Improved mortality analysis in early-phase dose-ranging clinical trials for emergency medical diseases using Bayesian time-to-event models with active comparators.

Emergency medical diseases (EMDs) are the leading cause of death worldwide. A time-to-death analysis is needed to accurately identify the risks and describe the pattern of an EMD because the mortality rate can peak early and then decline. Dose-ranging Phase II clinical trials are essential for developing new therapies for EMDs. However, most dose-finding trials do not analyze mortality as a time-to-event endpoint. We propose three Bayesian dose-response time-to-event models for a secondary mortality analysis of a clinical trial: a two-group (active treatment vs control) model, a three-parameter sigmoid EMAX model, and a hierarchical EMAX model. The study also incorporates one specific active treatment as an active comparator in constructing three new models. We evaluated the performance of these six models and a very popular independent model using simulated data motivated by a randomized Phase II clinical trial focused on identifying the most effective hyperbaric oxygen dose to achieve favorable functional outcomes in patients with severe traumatic brain injury. The results show that the three-group, EMAX, and EMAX model with an active comparator produce the smallest averaged mean squared errors and smallest mean absolute biases. We provide a new approach for time-to-event analysis in early-phase dose-ranging clinical trials for EMDs. The EMAX model with an active comparator can provide valuable insights into the mortality analysis of new EMDs or other conditions that have changing risks over time. The restricted mean survival time, a function of the model's hazards, is recommended for displaying treatment effects for EMD research.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Women Physicians and Promotion in Academic Medicine.

BACKGROUND: In 2000, a landmark study showed that women who graduated from U.S. medical schools from 1979 through 1997 were less likely than their male counterparts to be promoted to upper faculty ranks in academic medical centers. It is unclear whether these differences persist. METHODS: We merged data from the Association of American Medical Colleges on all medical school graduates from 1979 through 2013 with faculty data through 2018, and we compared the percentages of women who would be expected to be promoted on the basis of the proportion of women in the graduating class with the actual percentages of women who were promoted. We calculated Kaplan-Meier curves and used adjusted Cox proportional-hazards models to examine the differences between the early cohorts (1979-1997) and the late cohorts (1998-2013). RESULTS: The sample included 559,098 graduates from 134 U.S. medical schools. In most of the cohorts, fewer women than expected were promoted to the rank of associate or full professor or appointed to the post of department chair. Findings were similar across basic science and clinical departments. In analyses that included all the cohorts, after adjustment for graduation year, race or ethnic group, and department type, women assistant professors were less likely than their male counterparts to be promoted to associate professor (hazard ratio, 0.76; 95% confidence interval [CI], 0.74 to 0.78). Similar sex disparities existed in promotions to full professor (hazard ratio, 0.77; 95% CI, 0.74 to 0.81) and appointments to department chair (hazard ratio, 0.46; 95% CI, 0.39 to 0.54). These sex differences in promotions and appointments did not diminish over time and were not smaller in the later cohorts than in the earlier cohorts. The sex differences were even larger in the later cohorts with respect to promotion to full professor. CONCLUSIONS: Over a 35-year period, women physicians in academic medical centers were less likely than men to be promoted to the rank of associate or full professor or to be appointed to department chair, and there was no apparent narrowing in the gap over time. (Funded by the University of Kansas Medical Center Joy McCann Professorship for Women in Medicine and the American Association of University Women.).

Designing and analyzing clinical trials for personalized medicine via Bayesian models.

Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.

T-cell trafficking plays an essential role in tumor immunity.

Distinct populations of effector memory T cells use different homing receptors to traffic to the skin and gut. Whether tissue-selective T cells are needed for early rejection of a neoplasm growing in these tissues remains an open question. We chose to study an allogeneic tumor model because growth of such a fully mismatched tumor would signify a profound immune deficit. We implanted allogeneic tumor cells in the skin or gut of mice deficient in either α(1,3) fucosyltransferases IV and VII, enzymes critical for generating E-selectin ligands on skin-homing T cells, or ß7 integrin, a component of the α4ß7 integrin ligand for the mucosal adressin MAdCAM. During the first 9 days after tumor implantation, FucTVII-/- mice showed a profoundly impaired capacity to reject tumors growing in the skin, but readily rejected tumors implanted in the gut. Rejection of tumors in the skin was even more impaired in mice deficient in both FucTIV and FucTVII. This impairment was corrected by infusion of T cells from normal mice. By contrast, ß7 integrin-/- mice showed profoundly impaired rejection of tumors in the gut, but no defect in the skin tumor rejection. These differences were unrelated to antigen recognition or effector function of T cells, since all strains of mice were capable of generating tumor-specific CTLs in vitro against the tumor cell line used in vivo. These results demonstrate that T-cell homing defects in vivo impair immune surveillance of peripheral epithelial tissues in a specific and selective fashion.

Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies.

BACKGROUND: Monitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise. METHODS: This article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application. RESULTS: First released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center. CONCLUSION: The Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.

Supplemental Screening With Automated Breast Ultrasound in Women With Dense Breasts: Comparing Notification Methods and Screening Behaviors.

OBJECTIVE: Supplemental screening with ultrasound has been shown to detect additional breast malignancies in women with dense breast tissue and normal mammogram findings. The frequency of supplemental screening with automated breast ultrasound and the effect and type of breast tissue density notification on automated screening breast ultrasound utilization rates are unknown. MATERIALS AND METHODS: We examined normal mammogram results letters for patients with heterogeneously or extremely dense breast tissue between July 1, 2013, and June 30, 2014, by type of results letter, notification method, and sociodemographic characteristics. Logistic regression was used to examine the association between type of results letter and subsequent automated screening breast ultrasound. RESULTS: Among 3012 women with dense breast tissue and normal mammogram findings, 15% returned for supplemental automated screening breast ultrasound within 18 months of results letter notification. Compared with a similarly sized control group of women who did not undergo automated ultrasound, a significantly greater proportion of patients (86.9%) returned for breast ultrasound if they received a results letter indicating breast density in combination with a courtesy phone call (p < 0.001). Patients who received results letters with breast density notification including a statement that they may benefit from additional screening with automated breast ultrasound examination were 9.91 times (95% CI, 6.08-16.16) more likely to return for the examination than patients who did not receive breast density notification or mention of supplemental screening. CONCLUSION: Patient breast density notification and radiologists' recommendations for supplemental screening with breast ultrasound increase patient utilization of automated screening breast ultrasound examinations.

Bayesian design for two-arm randomized Phase II clinical trials with endpoints from the exponential family using multiple constraints.

Frequentist design for two-arm randomized Phase II clinical trials with outcomes from the exponential dispersion family was proposed previously, where the total sample sizes are minimized under multiple constraints on the standard errors of the estimated group means and their difference. This design was generalized from an approach specific for dichotomous outcomes. The two previous approaches measure the central tendency of each group and treatment effect based on mean and difference in means. Other measures such as median or hazard ratio are more appropriate under certain situations. In addition, the frequentist approaches assume that unknown parameters are fixed values. This does not reflect the reality that uncertainty always exists for unknowns. Compared to the frequentist methods, the Bayesian approach offers a flexible way to measure central tendency and treatment effect, and incorporate uncertainty in parameters of interest into considerations. In this article, we generalize a Bayesian design for Phase II clinical trials with endpoints in the exponential family from the two previously developed frequentist approaches. The proposed design minimizes the total sample sizes under pre-specified constraints on the expected length of posterior credible intervals for measures of treatment effect and central tendency in each group. The design is applicable for trials with fixed or optimal randomization allocation ratio and can be applied under adaptive procedure. Examples of method implementations are provided for different types of endpoints from the exponential family in both fixed and adaptive settings.

Formula with long-chain polyunsaturated fatty acids reduces incidence of allergy in early childhood.

BACKGROUND: Allergy has sharply increased in affluent Western countries in the last 30 years. N-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs) may protect the immune system against development of allergy. METHODS: We prospectively categorized illnesses by body system in a subset of 91 children from the Kansas City cohort of the DIAMOND (DHA Intake and Measurement of Neural Development) study who had yearly medical records through 4 years of age. As infants, they were fed either a control formula without LCPUFA (n = 19) or one of three formulas with LCPUFA from docosahexaenoic acid (DHA) and arachidonic acid (ARA) (n = 72). RESULTS: Allergic illnesses in the first year were lower in the combined LCPUFA group compared to the control. LCPUFAs significantly delayed time to first allergic illness (p = 0.04) and skin allergic illness (p = 0.03) and resulted in a trend to reduced wheeze/asthma (p = 0.1). If the mother had no allergies, LCPUFAs reduced the risk of any allergic diseases (HR = 0.24, 95% CI = 0.1, 0.56, p = 0.0.001) and skin allergic diseases (HR = 0.35, 95% CI = 0.13, 0.93, p = 0.04). In contrast, if the mother had allergies, LCPUFAs reduced wheezing/asthma (HR = 0.26, 95% CI = 0.07, 0.9, p = 0.02). CONCLUSIONS: LCPUFA supplementation during infancy reduced the risk of skin and respiratory allergic diseases in childhood with effects influenced by maternal allergies.

