title: Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes.
abstract: OBJECTIVE
  To determine the optimal sampling time design of a drug-drug interaction (DDI) study for the estimation of apparent clearances (CL/F) of two co-administered drugs (SX, a phase I compound, potentially a CYP3A4 inhibitor, and MDZ, a reference CYP3A4 substrate) without any in vivo data using physiologically based pharmacokinetic (PBPK) predictions, population PK modelling and multiresponse optimal design.
  
  
  METHODS
  PBPK models were developed with AcslXtreme using only in vitro data to simulate PK profiles of both drugs when they were co-administered. Then, using simulated data, population PK models were developed with NONMEM and optimal sampling times were determined by optimizing the determinant of the population Fisher information matrix with PopDes using either two uniresponse designs (UD) or a multiresponse design (MD) with joint sampling times for both drugs. Finally, the D-optimal sampling time designs were evaluated by simulation and re-estimation with NONMEM by computing the relative root mean squared error (RMSE) and empirical relative standard errors (RSE) of CL/F.
  
  
  RESULTS
  There were four and five optimal sampling times (=nine different sampling times) in the UDs for SX and MDZ, respectively, whereas there were only five sampling times in the MD. Whatever design and compound, CL/F was well estimated (RSE < 20% for MDZ and <25% for SX) and expected RSEs from PopDes were in the same range as empirical RSEs. Moreover, there was no bias in CL/F estimation. Since MD required only five sampling times compared to the two UDs, D-optimal sampling times of the MD were included into a full empirical design for the proposed clinical trial. A joint paper compares the designs with real data.
  
  
  CONCLUSIONS
  This global approach including PBPK simulations, population PK modelling and multiresponse optimal design allowed, without any in vivo data, the design of a clinical trial, using sparse sampling, capable of estimating CL/F of the CYP3A4 substrate and potential inhibitor when co-administered together.
authors.affiliation: Institut de Recherches Internationales Servier, 6 place des Pléiades, 92415, Courbevoie Cedex, France. marylore_chenel@hotmail.com
authors.forename: Marylore
authors.initials: M
authors.lastname: Chenel
authors.forename: François
authors.initials: F
authors.lastname: Bouzom
authors.forename: Leon
authors.initials: L
authors.lastname: Aarons
authors.forename: Kayode
authors.initials: K
authors.lastname: Ogungbenro
mesh_terms: D017321:Clinical Trials, Phase I as Topic; D003198:Computer Simulation; D051544:Cytochrome P-450 CYP3A; D065692:Cytochrome P-450 CYP3A Inhibitors; D004347:Drug Interactions; D004791:Enzyme Inhibitors / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology; D006801:Humans; D008874:Midazolam / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology; D008954:Models, Biological; D011237:Predictive Value of Tests; D012107:Research Design; D013997:Time Factors
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D004791:Enzyme Inhibitors; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; D008874:Midazolam