Conformational dynamics of androgen receptors bound to agonists and antagonists

PROTACs targeting androgen receptor signaling: Potential therapeutic agents for castration-resistant prostate cancer

After the initial androgen deprivation therapy (ADT), part of the prostate cancer may continuously deteriorate into castration-resistant prostate cancer (CRPC). The majority of patients suffer from the localized illness at primary diagnosis that could rapidly assault other organs. This disease stage is referred as metastatic castration-resistant prostate cancer (mCRPC). Surgery and radiation are still the treatment of CRPC, but have some adverse effects such as urinary symptoms and sexual dysfunction. Hormonal castration therapy interfering androgen receptor (AR) signaling pathway is indispensable for most advanced prostate cancer patients, and the first- and second-generation of novel AR inhibitors could effectively cure hormone sensitive prostate cancer (HSPC). However, the resistance to these chemical agents is inevitable, so many of patients may experience relapses. The resistance to AR inhibitor mainly involves AR mutation, splice variant formation and amplification, which indicates the important role in CRPC. Proteolysis-targeting chimera (PROTAC), a potent technique to degrade targeted protein, has recently undergone extensive development as a biological tool and therapeutic drug. This technique has the potential to become the next generation of antitumor therapeutics as it could overcome the shortcomings of conventional small molecule inhibitors. In this review, we summarize the molecular mechanisms on PROTACs targeting AR signaling for CRPC, hoping to provide insights into drug development and clinical medication.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1β-Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2-Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS-Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS-Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1β-Inulin achieved a single conformation with the lowest energy, while COX2-Inulin and iNOS-Inulin exhibited two lowest-energy conformations each. IL-1β-Inulin and COX2-Inulin displayed total binding free energies of - 27.76 kcal/mol and - 37.78 kcal/mol, respectively, while iNOS-Inulin demonstrated the best binding free energy results at - 45.89 kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes

Introduction

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease.

Methods

In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability.

Results

Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type.

Discussion

Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Androgen Deprivation Therapy for Prostate Cancer: Focus on Cognitive Function and Mood

Prostate cancer is the second leading cause of cancer death in men in the United States. Androgen deprivation therapy (ADT) is currently the primary treatment for metastatic prostate cancer, and some studies have shown that the use of anti-androgen drugs is related to a reduction in cognitive function, mood changes, diminished quality of life, dementia, and possibly Alzheimer's disease. ADT has potential physiological effects such as a reduction in white matter integrity and a negative impact on hypothalamic functions due to the lowering of testosterone levels or the blockade of downstream androgen receptor signaling by first- and second-generation anti-androgen drugs. A comparative analysis of prostate cancer patients undergoing ADT and Alzheimer patients identified over 30 shared genes, illustrating common ground for the mechanistic underpinning of the symptomatology. The purpose of this review was to investigate the effects of ADT on cognitive function, mood, and quality of life, as well as to analyze the relationship between ADT and Alzheimer's disease. The evaluation of prostate cancer patient cognitive ability via neurocognitive testing is described. Future studies should further explore the connection among cognitive deficits, mood disturbances, and the physiological changes that occur when hormonal balance is altered.

Novel androgen receptor inhibitors for metastatic hormone-sensitive prostate cancer: Current application and future perspectives

Androgen receptor (AR) signaling is essential in prostate cancer treatment. For many years, androgen deprivation therapy (ADT) has been primarily applied to manage advanced prostate cancer. However, most individuals with metastatic hormone-sensitive prostate cancer (mHSPC) administered ADT alone are at risk of developing metastatic castration-resistant prostate cancer (mCRPC) in less than two years. New approaches employing novel AR inhibitors (ARi) as intensified upfront systemic treatment in mHSPC have recently demonstrated substantial benefits in delaying disease progression and prolonging overall survival. Administration of novel ARi has become the new standard of care in mHSPC. The new landscape simultaneously makes treatment choice more challenging. This review provides comprehensive data on molecular structure, pharmaceutical properties, and efficacy and safety profiles reported by pivotal clinical trials. We also discuss future directions with ongoing Phase III trials of novel ARi in mHSPC. Considering these biological and clinical insights, this review aimed to provide a comprehensive understanding of differences in the development and applications of novel ARi for mHSPC, which may be helpful in designing strategies for first-line treatment choices.

