An Introduction to:In Vitro-In VivoExtrapolation (IVIVE)


Published on

  • Interesting piece . Apropos , you are searching for a NY LS 54 , my colleague saw a fillable form here
    Are you sure you want to  Yes  No
    Your message goes here
  • That's very kind of you.
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

An Introduction to:In Vitro-In VivoExtrapolation (IVIVE)

  1. 1. An Introduction to: In Vitro - In Vivo Extrapolation (IVIVE) Masoud Jamei Senior Scientific Advisor, Head of M&S Honorary Lecturer, University of Sheffield The University of Greenwich, 29th Oct 2009, UK IN CONFIDENCE © 2001-2009
  2. 2. Acknowledgement: The Team Current: Geoff Tucker, Amin Rostami-Hodjegan, Mohsen Aarabi, Khalid Abduljalil, Malidi Ahamadi, Lisa Almond, Steve Andrews, Adrian Barnett, Zoe Barter, Kim Crewe, Helen Cubitt, Duncan Edwards, Kevin Feng, Cyrus Ghobadi, Matt Harwood, Phil Hayward, Masoud Jamei, Trevor Johnson, James Kay, Kristin Lacy, Susan Lundie, Steve Marciniak, Claire Millington, Himanshu Mishra, Chris Musther, Helen Musther, Sibylle Neuhoff, Sebastian Polak, Camilla Rosenbaum, Karen Rowland-Yeo, Farzaneh Salem, David Turner, Kris Wragg Previous: Aurel Allabi, Mark Baker, Kohn Boussery, Hege Christensen, Gemma Dickinson, Eleanor Howgate, Jim Grannell, Shin-Ichi Inoue, Hisakazu Ohtani, Mahmut Ozdemir, Helen Perrett, Maciej Swat, Linh Van, Hua Wang, Jiansong Yang & .... Many others IN CONFIDENCE © 2001-2009
  3. 3. Grants Received by Simcyp IN CONFIDENCE © 2001-2009
  4. 4. Simcyp Background “Simcyp” stands for simulating CYPs (a super family of metabolising enzymes). Simcyp is a spin-out company of the University of Sheffield founded in 2001. Simcyp activities and future developments are guided by a consortium of pharmaceutical companies (the Simcyp consortium). The Simcyp® Population-Based ADME Simulator is a platform and database for „bottom-up‟ mechanistic modelling and simulation of the ADME processes of drugs and drug candidates in healthy and disease populations. IN CONFIDENCE © 2001-2009
  5. 5. Pharmacology, PK and PD Pharmacology is the study of how drugs interact with living organisms to produce a change in function. The field encompasses drug composition and properties, interactions, toxicology, therapy, and medical applications and antipathogenic capabilities. Pharmacokinetics (PK) is a branch of pharmacology dedicated to the determination of the fate of substances administered externally to a living organism. Or, what the body does to a substance. Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs on the body, the mechanisms of drug action and the relationship between drug concentration and effect. Or, what the substance does to the body. Source: Wikipedia IN CONFIDENCE © 2001-2009
  6. 6. In Vitro - In Vivo Extrapolation (IVIVE) In vitro (Latin: within the glass) refers to the technique of performing a given procedure in a controlled environment outside of a living organism. In vivo (Latin for "within the living") refers to experimentation using a whole, living organism as opposed to a partial or dead organism. Mechanistic approach Drug fate in body in vitro in vivo IN CONFIDENCE © 2001-2009
  7. 7. One Source of the Problem PRE-CLINICAL CLINICAL Ki ED50 LogP Kinact IN CONFIDENCE © 2001-2009
  8. 8. A Timeline of Traditional Drug Discovery and Development Hoffman J M et al. Radiology 2007;245:645-660 IN CONFIDENCE © 2001-2009