Potential factors associated with perceived cognitive impairment in breast cancer survivors.

PURPOSE: This cross-sectional study was designed to explore potential factors associated with perceived cognitive impairment (PCI) in breast cancer survivors compared to controls and gain insight into perceived levels of severity for cognitive complaints. METHODS: Women (N = 363, 317: breast cancer, 46: healthy controls) completed demographic questionnaire, MD Anderson Symptom Inventory, Attentional Function Index, and Functional Assessment for Cancer Therapy-Cognition. Group classification included pre-chemotherapy, current chemotherapy, and postchemotherapy (<1, >1- < 2, >2- < 5, >5 years). RESULTS: A significant group effect was seen for PCI (F 6, 355 = 7.01, p < 0.0001). Controls reported less PCI than all other groups. Neuropathy was inversely correlated with PCI (r = -0.23; p < 0.0001) for participants with breast cancer. A significant association was demonstrated between exercise frequency and PCI in women exposed to chemotherapy (F 3, 135 = 3.78, p < 0.05). A multiple linear regression model built using forward selection methods explained 24 % of the variance (adjusted R (2)) for PCI in breast cancer participants and included group, body mass index (BMI), exercise, fatigue, and distress. Exercise frequency moderated the relationship between BMI and PCI for breast cancer participants (F 3, 198 = 2.4, p = 0.07) and reduced the negative effects of high BMI. The moderating effect of exercise was significant (F 3, 133 = 3.1, p = 0.03) when limited to participants exposed to chemotherapy. CONCLUSIONS: PCI decreased for women >5 years postchemotherapy. Overweight survivors who exercised frequently reported less PCI than sedentary survivors. Study results provide support for a relationship between BMI and PCI in breast cancer survivors and exercise as a potential intervention for cognitive complaints. Further investigation of the influence of weight and exercise on cognitive function is warranted.

Combined use of multiday palonosetron with aprepitant and low-dose dexamethasone in prevention of nausea and emesis among patients with multiple myeloma and lymphoma undergoing autologous hematopoietic stem cell transplant: A pilot study.

BACKGROUND: The current standard for prevention of chemotherapy-induced nausea/vomiting in autologous stem cell transplant only achieves 4-20% emetic control. OBJECTIVES: To assess emetic responses to multiday palonosetron, aprepitant, and low-dose dexamethasone among patients with myeloma and lymphoma undergoing autologous hematopoietic stem cell transplant. METHODS: Oral aprepitant 125/80/80 mg was administered with intravenous dexamethasone 4 mg and palonosetron 0.25 mg on days -3, -2, -1 for multiple myeloma and days -7 through -3 for lymphoma. Palonosetron was repeated day +3 in both groups. RESULTS: A total of 20 patients were enrolled and 18 analyzed. None experienced emetic failure with complete control achieved in 78, 33, and 17% in the acute, delayed, and extended phases, respectively. Nausea occurred in 78% although not significant in 61%, with median Nausea Visual Score of 4.5. Quality of life correlated with emetic and nausea control. Eight patients developed grade 2-3 nonhematologic toxicities with only one event attributed to the study medications. CONCLUSIONS: This triplet regimen was feasible with acceptable safety profile in the autologous hematopoietic stem cell transplant setting. Emetic control was best achieved in the acute phase. Lesser degree of emetic and nausea control in the delayed and extended phases impacted quality of life. Our results warrant further evaluation in a larger autologous hematopoietic stem cell transplant population.

Expediting Clinical and Translational Research via Bayesian Instrument Development.

Developing valid and reliable instruments is crucial but costly and time-consuming in health care research and evaluation. The Food and Drug Administration and the National Institutes of Health have set up guidelines for developing patient-reported outcome instruments. However, the guidelines are not applicable to cases of small sample sizes. Instead of using an exact estimation procedure to examine psychometric properties, our Bayesian Instrument Development (BID) method integrates expert data and participant data into a single seamless analysis. Using a novel set of priors, we use simulated data to compare BID to classical instrument development procedures and test the stability of BID. To display BID to non-statisticians, a graphical user interface based on R and WINBUGS is developed and demonstrated with data on a small sample of heart failure patients. Costs were saved by eliminating the need for unnecessary continuation of data collection for larger samples as required by the classical instrument development approach.

DHA supplementation for early preterm birth prevention: An application of Bayesian finite mixture models to adaptive clinical trial design optimization.

