BENCHMARKING AND IMPLEMENTATION OF A NEW INDEPENDENT MONTE CARLO DOSE CALCULATION QUALITY ASSURANCE AUDIT TOOL FOR CLINICAL TRIALS

Introduction Commercial treatment planning systems employ a variety of dose calculation algorithms to plan and predict the dose distributions a patient receives during external beam radiation therapy. Traditionally, the Radiological Physics Center has relied on measurements to assure that institutions participating in the National Cancer Institute sponsored clinical trials administer radiation in doses that are clinically comparable to those of other participating institutions. To complement the effort of the RPC, an independent dose calculation tool needs to be developed that will enable a generic method to determine patient dose distributions in three dimensions and to perform retrospective analysis of radiation delivered to patients who enrolled in past clinical trials. Methods A multi-source model representing output for Varian 6 MV and 10 MV photon beams was developed and evaluated. The Monte Carlo algorithm, know as the Dose Planning Method (DPM), was used to perform the dose calculations. The dose calculations were compared to measurements made in a water phantom and in anthropomorphic phantoms. Intensity modulated radiation therapy and stereotactic body radiation therapy techniques were used with the anthropomorphic phantoms. Finally, past patient treatment plans were selected and recalculated using DPM and contrasted against a commercial dose calculation algorithm. Results The multi-source model was validated for the Varian 6 MV and 10 MV photon beams. The benchmark evaluations demonstrated the ability of the model to accurately calculate dose for the Varian 6 MV and the Varian 10 MV source models. The patient calculations proved that the model was reproducible in determining dose under similar conditions described by the benchmark tests. Conclusions The dose calculation tool that relied on a multi-source model approach and used the DPM code to calculate dose was developed, validated, and benchmarked for the Varian 6 MV and 10 MV photon beams. Several patient dose distributions were contrasted against a commercial algorithm to provide a proof of principal to use as an application in monitoring clinical trial activity.

The local safety net, health insurance, preventable hospitalizations and costs: Harris County (Houston), Texas

The aim of this study was to examine the association between determinants of access to healthcare and preventable hospitalizations, based on Davidson et al.'s framework for evaluating the effects of individual and community determinants on access to healthcare. The study population consisted of the low income, non-elderly, hospitalized adults residing in Harris County, Texas in 2004. The objectives of this study were to examine the proportion of the variance in preventable hospitalizations at the ZIP-code level, to analyze the association between the proximity to the nearest safety net clinic and preventable hospitalizations, to examine how the safety net capacity relates to preventable hospitalizations, to compare the relative strength of the associations of health insurance and the proximity to the nearest safety net clinic with preventable hospitalizations, and to estimate and compare the costs of preventable hospitalizations in Harris County with the average cost in the literature. The data were collected from Texas Health Care Information Collection (2004), Census 2000, and Project Safety Net (2004). A total of 61,841 eligible individuals were included in the final data analysis. A random-intercept multi-level model was constructed with two different levels of data: the individual level and the ZIP-code level. The results of this study suggest that ZIP-code characteristics explain about two percent of the variance in preventable hospitalizations and safety net capacity was marginally significantly associated with preventable hospitalizations (p= 0.062). Proximity to the nearest safety net clinic was not related to preventable hospitalizations; however, health insurance was significantly associated with a decreased risk of preventable hospitalization. The average direct cost was $6,466 per preventable hospitalization, which is significantly different from reports in the literature. ^

Imaging of pulmonary embolism and t-PA therapy effects using MDCT and liposomal iohexol blood pool agent – preliminary results in a rabbit model

Hypothesis and Objectives PEGylated liposomal blood pool contrast agents maintain contrast enhancement over several hours. This study aimed to evaluate (long-term) imaging of pulmonary arteries, comparing conventional iodinated contrast with a liposomal blood pool contrast agent. Secondly, visualization of the (real-time) therapeutic effects of tissue-Plasminogen Activator (t-PA) on pulmonary embolism (PE) was attempted. Materials and Methods Six rabbits (approximate 4 kg weight) had autologous blood clots injected through the superior vena cava. Imaging was performed using conventional contrast (iohexol, 350 mg I/ml, GE HealthCare, Princeton, NJ) at a dose of 1400 mgI per animal and after wash-out, animals were imaged using an iodinated liposomal blood pool agent (88 mg I/mL, dose 900 mgI/animal). Subsequently, five animals were injected with 2mg t-PA and imaging continued for up to 4 ½ hours. Results Both contrast agents identified PE in the pulmonary trunk and main pulmonary arteries in all rabbits. Liposomal blood pool agent yielded uniform enhancement, which remained relatively constant throughout the experiments. Conventional agents exhibited non uniform opacification and rapid clearance post injection. Three out of six rabbits had mistimed bolus injections, requiring repeat injections. Following t-PA, Pulmonary embolus volume (central to segmental) decreased in four of five treated rabbits (range 10–57%, mean 42%). One animal showed no response to t-PA. Conclusions Liposomal blood pool agents effectively identified acute PE without need for re-injection. PE resolution following t-PA was quantifiable over several hours. Blood pool agents offer the potential for repeated imaging procedures without need for repeated (nephrotoxic) contrast injections

Imaging of pulmonary embolism and t-PA therapy effects using MDCT and liposomal iohexol blood pool agent: preliminary results in a rabbit model.

RATIONALE AND OBJECTIVES: Polyethylene glycol-coated liposomal blood pool contrast agents maintain contrast enhancement over several hours. This study aimed to evaluate (long-term) imaging of pulmonary arteries, comparing conventional iodinated contrast with a liposomal blood pool contrast agent. Also, visualization of the (real-time) therapeutic effects of tissue plasminogen activator (t-PA) on pulmonary embolism (PE) was attempted. MATERIALS AND METHODS: Six rabbits (weight approximately 4 kg) had autologous blood clots injected through the superior vena cava. Imaging was performed using conventional contrast (iohexol, 350 mg I/ml; GE HealthCare, Princeton, NJ) at a dose of 1400 mg I per animal, and after wash-out, animals were imaged using an iodinated liposomal blood pool agent (88 mg I/mL, dose 900 mg I/animal). Subsequently, five animals were injected with 2 mg of t-PA and imaging continued for up to 4(1/2) hours. RESULTS: Both contrast agents identified PE in the pulmonary trunk and main pulmonary arteries in all rabbits. Liposomal blood pool agent yielded uniform enhancement, which remained relatively constant throughout the experiments. Conventional agents exhibited nonuniform opacification and rapid clearance postinjection. Three of six rabbits had mistimed bolus injections, requiring repeat injections. Following t-PA, pulmonary embolus volume (central to segmental) decreased in four of five treated rabbits (range 10-57%, mean 42%). One animal showed no response to t-PA. CONCLUSIONS: Liposomal blood pool agents effectively identified acute PE without need for reinjection. PE resolution following t-PA was quantifiable over several hours. Blood pool agents offer the potential for repeated imaging procedures without need for repeated (nephrotoxic) contrast injections.

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^