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR ). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.

A partially open conformation of an androgen receptor ligand-binding domain with drug-resistance mutations

Mutations in the androgen receptor (AR) ligand-binding domain (LBD) can cause resistance to drugs used to treat prostate cancer. Commonly found mutations include L702H, W742C, H875Y, F877L and T878A, while the F877L mutation can convert second-generation antagonists such as enzalutamide and apalutamide into agonists. However, pruxelutamide, another second-generation AR antagonist, has no agonist activity with the F877L and F877L/T878A mutants and instead maintains its inhibitory activity against them. Here, it is shown that the quadruple mutation L702H/H875Y/F877L/T878A increases the soluble expression of AR LBD in complex with pruxelutamide in Escherichia coli. The crystal structure of the quadruple mutant in complex with the agonist dihydrotestosterone (DHT) reveals a partially open conformation of the AR LBD due to conformational changes in the loop connecting helices H11 and H12 (the H11-H12 loop) and Leu881. This partially open conformation creates a larger ligand-binding site for AR. Additional structural studies suggest that both the L702H and F877L mutations are important for conformational changes. This structural variability in the AR LBD could affect ligand binding as well as the resistance to antagonists.

STEAP1 regulation and its influence modulating the response of LNCaP prostate cancer cells to bicalutamide, enzalutamide and apalutamide

Anti‑androgen drugs are the standard pharmacological therapies for treatment of non‑metastatic prostate cancer (PCa). However, the response of PCa cells may depend on the anti‑androgen used and often patients become resistant to treatment. Thus, studying how the anti‑androgen drugs affect oncogenes expression and action and the identification of the best strategy for combined therapies are essential to improve the efficacy of treatments. The Six Transmembrane Epithelial Antigen of the Prostate 1 (STEAP1) is an oncogene associated with PCa progression and aggressiveness, although its relationship with the androgen receptor signaling remains to be elucidated. The present study aimed to evaluate the effect of anti‑androgens in regulating STEAP1 expression and investigate whether silencing STEAP1 can make PCa cells more sensitive to anti‑androgen drugs. For this purpose, wild‑type and STEAP1 knockdown LNCaP cells were exposed to bicalutamide, enzalutamide and apalutamide. Bicalutamide decreased the expression of STEAP1, but enzalutamide and apalutamide increased its expression. However, decreased cell proliferation and increased apoptosis was observed in response to all drugs. Overall, the cellular and molecular effects were similar between LNCaP wild‑type and LNCaP‑STEAP1 knockdown cells, except for c‑myc expression levels, where a cumulative effect between anti‑androgen treatment and STEAP1 knockdown was observed. The effect of STEAP1 knockdown alone or combined with anti‑androgens in c‑myc levels is required to be addressed in future studies.

Androgens, aging, and prostate health

Due to late onset hypogonadism (LOH), there is an increased usage of testosterone replacement therapy (TRT) in the aging male population. Since prostate is a target organ for androgens and anti-androgenic strategies are used to treat and palliate benign prostate hyperplasia (BPH) and prostate cancer (PC), the prevalence of both increases with age, the possible influence of TRT on prostate health becomes highly relevant. The present review summarizes existing data on the associations between endogenous hormone concentrations and prostate growth and concludes that circulating concentrations of androgens do not appear to be associated with the risks of development of BPH or initiation or progression of PC. The explanation for these findings relates to an apparent insensitivity of prostatic tissue to changes of testosterone concentrations within the physiological range.

