Metformin shows anti-neoplastic properties by inhibition of oxidative phosphorylation and glycolysis in epidermolysis bullosa-associated aggressive cutaneous squamous cell carcinoma.

BACKGROUND: While most cutaneous squamous cell carcinomas (cSCCs) are treatable, certain high-risk cSCCs, such as those in recessive dystrophic epidermolysis bullosa (RDEB) patients, are particularly aggressive. Owing to repeated wounding, inflammation and unproductive healing, RDEB patients have a 68% cumulative risk of developing life-threatening cSCCs by the age of 35, and a 70% risk of death by the age of 45. Despite aggressive treatment, cSCC represents the leading cause of premature mortality in these patients, highlighting an unmet clinical need. Increasing evidence points to a role of altered metabolism in the initiation and maintenance of cSCC, making metabolism a potential therapeutic target. OBJECTIVES: We sought to determine the feasibility of targeting tumour cell energetics as a strategy to selectively hinder the growth advantage of aggressive cSCC. METHODS: We evaluated the cell energetics profiles of RDEB-SCC cells by analysing available gene expression data against multiple gene signatures and single-gene targets linked to metabolic reprogramming. Additionally, we employed real-time metabolic profiling to measure glycolysis and respiration in these cells. Furthermore, we investigated the anti-neoplastic properties of the metformin against human and murine high-risk cSCCs in vitro and in vivo. RESULTS: Gene expression analyses highlighted a divergence in cell energetics profiles between RDEB-SCC and non-malignant RDEB keratinocytes, with tumour cells demonstrating enhanced respiration and glycolysis scores. Real-time metabolic profiling supported these data and additionally highlighted a metabolic plasticity of RDEB-SCC cells. Against this background, metformin exerted an anti-neoplastic potential by hampering both respiration and glycolysis, and by inhibiting proliferation in vitro. Metformin treatment in an analogous model of fast-growing murine cSCC resulted in delayed tumour onset and slower tumour growth, translating to a 29% increase in median overall survival. CONCLUSIONS: Our data indicate that metformin exerts anti-neoplastic properties in aggressive cSCCs that exhibit high-risk features by interfering with respiration and glycolytic processes.

[Urgent need of action for the future of digital hospitals]. / Dringender Handlungsbedarf für die Zukunft des digitalen Krankenhauses.

BACKGROUND: Digitalization is significantly changing the entire healthcare sector. Hospitals must act now in order to remain competitive. OBJECTIVE: To assess which approaches exist for digitalization along the treatment chain and how digitalization can be strategically implemented. MATERIALS AND METHODS: An analysis of relevant examples from consulting work and success stories from the specialist press is presented, as is a discussion of expert recommendations. RESULTS: More than 90% of German hospitals are currently investing in digitalization. However, they often fail to understand that first and foremost, digitalization offers them the chance to successfully innovate their entire business model. Digital solutions can enhance quality of care, boost efficiency, and greatly improve attractiveness for the employees entrusted with these processes. CONCLUSION: German hospitals should be doing much more to exploit the many digital opportunities that exist. They can do so by deciding where to act first in the treatment chain, prioritizing the underlying actions, and making the necessary funds available. Other key success factors include forming strategic partnerships with startups and "medtech" companies, and introducing an agile corporate culture.

Levofloxacin population pharmacokinetics and creation of a demographic model for prediction of individual drug clearance in patients with serious community-acquired infection.