  9. 9. Estimate of the Total Investment required to “launch” Hoffman J M et al. Radiology 2007;245:645-660 Windhover's in vivo: the business and medicine report, Bain drug economics model, Nov 2003 IN CONFIDENCE © 2001-2009
  10. 10. ADME PK is often divided into several areas including, but not limited to, the extent and rate of Absorption, Distribution, Metabolism and Excretion (ADME). Absorption is the process of a substance entering the body through mouth. Distribution is the dispersion or dissemination of substances throughout the fluids and tissues of the body. Metabolism is the irreversible transformation of substances and its daughter metabolites. Excretion is the elimination of the substances from the body. In rare cases, some drugs irreversibly accumulate in a tissue in the body. The biological, physiological, and physicochemical factors influence the rate and extent of ADME of drugs in the body. Source: Wikipedia IN CONFIDENCE © 2001-2009
  11. 11. ADME: The Roadmap to Site of Effect Drug Food, environment, Tablet Compliance in Faeces in Tablet Comprehension genetic, race, gender, Excretion etc effects! Drug Drug Drug in Tablet in Gut Release Metabolites in Faeces Drug in Gut Absorption Drug Excretion Metabolites Metabolism Drug in Drug in Blood Metabolism Urine, Bile, Milk Drug Metabolites Excretion Drug in Tissues Distribution Metabolism Drug at Receptor Metabolite at Receptor NO DESIRED UNWANTED RESPONSE RESPONSE RESPONSE NO CHANGE THERAPY TOXICITY IN CONFIDENCE © 2001-2009
  12. 12. PK Models Different PK models: 1 C=Cie-kit 2 Empirical Compartmental Physiological GT Tucker (Basic PK Course) IN CONFIDENCE © 2001-2009
  13. 13. Combining Physiological and Drug-dependent Data Drug Data Systems Trial Data Design Mechanistic IVIVE & PBPK Population Pharmacokinetics & Covariates of ADME (Jamei et al., 2009) IN CONFIDENCE © 2001-2009
  14. 14. The Challenge of Population Variability Environment Disease Genetics IN CONFIDENCE © 2001-2009
  15. 15. Relationships Between Covariates Affecting ADME Genotypes (Distribution in Population) Renal Function Body Ethnicity Disease Fat Serum Creatinine Sex Age (Distribution in Population) (Distribution in Population) Height Brain Heart Body Volume Volume Surface Area Weight MPPGL Cardiac HPGL Liver Cardiac Output Index Enzyme Volume Abundance Liver Intrinsic Weight Clearance (Jamei et al., 2009) IN CONFIDENCE © 2001-2009
  16. 16. Covariates of Determining Tissue Volumes Age Sex Weight Height Adipose Erythrocytes Brain Plasma Bone Spleen Gut Heart Kidney Liver Lung Muscle Skin IN CONFIDENCE © 2001-2009
  17. 17. Models to Predict Tissue Volumes Price et al., 2003 Volume of Brain (L) for M&F aged 0-19 (including adult F)  Male = (-90.7 * BH(m) + 178.1) * BW(kg) / 1040;  Female = (-97.5 * BH(m) + 181.2) * BW(kg) / 1040; Volume of Heart (L) in Adults  Male = 9.22 * BW(kg)0.853 / 1040;  Female = 9 * BW(kg)0.855 / 1040; Volume of Heart (L) for others  Male = (22.81 * BH(m) * BW0.5 - 4.15) / 1040;  Female = (19.99 * BH(m) * BW0.5-1.53) / 1040; 1.6 Male 1.6 Female 1.4 1.4 Brain Volume (L) 1.2 Brain Volume (L) 1.2 1 1 0.8 0.8 0.6 ICRP 0.6 ICRP 0.4 Predicted 0.4 Predicted 0.2 0.2 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Age (year) Age (year) IN CONFIDENCE © 2001-2009
