15 research outputs found

    Directed Evolution Generates a Novel Oncolytic Virus for the Treatment of Colon Cancer

    Get PDF
    Background Viral-mediated oncolysis is a novel cancer therapeutic approach with the potential to be more effective and less toxic than current therapies due to the agents selective growth and amplification in tumor cells. To date, these agents have been highly safe in patients but have generally fallen short of their expected therapeutic value as monotherapies. Consequently, new approaches to generating highly potent oncolytic viruses are needed. To address this need, we developed a new method that we term “Directed Evolution” for creating highly potent oncolytic viruses. Methodology/Principal Findings Taking the “Directed Evolution” approach, viral diversity was increased by pooling an array of serotypes, then passaging the pools under conditions that invite recombination between serotypes. These highly diverse viral pools were then placed under stringent directed selection to generate and identify highly potent agents. ColoAd1, a complex Ad3/Ad11p chimeric virus, was the initial oncolytic virus derived by this novel methodology. ColoAd1, the first described non-Ad5-based oncolytic Ad, is 2–3 logs more potent and selective than the parent serotypes or the most clinically advanced oncolytic Ad, ONYX-015, in vitro. ColoAd1's efficacy was further tested in vivo in a colon cancer liver metastasis xenograft model following intravenous injection and its ex vivo selectivity was demonstrated on surgically-derived human colorectal tumor tissues. Lastly, we demonstrated the ability to arm ColoAd1 with an exogenous gene establishing the potential to impact the treatment of cancer on multiple levels from a single agent. Conclusions/Significance Using the “Directed Evolution” methodology, we have generated ColoAd1, a novel chimeric oncolytic virus. In vitro, this virus demonstrated a >2 log increase in both potency and selectivity when compared to ONYX-015 on colon cancer cells. These results were further supported by in vivo and ex vivo studies. Furthermore, these results have validated this methodology as a new general approach for deriving clinically-relevant, highly potent anti-cancer virotherapies

    DNA methylation-based measures of biological age:meta-analysis predicting time to death

    Get PDF
    Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p ≤ 8.2 x 10-9), independent of chronological age, even after adjusting for additional risk factors (p < 5.4 x 10-4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p≤ 7.5 x 10-43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality

    Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care.

    No full text
    Inadequate quality of care in healthcare facilities is one of the primary causes of patient mortality in low- and middle-income countries, and understanding the behavior of healthcare providers is key to addressing it. Much of the existing research concentrates on improving resource-focused issues, such as staffing or training, but these interventions do not fully close the gaps in quality of care. By contrast, there is a lack of knowledge regarding the full contextual and internal drivers-such as social norms, beliefs, and emotions-that influence the clinical behaviors of healthcare providers. We aimed to provide two conceptual frameworks to identify such drivers, and investigate them in a facility setting where inadequate quality of care is pronounced. Using immersion interviews and a novel decision-making game incorporating concepts from behavioral science, we systematically and qualitatively identified an extensive set of contextual and internal behavioral drivers in staff nurses working in reproductive, maternal, newborn, and child health (RMNCH) in government public health facilities in Uttar Pradesh, India. We found that the nurses operate in an environment of stress, blame, and lack of control, which appears to influence their perception of their role as often significantly different from the RMNCH program's perspective. That context influences their perceptions of risk for themselves and for their patients, as well as self-efficacy beliefs, which could lead to avoidance of responsibility, or incorrect care. A limitation of the study is its use of only qualitative methods, which provide depth, rather than prevalence estimates of findings. This exploratory study identified previously under-researched contextual and internal drivers influencing the care-related behavior of staff nurses in public facilities in Uttar Pradesh. We recommend four types of interventions to close the gap between actual and target behaviors: structural improvements, systemic changes, community-level shifts, and interventions within healthcare facilities
    corecore