Respiratory syncytial virus is a major cause of acute lower respiratory tract infection in young children, immunocompromised adults, and the elderly. Intervention with small-molecule antivirals specific for respiratory syncytial virus presents an important therapeutic opportunity, but no such compounds are approved today. Here we report the structure of JNJ-53718678 bound to respiratory syncytial virus fusion (F) protein in its prefusion conformation, and we show that the potent nanomolar activity of JNJ-53718678, as well as the preliminary structure–activity relationship and the pharmaceutical optimization strategy of the series, are consistent with the binding mode of JNJ-53718678 and other respiratory syncytial virus fusion inhibitors. Oral treatment of neonatal lambs with JNJ-53718678, or with an equally active close analog, efficiently inhibits established acute lower respiratory tract infection in the animals, even when treatment is delayed until external signs of respiratory syncytial virus illness have become visible. Together, these data suggest that JNJ-53718678 is a promising candidate for further development as a potential therapeutic in patients at risk to develop respiratory syncytial virus acute lower respiratory tract infection.
The mechanism by which COX inhibitors exert their analgesic effect is well established. However, data show no direct correlation between drug concentrations in plasma and the analgesic or adverse effects in chronic inflammatory conditions. This represents a major problem in the development of COX inhibitors, since it is difficult to predict the appropriate dosing regimen for the treatment of chronic inflammatory pain, based upon information from pre-clinical studies and eventually early clinical studies. The factors that determine response in inflammatory pain must be understood in order to make predictions about the time course of the analgesic effect. In this review the determinants of drug response and their variability will be discussed: physicochemical properties, pharmacokinetics (PK), pathophysiology and disease progression. From a mechanistic point of view, endogenous mediators of inflammation might be used as a biomarker for the analgesic effect and safety assessment. Such a biomarker can be an intermediate step between drug exposure and response. In addition, its concentration-effect relationship could be characterized by pharmacokinetic-pharmacodynamic (PK/PD) modelling. Indeed, recent investigations have shown that COX-2 inhibition, as determined by modelling of prostaglandin E2 (PGE2) levels in the whole blood assay in vitro can be used as a marker to predict drug effects (analgesia) in humans. A model-derived parameter, IC80, (total and unbound) was found to correlate directly with the analgesic plasma concentration of different COX inhibitors varying in enzyme selectivity. These findings indicate that PGE2 and thromboxane B2 inhibition can be used to predict and select efficacious doses in humans.
Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development.
Knowledge of drug concentration-time profiles at the central nervous system (CNS) target-site is critically important for rational development of CNS targeted drugs. Our aim was to translate a recently published comprehensive CNS physiologically-based pharmacokinetic (PBPK) model from rat to human, and to predict drug concentration-time profiles in multiple CNS compartments on available human data of four drugs (acetaminophen, oxycodone, morphine and phenytoin). Values of the system-specific parameters in the rat CNS PBPK model were replaced by corresponding human values. The contribution of active transporters for the four selected drugs was scaled based on differences in expression of the pertinent transporters in both species. Model predictions were evaluated with available pharmacokinetic (PK) data in human brain extracellular fluid and/or cerebrospinal fluid, obtained under physiologically healthy CNS conditions (acetaminophen, oxycodone, and morphine) and under pathophysiological CNS conditions where CNS physiology could be affected (acetaminophen, morphine and phenytoin). The human CNS PBPK model could successfully predict their concentration-time profiles in multiple human CNS compartments in physiological CNS conditions within a 1.6-fold error. Furthermore, the model allowed investigation of the potential underlying mechanisms that can explain differences in CNS PK associated with pathophysiological changes. This analysis supports the relevance of the developed model to allow more effective selection of CNS drug candidates since it enables the prediction of CNS target-site concentrations in humans, which are essential for drug development and patient treatment.
PurposePredicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition.MethodsA mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model.ResultsA common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%).ConclusionsA multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2065-3) contains supplementary material, which is available to authorized users.
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