  18. 18. Dosing Regimen and PK Parameters In many cases, pharmacological action, as well as toxicological action, is related to plasma concentration of drugs. Consequently, through the study of PK parameters, we will be able to individualise therapy for patients. Dosing regimen: How much? Dosing regimen: How often? Oral Half-life bioavailability Volume of Absorption Clearance distribution van de Waterbeemd and Gifford 2003, Drug Discovery IN CONFIDENCE © 2001-2009
  19. 19. Oral Absorption and the GI Tract From Moore & Dalley, 5th Ed IN CONFIDENCE © 2001-2009
  20. 20. Factors Affecting Solid Drug Absorption  Physicochemical &  Physiological issues Pharmaceutical issues  Disintegration  Gastric emptying  De-aggregation  Intestinal mobility  Dissolution  pH  Solubility  Intestinal metabolism  Precipitation  Disease state  Permeability  P-gp and other transporters  Intra-gut degradation  Intestinal blood flow  Food effects  GI-tract fluid secretion, re- absorption and motility IN CONFIDENCE © 2001-2009
  21. 21. Oral Absorption and First-Pass Effect Gut Lumen Portal Vein Gut Wall Liver Fa FG FH To Site of Action Metabolism Metabolism To Faeces Rowland and Tozer 1995 IN CONFIDENCE © 2001-2009
  22. 22. Oral Bioavailability Fraction escaped metabolism Fraction of dose released in enterocytes from formulation and Fraction escaped permeates through gut wall metabolism in hepatocytes Foral = fa . FG . FH Release Solubility Metabolism Metabolism Stability Permeability Transport Transit Binding Binding Permeability Blood Flow Blood Flow IN CONFIDENCE © 2001-2009
  23. 23. Solid Drug Absorption dissolution Solution Absorption precipitation dissolution disintegration deaggregation reaggregation IN CONFIDENCE © 2001-2009
  24. 24. Breakdown / Dissolution Stages kf,n-1AF,n-1 Drug in kf,nAF,n AF,n : the amount of solid mass trapped formulation in the formulation and not available for dissolution Release Rate kt,n-1AS,n-1 kt,nAS,n AS : the amount of solid mass available Solid drug for dissolution Precipitation Dissolution Rate Rate kt,n-1AD,n-1 kt,nAD,n AD : dissolved drug Dissolved drug Transport Absorption Rate Luminal Rate Degradation Absorbed drug Gut Wall To portal vein Jamei et al. (2009) AAPSJ Metabolism IN CONFIDENCE © 2001-2009
  25. 25. Some Differential Equations dAS ,n dAdiss,n dAF, n   kt ,n AS ,n  kt ,n  1 AS ,n  1  dt dt dt  k deg,n  kan  kt ,n AD,n  kt ,n1 AD,n1   nCLuintT , n fu gutCent, n dAD,n dAdiss,n  dt dt dCent, n dt  1 Vent, n ka An diss, n  Qent, nCent, n  CLuintG , n  CLuintT ,n  fu gutCent, n  dAdiss,n  1 1  AD ,n   4πr ( t )D2   C S ,n   dt  r( t ) h  Vlumen,n ( t )   eff   Jamei et al. (2009) AAPSJ 11:225 IN CONFIDENCE © 2001-2009
  26. 26. Advanced Dissolution Absorption & Metabolism Stomach Duodenum Jejunum I & II Ileum I Ileum II Ileum III Ileum IV Colon Solid Dosage Release Fine Particles Dissolution / Precipitation / Super-Saturation Dissolved Drug Degradation Pgp Absorption / Efflux Faeces Enterocytes Metabolism R distribution pH distribution PBPK Distribution Permeability distribution Portal Vein Liver Model CYPs+Pgp distribution Blood flow distribution After Agoram 2001 Jamei et al. 2009 IN CONFIDENCE © 2001-2009
  27. 27. Fluid Dynamics in the GI-tract Rsec, j Ktj-1 Vj-1 Ktj Vj Vj KRe-Abs, j Rsec, j: Fluid secretion rate into jth gut segment (1/h) KRe-Abs, j: Fluid re-absorption rate constant from jth segment (1/h) Vj: Volume of fluid in jth segment (mL) Ktj: Transit rate constant in jth segment (1/h) dV j  Kt j 1V j 1  Rsec, j  K Re  Abs, jV j  Kt jV j dt IN CONFIDENCE © 2001-2009
