Immunogenicity in Clinical Practice and Drug Development: When is it Significant?

Managing immunogenicity in clinical practice and during drug development was a recent topic at the ASCPT 2019 annual meeting. This commentary expands on the discussion to facilitate a broader engagement across the community. The intent is to provide a rationale for ongoing research into the current gaps in assessing and interpreting immunogenicity in drug development and managing clinical immunogenicity for an approved drug. The following are highlighted: (i) Immunogenicity Considerations in Clinical Practice, (ii) Immunogenicity Testing and Current Limitations, (iii) Immunogenicity Risk Assessment and Mitigation, and (iv) Quantitative Systems Pharmacology (QSP) models of Immunogenicity

HuR cytoplasmic expression is associated with increased cyclin A expression and poor outcome with upper urinary tract urothelial carcinoma

BACKGROUND: HuR is an RNA-binding protein that post-transcriptionally modulates the expressions of various target genes implicated in carcinogenesis, such as CCNA2 encoding cyclin A. No prior study attempted to evaluate the significance of HuR expression in a large cohort with upper urinary tract urothelial carcinomas (UTUCs). METHODS: In total, 340 cases of primary localized UTUC without previous or concordant bladder carcinoma were selected. All of these patients received ureterectomy or radical nephroureterectomy with curative intents. Pathological slides were reviewed, and clinical findings were collected. Immunostaining for HuR and cyclin A was performed and evaluated by using H-score. The results of cytoplasmic HuR and nuclear cyclin A expressions were correlated with disease-specific survival (DSS), metastasis-free survival (MeFS), urinary bladder recurrence-free survival (UBRFS), and various clinicopathological factors. RESULTS: HuR cytoplasmic expression was significantly related to the pT status, lymph node metastasis, a higher histological grade, the pattern of invasion, vascular and perineurial invasion, and cyclin A expression (p = 0.005). Importantly, HuR cytoplasmic expression was strongly associated with a worse DSS (p < 0.0001), MeFS (p < 0.0001), and UBRFS (p = 0.0370) in the univariate analysis, and the first two results remained independently predictive of adverse outcomes (p = 0.038, relative risk [RR] = 1.996 for DSS; p = 0.027, RR = 1.880 for MeFS). Cyclin A nuclear expression was associated with a poor DSS (p = 0.0035) and MeFS (p = 0.0015) in the univariate analysis but was not prognosticatory in the multivariate analyses. High-risk patients (pT3 or pT4 with/without nodal metastasis) with high HuR cytoplasmic expression had better DSS if adjuvant chemotherapy was performed (p = 0.015). CONCLUSIONS: HuR cytoplasmic expression was correlated with adverse phenotypes and cyclin A overexpression and also independently predictive of worse DSS and MeFS, suggesting its roles in tumorigenesis or carcinogenesis and potentiality as a prognostic marker of UTUC. High HuR cytoplasmic expression might identify patients more likely to be beneficial for adjuvant chemotherapy

Development of Deep Learning with RDA U-Net Network for Bladder Cancer Segmentation

In today’s high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resolution black-and-white image composed of gray scales. It is expected to assist doctors in making judgments through deep learning based on the image recognition technology of artificial intelligence. It used CT images to identify the bladder and lesions and then segmented them in the images. The images can achieve high accuracy without using a developer. In this study, the U-Net neural network, commonly used in the medical field, was used to extend the encoder position in combination with the ResBlock in ResNet and the Dense Block in DenseNet, so that the training could maintain the training parameters while reducing the overall identification operation time. The decoder could be used in combination with Attention Gates to suppress the irrelevant areas of the image while paying attention to significant features. Combined with the above algorithm, we proposed a Residual-Dense Attention (RDA) U-Net model, which was used to identify organs and lesions from CT images of abdominal scans. The accuracy (ACC) of using this model for the bladder and its lesions was 96% and 93%, respectively. The values of Intersection over Union (IoU) were 0.9505 and 0.8024, respectively. Average Hausdorff distance (AVGDIST) was as low as 0.02 and 0.12, respectively, and the overall training time was reduced by up to 44% compared with other convolution neural networks

Individualized Dosing of Therapeutic Monoclonal Antibodies—a Changing Treatment Paradigm?

The introduction of monoclonal antibodies (mAbs) to the treatment of inflammatory bowel disease (IBD) was an important medical milestone. MAbs have been demonstrated as safe and efficacious treatments of IBD. However, a large percentage of patients either fail to respond initially or lose response to therapy after a period of treatment. Although there are factors associated with poor treatment outcomes in IBD, one cause for treatment failure may be low mAb exposure. Consequently, gastroenterologists have begun using therapeutic drug monitoring (TDM) to guide dose adjustment. However, while beneficial, TDM does not provide sufficient information to effectively adjust doses. The pharmacokinetics (PK) and pharmacodynamics (PD) of mAbs are complex, with numerous factors impacting on mAb PK and PD. The concept of dashboard-guided dosing based on Bayesian PK models allows physicians to combine TDM with factors influencing mAb PK to individualize therapy more effectively. One issue with TDM has been the slow turnaround of assay results, either necessitating an additional clinic visit for a sample or reacting to TDM results at a subsequent, rather than the current, dose. New point-of-care (POC) assays for mAbs are being developed that would potentially allow physicians to determine drug concentration quickly. However, work remains to understand how to determine what target exposure is needed for an individual patient, and whether the combination of POC assays and dashboards presents a safe approach with substantial outcome benefit over the current standard of care

Monascuspiloin enhances the radiation sensitivity of human prostate cancer cells by stimulating endoplasmic reticulum stress and inducing autophagy.

Prostate cancer is a very common cancer among males. Traditional treatments for prostate cancer have limited efficacy; therefore, new therapeutic strategies and/or new adjuvant drugs must be explored. Red yeast rice (RYR) is a traditional food spice made in Asia by fermenting white rice with Monascus purpureus Went yeast. Accumulating evidence indicates that RYR has antitumor activity. In this study, PC-3 cells (human prostate cancer cells) were used to investigate the anti-cancer effects of ionizing radiation (IR) combined with monascuspiloin (MP, a yellow pigment isolated from Monascus pilosus M93-fermented rice) and to determine the underlying mechanisms of these effects in vitro and in vivo. We found that IR combined with MP showed increased therapeutic efficacy when compared with either treatment alone in PC-3 cells. In addition, the combined treatment enhanced DNA damage and endoplasmic reticulum (ER) stress. The combined treatment induced primarily autophagy in PC-3 cells, and the cell death that was induced by the combined treatment was chiefly the result of inhibition of the Akt/mTOR signaling pathways. In an in vivo study, the combination treatment showed greater anti-tumor growth effects. These novel findings suggest that the combined treatment could be a potential therapeutic strategy for prostate cancer