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.

The mechanism of the pharmacological actions of dexrazoxane on different tissue types

Approximately 6,600 people die from acute myelogenous leukemia (AML) on an annual basis. During the past 10 to 15 years, there has been gradual overall improvements in the therapy of this disease, yet the majority of patients with AML succumb to this disease. In an attempt to improve current therapeutic strategies for AML, we became interested in a commercially available drug, dexrazoxane, which protects against anthracycline-induced cardiotoxicity. We have investigated dexrazoxane's (DEX) effects on different tissue types in an effort to determine its unique mechanism of action. Colony forming assays were used to evaluate stem-cell renewal of myeloid cells in vitro and median effect analysis was used to evaluate antagonism, synergism, or additivity. The anthracyclines, doxorubicin, daunorubicin, and idarubicin were individually combined with DEX in leukemic myeloid models to determine if the combination of the two drugs resulted in a synergistic, additive or antagonistic effect. Etoposide and cytosine arabinoside were also evaluated in combination with DEX using the same in vitro model and evaluated similarly. ^ Dexrazoxane in combination with any of the anthracyclines was schedule dependent. The combination of DEX and anthracycline resulted in a greater antitumor effect than anthracycline alone except for DEX administered 24 hours before doxorubicin or daunorubicin. These data were corroborated through median effect analysis. Etoposide in combination with dexrazoxane was synergistic for all combinations, and the combination of cytosine arabinoside and DEX was schedule dependent. In contrast, using an in vivo gastrointestinal model, DEX in combination with doxorubicin was antagonistic for almost all of the ratios used, except for the highest. A Withers' assay was used to evaluate toxicity on jejunal crypt cells. No effect was apparent for the combination of idarubicin and DEX, however, as seen with RZ, DEX in addition to radiation greatly potentiated the cytotoxic effects of radiation on crypts. These paradoxical effects of dexrazoxane were initially enigmatic, but after additional investigation, we propose a model that explains our findings. We conclude that DEX in combination with anthracyclines produces an additive to synergistic antileukemic response and may have therapeutic potential clinically. Additionally, DEX protects the gastrointestinal tract from doxorubicin toxicity, which could have clinical implications for the administration of greater doses of doxorubicin. ^

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. ^

Retrospective cohort study to determine the effect of metformin on survival of diabetic patients with pancreatic adenocarcinoma

Background: Pancreatic cancer is the fourth most common cause of cancer death in the United States. Despite advances in cancer treatment, prognosis of pancreatic cancer remains extremely poor with survival rates of 24% and 5% in 1 and 5 years, respectively. Many patients with pancreatic cancer have a history of diabetes and are treated with various antidiabetic regimens including metformin. In multiple retrospective studies, metformin has been associated with decreased risk of cancer and cancer-related mortality. Metformin has also been reported to inhibit the growth of cancer cells, both in vitro and in vivo.^ Methods: We conducted a retrospective cohort study to examine the survival benefit of metformin in diabetic patients with pancreatic cancer at MD Anderson Cancer Center (MDACC). A dataset of 397 patients who carried the diagnosis of "Diabetes Mellitus" and "Pancreatic Cancer" at MD Anderson were screened for this study. ^ Results: Mean age of patients at diagnosis of cancer was 64.0 ± 8.7 years (range 37-84). The majority of the patients were male (65.6%) and of Caucasian race (78.5%). The most common antidiabetic regimen used were insulin and metformin (in 39.1% and 38.7%, respectively). Patients' cancer were staged as resectable in 34.1%, locally advanced unresectable in 29.1%, and disseminated disease in 36.7% of cases. Overall 1-year and 3-year survival rates for all stages combined were 51.8% and 7.6%, respectively. Earlier stage, metformin use, low CA19-9 level, better ECOG performance status, surgical intervention, negative surgical margins, and smaller tumor size were associated with longer survival. Metformin use was associated with a 33% decrease in risk of death (HR: 0.67; 95% CI: 0.51-0.88). Multivariate Cox proportional hazard regression showed hazard ratio of 1.77 (95% CI 1.49-2.10) for cancer stage, 0.65 (95% CI 0.49-0.86) for metformin use, and 1.68 (95% CI 1.26-2.23) for CA 19-9 level above population median. ^ Conclusion: Our study suggests that metformin may improve the outcome in diabetic patients with pancreatic cancer independently of other known prognostic factors. Pancreatic cancer carries extremely poor prognosis; metformin may provide a suitable adjunct therapeutic option for pancreatic cancer in patients with and without diabetes mellitus.^

