Teicoplanin physiologically based pharmacokinetic modeling offers a quantitative assessment of a theoretical influence of serum albumin and renal function on its disposition.

PURPOSE: Variability in teicoplanin pharmacokinetics has been explained by multiple factors such as body weight, renal function, and serum albumin level. To improve mechanistic understanding of the causes of variability, a physiologically based pharmacokinetic (PBPK) model can be used as a systematic platform. In this study, a PBPK model of teicoplanin was developed to quantitatively assess the effects of physiological changes due to disease status using virtual populations. METHODS: Predictive performance of the models was evaluated by comparing simulated and observed concentration-time profiles of teicoplanin. Subsequently, sensitivity analyses were conducted to identify potential factors contributing to individual differences in teicoplanin PK. RESULTS: The developed PBPK model generated concentration-time profiles that were comparable to clinical observations in healthy adults, including Caucasians and Japanese, and after single-dose and multiple-dose administration. The predicted PK parameters (i.e., Cmax, AUC, clearance) were within a two-fold range of the observed data in patients with renal impairments as well as healthy adults. Changes in total and unbound teicoplanin concentrations at 72 h, after various dosing regimens (tested 4-14 mg/kg q12h for three doses as a loading dose and then 4-14 mg/kg daily as a maintenance dose), were sensitive to renal function and serum albumin concentrations. CONCLUSION: The PBPK model of teicoplanin provides mechanistic insight into the factors altering its disposition and allows assessments of the theoretical and quantitative impact of individual changes in physiological parameters on its PK even when an actual assessment with adequate sample sizes of patients is challenging.

Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: expected variations in clearance.

BACKGROUND AND OBJECTIVES: The maintenance dose of a drug is dependent on drug clearance, and thus any biochemical and physiological changes in obesity that affect parameters such as cardiac output, renal function, expression of drug-metabolizing enzymes and protein binding may result in altered clearance compared with that observed in normal-weight subjects (corrected or uncorrected for body weight). Because of the increasing worldwide incidence of obesity, there is a need for more information regarding the optimal dosing of drug therapy to be made available to prescribers. This is usually provided via clinical studies in obese people; however, such studies are not available for all drugs that might be used in obese subjects. Incorporation of the relevant physiological and biochemical changes into predictive bottom-up pharmacokinetic models in order to optimize dosage regimens may offer a logical way forward for the cases where no clinical data exist. The aims of the current report are to apply such a 'systems approach' to identify the likelihood of observing variations in the clearance of drugs in obesity and morbid obesity for a set of compounds for which clinical data, as well as the necessary in vitro information, are available, and to provide a framework for assessing other drugs in the future. METHODS: The population-specific changes in demographic, physiological and biochemical parameters that are known to be relevant to obese and morbidly obese subjects were collated and incorporated into two separate population libraries. These libraries, together with mechanistic in vitro-in vivo extrapolations (IVIVE) within the Simcyp Population-based Simulator™, were used to predict the clearance of oral alprazolam, oral caffeine, oral chlorzoxazone, oral ciclosporin, intravenous and oral midazolam, intravenous phenytoin, oral theophylline and oral triazolam. The design of the simulated studies was matched as closely as possible with that of the clinical studies. Outcome was measured by the predicted ratio of the clearance of the drug in obese and lean subjects ± its 90% confidence interval, compared with observed values. The overall statistical measures of the performance of the model to detect differences in compound clearance between obese and lean populations were investigated by measuring sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). A power calculation was carried out to investigate the impact of the sample size on the overall outcome of clinical studies. RESULTS: The model was successful in predicting clearance in obese subjects, with the degree to which simulations could mimic the outcome of in vivo studies being greater than 60% for six of the eight drugs. A clear difference in the clearance of chlorzoxazone was correctly picked up via simulation. The overall statistical measures of the performance of the Simcyp Simulator were 100% sensitivity, 66% specificity, 60% PPV and 100% NPV. Studies designed on the basis of the ratio of the absolute values required substantial numbers of participants in order to detect a significant difference, except for phenytoin and chlorzoxazone, where the ratios of the weight-normalized clearances generally showed statistically significant differences with a smaller number of subjects. CONCLUSION: Extension of a mechanistic predictive pharmacokinetic model to accommodate physiological and biochemical changes associated with obesity and morbid obesity allowed prediction of changes in drug clearance on the basis of in vitro data, with reasonable accuracy across a range of compounds that are metabolized by different enzymes. Prediction of the effects of obesity on drug clearance, normalized by various body size scalars, is of potential value in the design of clinical studies during drug development and in the introduction of dosage adjustments that are likely to be needed in clinical practice.

Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children.

BACKGROUND: Prediction of the exposure of neonates, infants and children to xenobiotics is likely to be more successful using physiologically based pharmacokinetic models than simplistic allometric scaling, particularly in younger children. However, such models require comprehensive information on the ontogeny of anatomical, physiological and biochemical variables; data that are not available from single sources. The Simcyp software integrates demographic, genetic, physiological and pathological information on adults with in vitro data on human drug metabolism and transport to predict population distributions of drug clearance (CL) and the extent of metabolic drug-drug interactions. The algorithms have now been extended to predict clearance and its variability in paediatric populations by incorporating information on developmental physiology and the ontogeny of specific cytochrome P450s. METHODS: Values of the clearance (median and variability) of 11 drugs (midazolam [oral and intravenous], caffeine, carbamazepine, cisapride, theophylline, diclofenac, omeprazole, S-warfarin, phenytoin, gentamicin and vancomycin) were predicted for 2,000 virtual subjects (birth to 18 years). In vitro enzyme pharmacokinetic parameters (maximum rate of metabolism [Vmax] and Michaelis-Menten constant [Km]) and in vivo clearance data were obtained from the literature. RESULTS: In neonates 70% (7/10) of predicted median clearance values were within 2-fold of the observed values. Corresponding results for infants, children and adolescents were 100% (9/9), 89% (17/19) and 94% (17/18), respectively. Predicted variability (95% confidence interval) was within 2-fold of the observed values in 70% (7/10), 67% (6/9), 63% (12/19) and 55% (10/18) of cases, respectively. The accuracy of the physiologically based model incorporated in the Simcyp software was superior to that of simple allometry, especially in children <2 years old. CONCLUSION: The in silico prediction of pharmacokinetic behaviour in paediatric patients is not intended to replace clinical studies. However, it provides a valuable aid to decision-making with regard to first-time dosing in children and study design. The clinical study then becomes 'confirmatory' rather than 'exploratory'.