Comparison of strategies for assessing metabolic stability in vitro Michael Griffin PhD
Summary of presentation Why study metabolic stability? Common systems used for assessing in vitro metabolic stability In vitro to in vivo scaling methodology In house comparison of in vitro systems Conclusions
Why is metabolic stability important? CL int - (µl/min/mg protein or 10 6 cells) = Intrinsic clearance is a measure of how quickly a substance is metabolised in the site of interest usually the liver or intestine. Typically compounds with a high intrinsic clearance will be metabolised rapidly and will only have a narrow therapeutic range in vivo. Compounds with a low intrinsic clearance will have a long half life and may potentially remain in the body for a prolonged period of time with the potential to lead to toxicity. Influence the chemical design of New Chemical Entities (NCE s) by identifying structural changes to alter the metabolic stability to improve efficacy. Calculation of the in vitro intrinsic clearance (CL int ) for a given compound may be used to predict the in vivo metabolic clearance for any given species including man. Can be measured in a number of in vitro systems including microsomes, S9 and hepatocytes
Preparation of microsomes and S9 Microsomes and S9 are prepared by homogenisation and centrifugation of liver tissue. Homogenise 9,000g centrifugation S9 100,000g centrifugation P450, UGT, FMO NAT, GST, SULT Microsome Cytosol
Systems used to probe metabolic stability Microsomes Most commonly used system to probe in vitro metabolic stability Easily accessible, relatively cheap, numerous species can be studied Mainly used to probe phase I mediated metabolism Activation of UDPGT with pore forming antibiotic alamethicin allows investigation of glucuronidation Useful to probe different pathways of metabolism
Systems used to probe metabolic stability S9 Liver supernatant Phase I and II enzymes Available in numerous species Require addition of cofactors Lower levels of activity than liver microsomes Recent upsurge in interest
Systems used to probe metabolic stability Hepatocytes Contain full complement of hepatic enzymes and cofactors Cell membrane containing transporters More analogous to in vivo situation than microsomes Cryopreserved hepatocytes allow a ready supply More expensive than microsomes
Traditional approach to scaling CL int in vitro (µl/min/mg or 10 6 cells) CL int in vivo (ml/min/kg) CL h (ml/min/kg) System binding Scaling factor Liver weight Body weight Protein-binding Blood flow 1. Houston and Carlile, Drug Metab Rev, 29(4), 891-922, 1997. 2. Ito et al., Ann Rev Pharmacol Toxicol, 38, 461-499, 1998.
In vitro system binding Microsomes Binding can be measured by equilibrium dialysis Predicted from physicochemical properties 1 Hepatocytes Can be measured by silicone oil method Predicted from physicochemical properties 2 S9 Measured by equilibrium dialysis No prediction method to date 1. Hallifax and Houston, 2006, Drug Met Dispo. 34:724-726 2. Austin et al, 2005, Drug Met Dispo. 33:419-425
Predicted in vitro system binding 1.20 1.00 Fu incubation 0.80 0.60 0.40 Microsomes Hepatocytes 0.20 0.00 Acids Bases Neutrals Total Data shown is the overall mean, maximum and minimum predicted Microsomal binding predicted using Hallifax and Houston; 2006, Drug Met Dispo. 34:724-726 Hepatocyte binding predicted using Austin et al, 2005, Drug Met Dispo. 33:419-425
Scaling parameters Parameters Human Monkey Dog Rat Mouse Q H (ml/min/kg) 1 20.7 43.6 30.9 55.2 90 Liver weight (g liver/kg body weight) 1 25.7 30 32 40 87.5 Microsomal concentration (mg/g 40 50* 78 45 50* liver) 2 Hepatocyte concentration (10 6 99 120* 215 117 135 cells/g liver) 3 1. Davies and Morris, 1993, 10 (7) pp 1093-1095. 2. Barter et al, 2007, Curr Drug Metab, 8(1), pp 33-45, Iwatsubo et al, 1997, JPET, 283 pp 462-469. *Monkey and Mouse assumed. 3. Barter et al, 2007, Curr Drug Metab, 8(1), pp 33-45; Sohlenius-Sternbeck AK, 2006, Toxicol in Vitro, (20), pp 1582-1586; *Monkey assumed.
