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Cancer Therapy: Preclinical |
Authors' Affiliations: 1 Roswell Park Cancer Institute and 2 State University of New York at Buffalo, Buffalo, New York
Requests for reprints: Meir Wetzler, Leukemia Section, Department of Medicine, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263. Phone: 716-845-8447; Fax: 716-845-2343; E-mail: meir.wetzler{at}roswellpark.org.
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Abstract |
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50% of acute myelogenous leukemia cases and associated with adverse treatment outcome, is down-regulated by arsenic trioxide (ATO). Heat shock protein (HSP) 90 is a molecular chaperone involved in signal transduction pathways. We hypothesized that HSP90 inhibitors will potentiate ATO effect on constitutive STAT3 activity and cell killing. One concern was that the effect of ATO and HSP90 inhibitors will result in up-regulation of HSP70, a protein known to inhibit apoptosis. Experimental Design: We have used a semimechanistic pharmacodynamic model to characterize concentration-effect relationships of ATO and HSP90 inhibitors on constitutive STAT3 activity, HSP70 expression, and cell death in a cell line model.
Results: Pharmacodynamic interaction of ATO and three HSP90 inhibitors showed synergistic interactions in inhibiting constitutive STAT3 activity and inducing cell death, in spite of a concurrent synergistic up-regulation of HSP70.
Conclusions: These preliminary results provide a basis for studying the combined role of ATO with HSP90 inhibitors in acute myelogenous leukemia with constitutive STAT3 activity.
Identifying the type and extent of drug-drug interactions has been a challenge since the early 1900s. When the mechanisms of action of two pharmacologic agents are not known, empirical drug-drug interaction models such as Loewe additivity (11), Bliss independence (12), or the Chou and Talalay method (13, 14) can be applied. When the true behavior is well appreciated, mechanistic models offer insight into the physiologic processes influencing the degree of interaction (15–17). The HSP90 inhibitors act by binding HSP90 and preventing the stabilization of "client" protein complexes, involving cancer targets such as mutated p53, Raf-1, ErbB2, and other proteins associated with signal transduction. On the other hand, the mechanism of ATO action toward DNA fragmentation and cell death is not completely understood. It is clear, however, that when given in combination, ATO and HSP90 inhibitors may interact noncompetitively through different pathways.
We examined the combined effects of each HSP90 inhibitor with ATO on constitutive STAT3, HSP70, and HSP90 protein levels using the Ariens noncompetitive functional interaction model (15, 16) with an interaction parameter (
). Interaction parameters may be useful in various mechanism-based models to account for the synergism or antagonism not predicted by the mechanistic expectations of the modeling scheme (17–19). The estimated value of this parameter indicates the intensity of the drug-drug interaction when compared with the no-interaction value (i.e., the value that does not influence the underlying mechanistic model, based on single drug effect alone). This interaction model is not limited to the level of mass-balance drug-receptor binding equations, but assumes that each drug contributes to the interaction after binding to their respective targets. Effect is assumed to be a function of bound drug-target, and the Hill equation relates single drug concentrations to effect.
The cell killing effects of ATO and 17-DMAG (currently in clinical trials) were captured in a time-dependent manner. A mechanistic drug-drug interaction model was developed, incorporating time-dependent natural cell growth and death in the system. A modified functional interaction model was used to characterize the type of interaction. These studies were designed to enhance the effect of ATO on constitutive STAT3 activity.
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Materials and Methods |
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Cell line and culture conditions. The AML cell line, HEL, a cytokine-independent human erythroleukemia cell line that has constitutive STAT3 activity, served as a model system. The cells were exposed for 6 to 48 h to ATO, geldanamycin, 17-AAG and 17-DMAG. Cell viability was determined by the trypan blue dye (Life Technologies, Grand Island, NY) exclusion assay.
Western blotting. Tyrosine phosphorylated (P) and unphosphorylated STAT3 were quantitated by Western blot analysis as previously described (1, 2, 20, 21). In brief, whole-cell extracts were separated on 7.5% polyacrylamide SDS gels and the proteins were transferred onto nitrocellulose membranes. The membranes were incubated with antibodies against PSTAT3 (Y705; Upstate Biotechnology, Lake Placid, NY). To detect nonphosphorylated proteins, immunoblots were reacted with antibody against the NH2 termini of STAT3 (Transduction Laboratories, Lexington, KY), HSP70 (R&D Systems, Minneapolis, MN), and HSP90 (Santa Cruz Biotechnology, Santa Cruz, CA). The immune complexes were visualized by the enhanced chemiluminescence reaction (Amersham Life Science, Arlington Heights, IL).
