Pain and distress caused by endotracheal suctioning in neonates is better quantified by behavioural than physiological items: a comparison based on item response theory modelling

Pain cannot be directly measured in neonates. Therefore, scores based on indirect behavioural signals such as crying, or physiological signs such as blood pressure, are used to quantify neonatal pain both in clinical practice and in clinical studies. The aim of this study was to determine which of the physiological and behavioural items of 2 validated pain assessment scales (COMFORT and premature infant pain profile) are best able to detect pain during endotracheal and nasal suctioning in ventilated newborns. We analysed a total of 516 PIPP and COMFORT scores from 118 newborns. A graded response model was built to describe the data and item information was calculated for each of the behavioural and physiological items. We found that the graded response model was able to well describe the data, as judged by agreement between the observed data and model simulations. Furthermore, a good agreement was found between the pain estimated by the graded response model and the investigator-assessed visual analogue scale scores (Spearman rho correlation coefficient = 0.80). The information scores for the behavioural items ranged from 1.4 to 27.2 and from 0.0282 to 0.131 for physiological items. In these data with mild to moderate pain levels, behavioural items were vastly more informative of pain and distress than were physiological items. The items that were the most informative of pain are COMFORT items "calmness/agitation," "alertness," and "facial tension.

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach

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.Pharmacolog

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

Purpose Predicting 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. Methods A 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. Results A 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 concentrationtime profiles in different brain compartments well (symmetric mean absolute percentage error <90%). Conclusions A 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