title: Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: expected variations in clearance.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Simcyp Limited, Sheffield, UK.
authors.forename: Cyrus
authors.initials: C
authors.lastname: Ghobadi
authors.forename: Trevor N
authors.initials: TN
authors.lastname: Johnson
authors.forename: Mohsen
authors.initials: M
authors.lastname: Aarabi
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Almond
authors.forename: Aurel Constant
authors.initials: AC
authors.lastname: Allabi
authors.forename: Karen
authors.initials: K
authors.lastname: Rowland-Yeo
authors.forename: Masoud
authors.initials: M
authors.lastname: Jamei
authors.forename: Amin
authors.initials: A
authors.lastname: Rostami-Hodjegan
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001569:Benzodiazepines / Q000493:pharmacokinetics; D001837:Body Weights and Measures; D002110:Caffeine / Q000493:pharmacokinetics; D002753:Chlorzoxazone / Q000493:pharmacokinetics; D003198:Computer Simulation; D016572:Cyclosporine / Q000493:pharmacokinetics; D005260:Female; D041981:Gastrointestinal Tract / Q000098:blood supply / Q000378:metabolism; D006801:Humans; D007668:Kidney / Q000098:blood supply / Q000378:metabolism; D008099:Liver / Q000098:blood supply / Q000378:metabolism; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000378:metabolism*; D009767:Obesity, Morbid / Q000378:metabolism*; D010599:Pharmacokinetics; D010672:Phenytoin / Q000493:pharmacokinetics; D012039:Regional Blood Flow; D013806:Theophylline / Q000493:pharmacokinetics; D055815:Young Adult
chemical_list: D001569:Benzodiazepines; D002110:Caffeine; D010672:Phenytoin; D016572:Cyclosporine; D013806:Theophylline; D002753:Chlorzoxazone
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