BACKGROUND: Early preterm birth (ePTB) - born before 34 weeks of gestation - poses a significant public health challenge. Two randomized trials indicated an ePTB reduction among pregnant women receiving high-dose docosahexaenoic acid (DHA) supplementation. One of them is Assessment of DHA on Reducing Early Preterm Birth (ADORE). A survey employed in its secondary analysis identified women with low DHA levels, revealing that they derived greater benefits from high-dose DHA supplementation. This survey's inclusion in future trials can provide critical insights for informing clinical practices. OBJECTIVE: To optimize a Phase III trial design, ADORE Precision, aiming at assessing DHA supplement (200 vs. 1000 mg/day) on reducing ePTB among pregnant women with a low baseline DHA. METHODS: We propose a Bayesian Hybrid Response Adaptive Randomization (RAR) Design utilizing a finite mixture model to characterize gestational age at birth. Subsequently, a dichotomized ePTB outcome is used to inform trial design using RAR. Simulation studies were conducted to compare a Fixed Design, an Adaptive Design with early stopping, an ADORE-like Adaptive RAR Design, and two new Hybrid Designs with different hyperpriors. DISCUSSION: Simulation reveals several advantages of the RAR designs, such as higher allocation to the more promising dose and a trial duration reduction. The proposed Hybrid RAR Designs addresses the statistical power drop observed in Adaptive RAR. The new design model shows robustness to hyperprior choices. We recommend Hybrid RAR Design 1 for ADORE Precision, anticipating that it will yield precise determinations, which is crucial for advancing our understanding in this field.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

Using a Bayesian model of the joint distribution of pain and time on medication to decide on pain medication for neuropathy.

The PAIN-CONTRoLS trial compared four medications in treating Cryptogenic sensory polyneuropathy. The primary outcome was a utility function that combined two outcomes, patients' pain score reduction and patients' quit rate. However, additional analysis of the individual outcomes could also be leveraged to inform selecting an optimal medication for future patients. We demonstrate how joint modeling of longitudinal and time-to-event data from PAIN-CONTRoLS can be used to predict the effects of medication in a patient-specific manner and helps to make patient-focused decisions. A joint model was used to evaluate the two outcomes while accounting for the association between the longitudinal process and the time-to-event processes. Results suggested no significant association between the patients' pain scores and time to the medication quit in the PAIN-CONTRoLS study, but the joint model still provided robust estimates and a better model fit. Using the model estimates, given patients' baseline characteristics, a drug profile on both the pain reduction and medication time could be obtained for each drug, providing information on how likely they would quit and how much pain reduction they should expect. Our analysis suggested that drugs viable for one patient may not be beneficial for others.

Peripherally inserted central catheters are equivalent to centrally inserted catheters in intensive care unit patients for central venous pressure monitoring.

To determine the equivalency of pressure measurements from peripherally inserted central catheters(PICCs) versus centrally inserted central venous catheters(CVCs) in vitro as well as in vivo. The in vitro study was performed in a clinical laboratory. Static pressure measurements from PICCs and CVCs were obtained in vitro over a physiologic range of 5­25 mmHg. Triple and dual lumen PICCs were directly compared to CVC controls.Dynamic pressure waveforms were recorded to simulate physiologic intravascular pressure variation. The in vivo study was executed in the medical intensive care unit(MICU) of a tertiary-level academic medical center. Data was collected from ten adult patients with both a PICC and a CVC in place for on-going clinical care. Measurements of central venous pressure (CVP) were recorded simultaneously from PICCs and CVCs. Duplicate measurements were taken after a stable waveform was recorded. For the in vitro study, a total of 540 pressure measurements were recorded. The average bias determined by Bland­Altman plot was 0 mmHg for the 5Fr PICC and 0.071 mmHg for the 6Fr PICC. The correlation coefficient for both catheters was 1.0 (P<0.001). Dynamic pressure waveforms revealed equivalent amplitude. During the in vivo trial, 70CVP measurements were collected. The paired CVP measurements were found to be highly reliable across subjects (r = 0.99, P<0.0001). No significance in the average difference in CVP measurement (PICC­CVC) was determined by the Wilcoxon Signed Rank test (S = 1,P = 0.93). In conclusion, PICCs are equivalent to CVCs when measuring static and dynamic pressure in vitro and CVP in ICU patients.

Predictors of early versus late smoking abstinence within a 24-month disease management program.