Interaction assays. All assays were conducted at least in triplicates. The Hill function was fitted to each concentration-response curve for each drug. After fitting and determination of the IC50, five combination ratios of the IC50 (ATO/HSP90 inhibitors; 1:1, 1:4, 4:1, 1.5:3, 3:1.5) were characterized.
Pharmacodynamic drug-drug interaction model. Pharmacodynamic drug-drug interactions on PSTAT3 were evaluated with the following noncompetitive equation relating concentrations of both drugs to the overall effect, E.
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| (A) |
The interactions of these drugs on the stimulation of HSP70 expression was characterized by a stimulatory version of Eq. A. All negative terms are made positive in this form, and Imax and IC50 parameters take on a stimulatory meaning (i.e., Smax, SC50).
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| (B) |
Equation A was originally proposed by Ariens (15) for drugs that interact noncompetitively and was modified by Chakraborty and Jusko (18) to include an interaction parameter, , describing a mutual influence of each drug on the IC50 value of the other. When < 1, less total drug is required to elicit the same response compared with either drug administered alone. This is denoted as apparent synergy. When > 1, more total drug is required to achieve the same maximal response and this is defined as apparent antagonism. When = 1, there is no mutual effect on the IC50 value of either drug. This condition is termed no-interaction and is our reference model based on the noncompetitive assumptions of Ariens functional interaction. The stimulatory function obeys these properties as well.
In the above equations, when the concentration of A or B is zero, the equation reduces to the form of the basic Hill function. For example, in Eq. A when concentrations of drug B equal zero
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| (C) |
is assumed to be 1 in the absence of either drug. In Eq. B, when concentrations of drug B equal zero
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| (D) |
is assumed to be 1 in the absence of either drug.
Pharmacodynamic drug-drug interaction model for time-dependent cell-killing data. Natural growth parameters of the system were determined by fitting the logistic function to live cell data without the presence of drug, over a 72-h time course. A natural cell loss term was necessary to account for the loss of cells noted in the viable cell counts reported. The underlying model for natural cell growth in the system is
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| (E) |
Cell survival data for single drug effect was best described by a linear time-dependent cell-killing model. Drug effect was modeled as a stimulation of natural cell loss.
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| (F) |
The full model for interaction included an empirical second-order term in which the effect is modified by an interaction in the presence of both drugs.
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| (G) |
. If > 0, then there is an enhancement of effect or apparent synergy. When < 0, there is an apparent antagonism. When = 0, this is the no-interaction reference model for this interaction paradigm.
Effect was reported as percentage of viable cells. The equation is
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Data analysis. Nonlinear regression fitting was done using ADAPT II software (22). For all models, to resolve the pharmacologic parameters (i.e., Imax, IC50, and Smax) specific to each drug, single drug equations were fit to both protein expression and cell survival data for incubation with drug A alone and with drug B alone. In the case of cell killing, the base model was fit to control data with no drug to initially resolve the growth parameters of the system. These parameters were then fixed and the single drug equation was used to fit single drug data. When fitting the interaction data and using full interaction models, the interaction parameter is the only parameter fitted in each model, allowing for an estimation of the potency of the interaction.
For all models, isobolograms were generated using a numerical bisection method to determine the value of drug A that produces the 50% effect while varying the amount of drug B in the system (23). For each concentration-effect model, isobole curves were generated for the fitted interaction and for the simulated no-interaction cases. For the no-interaction isobole, the interaction term was set to its no-interaction value of 1 or 0.
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Results |
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is fixed to 1.0) well characterized the data and the fitted parameters are listed in Table 1
. These plots and fittings suggest that geldanamycin, with the lowest IC50 value of 46.8 nmol/L, is the most potent inhibitor, whereas ATO exhibited the least potency with an IC50 of 1,334 nmol/L.
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is fixed to 1.0 for the single drug case). Paremeters for each model fitting are reported in Table 2
. Similar to PSTAT3 regulation, 17-AAG had the highest SC50, whereas geldanamycin had the lowest value. We have limited geldanamycin concentrations because it is dissolved in DMSO, a solvent with known toxicity. These single-drug pharmacologic effect parameters for effects on PSTAT3 and HSP70 were used in all subsequent drug-drug interaction models.
