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- Smits, Anne, et al.
(författare)
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Current knowledge, challenges and innovations in developmental pharmacology: A combined conect4children Expert Group and European Society for Developmental, Perinatal and Paediatric Pharmacology White Paper.
- 2021
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Ingår i: British journal of clinical pharmacology. - : Wiley. - 1365-2125 .- 0306-5251. ; 88:12, s. 4965-84
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Tidskriftsartikel (refereegranskat)abstract
- Developmental pharmacology describes the impact of maturation on drug disposition (pharmacokinetics, PK) and drug effects (pharmacodynamics, PD) throughout the paediatric age range. This paper, written by a multidisciplinary group of experts, summarizes current knowledge, and provides suggestions to pharmaceutical companies, regulatory agencies and academicians on how to incorporate the latest knowledge regarding developmental pharmacology and innovative techniques into neonatal and paediatric drug development. Biological aspects of drug absorption, distribution, metabolism and excretion throughout development are summarized. Although this area made enormous progress during the last two decades, remaining knowledge gaps were identified. Minimal risk and burden designs allow for optimally informative but minimally invasive PK sampling, while concomitant profiling of drug metabolites may provide additional insight in the unique PK behaviour in children. Furthermore, developmental PD needs to be considered during drug development, which is illustrated by disease- and/or target organ-specific examples. Identifying and testing PD targets and effects in special populations, and application of age- and/or population-specific assessment tools are discussed. Drug development plans also need to incorporate innovative techniques such as preclinical models to study therapeutic strategies, and shift from sequential enrolment of subgroups, to more rational designs. To stimulate appropriate research plans, illustrations of specific PK/PD-related as well as drug safety-related challenges during drug development are provided. The suggestions made in this joint paper of the Innovative Medicines Initiative conect4children Expert group on Developmental Pharmacology and the European Society for Developmental, Perinatal and Paediatric Pharmacology, should facilitate all those involved in drug development.
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| 2. |
- Germovsek, Eva, et al.
(författare)
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Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants
- 2016
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Ingår i: Antimicrobial Agents and Chemotherapy. - 0066-4804 .- 1098-6596. ; 60:8, s. 4869-4877
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Tidskriftsartikel (refereegranskat)abstract
- Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC(0-t)). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC(0-t) values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg center dot h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.
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