactions of the receptor (with the T877A mutation) with the ligands analyzed above showed that the affinity of the natural ligands is low compared to that studied in the wild receptor. The affinity of the antiandrogen Bicalutamide, if it has an affinity of -8.3 for the receptor with the T877A mutation, decreased to -4.3. However, the affinity of Flutamide and Nilutamide increased from -7.7 and -8.6 to -8.7 and -9.3, respectively, and second-generation antiandrogens (Enzalutamide and Apalutamide) were stable, as the T877A mutation is not found at the site where these two drugs bind to inhibit the receptor (Tables 7 and 8).¹⁷ It was noted that a drug binds to the receptors somewhat well, and this confirms the studies that led to the adoption of a drug for treatment.¹⁸ On the other hand, apalutamide and enzalutamide binding affinities did not change for both mutated and wild-type androgenic receptors, which provides an idea to use them even in patient with CRPC and this result encouraged the FDA on (9-2021) approvals of apalutamide for treatment of non-metastatic castration-resistant prostate cancer.

The interacting residues in each of the mutated AR-ligand complexes were visualized with the PyMOL program. The residues that are modified in the regular interaction of the natural ligands of AR were Threonine 877 so that the union with the natural ligands was between Arginine 752 that binds to O of the Testosterone ligand (2.8Å), and the Glutamine residue 711 attached to H of the ligand (2.0Å). In the AR-Dihydrotestosterone complex, the amino acids involved are Arginine 752 and Glutamine 711 at distances of 2.9Å with O and 2.4Å with H, respectively (Figure 4).^19,20

Figure 4. Inhibitor-mutated androgen receptor interaction.

a) Nilutamide, b) Bicalutamide, c) Flutamide, d) Enzalutamide and e) Apalutamide.

Conclusion

Prostate cancer, specifically the castration-resistant form (CRPC), has continued to be a significant challenge in the medical field. Our study, centering on the identification of binding sites for wild-type and mutated androgenic receptors, provides invaluable insights into potential therapeutic interventions for CRPC. Using in-silico methodologies and computational docking techniques, we assessed the binding affinities of known ligands and inhibitors to the androgen receptor (AR), both in its wild and mutated (T877A) forms.

Our findings affirmed that the Dihydrotestosterone (DHT) ligand possesses a higher affinity to the AR compared to testosterone. The different affinities of first and second-generation antiandrogens were elucidated, providing a broader perspective on their effectiveness. Importantly, second-generation antiandrogens, specifically Enzalutamide and Apalutamide, demonstrated alternative binding sites on the AR, which could be an underlying factor contributing to their different therapeutic outcomes in CRPC patients.

Moreover, the observed interactions between the AR and its ligands, facilitated by residues like threonine 877 and arginine 752, offer deeper molecular insights into their binding mechanisms. Such insights are invaluable as they lay the groundwork for designing more targeted therapies in the future.

It is evident from our study that there is a complex interplay of forces governing the interaction between AR and its ligands. This interaction is further complicated by the presence of mutations in the AR, which can potentially alter its binding behavior and drug resistance patterns. Computational methods, such as the ones employed in our study, thus serve as a powerful tool for unraveling these complexities and guiding the future design of therapeutics.

Finally, our study sheds light on the intricate interactions between AR and various ligands, both natural and therapeutic. By understanding these interactions at a molecular level, we open the door to more targeted and effective treatments for CRPC in the future. Further research is essential to translate these findings into tangible clinical outcomes, but the foundation laid by this study is undeniably promising.

Data availability