Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Relevant drug to drug interactions between itraconazole and non-nucleoside reverse transcriptase inhibitors in HIV-infected patients on maintenance therapy for disseminated histoplasmosis

Objective. Itraconazole is recommended life-long for preventing relapse of disseminated histoplasmosis in HIV-infected patients. I sought to determine if serum itraconazole levels are affected by the type of Highly Active Anti-Retroviral Therapy (NNRTI or PI) being taken concomitantly to treat HIV. ^ Design. Retrospective cohort. ^ Methods. De-identified data were used from an IRB-approved parent study which identified patients on HAART and maintenance itraconazole for confirmed disseminated histoplasmosis between January 2003 and December 2006. Available itraconazole blood levels were abstracted as well as medications taken by each patient at the time of the blood tests. Mean itraconazole levels were compared using the student's t-test. ^ Results. 11 patients met study criteria. Patient characteristics were: median age 36, 91% men, 18% white, 18% black, 55% Hispanic and 9% Asians, median CD4 cell count 120 cells/mm3. 14 blood levels were available for analysis—8 on PI, 4 on NNRTI and 2 on both. 8/8 itraconazole levels obtained while taking concomitant PI were therapeutic (>0.4 μg/mL) in contrast to 0/4 obtained while taking NNRTI. Two patients switched from NNRTI to PI and reached therapeutic levels. Mean levels on NNRTI (0.05 μg/mL, s.d. 0.0) and on PI (2.45 μg/mL, s.d. 0.21) for these two patients were compared via a paired t-test (t = 16.00, d.f. = 1, P = 0.04). Remaining patient levels were compared using an unpaired t-test. Mean itraconazole on concomitant PI (n = 6) was 1.37 μg/mL (s.d. 0.74), while the mean on concomitant NNRTI was 0.05 μg/mL (s.d. 0.0), t = 2.39, d.f. = 6, P = 0.05. ^ Conclusions. Co-administration of NNRTI and itraconazole results in significant decreases in itraconazole blood levels, likely by inducing the CYP3A4 enzyme system. Itraconazole drug levels should be monitored in patients on concomitant NNRTI. PI-based HAART may be preferred over NNRTI-based HAART when using itraconazole to treat HIV-infected patients with disseminated histoplasmosis. ^

Effect of indomethacin on bile acid-phospholipid interactions: implication for small intestinal injury induced by nonsteroidal anti-inflammatory drugs.

The injurious effect of nonsteroidal anti-inflammatory drugs (NSAIDs) in the small intestine was not appreciated until the widespread use of capsule endoscopy. Animal studies found that NSAID-induced small intestinal injury depends on the ability of these drugs to be secreted into the bile. Because the individual toxicity of amphiphilic bile acids and NSAIDs directly correlates with their interactions with phospholipid membranes, we propose that the presence of both NSAIDs and bile acids alters their individual physicochemical properties and enhances the disruptive effect on cell membranes and overall cytotoxicity. We utilized in vitro gastric AGS and intestinal IEC-6 cells and found that combinations of bile acid, deoxycholic acid (DC), taurodeoxycholic acid, glycodeoxycholic acid, and the NSAID indomethacin (Indo) significantly increased cell plasma membrane permeability and became more cytotoxic than these agents alone. We confirmed this finding by measuring liposome permeability and intramembrane packing in synthetic model membranes exposed to DC, Indo, or combinations of both agents. By measuring physicochemical parameters, such as fluorescence resonance energy transfer and membrane surface charge, we found that Indo associated with phosphatidylcholine and promoted the molecular aggregation of DC and potential formation of larger and isolated bile acid complexes within either biomembranes or bile acid-lipid mixed micelles, which leads to membrane disruption. In this study, we demonstrated increased cytotoxicity of combinations of bile acid and NSAID and provided a molecular mechanism for the observed toxicity. This mechanism potentially contributes to the NSAID-induced injury in the small bowel.

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. ^