  28. 28. Inter-individual Variability & fa fa vs Peff and Tsi (R=1.7 cm) 250 120% 100% 200 100 80% 150 fa (%) 60% 50 Frequency 100 40% 50 0 20% 4 0 0% 10 52 135 207 288 365 447 570 2 5 Peff (cm/h) Intestinal Transit Time (min) 0 0 Tsi (h) Yu et al. (1998) M Jamei et al, 2009 Probability distribution fitting Sensitivity Analysis IN CONFIDENCE © 2001-2009
  29. 29. Clearance (CL) The Clearance (Cl) of a drug is the volume of plasma from which the drug is completely removed per unit time. The amount eliminated is proportional to the concentration of the drug in the blood. Mass Balance Q x CA Q x CV Rate of Extraction= E = (CA-CV)/CA Q(CA - CV) Clearance = QE IN CONFIDENCE © 2001-2009
  30. 30. Metabolism in the liver Metabolism mainly happens in the liver but it can happen in the gut and to much lesser degree in the kidney. Intrinsic hepatic (gut) clearance (CLint): The ability of the liver (gut) to remove xenobiotic from the blood in the absence of other confounding factors (e.g., QH). fuB.CLuint EH = QH + fuB.CLuint QH.fuB.CLuint CLH = QH + fuB.CLuint Can we find Cluint from in vitro assays? How? IN CONFIDENCE © 2001-2009
  31. 31. Scaling Factors for Hepatic Clearance In vitro CLuint per CLuint g Liver In vitro Scaling Scaling CLu per int system Factor 1 Factor 2 Liver HLM µL.min-1 MPPGL mg mic protein X HHEP µL.min-1 Liver X HPGL X 106 cells Weight rhCYP µL.min-1 pmol P450 isoformX MPPGL X mg mic protein pmol P450 isoform IN CONFIDENCE © 2001-2009
  32. 32. IVIVE - Metabolism CLint per CYP/mg x MPPGL Overall CYPs fuB Specific CYP (pmol/g liver) Liver Weight CLint per mg of MPPGL Microsomal Protein (mg/g liver) CLint Liver Microsomal Protein CLint per HPGL Hepatocellularity Hepatocyte (106/g liver) Liver Blood Flow CLH CLpo fa, FG Genetic/Environmental/rac e/age/sex/disease considerations Gut Blood Flow Gut Surface Area Total CYP in gut Overal CYPs CLint per CYP in gut CLint Gut Gut Wall Permeability IN CONFIDENCE © 2001-2009
  33. 33. Rate per pmol of “Each Enzyme” Knowing:  the abundance of each CYP isoform per mg of microsomal protein  the isoform(s) responsible for specific metabolic routes  n  m Vmax (rhCYPj )i  CYP jabundance  CLuint [ L / h]       MPPGL Liver Weig ht  j1  i 1   K m (rhCYPj )i   Proctor et al. Xenobiotica 2004 Vmax Americans/Europeans CLint  CYP1A2 Km  [ S ] CYP2A6 CYP2B6 CYP2C8 CYP2C9 CYP2C18 CYP2C19 CYP2D6 CYP2E1 CYP2J2 CYP3A4 CYP3A5 Japanese/Chinese IN CONFIDENCE © 2001-2009
  34. 34. Mechanistic Model for Expressing Enzyme Pool [S] [P] [E·S] Rsys [E] Induction kdegrad [E·I] [I] kinact [PI] [E·MI] Accelerated Deactivation IN CONFIDENCE © 2001-2009
  35. 35. Mutual Interactions: Drugs/Metabolites/Self-Induction/Inhibition Comp, MBI, Ind Comp, MBI, Ind Comp, MBI, Ind Sub Sub Met Inh 1 Inh1 Met Inh 2 Inh 3 Comp, MBI, Ind Comp, MBI, Ind Comp, MBI, Ind IN CONFIDENCE © 2001-2009
  36. 36. Predicting Volume of Distribution (Vss) Vss knowing distribution into individual tissues is (Sawada et al., 1984): Vss  Vp  Ve  E : P   Vt  Pt:p t Vp = volume of plasma; Vt = tissue (t) volume Ce, ss Erythrocyte : Plasma partition coefficient E:P C p , ss Ct ,ss Tissue : Plasma partition coefficient K p  Pt: p  C p ,ss IN CONFIDENCE © 2001-2009
