A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

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. ^

Design, synthesis and cellular and molecular pharmacology of 2$\sp\prime$-bromo-4$\sp\prime$-epidaunorubicin (WP401): A novel non-cross-resistant anthracycline antibiotic with the intrinsic ability to circumvent P -glycoprotein-mediated drug resistance

We designed and synthesized a novel daunorubicin (DNR) analogue that effectively circumvents P-glycoprotein (P-gp)-mediated drug resistance. The fully protected carbohydrate intermediate 1,2-dibromoacosamine was prepared from acosamine and effectively coupled to daunomycinone in high yield. Deprotection under alkaline conditions yielded 2$\sp\prime$-bromo-4$\sp\prime$-epidaunorubicin (WP401). The in vitro cytotoxicity and cellular and molecular pharmacology of WP401 were compared with those of DNR in a panel of wild-type cell lines (KB-3-1, P388S, and HL60S) and their multidrug-resistant (MDR) counterparts (KB-V1, P388/DOX, and HL60/DOX). Fluorescent spectrophotometry, flow cytometry, and confocal laser scanning microscopy were used to measure intracellular accumulation, retention, and subcellular distribution of these agents. All MDR cell lines exhibited reduced DNR uptake that was restored, upon incubation with either verapamil (VER) or cyclosporin A (CSA), to the level found in sensitive cell lines. In contrast, the uptake of WP401 was essentially the same in the absence or presence of VER or CSA in all tested cell lines. The in vitro cytotoxicity of WP401 was similar to that of DNR in the sensitive cell lines but significantly higher in resistant cell lines (resistance index (RI) of 2-6 for WP401 vs 75-85 for DNR). To ascertain whether drug-mediated cytotoxicity and retention were accompanied by DNA strand breaks, DNA single- and double-strand breaks were assessed by alkaline elution. High levels of such breaks were obtained using 0.1-2 $\mu$g/mL of WP401 in both sensitive and resistant cells. In contrast, DNR caused strand breaks only in sensitive cells and not much in resistant cells. We also compared drug-induced DNA fragmentation similar to that induced by DNR. However, in P-gp-positive cells, WP401 induced 2- to 5-fold more DNA fragmentation than DNR. This increased DNA strand breakage by WP401 was correlated with its increased uptake and cytotoxicity in these cell lines. Overall these results indicate that WP401 is more cytotoxic than DNR in MDR cells and that this phenomenon might be related to the reduced basicity of the amino group and increased lipophilicity of WP401. ^