INTRODUCTION: Standard smoking cessation treatment studies have been limited to 6- to 12-month follow-up, and examination of predictors of abstinence has been restricted to this timeframe. The KanQuit study enrolled 750 rural smokers across all stages of readiness to stop smoking and provided pharmacotherapy management and/or disease management, including motivational interviewing (MI) counseling every 6 months over 2 years. This paper examines differences in predictors of abstinence following initial (6-month) and extended (24-month) intervention. METHODS: Baseline variables were analyzed as potential predictors of self-reported smoking abstinence at Month 6 and at Month 24. Chi-square tests, 2-sample t tests, and multiple logistic regression analyses were used to identify predictors of abstinence among 592 participants who completed assessment at baseline and Months 6 and 24. RESULTS: Controlling for treatment group, the final regression models showed that male gender and lower baseline cigarettes per day predicted abstinence at both 6 and 24 months. While remaining significant, the relative advantage of being male decreased over time. Global motivation to stop smoking, controlled motivation, and self-efficacy predicted abstinence at 6 months but did not predict abstinence at Month 24. In contrast, stage of change was strongly predictive of 24-month smoking status. CONCLUSIONS: While the importance of some predictors of successful smoking cessation appeared to diminish over time, initial lack of interest in cessation and number of cigarettes per day strongly predicted continued smoking following a 2-year program.

Improving the efficiency of clinical trials by standardizing processes for Investigator Initiated Trials.

Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

Programming of infant neurodevelopment by maternal obesity: potential role of maternal inflammation and insulin resistance.

BACKGROUND AND OBJECTIVES: Recent studies show that maternal obesity is associated with impaired offspring neurodevelopmental outcomes. The mechanism underlying the association is unclear. However, there is evidence to suggest a role for intra-uterine exposure to inflammation and insulin resistance (IR). We aimed to determine if maternal IR and inflammation were associated to fetal neurodevelopment as indicated by fetal heart rate variability (HRV), an index of fetal cardiac autonomic nervous system development. METHODS AND STUDY DESIGN: A total of 44 healthy maternal-fetal pairs (maternal pre-pregnancy BMI distribution: n=20 normal weight, 8 overweight, 16 obese) were analyzed. We assessed maternal inflammation (plasma IL-6 and TNF-α) and IR (HOMA index). Fetal HRV, a proxy for fetal neurodevelopment, was assessed using fetal magnetocardiogram at the 36th week of pregnancy. The relationships between maternal inflammation and IR with fetal HRV (SD1 and SD2) were estimated individually by Pearson bivariate correlations. RESULTS: No correlations were observed between the fetal HRV components with maternal HOMA-IR and maternal plasma levels of IL-6 and TNF-α (all p<0.05). However, the negative association between maternal TNF-α level and fetal SD2 approached significance (correlation coefficient=-0.29, 95% confidence interval=-0.62,-0.03, p=0.07). CONCLUSION: Maternal IR and inflammation during pregnancy were not associated with fetal cardiac autonomic nervous system development. Further studies with a larger sample size and more maternal inflammatory indicators are needed to explore these relationships.

A phase I study of intraperitoneal nanoparticulate paclitaxel (Nanotax®) in patients with peritoneal malignancies.

PURPOSE: This multicenter, open-label, dose-escalating, phase I study evaluated the safety, tolerability, pharmacokinetics and preliminary tumor response of a nanoparticulate formulation of paclitaxel (Nanotax®) administered intraperitoneally for multiple treatment cycles in patients with solid tumors predominantly confined to the peritoneal cavity for whom no other curative systemic therapy treatment options were available. METHODS: Twenty-one patients with peritoneal malignancies received Nanotax® in a modified dose-escalation approach utilizing an accelerated titration method. All patients enrolled had previously received chemotherapeutics and undergone surgical procedures, including 33 % with optimal debulking. Six doses (50-275 mg/m(2)) of Cremophor-free Nanotax® were administered intraperitoneally for one to six cycles (every 28 days). RESULTS: Intraperitoneal (IP) administration of Nanotax® did not lead to increases in toxicity over that typically associated with intravenous (IV) paclitaxel. No patient reported ≥Grade 2 neutropenia and/or ≥Grade 3 neurologic toxicities. Grade 3 thrombocytopenia unlikely related to study medication occurred in one patient. The peritoneal concentration-time profile of paclitaxel rose during the 2 days after dosing to peritoneal fluid concentrations 450-2900 times greater than peak plasma drug concentrations and remained elevated through the entire dose cycle. Best response assessments were made in 16/21 patients: Four patients were assessed as stable or had no response and twelve patients had increasing disease. Five of 21 patients with advanced cancers survived longer than 400 days after initiation of Nanotax® IP treatment. CONCLUSIONS: Compared to IV paclitaxel administration, Cremophor-free IP administration of Nanotax® provides higher and prolonged peritoneal paclitaxel levels with minimal systemic exposure and reduced toxicity.