  37. 37. Minimal Physiologically-Based PK Model 1-fa PO Gut Lumen Faeces fa 1-FG Gut Wall Gut Metabolism Portal Vein FG QPV QPV QHA FH Systemic Liver IV QPV+HA Compartment CLH Hepatic CLR Renal Clearance Clearance IN CONFIDENCE © 2001-2009
  38. 38. Whole Body Physiologically-based PK Parameters Physiologically-based pharmacokinetics (PBPK) models need different sets of parameters which can be divided into: Physiological parameters including: • tissue volumes, • tissue compositions, • blood flow to each organ/tissue, • Enzyme abundances and distributions, • Transporters abundances and distributions Drug-dependent parameters including: • Physicochemical and blood/plasma binding data (MW, LogP, pKa, fu, B:P, etc), • Absorption data (fa, ka, permeability, solubility, particle size, etc), • Metabolism data (CL, CLint, etc), • Distribution data (tissue:plasma ratios (Kp)) • Transport data (Jmax, Km, REF, CLPD, etc) IN CONFIDENCE © 2001-2009
  39. 39. Full PBPK Model with Time-Dependent Volume Lung Adipose Bone Brain Heart Venous Arterial Kidney Blood Blood Muscle Skin Liver Spleen Portal Vein Gut IV Dose PO Dose IN CONFIDENCE © 2001-2009
  40. 40. Multicompartment Mammillary Model Plasma Water KKtP-off P-on P +ve P KP-off pH=7.4 KtEW-in KtEW-out KtP-off +ve P KtP-on +ve EW pH=7.4 KtIW-in KtIW-out KtNP-on +ve KtNP-off KtAP-on KtAP-off NP Ktel KtNL-on KtNL-off +ve AP -ve NL IW pH=7 EW: Extracellular Water NL: Neutral Lipids AP: Acidic Phospholipids IW: Intracellular Water NP: Neutral Phospholipids IN CONFIDENCE © 2001-2009
  41. 41. Prediction of Tissue to Plasma Partition Coefficients Strong bases (pKa ≥ 7) and Zwitterions (pKa ≥ 7) K pu  f EW X   f IW         P  f NL  0.3P  0.7 f NP   Ka AP AP T  a      Y   Y   Y  Other compounds (Zwitterions pKa < 7, neutrals, acids and weak bases) X   P  f NL  0.3P  0.7 f NP  K pu  f EW   f IW      KaPR PR T  Y   Y  Rodgers and Rowland 2006, 2007 IN CONFIDENCE © 2001-2009
  42. 42. Active and Passive Transport QT QT Capillary blood Extracellular fluid Phospholipid bilayer Intracellular fluid For most drugs the capillary membrane is very permeable and diffusion to the interstitial fluid is very fast (Gibaldi and Perrier 1975). The drug movement across the cell membrane can be either passive or/and active.  Perfusion-limited penetration (permeability is NOT rate limiting)  Permeability-limited penetration (permeability is rate limiting) IN CONFIDENCE © 2001-2009
  43. 43. Known Human Transporters! > 50 human ABC transporters are identified; 7 sub-families (A-G) > 360 human SLC transporters; 48 sub-families IN CONFIDENCE © 2001-2009
  44. 44. Tissues Transporters Ho and Kim, 2005 IN CONFIDENCE © 2001-2009
  45. 45. Permeability-limited Liver Model - Hepatobiliary Transporters Capillary blood KP-on KtP-off P +ve P KP-off pH=7.4 KtEW-in KtEW-out KtP-off +ve P KtP-on +ve EW pH=7.4 Sinusoidal OATP1B1 OATP1B3 OCT1 MRP3 KtIW-in KtIW-out membrane Tight junction KtNP-on P-gp +ve KtNP-off KtAP-on KtAP-off NP MRP2 KtNL-on KtNL-off Bile Ktel +ve BCRP AP -ve NL IW pH=7 EW: Extracellular Water NL: Neutral Lipids AP: Acidic Phospholipids Canalicular IW: Intracellular Water NP: Neutral Phospholipids membrane IN CONFIDENCE © 2001-2009
  46. 46. Parameter Estimation Module Tune design parameters to fit observations Simcyp simulation Trial and Error Parameter Estimation (PE) Module IN CONFIDENCE © 2001-2009