Population pharmacokinetic modeling is a useful approach to obtaining estimates of both population and individual pharmacokinetic parameter values. The potential for relating pharmacokinetic parameters to pharmacodynamic outcome variables, such as efficacy and toxicity, exists. A logistic regression relationship between the probability of a successful clinical and microbiological outcome and the peak concentration-to-MIC ratio (and also the area under the plasma concentration-time curve [AUC]/MIC ratio) has previously been developed for levofloxacin; however, levofloxacin assays for determination of the concentration in plasma are not readily available. We attempted to derive and validate demographic variable models to allow prediction of the peak concentration in plasma and clearance (CL) from plasma for levofloxacin. Two hundred seventy-two patients received levofloxacin intravenously for the treatment of community-acquired infection of the respiratory tract, skin or soft tissue, or urinary tract, and concentrations in plasma, guided by optimal sampling theory, were obtained. Patient data were analyzed by the Non-Parametric Expectation Maximization approach. Maximum a posteriori probability Bayesian estimation was used to generate individual parameter values, including CL. Peak concentrations were simulated from these estimates. The first 172 patients were used to produce demographic models for the prediction of CL and the peak concentration. The remaining 100 patients served as the validation group for the model. A median bias and median precision were calculated. A two-compartment model was used for the population pharmacokinetic analysis. The mean CL and the mean volume of distribution of the central compartment (V1) were 9.27 liters/h and 0.836 liter/kg, respectively. The mean values for the intercompartmental rate constants, the rate constant from the central compartment to the peripheral compartment (Kcp) and the rate constant from the peripheral compartment to the central compartment (Kpc), were 0.487 and 0.647 h(-1), respectively. The mean peak concentration and the mean AUC values normalized to a dosage of 500 mg every 24 h were 8.67 microg/ml and 72.53 microg x h/ml, respectively. The variables included in the final model for the prediction of CL were creatinine clearance (CLCR), race, and age. The median bias and median precision were 0.5 and 18.3%, respectively. Peak concentrations were predicted by using the demographic model-predicted parameters of CL, V1, Kcp and Kpc, in the simulation. The median bias and the median precision were 3.3 and 21.8%, respectively. A population model of the disposition of levofloxacin has been developed. Population demographic models for the prediction of peak concentration and CL from plasma have also been successfully developed. However, the performance of the model for the prediction of peak concentration was likely insufficient to be of adequate clinical utility. The model for the prediction of CL was relatively robust, with acceptable bias and precision, and explained a reasonable amount of the variance in the CL of levofloxacin from plasma in the population (r2 = 0.396). Estimated CLCR, age, and race were the final model covariates, with CLCR explaining most of the population variance in the CL of levofloxacin from plasma. This model can potentially optimize the benefit derived from the pharmacodynamic relationships previously developed for levofloxacin.

Pharmacodynamics of levofloxacin: a new paradigm for early clinical trials.

CONTEXT: One purpose of early clinical trials is to establish the appropriate dose of an antibiotic for phase 3 trials. Development of a relationship between the ratio of drug exposure to organism minimum inhibitory concentration (MIC) and therapeutic response early in the development process would allow an optimal choice of dose to maximize response. OBJECTIVE: To prospectively quantitate the relationship between plasma levels of levofloxacin and successful clinical and/or microbiological outcomes and occurrence of adverse events in infected patients. DESIGN: Multicenter open-label trial. SETTING: Twenty-two enrolling university-affiliated medical centers. PATIENTS: A total of 313 patients with clinical signs and symptoms of bacterial infections of the respiratory tract, skin, or urinary tract. MAIN OUTCOME MEASURES: Clinical response and microbiological eradication of pathogenic organisms. RESULTS: Of 313 patients, 272 had plasma concentration-time data obtained. Of these, 134 patients had a pathogen recovered from the primary infection site and had an MIC of the pathogen to levofloxacin determined. These patients constituted the primary analysis group for clinical outcome. Groups of 116 and 272 patients, respectively, were analyzed for microbiological outcome and incidence of adverse events. In a logistic regression analysis, the clinical outcome was predicted by the ratio of peak plasma concentration to MIC (Peak/MIC) and site of infection (P<.001). Microbiological eradication was predicted by the Peak/MIC ratio (P<.001). Both clinical and microbiological outcomes were most likely to be favorable if the Peak/MIC ratio was at least 12.2. CONCLUSIONS: Levofloxacin generated clinical and microbiological response rates of 95% and 96%, respectively. These response rates included fluoroquinolone "problem pathogens," such as Streptococcus pneumoniae and Staphylococcus aureus. Exposure to levofloxacin was significantly associated with successful clinical and microbiological outcomes. The principles used in these analyses can be applied to other classes of drugs to develop similar relationships between exposure and outcome. This pharmacokinetic modeling could be used to determine optimal treatment dose in clinical trials in a shorter time frame with fewer patients. This modeling also should be evaluated for its potential to improve outcomes (maximizing therapeutic response, preventing emergence of resistance, and minimizing adverse events) of patients treated with this drug.