17β-Estradiol Prevents Early-Stage Atherosclerosis in Estrogen Receptor-Alpha Deficient Female Mice

Estrogen is atheroprotective and a high-affinity ligand for both known estrogen receptors, ERα and ERβ. However, the role of the ERα in early-stage atherosclerosis has not been directly investigated and is incompletely understood. ERα-deficient (ERα−/−) and wild-type (ERα+/+) female mice consuming an atherogenic diet were studied concurrent with estrogen replacement to distinguish the actions of 17β-estradiol (E2) from those of ERα on the development of early atherosclerotic lesions. Mice were ovariectomized and implanted with subcutaneous slow-release pellets designed to deliver 6 or 8 μg/day of exogenous 17β-estradiol (E2) for a period of up to 4 months. Ovariectomized mice (OVX) with placebo pellets (E2-deficient controls) were compared to mice with endogenous E2 (intact ovaries) and exogenous E2. Aortas were analyzed for lesion area, number, and distribution. Lipid and hormone levels were also determined. Compared to OVX, early lesion development was significantly (p < 0.001) attenuated by E2 with 55–64% reduction in lesion area by endogenous E2 and >90% reduction by exogenous E2. Compared to OVX, a decline in lesion number (2- to 4-fold) and lesser predilection (~4-fold) of lesion formation in the proximal aorta also occurred with E2. Lesion size, development, number, and distribution inversely correlated with circulating plasma E2 levels. However, atheroprotection was independent of ERα status, and E2 athero-protection in both genotypes was not explained by changes in plasma lipid levels (total cholesterol, triglyceride, and high-density lipoprotein cholesterol). The ERα is not essential for endogenous/exogenous E2-mediated protection against early-stage atherosclerosis. These observations have potentially significant implications for understanding the molecular and cellular mechanisms and timing of estrogen action in different estrogen receptor (ER) deletion murine models of atherosclerosis, as well as implications to human studies of ER polymorphisms and lipid metabolism. Our findings may contribute to future improved clinical decision-making concerning the use of hormone therapy

Example depicting dose schedule definition for one cycle of treatment with <i>n</i> = 3 for all optimization classes.

<p>This schematic shows the process by which one cycle of treatment is defined for each optimization class with <i>n</i> = 3. A cycle in Class 1 or 3 contains a standard erlotinib dosing schedule of 150 mg/day, whereas a cycle in Class 2 contains a low-dose erlotinib schedule of 7 mg twice daily. When <i>n</i> = 3, each cycle has length <i>L</i> = 168 (one week). For Classes 1 and 2, the evofosfamide dose in each cycle is given 24 hours before the end of the week, and for Class 3 the evofosfamide dose in each cycle is given 6 hours before the end of the week. This is all depicted in step 1. Step 2 shows the removal of erlotinib doses required to satisfy the combination toxicity constraint. Each of these cycles is then repeated to form the entire dosing schedule.</p

Tumor evolutionary dynamics over time, given a variety of single-agent and combination therapies.

<p>Mean tumor size (A) and probability of resistance (B) are calculated up to recurrence time for a tumor with an initial population of 1.6 ⋅ 10⁶ sensitive cells undergoing treatment with each of the ten dosing schedules defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005077#pcbi.1005077.t003" target="_blank">Table 3</a>. Each labeled curve corresponds to the dosing schedule with the matching letter in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005077#pcbi.1005077.t003" target="_blank">Table 3</a>. For the sake of comparison, results due to dosing schedules using erlotinib alone are shown in red, results due to dosing schedules using evofosfamide alone are shown in blue, and results due to combination therapies are shown in green. Mean tumor size for one of each of these three types of dosing schedules is broken down into the means of sensitive and resistant cells in (C). (D) shows the expected tumor size for combination strategies, conditioned upon the event of developing resistance.</p

Tumor microenvironment modeling process.