  47. 47. Parameter Estimation Process During a parameter estimation process the design parameters are changed, according to a specific algorithm, to get the model outputs as close as possible to the observed DVs. Design parameters: Vss, CL, fu, BP, … Model: one-compartment absorption and/or PBPK model DVs: plasma concentrations 3 2 C(t) 1 0 t1 t2 t3 IN CONFIDENCE © 2001-2009
  48. 48. Least Squares (LS) Objective Function 3 2 e (t1) e (t2) C(θ, t) 1 e (t3) 0 0 t1 20 t2 40 t3 60 80 i n i n WLS  min  w i e( t i )  min  w i y( t i )  C, t i  ˆ 2 2 i 1 i 1 in yi  f (, t i )2 in yi  f (, t i )2 in yi  f (, t i )2 in yi  f (, t i )2  i 1 yi  i 1 yi2 i 1 f (, t i )  i 1 f (, t i ) 2 IN CONFIDENCE © 2001-2009
  49. 49. Optimisation Algorithms  Direct/random search methods (Hooke-Jeeves, Nelder-Mead, …);  Genetic Algorithms (GA);  Combined Algorithms: Begin with a global optimisation method (GA) and then switch to a local optimisation method; e.g., HJ or NM. IN CONFIDENCE © 2001-2009
  50. 50. Genetic Algorithms Evaluate Candidates Randomly Assigned Set of Candidate Candidates Parameters Select a New Set of Rank Candidates Candidates Recombination and Reproduce New Mutation Candidates IN CONFIDENCE © 2001-2009
  51. 51. Maximum Likelihood (ML) Estimation In a population, the model parameters and observations are different for different subjects and we are interested in predicting individual as well as population parameters. l(θ|y2) 3 l(θ|y1) 2 l(θ|y3) C(θ, t) 1 0 0 t1 20 t2 40 t3 60 80 Assuming normal distribution of parameters N(C(θ, t1), σ12)    y i  C  , ti 2   | y    1 Likelihood function: exp    i 2  2 i2    IN CONFIDENCE © 2001-2009
  52. 52. Maximum a Posterior (MAP) Objective Function MAP estimation is a Bayesian approach in the sense that it can exploit an additional information on the supplied experimental data. Consequently if the user has prior knowledge regarding the experimental data then the MAP should in theory provide more accurate estimations of the design parameters than the Maximum Likelihood which only requires experimental measurements. MAP differs from ML in that MAP assumes the parameter θ is also a random variable which has a prior distribution p(θ)  ( yi  f (, t i )) 2  P  ( j   j ) 2    N O MAP ()     ln (b 0  b1f (, t i ) b 2 ) 2     ln( j ) 2 i 1  ( b 0  b1f (, t i ) )  j  j b2 2 2    Where β={b0, b1, b2} vector defines the variance model: Additive β={b0, 0, 1} Proportional β={0, b1, 1} Combined β={b0, b1, 1} IN CONFIDENCE © 2001-2009
  53. 53. Expectation-Maximisation (EM) Algorithm In order to determine the ML or MAP estimations we need to use an optimisation algorithm. The Expectation-Maximisation (EM) algorithm is one of the most popular algorithms for the iterative calculation of the likelihood estimates. The EM algorithm was first introduced by Dempster et al (Dempster, Laird et al. 1977) and was applied to a variety of incomplete-data problems and has two steps which are the E-step and the M-step. E-step: Determining the conditional expectation using Monte Carlo (MC) sampling and updating MC pool for each individual after each iteration M-step: Maximise this expectation with respect to θ and updating population parameters and variance model parameters IN CONFIDENCE © 2001-2009
  54. 54. Useful Simulations vs Accurate Predictions Rostami-Hodjegan & Tucker, Drug Discovery Today: Technologies, V4, Dec 2004 IN CONFIDENCE © 2001-2009