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, the interaction parameter. For all combinations of ATO with either geldanamycin, 17-AAG, or 17-DMAG, the fitted parameters indicate a mechanism-based synergy as the value of is <1.0, mutually reducing the apparent IC50 values of each compound for the given combination.
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= 1.14 for geldanamycin with ATO indicates mechanism-based antagonism on HSP70 up-regulation, as, in combination, mutually increases the SC50 of each compound. In contrast, interactions for ATO with either 17-AAG or 17-DMAG on HSP70 expression were apparently synergistic ( = 0.785 and 0.654, respectively).
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= 1.14), meaning that more drug is required to achieve the same effect. On the other hand, for all other combinations, the interaction lines are beneath the no-interaction lines (i.e., less total drug is required to achieve the same effect).
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Drug-drug effects on cell survival. Cell survival at 24 and 48 h and model fittings are shown in Fig. 7A and B
. The same concentrations were used for each incubation time. With increasing concentration, it is obvious that cell survival is diminished. However, the concentrations required to observe changes in cell death are 100- to 1,000-fold higher than those altering PSTAT3 and HSP70 protein expression. Additionally, the length of incubation time necessary to observe an effect at these higher concentrations is four to eight times longer than that necessary for changes in protein expression. By comparing the slopes of the data on the ATO-effect plane with the slopes of the data lying in the 17-DMAG-effect plane, ATO seems to have a greater effect on cell death (90-95% reduction at 48 h) than 17-DMAG (30% reduction at 48 h). It is also clear that fewer cells survive during a longer incubation period for both effect curves and for the combination of ATO and 17-DMAG as well.
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Isobolograms were also produced to compare joint effects of ATO and 17-DMAG on cell survival with their effects on protein expression (Fig. 7D). Isobolograms permit a visual comparison of the concentrations necessary to produce the half-maximal effect, independent of the magnitude of the interaction parameters in each model. For cell killing, it is apparent from model fittings that more 17-DMAG (IC50 500 nmol/L) is needed to kill half the cells than to down-regulate 50% of PSTAT3 (IC50 395 nmol/L). Strikingly, for ATO, significantly more drug is needed to achieve the same cell killing effect (IC50,6 h 80,000 nmol/L) than to down-regulate PSTAT3 (IC50 1334 nmol/L).
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Discussion |
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50% of AML cases). These models not only indicated the presence of a synergy in inhibiting PSTAT3 expression but also indicated that this synergy occurs despite a concurrent synergy in the up-regulation of HSP70. These findings are of special significance because others have shown that up-regulation of HSP70 in an AML cell line model inhibited cytarabine and etoposide-induced apoptosis (24). The interactions between ATO and HSP90 inhibitors have been previously studied using the Chou and Talalay method (13). However, several features of this popular approach necessitate an alternative method for assessing our drug-drug interactions. Their published equation for the mutually exclusive case is not derived rigorously and does not adequately address the mutually nonexclusive case (17). Additionally, Chou and Talalay did not indicate how to statistically distinguish between the mutually exclusive and nonexclusive cases, through differences in effect-slope values (17). This method also uses log linearization to simplify the analysis of the data. With the current software available, entire mechanistic equations (not requiring linearization) may be fitted by nonlinear regression, permitting statistical assessment of any interaction. Our approach is semimechanistic, does not require the distinction between mutually exclusive and nonexclusive interactions, and provides one statistical value indicating an overall intensity of the interaction. This semimechanistic pharmacodynamic model, a modified form of the Ariens noncompetitive functional interaction, was used to characterize concentration-effect relationships of ATO and HSP90 inhibitor effects on PSTAT3 and HSP70 protein levels.
Mechanistic models are often applied but may be incapable of characterizing intense apparent synergy or antagonism. Although mechanistically relevant, this model could not predict the degree of synergy observed without the addition of a parameter to account for the interaction (). The model was modified to include as a mutual factor of the EC50 values of both drugs. When this value was <1, its influence on both EC50 values indicated that less total drug for each agent was required to achieve the same effect, an apparent mechanism-based synergy. When the value was >1, more drug would be required, indicating an apparent mechanism-based antagonism. Although often empirical, the interaction parameters are useful not only for their ability to help the model capture the interaction better; however, more importantly, they permit a statistical measure of the interaction intensity.