<p>This schematic shows the process used to model the tumor microenvironment as a set of discrete compartments. A series of compartments is defined based on various distances from the nearest blood vessel, and the oxygen concentration in each compartment is calculated accordingly. The relative weights of the compartments are determined based on experimental observations of oxygen partial pressure distribution in solid tumors.</p

Dosing schedules considered in the comparison of single-agent and combination therapies.

<p>Each lettered column denotes a distinct dosing schedule containing repeating 3-week cycles defined by the dosing protocols in that column. The entries with subscripts of 1 are doses of erlotinib in mg and the entries with subscripts of 2 are doses of evofosfamide in mg/m². For a fixed schedule (column) and day (row), a single entry represents the one dose of either erlotinib or evofosfamide scheduled for that day. A missing entry for a fixed schedule and day corresponds to a day with neither erlotinib nor evofosfamide. Two entries for a given schedule on a single day represent the scheduling of two doses, either one dose of each drug or two doses of the same drug.</p

Leveraging Hypoxia-Activated Prodrugs to Prevent Drug Resistance in Solid Tumors

<div><p>Experimental studies have shown that one key factor in driving the emergence of drug resistance in solid tumors is tumor hypoxia, which leads to the formation of localized environmental niches where drug-resistant cell populations can evolve and survive. Hypoxia-activated prodrugs (HAPs) are compounds designed to penetrate to hypoxic regions of a tumor and release cytotoxic or cytostatic agents; several of these HAPs are currently in clinical trial. However, preliminary results have not shown a survival benefit in several of these trials. We hypothesize that the efficacy of treatments involving these prodrugs depends heavily on identifying the correct treatment schedule, and that mathematical modeling can be used to help design potential therapeutic strategies combining HAPs with standard therapies to achieve long-term tumor control or eradication. We develop this framework in the specific context of EGFR-driven non-small cell lung cancer, which is commonly treated with the tyrosine kinase inhibitor erlotinib. We develop a stochastic mathematical model, parametrized using clinical and experimental data, to explore a spectrum of treatment regimens combining a HAP, evofosfamide, with erlotinib. We design combination toxicity constraint models and optimize treatment strategies over the space of tolerated schedules to identify specific combination schedules that lead to optimal tumor control. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, (ii) sequentially alternating single doses of each drug leads to minimal tumor burden and maximal reduction in probability of developing resistance, and (iii) strategies minimizing the length of time after an evofosfamide dose and before erlotinib confer further benefits in reduction of tumor burden. These results provide insights into how hypoxia-activated prodrugs may be used to enhance therapeutic effectiveness in the clinic.</p></div

Drug tolerability data from clinical trials.

<p>Details regarding erlotinib and evofosfamide dosing schedules tested in clinical trials and how this data informs the construction of the toxicity constraint curve for each drug. The dose administered is shown in the first column, and the corresponding dosing schedule is shown in the second column. The third column shows whether or not this particular dosing schedule was tolerated in the clinical trial. The fourth column shows the ordered pair this dosing schedule corresponds to, and the last column shows the location of this point relative to the toxicity constraint curve. We assume that any point corresponding to a maximum tolerated dosing schedule tested in a clinical trial can either lie on or below the toxicity constraint curve to account for the possibility that a higher dose (which was not tested in the trial) is tolerated.</p

Toxicity constraint curves for erlotinib and evofosfamide.

<p>These curves depict the maximum tolerated doses of erlotinib (A) and evofosfamide (B) as functions of frequency of dose administration. The black points are the coordinates from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005077#pcbi.1005077.t002" target="_blank">Table 2</a> corresponding to tolerated dosing schedules, and the red points are the ordered pairs in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005077#pcbi.1005077.t002" target="_blank">Table 2</a> associated to dosing schedules that were not tolerated in clinical trials. All points contained in the areas on and below these two curves make up the space of tolerated monotherapy dosing schedules, and all points contained in the areas above these two curves make up the space of dosing schedules which lead to dose-limiting toxicities. The curves themselves represent the space of all monotherapy maximum tolerated dosing schedules.</p