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. ^

Maximizing efficacy of the hypomethylating drug 5-aza-2'-deoxycytidine in human leukemia

5-aza-2'-deoxycytidine (DAC) is a cytidine analogue that strongly inhibits DNA methylation, and was recently approved for the treatment of myelodysplastic syndromes (MDS). To maximize clinical results with DAC, we investigated its use as an anti-cancer drug. We also investigated mechanisms of resistance to DAC in vitro in cancer cell lines and in vivo in MDS patients after relapse. We found DAC sensitized cells to the effect of 1-β-D-Arabinofuranosylcytosine (Ara-C). The combination of DAC and Ara-C or Ara-C following DAC showed additive or synergistic effects on cell death in four human leukemia cell lines in vitro, but antagonism in terms of global methylation. RIL gene activation and H3 lys-9 acetylation of short interspersed elements (Alu). One possible explanation is that hypomethylated cells are sensitized to cell killing by Ara-C. Turning to resistance, we found that the IC50 of DAC differed 1000 fold among and was correlated with the dose of DAC that induced peak hypomethylation of long interspersed nuclear elements (LINE) (r=0.94, P<0.001), but not with LINE methylation at baseline (r=0.05, P=0.97). Sensitivity to DAC did not significantly correlate with sensitivity to another hypomethylating agent 5-azacytidine (AZA) (r=0.44, P=0.11). The cell lines most resistant to DAC had low dCK, hENT1, and hENT2 transporters and high cytosine deaminase (CDA). In an HL60 leukemia cell line, resistance to DAC could be rapidly induced by drug exposure, and was related to a switch from monoallelic to biallelic mutation of dCK or a loss of wild type DCK allele. Furthermore, we showed that DAC induced DNA breaks evidenced by histone H2AX phosphorylation and increased homologous recombination rates 7-10 folds. Finally, we found there were no dCK mutations in MDS patients after relapse. Cytogenetics showed that three of the patients acquired new abnormalities at relapse. These data suggest that in vitro spontaneous and acquired resistance to DAC can be explained by insufficient incorporation of drug into DNA. In vivo resistance to DAC is likely due to methylation-independent pathways such as chromosome changes. The lack of cross resistance between DAC and AZA is of potential clinical relevance, as is the combination of DAC and Ara-C. ^