  55. 55. 3 Pillars of Successful Knowledge Management - Intelligent Workforce - Reliable Data - Enabling Tools Regular Hands-on Workshops to give update on latest IVIVE activities applied to ADME to ALL key players in the drug development scene (e.g. scientists in regulatory agencies, different sections of industry) Amount of CYP3A4 in the Gut 8.10 4 Gathering Data / Reaching Consensus on Common 6.10 4 50 mg (Pmol/gut) 100 mg IVIVE & ADME Parameters / Identifying Areas of 4.10 4 200 mg 400 mg 600 mg Further Research (defining specific projects in the form of 2.10 4 800 mg focus groups) 0 0 50 100 150 200 250 300 Time (hour) Continuous Development and Update of a user friendly and mechanistic platform for easier integration of ADME models & databases (simulation of candidate drugs in virtual populations) IN CONFIDENCE © 2001-2009
  56. 56. Organising IVIVE Workshops Washington – April Leiden - May Sheffield – September La Jolla – November IN CONFIDENCE © 2001-2009
  57. 57. Annual Simcyp IVIVE Awards Academic (Research & Teaching) For academic and research institutions leading the field of IVIVE, ADME, Pharmaceutics and Modelling and Simulation ‘Most Informative Scientific Report’ • Awarded to lead author • Receives bursary towards scientific meeting / sabbatical at Simcyp ‘Most Innovative Teaching Application’ • Awarded to course leader • Receives contribution towards computer hardware or software / sabbatical at Simcyp IN CONFIDENCE © 2001-2009
  58. 58. Publications: Peer Reviewed Articles Research Articles Published/In Press 1. Johnson TN, Boussery K, Tucker GT, Rostami-Hodjegan A. Prediction of the increased exposure to drugs in liver cirrhosis: A systems biology approach integrating prior information on disease with in vitro data on drug disposition, Clin Pharmacokin 2009 (in press) 2. Johnson TN, Kerbusch T, Jones B, Tucker GT, Rostami-Hodjegan A, Milligan P. Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug-drug interactions: Using in vitro data as an aid to assess study power, Pharm Stats 2009 (Epub ahead of print) 3. Van LM, Sarda S, Hargreaves JA and Rostami-Hodjegan A. Metabolism of Dextrorphan by CYP2D6 in Different Recombinantly Expressed Systems and its Implications for the In Vitro Assessment of Dextromethorphan Metabolism, J Pharm Sci 2009, 98(2): 763-71 4. O‟Mahoney B, Farre Albaladejo M, Rostami-Hodjegan A, Yang J, Cuyas Navarro E, Torrens Melich M, Pardo Lozano R, Abanades S, Maluf S, Tucker GT and De La Torre Fornell R. The consequences of 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy) induced CYP2D6 inhibition in humans, J Clin Psychopharm 2008, 28(5): 523-9 5. Barter Z, Chowdry J, Harlow JR, Snawder JE, Lipscomb JC and Rostami-Hodjegan A. Co variation of human microsomal protein per gram of liver with age: Absence of influence of operator and sample storage may justify inter laboratory data pooling, Drug Metab Dispos. 2008, 36(12): 2405-9 Review Articles Published/In Press 1. Almond LM, Yang J, Jamei M, Tucker GT, Rostami-Hodjegan A. Towards a quantitative framework for the prediction of DDI‟s arising from Cytochrome P450 induction, Curr Drug Metab 2009, 10(4): 420-432 2. Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan A, Tucker GT. Population-based Mechanistic Prediction of Oral Drug Absorption, The AAPS Journal 2009, 11(2): 225-237 3. Jamei M, Dickinson GL, Rostami-Hodjegan A. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of „bottom-up‟ vs „top-down‟ recognition of covariates, DMPK 2009, 24(1): 53-75 4. Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp population-based ADME simulator, Expert Opinion on Drug Metabolism & Toxicology 2009, 5(2): 211-223 IN CONFIDENCE © 2001-2009
