Intracellular Pharmacodynamic Modeling Is Predictive of the Clinical Activity of Fluoroquinolones against Tuberculosis.

Clinical studies of new antitubercular drugs are costly and time-consuming. Owing to the extensive tuberculosis (TB) treatment periods, the ability to identify drug candidates based on their predicted clinical efficacy is vital to accelerate the pipeline of new therapies. Recent failures of preclinical models in predicting the activity of fluoroquinolones underline the importance of developing new and more robust predictive tools that will optimize the design of future trials. Here, we used high-content imaging screening and pharmacodynamic intracellular (PDi) modeling to identify and prioritize fluoroquinolones for TB treatment. In a set of studies designed to validate this approach, we show moxifloxacin to be the most effective fluoroquinolone, and PDi modeling-based Monte Carlo simulations accurately predict negative culture conversion (sputum sterilization) rates compared to eight independent clinical trials. In addition, PDi-based simulations were used to predict the risk of relapse. Our analyses show that the duration of treatment following culture conversion can be used to predict the relapse rate. These data further support that PDi-based modeling offers a much-needed decision-making tool for the TB drug development pipeline.

Fluoroquinolone Antibiotic Prophylaxis to Prevent Post-Traumatic Bacterial Infectious Endophthalmitis: Using Monte Carlo Simulation to Evaluate the Probability of Success.

Purpose: Patients with open globe injuries routinely receive fluoroquinolone (FQ) prophylaxis to prevent bacterial infectious endophthalmitis. Owing to the rarity of this infection, there is an absence of clinical trials evaluating optimal prophylactic FQ dosing. To address this knowledge gap, we conducted a Monte Carlo simulation (MCS)-based study to identify the FQ dosing option(s) that optimize pharmacokinetic-pharmacodynamic FQ target attainment against common bacterial pathogens implicated in post-traumatic bacterial infectious endophthalmitis (PTBIE). Methods: Weighted mean pharmacokinetic parameters and standard deviations for ciprofloxacin, levofloxacin, and moxifloxacin were calculated from published studies in healthy volunteers. The incidence and FQ susceptibility profiles for the most common bacteria causing PTBIE were extracted from the literature. MCS was used to determine the cumulative fraction of response (CFR) for 5 FQ dosing options to determine the probability of attaining pathogen-specific target 24-hour area under the curve to minimum inhibitory concentration ratios in the vitreous humor of the eye against the 4 most common causative bacteria seen in PTBIE. Results: Moxifloxacin 400 mg po daily (M400) achieved the highest CFR (72%). Levofloxacin dosing options achieved CFRs between 54% and 63%. Ciprofloxacin dosing options achieved CFRs between 28% and 35%. Conclusion: M400 optimized the likelihood of prophylactic success in the prevention of PTBIE, and based on the study findings, M400 is predicted to optimize the probability of success compared with ciprofloxacin and levofloxacin dosing options currently endorsed by expert opinion.

Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin.

Drug-induced cardiac arrhythmia, especially occurrence of torsade de pointes (TdP), has been a leading cause of attrition and post-approval re-labeling and withdrawal of many drugs. TdP is a multifactorial event, reflecting more than just drug-induced cardiac ion channel inhibition and QT interval prolongation. This presents a translational gap in extrapolating pre-clinical and clinical cardiac safety assessment to estimate TdP risk reliably, especially when the drug of interest is used in combination with other QT-prolonging drugs for treatment of diseases such as tuberculosis. A multi-scale mechanistic modeling framework consisting of physiologically based pharmacokinetics (PBPK) simulations of clinically relevant drug exposures combined with Quantitative Systems Toxicology (QST) models of cardiac electro-physiology could bridge this gap. We illustrate this PBPK-QST approach in cardiac risk assessment as exemplified by moxifloxacin, an anti-tuberculosis drug with abundant clinical cardiac safety data. PBPK simulations of moxifloxacin concentrations (systemic circulation and estimated in heart tissue) were linked with in vitro measurements of cardiac ion channel inhibition to predict the magnitude of QT prolongation in healthy individuals. Predictions closely reproduced the clinically observed QT interval prolongation, but no arrhythmia was observed, even at ×10 exposure. However, the same exposure levels in presence of physiological risk factors, e.g., hypokalemia and tachycardia, led to arrhythmic event in simulations, consistent with reported moxifloxacin-related TdP events. Application of a progressive PBPK-QST cardiac risk assessment paradigm starting in early development could guide drug development decisions and later define a clinical "safe space" for post-approval risk management to identify high-risk clinical scenarios.