Models used for clearance prediction Three models used for prediction well-stirred, parallel tube and convective-dispersion model Share three common assumptions that the distribution into the liver is perfusion rate limited with no diffusion barriers only unbound drug crosses the cell membrane and occupies the enzyme site there is a homogenous distribution of metabolic enzymes in the liver However, different assumptions are made regarding the concentration gradient of drug within the liver
Models used for clearance prediction Well-Stirred Model Assumes that the liver is a homogenous compartment with no concentration gradient in the direction of blood flow Easy to use Most commonly used in industry Least physiologically relevant Reasonable predictions but limitations for high clearance compounds CL h Q Q h h f u f u CL CL int int Ito K and Houston JB, 2005,Pharm.Res. 22:103-112.
Models used for clearance prediction Parallel tube model More complex than well-stirred model Sinusoids in the liver represented as parallel tubes assume that solutes enters the liver at the same time moving though the liver with constant and equal velocities and that drug declines exponentially as it moves through the liver Improvement for high clearance compounds CL h Q h 1- exp Ito K and Houston JB, 2005,Pharm.Res. 22:103-112. - f u CL Q h int
and Models used for clearance prediction Convective-Dispersion model More complex but representative model of the liver Sinusoids in the liver represented as cylinders in which mixing of blood occurs due to changes in blood flow and convective forces within the liver 4a CLh Q 1-2 2 (1 a) exp (a h 1)/2Dn (1 a) exp- (a 1)/2Dn Where Dn=0.17 (Roberts & Rowland, 1986), a 1 4RnDn, Rn fu CLint Q h Ito K and Houston JB, 2005,Pharm.Res. 22:103-112.
Current literature data Microsomal CL int collated for 57 compounds from literature publications 1 In vitro CL int scaled to in vivo CL int CL int adjusted for in vitro binding 2 Literature in vivo CL B scaled down to in vivo CL int using the well stirred liver model Fu p and R B taken into account 1. Brown, Griffin and Houston, 2007, Drug Met Dispo. 35: 293-301 2. Hallifax and Houston, 2006, Drug Met Dispo. 34:724-726
and Well-stirred model deconvoluted Well Stirred model CL int ( b 1- ) Q CL CL H b fu R p B Ito K and Houston JB, 2005,Pharm.Res. 22:103-112.
Statistical analysis Bias - measure of the fold under/over prediction afe 10 1 N log Predicted Observed Precision - measure of variance mse 1 2 N (Predicted - Observed), rmse mse
Microsome prediction literature data 1e5 10000 Microsome CLint, u (ml/min/kg) 1000 100 10 1 Statistics Fold underprediction CL int,u (ml/min/kg) 5.0 rmse 5796 n 57 0.1 0.01 0.01 0.1 1 10 100 1000 10000 1e5 In vivo CLint (ml/min/kg)
In vitro experimental investigations Utility of in house microsomes to assess clearance Can this be improved by the addition of UDPGA? Addition of alamethicin beneficial for sequential and direct phase II metabolism Investigate clearance of 11 test compounds with reported phase I and II metabolism in human liver microsomes (HLM), activated human liver microsomes (ahlm) and S9 Compare to CL int determined in hepatocytes and scale to in vivo CL int
In vitro experimental conditions Clozapine, codeine, diazepam, diclofenac, etodolac, gemfibrozil, isradipine, ketoprofen, midazolam, naloxone, naproxen, and tolmetin. Incubations consisted of HLM (0.5 mg/ml) or S9 (1 mg/ml), 0.1 M phosphate buffer (ph 7.4) and test compound (3 µm). For all ahlm incubations, HLM was pre-incubated with alamethicin (25 µg/mg protein). Samples pre-incubated at 37 C for 5 minutes prior to addition of cofactors to initiate the reaction. Co-factors used: NADPH, UDPGA or NADPH and UDPGA (c.f. 1mM). Compounds incubated for 0, 5, 15, 30 and 45 minutes, terminated with MeOH Sample supernatants were analysed by LC-MS/MS.