Structural and molecular insights into the mechanism of resistance to enzalutamide by the clinical mutants in androgen receptor (AR) in castration-resistant prostate cancer (CRPC) patients

Androgen receptor (AR) is a key contributing element in the prostate cancer (PCa) instigation, progression and it is among the vastly discovered target for prostate cancer. Numerous mechanisms trigger the expansion of CRPC among which the aberrant AR gene is considered as the prime factor. Recently three essential substitutions H875Y, F877L, and T878A are reported to cause resistance to Enzalutamide. However, no detailed study is available to explore the key events that contribute to the resistance. Hence, considering the applicability of structural bioinformatics and molecular simulation-based methods in the current study, we assessed the impact of these mutations on the binding of Enzalutamide. Using a long-run simulation approach the binding stability, residues flexibility, hydrogen bonding, and protein compactness for each complex were determined to reveal the dynamic variations induced by these mutations. We discovered that the binding mode of Enzalutamide is altered by these mutations which misstarget the key residues required for the antagonistic activity. Molecular simulation of each complex revealed that the wild type H11 and H12 are more flexible moving outside and provides more volume for the ligand optimization. In the mutant complexes, the H12 remained tighter pushing out enzalutamide from the key residues which then essentially misstarget the correct orientation for the antagonist activity. The binding free energy (BFE) for the wild type was computed to be -59.92 ± 0.18 kcal/mol, for H875Y the BFE was -55.92 ± 0.18 kcal/mol, -54.82 ± 0.15 kcal/mol for F877L and -53.87 ± 0.18 kcal/mol for T878A, which further demonstrate that these mutations have destabilized the binding of enzalutamide. The proteins' motion and FEL further validated the aforementioned findings where the wild type reported different dynamic features than the mutant complexes. In conclusion, this study provides a structural basis for the resistance to Enzalutamide, which can be used to design novel effective drugs using structure-based and rationale approaches.

Second generation androgen receptor antagonists and challenges in prostate cancer treatment

Prostate cancer is a hormone-dependent malignancy, whose onset and progression are closely related to the activity of the androgen receptor (AR) signaling pathway. Due to this critical role of AR signaling in driving prostate cancer, therapy targeting the AR pathway has been the mainstay strategy for metastatic prostate cancer treatment. The utility of these agents has expanded with the emergence of second-generation AR antagonists, which began with the approval of enzalutamide in 2012 by the United States Food and Drug Administration (FDA). Together with apalutamide and darolutamide, which were approved in 2018 and 2019, respectively, these agents have improved the survival of patients with prostate cancer, with applications for both androgen-dependent and castration-resistant disease. While patients receiving these drugs receive a benefit in the form of prolonged survival, they are not cured and ultimately progress to lethal neuroendocrine prostate cancer (NEPC). Here we summarize the current state of AR antagonist development and highlight the emerging challenges of their clinical application and the potential resistance mechanisms, which might be addressed by combination therapies or the development of novel AR-targeted therapies.

The importance of sulfur-containing motifs in drug design and discovery

INTRODUCTION

Sulfur-containing functional groups are privileged motifs that occur in various pharmacologically effective substances and several natural products. Various functionalities are found with a sulfur atom at diverse oxidation states, as illustrated by thioether, sulfoxide, sulfone, sulfonamide, sulfamate, and sulfamide functions. They are valuable scaffolds in the field of medicinal chemistry and are part of a large array of approved drugs and clinical candidates.

AREA COVERED

Herein, the authors review the current research on the development of organosulfur-based drug discovery. This article also covers details of their roles in the new lead compounds reported in the literature over the past five years 2017-2021.

EXPERT OPINION

Given its prominent role in medicinal chemistry and its importance in drug discovery, sulfur has attracted continuing interest and has been used in the design of various valuable compounds that demonstrate a variety of biological and pharmacological feature activities. Overall, sulfur's role in medicinal chemistry continues to grow. However, many sulfur functionalities remain underused in small-molecule drug discovery and deserve special attention in the armamentarium for treating diverse diseases. Research efforts are also still required for the development of a synthetic methodology for direct access to these functions and late-stage functionalization.