title: Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice.
abstract: BACKGROUND
  Majority of reported studies so far developed correlation regression equations using the rat muscle-to-plasma drug concentration ratio (Kp-muscle) to predict tissue-to-plasma drug concentration ratios (Kp-tissues). Use of regression equations derived from rat Kp-muscle may not be ideal to predict the mice tissue-Kps as there are species differences.
  
  
  OBJECTIVE
  (i) To develop the linear regression equations using mouse tissue-Kps; (ii) to assess the correlation between organ blood flow and/or organ weight with tissue-Kps and (iii) compare the observed tissue-Kps from mice with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations.
  
  
  METHODS
  Disposition of 12 small molecules were investigated extensively in mouse plasma and tissues after a single oral dose administration. Linear correlation was assessed for each of the tissue with rest of the other tissues, separately for weak and strong bases.
  
  
  RESULTS
  Newly developed regression equations using mice tissue-Kps, predicted 79% data points within twofold. As observed correlation r2range was 0.75-0.98 between Kp-muscle and Kp-brain, -spleen, -skin, -liver, -lung, suggesting superior correlation between the tissue-Kps. Order of tissue-Kps, showed that tissue concentrations were directly proportional to the organ blood flow and inversely to the organ weight. Further, the observed tissue-Kps from mice were compared with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. Overall, 46, 54 and 63% data points were under predicted (<0.5-fold) for liver, spleen and lung, respectively. Whereas 63 and 75% data points were over predicted (>twofold) for skin and brain, respectively. These findings suggest that cross species extrapolation predictability is poor.
  
  
  CONCLUSIONS
  All these findings together suggest that mouse specific regression equations developed under controlled experimental conditions could be most appropriate for predicting mouse tissue-Kps for compounds with wide range of volume of distribution.
authors.affiliation: Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India. prashantnigade@lupin.com.|DMPK, Novel Drug Discovery and Development Department, Lupin Limited (Research Park), 46A/47A, Village Nande, Taluka Mulshi, Pune, 412 115, India. prashantnigade@lupin.com.
authors.forename: Prashant B
authors.initials: PB
authors.lastname: Nigade
authors.affiliation: Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India.
authors.forename: Jayasagar
authors.initials: J
authors.lastname: Gundu
authors.affiliation: Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal University, Manipal, India.
authors.forename: K
authors.initials: K
authors.lastname: Sreedhara Pai
authors.affiliation: Department of Pharmacology, Lupin Limited (Research Park), Pune, India.
authors.forename: Kumar V S
authors.initials: KVS
authors.lastname: Nemmani
mesh_terms: D000818:Animals; D001921:Brain / Q000378:metabolism; D005260:Female; D016014:Linear Models; D008099:Liver / Q000378:metabolism; D051379:Mice; D008807:Mice, Inbred BALB C; D008954:Models, Biological; D009132:Muscles / Q000378:metabolism; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010949:Plasma / Q000378:metabolism*; D051381:Rats; D014018:Tissue Distribution
chemical_list: D004364:Pharmaceutical Preparations
keywords: Dasatinib; Gefitinib; Imatinib; Lapatinib; Sunitinib