Development and Characterization of a Novel Non-Lytic Cancer Immunotherapy Using a Recombinant Arenavirus Vector Platform.

Engineered viral vectors represent a promising strategy to trigger antigen-specific antitumor T cell responses. Arenaviruses have been widely studied because of their ability to elicit potent and protective T cell responses. Here, we provide an overview of a novel intravenously administered, replication-competent, non-lytic arenavirus-based vector technology that delivers tumor antigens to induce antigen-specific anti-cancer T cell responses. Preclinical studies in mice and cell culture experiments with human peripheral blood mononuclear cells demonstrate that arenavirus vectors preferentially infect antigen-presenting cells. This, in conjunction with a non-lytic functional activation of the infected antigen-presenting cells, leads to a robust antigen-specific CD8+ T cell response. T cell migration to, and infiltration of, the tumor microenvironment has been demonstrated in various preclinical tumor models with vectors encoding self- and non-self-antigens. The available data also suggest that arenavirus-based vector therapy can induce immunological memory protecting from tumor rechallenge. Based on promising preclinical data, a phase 1/2 clinical trial was initiated and is currently ongoing to test the activity and safety of arenavirus vectors, HB-201 and HB-202, created using lymphocytic choriomeningitis virus and Pichinde virus, respectively. Both vectors have been engineered to deliver non-oncogenic versions of the human papilloma virus 16 (HPV16) antigens E7 and E6 and will be injected intravenously with or without an initial intratumoral dose. This dose escalation/expansion study is being conducted in patients with recurrent or metastatic HPV16+ cancers. Promising preliminary data from this ongoing clinical study have been reported. Immunogenicity data from several patients demonstrate that a single injection of HB-201 or HB-202 monotherapy is highly immunogenic, as evidenced by an increase in inflammatory cytokines/chemokines and the expansion of antigen-specific CD8+ T cell responses. This response can be further enhanced by alternating injections of HB-202 and HB-201, which has resulted in frequencies of circulating HPV16 E7/E6-specific CD8+ T cells of up to 40% of the total CD8+ T cell compartment in peripheral blood in analyses to date. Treatment with intravenous administration also resulted in a disease control rate of 73% among 11 evaluable patients with head and neck cancer dosed every three weeks, including 2 patients with a partial response.

Phase I Study of the Prolactin Receptor Antagonist LFA102 in Metastatic Breast and Castration-Resistant Prostate Cancer.

LESSONS LEARNED: Despite evidence for a role for prolactin signaling in breast and prostate tumorigenesis, a prolactin receptor-binding monoclonal antibody has not produced clinical efficacy.Increased serum prolactin levels may be a biomarker for prolactin receptor inhibition.Results from the pharmacokinetic and pharmacodynamics (PD) studies suggest that inappropriately long dosing intervals and insufficient exposure to LFA102 may have resulted in lack of antitumor efficacy.Based on preclinical data, combination therapy of LFA102 with those novel agents targeting hormonal pathways in metastatic castration-resistant prostate cancer and metastatic breast cancer is promising.Given the PD evidence of prolactin receptor blockade by LFA102, this drug has the potential to be used in conditions such as hyperprolactinemia that are associated with high prolactin levels. BACKGROUND: Prolactin receptor (PRLR) signaling is implicated in breast and prostate cancer. LFA102, a humanized monoclonal antibody (mAb) that binds to and inhibits the PRLR, has exhibited promising preclinical antitumor activity. METHODS: Patients with PRLR-positive metastatic breast cancer (MBC) or metastatic castration-resistant prostate cancer (mCRPC) received doses of LFA102 at 3-60 mg/kg intravenously once every 4 weeks. Objectives were to determine the maximum tolerated dose (MTD) and/or recommended dose for expansion (RDE) to investigate the safety/tolerability of LFA102 and to assess pharmacokinetics (PK), pharmacodynamics (PD), and antitumor activity. RESULTS: A total of 73 patients were enrolled at 5 dose levels. The MTD was not reached because of lack of dose-limiting toxicities. The RDE was established at 60 mg/kg based on PK and PD analysis and safety data. The most common all-cause adverse events (AEs) were fatigue (44%) and nausea (33%) regardless of relationship. Grade 3/4 AEs reported to be related to LFA102 occurred in 4% of patients. LFA102 exposure increased approximately dose proportionally across the doses tested. Serum prolactin levels increased in response to LFA102 administration, suggesting its potential as a biomarker for PRLR inhibition. No antitumor activity was detected. CONCLUSION: Treatment with LFA102 was safe and well tolerated, but did not show antitumor activity as monotherapy at the doses tested.

Effect of temperature on microbial growth rate-mathematical analysis: the Arrhenius and Eyring-Polanyi connections.

The objective of this work is to develop a mathematical model for evaluating the effect of temperature on the rate of microbial growth. The new mathematical model is derived by combination and modification of the Arrhenius equation and the Eyring-Polanyi transition theory. The new model, suitable for both suboptimal and the entire growth temperature ranges, was validated using a collection of 23 selected temperature-growth rate curves belonging to 5 groups of microorganisms, including Pseudomonas spp., Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Escherichia coli, from the published literature. The curve fitting is accomplished by nonlinear regression using the Levenberg-Marquardt algorithm. The resulting estimated growth rate (µ) values are highly correlated to the data collected from the literature (R(2) = 0.985, slope = 1.0, intercept = 0.0). The bias factor (B(f) ) of the new model is very close to 1.0, while the accuracy factor (A(f) ) ranges from 1.0 to 1.22 for most data sets. The new model is compared favorably with the Ratkowsky square root model and the Eyring equation. Even with more parameters, the Akaike information criterion, Bayesian information criterion, and mean square errors of the new model are not statistically different from the square root model and the Eyring equation, suggesting that the model can be used to describe the inherent relationship between temperature and microbial growth rates. The results of this work show that the new growth rate model is suitable for describing the effect of temperature on microbial growth rate. Practical Application: Temperature is one of the most significant factors affecting the growth of microorganisms in foods. This study attempts to develop and validate a mathematical model to describe the temperature dependence of microbial growth rate. The findings show that the new model is accurate and can be used to describe the effect of temperature on microbial growth rate in foods.