The interaction parameters here were used to compare the degree of synergy between ATO and the different HSP90 inhibitors for each type of effect. For effects on PSTAT3 inhibition, the combination of ATO and geldanamycin had the lowest value of (0.662), indicating in the model that it took much less drug to achieve the same effect when compared with the other combinations. It is of interest that the most synergistic interaction affecting PSTAT3 (geldanamycin and ATO) had antagonistic effect on HSP70 ( = 1.14). This is the first comparison of the three HSP90 inhibitors with ATO; we do not have an explanation for this finding as we expected that inhibiting PSTAT3 will result in concomitant up-regulation of HSP70, as was seen with the other HSP90 inhibitors. Smith et al. (25) showed that in vitro comparison of 17-AAG and 17-DMAG, the two inhibitors that are being tested in clinical trials, resulted in more pronounced effect for 17-DMAG. Our results concur with these findings; the value for both inhibiting PSTAT3 and stimulating HSP70 were lower for 17-DMAG, indicating that much less drug was needed to achieve the same effect when compared with the other combination.
The analysis of the effects of these drugs on cell survival was limited to the combination of ATO and 17-DMAG due to better solubility of 17-DMAG in aqueous solutions and hence potential use in clinical trials. Cell survival studies with ATO and 17-DMAG were done to determine whether the synergy observed on PSTAT3 regulation was indeed a direct interaction on PSTAT3 regulation and not a synergy resulting from cell death. Interestingly, cell survival studies showed no significant drug effect after 6-h incubation. Therefore, incubations were extended for 24 and 48 h. The resulting data is time dependent, adding a fourth dimension. A mechanistic model for cell killing with an interaction term, similar to the modified functional interaction used previously, was developed to explain differences in a time-dependent manner. The model structures between cell survival and protein level differed significantly and any comparison of the interaction terms would certainly not yield the same degree of interaction. Therefore, isobolograms were used to compare the interaction between protein regulation at 6 h and the mechanistic time-dependent model prediction of cell killing at 6 h.
Isobolograms are useful because they allow a comparison of interactions between different types of effects. The lines in these plots represent all the combinations required to achieve a particular effect level (i.e., 50% of the maximal effect). Interactions for two different effects (with different scales or effect units) may be compared at one level (e.g., 50% maximal) by identifying the amount of each drug, for a given combination, required to elicit that response level. We used the isobolograms to compare the interactions between ATO and 17-DMAG on cell survival and PSTAT3 protein level after 6-h incubation. Our results show that at 6 h, the amount of drugs required to down-regulate PSTAT3 is significantly lower than the amount needed to cause cell death. These results suggest that combinations of these agents will need to be administered over prolonged periods of time to achieve cell death. Alternatively, these agents can be administered over a short period of time to down-regulate constitutive STAT3 activity and thus potentiate the effect of other drugs used to treat AML.
The IC50 values in our models were within the physiologic ranges [ATO (26, 27), 17-AAG (28), and 17-DMAG (29, 30)]. However, the drug effect was measured and modeled not only for the physiologically relevant concentrations but also beyond. Even then, some of the graphs do not display a maximum effect in the observed data. We caution regarding the utility of these models beyond the presented range of concentrations, as the maximal effect often influences the determination of other parameters such as EC50.
In summary, Parmar et al. (31) have shown that ATO alone, although effective in vitro in inducing apoptosis of AML cells, is not sufficiently effective in vivo in inducing remission in AML patients. Therefore, combination trials are needed. It is of interest that HSP90 inhibition sensitized AML cells to cytarabine in vitro (32). Finally, the combination of ATO with the HSP90 inhibitor, 17-AAG, has shown some promise in vitro (13); however, the pharmacodynamic modeling was not appropriate to address the mechanistic questions that the combination of these two type of drugs present. Of interest is the fact that both approaches, the one studying the interaction as a mutually exclusive case (13) and the one taking the mechanistic approach (current), reached the same conclusion. This supports further testing of combinations of ATO and 17-AAG or preferably 17-DMAG in preclinical AML in vivo models.
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Footnotes |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 10/17/06; revised 12/29/06; accepted 1/ 3/07.
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