Study on the effect of metformin in patients of metastatic renal cell cancer with diabetes receiving tyrosine kinase inhibitor therapy

Objective: The primary objective of our study was to study the effect of metformin in patients of metastatic renal cell cancer (mRCC) and diabetes who are on treatment with frontline therapy of tyrosine kinase inhibitors. The effect of therapy was described in terms of overall survival and progression free survival. Comparisons were made between group of patients receiving metformin versus group of patients receiving insulin in diabetic patients of metastatic renal cancer on frontline therapy. Exploratory analyses were also done comparing non-diabetic patients of metastatic renal cell cancer receiving frontline therapy compared to diabetic patients of metastatic renal cell cancer receiving metformin therapy. ^ Methods: The study design is a retrospective case series to elaborate the response rate of frontline therapy in combination with metformin for mRCC patients with type 2 diabetes mellitus. The cohort was selected from a database, which was generated for assessing the effect of tyrosine kinase inhibitor therapy associated hypertension in metastatic renal cell cancer at MD Anderson Cancer Center. Patients who had been started on frontline therapy for metastatic renal cell carcinoma from all ethnic and racial backgrounds were selected for the study. The exclusion criteria would be of patients who took frontline therapy for less than 3 months or were lost to follow-up. Our exposure variable was treatment with metformin, which comprised of patients who took metformin for the treatment of type 2 diabetes at any time of diagnosis of metastatic renal cell carcinoma. The outcomes assessed were last available follow-up or date of death for the overall survival and date of progression of disease from their radiological reports for time to progression. The response rates were compared by covariates that are known to be strongly associated with renal cell cancer. ^ Results: For our primary analyses between the insulin and metformin group, there were 82 patients, out of which 50 took insulin therapy and 32 took metformin therapy for type 2 diabetes. For our exploratory analysis, we compared 32 diabetic patients on metformin to 146 non-diabetic patients, not on metformin. Baseline characteristics were compared among the population. The time from the start of treatment until the date of progression of renal cell cancer and date of death or last follow-up were estimated for survival analysis. ^ In our primary analyses, there was a significant difference in the time to progression of patients receiving metformin therapy vs insulin therapy, which was also seen in our exploratory analyses. The median time to progression in primary analyses was 1259 days (95% CI: 659-1832 days) in patients on metformin therapy compared to 540 days (95% CI: 350-894) in patients who were receiving insulin therapy (p=0.024). The median time to progression in exploratory analyses was 1259 days (95% CI: 659-1832 days) in patients on metformin therapy compared to 279 days (95% CI: 202-372 days) in non-diabetic group (p-value <0.0001). ^ The median overall survival was 1004 days in metformin group (95% CI: 761-1212 days) compared to 816 days (95%CI: 558-1405 days) in insulin group (p-value<0.91). For the exploratory analyses, the median overall survival was 1004 days in metformin group (95% CI: 761-1212 days) compared to 766 days (95%CI: 649-965 days) in the non-diabetic group (p-value<0.78). Metformin was observed to increase the progression free survival in both the primary and exploratory analyses (HR=0.52 in metformin Vs insulin group and HR=0.36 in metformin Vs non-diabetic group, respectively). ^ Conclusion: In laboratory studies and a few clinical studies metformin has been proven to have dual benefits in patients suffering from cancer and type 2-diabetes via its action on the mammalian target of Rapamycin pathway and effect in decreasing blood sugar by increasing the sensitivity of the insulin receptors to insulin. Several studies in breast cancer patients have documented a beneficial effect (quantified by pathological remission of cancer) of metformin use in patients taking treatment for breast cancer therapy. Combination of metformin therapy in patients taking frontline therapy for renal cell cancer may provide a significant benefit in prolonging the overall survival in patients with metastatic renal cell cancer and diabetes. ^