Time-kill and synergism studies of ceftobiprole against Enterococcus faecalis, including beta-lactamase-producing and vancomycin-resistant isolates.

Ceftobiprole (BAL9141) is an investigational cephalosporin with broad in vitro activity against gram-positive cocci, including enterococci. Ceftobiprole MICs were determined for 93 isolates of Enterococcus faecalis (including 16 beta-lactamase [Bla] producers and 17 vancomycin-resistant isolates) by an agar dilution method following the Clinical and Laboratory Standards Institute recommendations. Ceftobiprole MICs were also determined with a high inoculum concentration (10(7) CFU/ml) for a subset of five Bla producers belonging to different previously characterized clones by a broth dilution method. Time-kill and synergism studies (with either streptomycin or gentamicin) were performed with two beta-lactamase-producing isolates (TX0630 and TX5070) and two vancomycin-resistant isolates (TX2484 [VanB] and TX2784 [VanA]). The MICs of ceftobiprole for 50 and 90% of the isolates tested were 0.25 and 1 microg/ml, respectively. All Bla producers and vancomycin-resistant isolates were inhibited by concentrations of

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

An Innovative Phase I Trial Design Allowing for the Identification of Multiple Potential Maximum Tolerated Doses with Combination Therapy of Targeted Agents

Treatment for cancer often involves combination therapies used both in medical practice and clinical trials. Korn and Simon listed three reasons for the utility of combinations: 1) biochemical synergism, 2) differential susceptibility of tumor cells to different agents, and 3) higher achievable dose intensity by exploiting non-overlapping toxicities to the host. Even if the toxicity profile of each agent of a given combination is known, the toxicity profile of the agents used in combination must be established. Thus, caution is required when designing and evaluating trials with combination therapies. Traditional clinical design is based on the consideration of a single drug. However, a trial of drugs in combination requires a dose-selection procedure that is vastly different than that needed for a single-drug trial. When two drugs are combined in a phase I trial, an important trial objective is to determine the maximum tolerated dose (MTD). The MTD is defined as the dose level below the dose at which two of six patients experience drug-related dose-limiting toxicity (DLT). In phase I trials that combine two agents, more than one MTD generally exists, although all are rarely determined. For example, there may be an MTD that includes high doses of drug A with lower doses of drug B, another one for high doses of drug B with lower doses of drug A, and yet another for intermediate doses of both drugs administered together. With classic phase I trial designs, only one MTD is identified. Our new trial design allows identification of more than one MTD efficiently, within the context of a single protocol. The two drugs combined in our phase I trial are temsirolimus and bevacizumab. Bevacizumab is a monoclonal antibody targeting the vascular endothelial growth factor (VEGF) pathway which is fundamental for tumor growth and metastasis. One mechanism of tumor resistance to antiangiogenic therapy is upregulation of hypoxia inducible factor 1α (HIF-1α) which mediates responses to hypoxic conditions. Temsirolimus has resulted in reduced levels of HIF-1α making this an ideal combination therapy. Dr. Donald Berry developed a trial design schema for evaluating low, intermediate and high dose levels of two drugs given in combination as illustrated in a recently published paper in Biometrics entitled “A Parallel Phase I/II Clinical Trial Design for Combination Therapies.” His trial design utilized cytotoxic chemotherapy. We adapted this design schema by incorporating greater numbers of dose levels for each drug. Additional dose levels are being examined because it has been the experience of phase I trials that targeted agents, when given in combination, are often effective at dosing levels lower than the FDA-approved dose of said drugs. A total of thirteen dose levels including representative high, intermediate and low dose levels of temsirolimus with representative high, intermediate, and low dose levels of bevacizumab will be evaluated. We hypothesize that our new trial design will facilitate identification of more than one MTD, if they exist, efficiently and within the context of a single protocol. Doses gleaned from this approach could potentially allow for a more personalized approach in dose selection from among the MTDs obtained that can be based upon a patient’s specific co-morbid conditions or anticipated toxicities.

Short-Term Plasticity in a Computational Model of the Tail-Withdrawal Circuit in Aplysia.

The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit responded preferentially to long-duration, low-frequency inputs.

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. ^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Estradiol and insulin cross -talk in breast cancer

Approximately 33% of clinical breast carcinomas require estrogens to proliferate. Epidemiological data show that insulin resistance and diabetes mellitus is 2–3 times more prevalent in women with breast cancer than those with benign breast lesions, suggesting a clinical link between insulin and estradiol. Insulin and estradiol have a synergistic effect on the growth of MCF7 breast cancer cells, and long-term estradiol treatment upregulates the expression of the key insulin signaling protein IRS-1. The goal of this study was to further define the mechanism(s) of cross-talk between insulin and estradiol in regulating the growth of breast cancer. Using MCF7 cells, acute treatment with insulin or estradiol alone was found to stimulate two activities associated with growth: Erk MAP kinase and PI 3-kinase. However, combined acute treatment had an antagonistic effect on both activities. Acute estradiol treatment inhibited the insulin-stimulated tyrosine phosphorylation of IRS-1 while increasing its serine phosphorylation; the serine phosphorylation was attenuated by the PI 3-kinase inhibitor wortmannin. The acute antagonism observed with combined estradiol and insulin are not consistent with the long-term synergistic effect on growth. In contrast, chronic estradiol treatment enhanced the insulin-sensitivity of breast cancer cells as measured by increases in total cellular insulin-stimulated tyrosine phosphorylation of IRS-1 and activation of PI 3-kinase. Estradiol stimulation of gene transcription was found to require PI 3-kinase activity but not MAP kinase activity. Insulin alone had no effect on ER transcriptional activity, but chronic treatment in combination with estradiol resulted in synergism of ER transcription. The synergistic effect of insulin and estradiol on MCF7 cell growth was also found to require PI 3-kinase but not MAP kinase activity. Therefore, chronic estradiol treatment increases insulin stimulation of PI 3-kinase, and PI 3-kinase is required for estradiol stimulation of gene transcription alone and in combined synergy with insulin. These data demonstrate that PI 3-kinase is the locus for the cross-talk between insulin and estradiol which results in enhanced breast cancer growth with long-term exposure to both hormones. This may have important clinical implications for women with high risk for breast cancer and/or diabetes mellitus. ^