Current androgen receptor antagonists under investigation for resistant prostate cancer

A prospective study on the early evaluation of response to androgen receptor-targeted agents with 11C-Choline, 68Ga-PSMA, and 18F-FACBC PET in metastatic castration-resistant prostate cancer: a single-center experience

BACKGROUND

The early identification of responsive and resistant patients to androgen receptor-targeting agents (ARTA) in metastatic castration-resistant prostate cancer (mCRPC) is not completely possible with prostate-specific antigen (PSA) assessment and conventional imaging. Considering its ability to determine metabolic activity of lesions, positron emission tomography (PET) assessment might be a promising tool.

PATIENTS AND METHODS

We carried out a monocentric prospective study in patients with mCRPC treated with ARTA to evaluate the role of different PET radiotracers: 49 patients were randomized to receive 11C-Choline, Fluorine 18 fluciclovine (anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid - FACBC) (18F-FACBC), or Gallium-68-prostate-specific-membrane-antigen (68Ga-PSMA) PET, one scan before therapy and one 2 months later. The primary aim was to investigate the performance of three novel PET radiotracers for the early evaluation of response to ARTA in metastatic CRPC patients; the outcome evaluated was biochemical response (PSA reduction ≥50%). The secondary aim was to investigate the prognostic role of several semiquantitative PET parameters and their variations with the different radiotracers in terms of biochemical progression-free survival (bPFS) and overall survival (OS). The study was promoted by the Italian Department of Health (code RF-2016-02364809).

RESULTS

Regarding the primary endpoint, at log-rank test a statistically significant correlation was found between metabolic tumor volume (MTV) (P = 0.018) and total lesion activity (TLA) (P = 0.025) percentage variation among the two scans with 68Ga-PSMA PET and biochemical response. As for the secondary endpoints, significant correlations with bPFS were found for 68Ga-PSMA total MTV and TLA at the first scan (P = 0.001 and P = 0.025, respectively), and MTV percentage variation (P = 0.031). For OS, statistically significant correlations were found for different 68Ga-PSMA and 18F-FACBC parameters and for major maximum standardized uptake value at the first 11C-Choline PET scan.

CONCLUSIONS

Our study highlighted that 11C-Choline, 68Ga-PSMA, and 18F-FACBC semiquantitative PET parameters and their variations present a prognostic value in terms of OS and bPFS, and MTV and TLA variations with 68Ga-PSMA PET a correlation with biochemical response, which could help to assess the response to ARTA.

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.

Interlaboratory prevalidation of a new in vitro transcriptional activation assay for the screening of (anti-)androgenic activity of chemicals using the UALH-hAR cell line

We report an interlaboratory evaluation of a recently developed androgen receptor (AR) transactivation assay using the UALH-hAR reporter cell line that stably expresses the luciferase gene under the transcriptional control of androgen receptor elements (AREs) with no glucocorticoid receptor (GR) crosstalk. Herein, a two-step prevalidation study involving three laboratories was conducted to assess performance criteria of the method such as transferability as well as robustness, sensitivity, and specificity. The first step consisted in the validation of the transfer of the cell line to participant laboratories through the testing of three reference chemicals: the AR agonist dihydrotestosterone, the AR antagonist hydroxyflutamide and the glucocorticoid dexamethasone. Secondly, a blinded study was conducted by screening a selection of ten chemicals, including four AR agonists, five AR antagonists, and one non-active chemical. All test compounds yielded the same activity profiles in all laboratories. The logEC₅₀ (agonist assay) or logIC₅₀ (antagonist assay) were in the same range, with intra-laboratory coefficients of variation (CVs) of 0.1-3.4% and interlaboratory CVs of 1-4%, indicating very good within- and between-laboratory reproducibility. Our results were consistent with literature and regulatory data (OECD TG458). Overall, this interlaboratory study demonstrated that the UALH-hAR assay is transferable, produces reliable, accurate and specific (anti)androgenic activity of chemicals, and can be considered for further regulatory validation.

GZ17-6.02 kills prostate cancer cells in vitro and in vivo

GZ17-6.02 is undergoing clinical evaluation in solid tumors and lymphoma. We defined the biology of GZ17-6.02 in prostate cancer cells and determined whether it interacted with the PARP1 inhibitor olaparib to enhance tumor cell killing. GZ17-6.02 interacted in a greater than additive fashion with olaparib to kill prostate cancer cells, regardless of androgen receptor expression or loss of PTEN function. Mechanistically, GZ17-6.02 initially caused peri-nuclear activation of ataxia-telangiectasia mutated (ATM) that was followed after several hours by activation of nuclear ATM, and which at this time point was associated with increased levels of DNA damage. Directly downstream of ATM, GZ17-6.02 and olaparib cooperated to activate the AMP-dependent protein kinase (AMPK) which then activated the kinase ULK1, resulting in autophagosome formation that was followed by autophagic flux. Knock down of ATM, AMPKα or the autophagy-regulatory proteins Beclin1 or ATG5 significantly reduced tumor cell killing. GZ17-6.02 and olaparib cooperated to activate protein kinase R which phosphorylated and inactivated eIF2α, i.e., enhanced endoplasmic reticulum (ER) stress signaling. Knock down of eIF2α also significantly reduced autophagosome formation and tumor cell killing. We conclude that GZ17-6.02 and olaparib interact to kill prostate cancer cells in vitro by increasing autophagy and by enhancing ER stress signaling. In vivo, GZ17-6.02 as a single agent profoundly reduced tumor growth and significantly prolonged animal survival. GZ17-6.02 interacted with olaparib to further suppress the growth of LNCaP tumors without ultimately enhancing animal survival. Our data support the consideration of GZ17-6.02 as a possible therapeutic agent in patients with AR+ prostate cancer.

Structure-Based Study to Overcome Cross-Reactivity of Novel Androgen Receptor Inhibitors

The mutation-driven transformation of clinical anti-androgen drugs into agonists of the human androgen receptor (AR) represents a major challenge for the treatment of prostate cancer patients. To address this challenge, we have developed a novel class of inhibitors targeting the DNA-binding domain (DBD) of the receptor, which is distanced from the androgen binding site (ABS) targeted by all conventional anti-AR drugs and prone to resistant mutations. While many members of the developed 4-(4-phenylthiazol-2-yl)morpholine series of AR-DBD inhibitors demonstrated the effective suppression of wild-type AR, a few represented by 4-(4-(3-fluoro-2-methoxyphenyl)thiazol-2-yl)morpholine (VPC14368) exhibited a partial agonistic effect toward the mutated T878A form of the receptor, implying their cross-interaction with the AR ABS. To study the molecular basis of the observed cross-reactivity, we co-crystallized the T878A mutated form of the AR ligand binding domain (LBD) with a bound VPC14368 molecule. Computational modelling revealed that helix 12 of AR undergoes a characteristic shift upon VPC14368 binding causing the agonistic behaviour. Based on the obtained structural data we then designed derivatives of VPC14368 to successfully eliminate the cross-reactivity towards the AR ABS, while maintaining significant anti-AR DBD potency.