title: Model-based prediction of the acute and long-term safety profile of naproxen in rats.
abstract: OBJECTIVE
  Despite the increasing importance of biomarkers as predictors of drug effects, toxicology protocols continue to rely on the experimental evidence of adverse events (AEs) as a basis for establishing the link between indicators of safety and drug exposure. Furthermore, biomarkers may facilitate the translation of findings from animals to humans. Combined with a model-based approach, biomarker data have the potential to predict long-term effects arising from prolonged drug exposure. Here, we used naproxen as a paradigm to explore the feasibility of a biomarker-guided approach for the prediction of long-term AEs in humans.
  
  
  METHODS
  An experimental toxicology protocol was set up for evaluating the effects of naproxen in rats, in which four active doses were tested (7.5, 15, 40 and 80 mg·kg(-1) ). In addition to AE monitoring and histology, a few blood samples were also collected for the assessment of drug exposure, TXB2 and PGE2 levels. Non-linear mixed effects modelling was used to analyse the data and identify covariate factors on the incidence and severity of AEs.
  
  
  RESULTS
  Modelling results showed that besides drug exposure, maximum PGE2 inhibition and treatment duration were also predictors of gastrointestinal ulceration. Although PGE2 levels were clearly linked to the incidence rates, it appeared that ulceration severity is better predicted by measures of drug exposure.
  
  
  CONCLUSIONS
  These results show that the use of a model-based approach provides the opportunity to integrate pharmacokinetics, pharmacodynamics and toxicity data, enabling optimization of the design, analysis and interpretation of toxicology experiments.
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: Tarjinder
authors.initials: T
authors.lastname: Sahota
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: Ian
authors.initials: I
authors.lastname: Sanderson
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
authors.forename: Meindert
authors.initials: M
authors.lastname: Danhof
authors.affiliation: Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.|Clinical Pharmacology, Modelling and Simulation, GlaxoSmithKline, Uxbridge, UK.|Clinical Pharmacology & Therapeutics, University College London, London, UK.
authors.forename: Oscar
authors.initials: O
authors.lastname: Della Pasqua
mesh_terms: D000818:Animals; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects* / Q000493:pharmacokinetics; D015415:Biomarkers / Q000097:blood; D015232:Dinoprostone / Q000097:blood; D004305:Dose-Response Relationship, Drug; D008297:Male; D008954:Models, Biological; D009288:Naproxen / Q000009:adverse effects* / Q000493:pharmacokinetics; D010437:Peptic Ulcer / Q000097:blood / Q000139:chemically induced; D051381:Rats; D013929:Thromboxane B2 / Q000097:blood
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal; D015415:Biomarkers; D013929:Thromboxane B2; D009288:Naproxen; D015232:Dinoprostone