In vitro to in vivo scaling in house data In vitro CL int scaled to in vivo CL int using literature scaling factors as previously discussed Predicted Fu inc used for microsomes and hepatocytes Fu inc for S9 measured by equilibrium dialysis In vivo CL B adjusted using the well-stirred and parallel tube model, Fu p and R B taken into account Literature R B taken where known, if unknown 0.55 (1-haematocrit) used for acidic compound, 1 used for basic and neutral compounds.
Comparison of Microsome and S9 binding Compound Microsome Fu S9 Fu Diazepam 0.80 0.69 Diclofenac 0.96 0.40 Etodolac 0.96 0.45 Gemfibrozil 0.94 0.61 Isradapine 0.62 0.22 Ketoprofen 0.97 0.86 Midazolam 0.94 0.79 Naproxen 0.96 0.86 Higher binding to S9 fraction observed
Comparison of all three systems 10000 1000 In vitro CLint (ml/min/kg) 100 10 Hepatocytes S9 Microsomes 1 0.1 0.1 1 10 100 1000 10000 In vivo CLint (ml/min/kg)
HLM and ahlm comparison System HLM ahlm HLM ahlm Model WS PT WS PT WS PT WS PT WS PT Co-factors NADPH UDPGA NADPH and UDPGA Bias 11 6.3 81.8 44.9 69.4 38.6 9.8 6.1 11.5 6.6 RMSE 7189 242 7395 353 7396 353 7251 250 7148 245 WS = Well-stirred model, PT = Parallel tube
S9 and hepatocyte data System Model WS PT WS PT WS PT S9 Co-factors NADPH UDPGA NADPH and UDPGA Bias 11.5 6.6 33.3 19.9 14.2 8.1 RMSE 7140 256 7389 339 7159 238 System Hepatocyte Model WS PT Bias 3.7 2.1 RMSE 7369 267 WS = Well-stirred model, PT = Parallel tube
Utility of alamethicin in direct glucuronidation 20 * 18 16 * CLint unbound (ml/min/kg) 14 12 10 8 6 4 UDPGA UDPGA and Alamethicin 2 0 Diclofenac Etodolac Gemf ibrozil Ketoprofen Naproxen Data shown is mean of 3 incubations (± S.D.). *P<0.05, paired student t-test
S9 data *** 300 * ** 35 * 250 30 CLint unbound (ml/min/kg) 200 150 100 CLint unbound (ml/min/kg) 25 20 15 10 Plus NADPH Plus NADPH+UDPGA Plus UDPGA 50 5 0 Diclofenac 0 Gemfibrozil Data shown is mean of 3 incubations (± S.D.). *P<0.05, **P<0.01, ***P<0.005, paired student t-test.
Conclusions No significant improvement with alamethicin when phase I and II metabolism is assessed Inclusion of alamethicin can be useful in increasing direct glucuronidation Microsomes and S9 unlikely to improve on hepatocytes for CL prediction however they are useful for ranking and probing pathways of metabolism Recommend the use of the parallel tube model over the well-stirred for clearance scaling or Cloe PK if more in vitro data available.
Why underprediction? Other clearance mechanisms to be considered: intestinal, renal and biliary Model assumptions? Loss of activity in microsomes, S9 and hepatocytes Population difference between in vitro and in vivo data Scaling factors correct? Is system/protein binding dynamic rather than static
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and Clearance equations deconvoluted Well Stirred model CL int CL ( b 1- ) Q CL H b fu R p B Parallel tube model CL int Q fu H B ln 1 CL Q H B