Prognostic Score and Benefit from Abiraterone in First-line Metastatic, Castration-resistant Prostate Cancer

Exploring 7β-amino-6-nitrocholestens as COVID-19 antivirals: in silico, synthesis, evaluation, and integration of artificial intelligence (AI) in drug design: assessing the cytotoxicity and antioxidant activity of 3β-acetoxynitrocholestane

In light of the ongoing pandemic caused by SARS-CoV-2, effective and clinically translatable treatments are desperately needed for COVID-19 and its emerging variants. In this study, some derivatives, including 7β-aminocholestene compounds, and 3β-acetoxy-6-nitrocholesta-4,6-diene were synthesized, in quantitative yields from 7β-bromo-6-nitrocholest-5-enes (1-3) with a small library of amines. The synthesized steroidal products were then thoroughly characterized using a range of physicochemical techniques, including IR, NMR, UV, MS, and elemental analysis. Next, a virtual screening based on structures using docking studies was conducted to investigate the potential of these synthesized compounds as therapeutic candidates against SARS-CoV-2. Specifically, we evaluated the compounds' binding energy of the reactants and their products with three SARS-CoV-2 functional proteins: the papain-like protease, 3C-like protease or main protease, and RNA-dependent RNA polymerase. Our results indicate that the 7β-aminocholestene derivatives (4-8) display intermediate to excellent binding energy, suggesting that they interact strongly with the receptor's active amino acids and may be promising drug candidates for inhibiting SARS-CoV-2. Although the starting steroid derivatives; 7β-bromo-6-nitrocholest-5-enes (1-3) and one steroid product; 3β-acetoxy-6-nitrocholesta-4,6-diene (9) exhibited strong binding energies with various SARS-CoV-2 receptors, they did not meet the Lipinski Rule and ADMET properties required for drug development. These compounds showed either mutagenic or reproductive/developmental toxicity when assessed using toxicity prediction software. The findings based on structure-based virtual screening, suggest that 7β-aminocholestaines (4-8) may be useful for reducing the susceptibility to SARS-CoV-2 infection. The docking pose of compound 4, which has a high score of -7.4 kcal mol-1, was subjected to AI-assisted deep learning to generate 60 AI-designed molecules for drug design. Molecular docking of these AI molecules was performed to select optimal candidates for further analysis and visualization. The cytotoxicity and antioxidant effects of 3β-acetoxy-6-nitrocholesta-4,6-diene were tested in vitro, showing marked cytotoxicity and antioxidant activity. To elucidate the molecular basis for these effects, steroidal compound 9 was subjected to molecular docking analysis to identify potential binding interactions. The stability of the top-ranked docking pose was subsequently assessed using molecular dynamics simulations.

Bellmunt risk score as a survival predictor in patients with metastatic castration-resistant prostate cancer

BACKGROUND

The prognosis of metastatic castration-resistant prostate cancer (mCRPC) is influenced by numerous individual factors. Despite various proposed prognostic models, the clinical application of these remains limited, probably due to complexity. Our study aimed to evaluate the predictive value of the Bellmunt risk score, which is well-known for urothelial carcinoma and easily assessed, in mCRPC patients.

METHODS

The Bellmunt risk score was calculated from three risk factors (Eastern Cooperative Oncology Group Performance Status (ECOG PS) ≥1, serum hemoglobin <10 g/dL, presence of liver metastases) in 125 patients who received first-line mCRPC treatment between 2005 and 2023. In addition, a modified score was established (one point each for hemoglobin <10 g/dL and the presence of liver metastases added to the ECOG PS). Associations with overall survival (OS) under first- and second-line therapy were tested using Cox regression analyzes, log-rank tests, concordance index (C-index) and time-dependent receiver operating characteristic.

RESULTS

There is a significant correlation between the level of the Bellmunt risk score and shorter OS (hazard ratio: 3.23, 95% confidence interval: 2.06-5.05; log-rank p < 0.001; C-index: 0.724). The semi-quantitative modified risk score showed even better prognostic discrimination (log-rank p < 0.001, C-index: 0.764). The score and its dynamics were also predictive in the second-line setting (log-rank p < 0.001 and = 0.01; C-index: 0.742 and 0.595).

CONCLUSIONS

The Bellmunt risk score is easy to assess and provides useful prognostic information in mCRPC, and can support physicians in their treatment decisions.

DHODH inhibition represents a therapeutic strategy and improves abiraterone treatment in castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) is an aggressive disease with poor prognosis, and there is an urgent need for more effective therapeutic targets to address this challenge. Here, we showed that dihydroorotate dehydrogenase (DHODH), an enzyme crucial in the pyrimidine biosynthesis pathway, is a promising therapeutic target for CRPC. The transcript levels of DHODH were significantly elevated in prostate tumors and were negatively correlated with the prognosis of patients with prostate cancer. DHODH inhibition effectively suppressed CRPC progression by blocking cell cycle progression and inducing apoptosis. Notably, treatment with DHODH inhibitor BAY2402234 activated androgen biosynthesis signaling in CRPC cells. However, the combination treatment with BAY2402234 and abiraterone decreased intratumoral testosterone levels and induced apoptosis, which inhibited the growth of CWR22Rv1 xenograft tumors and patient-derived xenograft organoids. Taken together, these results establish DHODH as a key player in CRPC and as a potential therapeutic target for advanced prostate cancer.

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

No consensus strategies exist for prognosticating metastatic castration-resistant prostate cancer (mCRPC). Circulating tumor DNA fraction (ctDNA%) is increasingly reported by commercial and laboratory tests but its utility for risk stratification is unclear. Here, we intersect ctDNA%, treatment outcomes, and clinical characteristics across 738 plasma samples from 491 male mCRPC patients from two randomized multicentre phase II trials and a prospective province-wide blood biobanking program. ctDNA% correlates with serum and radiographic metrics of disease burden and is highest in patients with liver metastases. ctDNA% strongly predicts overall survival, progression-free survival, and treatment response independent of therapeutic context and outperformed established prognostic clinical factors. Recognizing that ctDNA-based biomarker genotyping is limited by low ctDNA% in some patients, we leverage the relationship between clinical prognostic factors and ctDNA% to develop a clinically-interpretable machine-learning tool that predicts whether a patient has sufficient ctDNA% for informative ctDNA genotyping (available online: https://www.ctDNA.org ). Our results affirm ctDNA% as an actionable tool for patient risk stratification and provide a practical framework for optimized biomarker testing.

Factors influencing survival in metastatic castration resistant prostate cancer therapy

INTRODUCTION

The number of patients with metastatic castration resistant prostate cancer (mCRPC) is expecting to increase due to the long-life expectancy of those with advanced disease who are also more commonly diagnosed today because of stage migration. Several compounds are available for treating these patients.

AREAS COVERED

We reviewed currently available treatments for mCRPC, their mechanism of action and resistance and we explored possible predictors of treatment success useful to predict survival in mCRPC patients.

EXPERT OPINION

A combination of molecular, clinical, pathological, and imaging features is necessary to correctly estimate patients' risk of death. The combination of these biomarkers may allow clinicians to tailor treatments based on cancer history and patients' features. The search of predictive biomarkers remains an unmet medical need for most patients with mCRPC.