The Association between a Decrease in On-Treatment Neutrophil-to-Eosinophil Ratio (NER) at Week 6 after Ipilimumab Plus Nivolumab Initiation and Improved Clinical Outcomes in Metastatic Renal Cell Carcinoma

A lower baseline neutrophil-to-eosinophil ratio (NER) has been associated with improved responses to immune checkpoint inhibitors (ICI)-treated metastatic renal cell carcinoma (mRCC). This study investigated the decrease in NER at week 6 after ipilimumab/nivolumab (ipi/nivo) initiation and treatment responses in mRCC. A retrospective study of ipi/nivo-treated mRCC at two US academic cancer centers was conducted. A landmark analysis at week 6 was performed to assess the association between the change in NER and clinical responses (progression-free survival (PFS)/overall survival (OS)). Week 6 NER was modeled as a continuous variable, after log transformation (Ln NER), and a categorical variable by percent change. There were 150 mRCC patients included: 78% had clear cell histology, and 78% were IMDC intermediate/poor risk. In multivariable regression analysis, every decrease of 1 unit of Ln NER at week 6 was associated with improved PFS (adjusted hazard ratio (AHR): 0.78, p-value:0.005) and OS (AHR: 0.67, p-value: 0.002). When NER was modeled by percent change, decreased NER > 50% was associated with improved PFS (AHR: 0.55, p-value: 0.03) and OS (AHR: 0.37, p-value: 0.02). The decrease in week 6 NER was associated with improved PFS/OS in ipi/nivo-treated mRCC. Prospective studies are warranted to validate NER change as a biomarker to predict ICI responses.

Current Status of Monoclonal Antibodies-Based Therapies in Castration-Resistant Prostate Cancer: A Systematic Review and Meta-Analysis of Clinical Trials

Background

Multiple patients with prostate cancer become resistant to castration therapies, which is termed castration-resistant prostate cancer (CRPC).

Purpose

The purpose of this review is to assess the status of efficacy (≥50% decline in prostate-specific antigen (PSA), progression-free survival (PFS), and overall survival (OS)) and safety (grade 3-4 adverse effects) of monoclonal antibodies in CRPC.

Data source

We searched databases including PubMed, Embase, Cochrane, Web of Science, and ClinicalTrials.gov.

Results

Hazard ratios of PFS and OS were 0.77 (95% CI = 0.69-0.87, I² = 53%) and 0.98 (95% CI = 0.86-1.11, I² = 40%), respectively, in the favor of monoclonal antibodies as compared to placebo. Risk ratio (RR) of >50% decline in PSA was 1.99 (95% CI = 0.97-4.08, I² = 53%) in favor of monoclonal antibodies. Pooled incidence of >50% decline in PSA levels was 15% (95% CI = 0.1-0.23, I² = 83%), 29% (95% CI = 0.14-0.51, I² = 93%), 63% (95% CI = 0.49-0.76, I² = 77%), and 88% (95% CI = 0.81-0.93, I² = 0%) in single, two, three, and four-drug regimens, respectively.

Conclusion

Monoclonal antibodies are well tolerated and showed better PFS as compared to placebo. However, OS was only improved with ipilimumab. Denosumab delayed skeletal-related adverse events as compared to zoledronic acid. More multicenter double-blind clinical trials may be needed to confirm these results.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.