  59. 59. Publications: Others Book Chapters in Press 1. Rostami-Hodjegan A. Translation of in vitro metabolic data to predict in vivo drug-drug interactions: IVIVE and modeling and simulations, in “Enzymatic- and Transporter-Based Drug-Drug Interactions: Progress and Future Challenges” (Eds. Sandy K. Pang, David A. Rodrigues and Raimund M. Peter), Springer, 2009, In press 2. Rostami-Hodjegan A. Predicting Inter-individual Variability of Metabolic Drug-Drug Interactions: Identifying the Causes and Accounting for them Using Systems Approach, in “Enzyme Inhibition in Drug Discovery and Development: The Good and the Bad” (Eds. Chuang Lu and Albert P. Li), Wiley, 2009, In press 3. Yang J. Simulation of population variability in pharmacokinetics, in “Systems Biology in Drug Discovery and Development” (Eds. Daniel L. Young and S. Michelson), Wiley, In press Commentary Articles 1. Toon S, „Model Making – Virtual Reality‟, International Clinical Trials, November 2008 2. Toon S, „R&D in a Virtual World‟, Applied Clinical Trials, 17(10):82, October 2008 IN CONFIDENCE © 2001-2009
  60. 60. Publications: Growing Independent Research Applications of Simcyp 1. Wong H, Chen JZ, Chou B, Halladay JS, Kenny JR, La H, Marsters JC, Plise E, Rudewicz PJ, Robarge K, Shin Y, Wong S, Zhang C, Khojasteh SC. Preclinical assessment of the absorption, distribution, metabolism and excretion of GDC-0449 (2-chloro-N-(4-chloro-3-(pyridin-2- yl)phenyl)-4-(methylsulfonyl)benzamide), an orally bioavailable systemic Hedgehog signalling pathway inhibitor. Xenobiotica. 2009 Sep 2. [Epub ahead of print] 2. Polasek TM, Polak S, Doogue MP, Rostami-Hodjegan A, Miners JO. Assessment of inter-individual variability in predicted phenytoin clearance, Eu J Clin Pharm, 2009 (in press) 3. Gibson CR, Bergman A, Lu P, Kesisoglou F, Denney WS, Mulrooney E. Prediction of Phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling, Xenobiotica 2009, 39(9): 637-648 4. Foti RS, Pearson JT, Rock DA, Wahlstrom JL, Wienkers LC. In vitro inhibition of multiple cytochrome P450 isoforms by xanthone derivatives from mangosteen extract, Drug Metabolism & Disposition 2009, 37(9): 1848-55 5. Fahmi OA, Hurst S, Plowchalk D, Cook J, Guo F, Youdim K, Dickins M, Phipps A, Darekar A, Hyland R, Obach RS. Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction, Drug Metabolism & Disposition 2009, 37(8): 1658-1666 6. Thelingwani RS, Zvada SP, Hughes D, Ungell AL, Masimirembwa CM. In vitro and in silico identification and characterisation of thiabendazole as a mechanism-based inhibitor of CYP1A2 and simulation of possible pharmacokinetic drug-drug interactions, Drug Metabolism & Disposition 2009, 37(6): 1286-1294 7. Hyland R, Osborne T, Payne A, Kempshall S, Logan YR, Ezzeddine K, Jones B. In vitro and in vivo glucuronidation of midazolam in humans, British Journal of Clinical Pharmacology 2009, 67(4): 445-454 8. Ping Z, Ragueneau-Majlessi I, Zhang L, Strong JM, Reynolds KS, Levy RH, Thummel KE, Huang SM. Quantitative Evaluation of Pharmacokinetic Inhibition of CYP3A Substrates by Ketoconazole: A Simulation Study, J Clin Pharmacol 2009, 49: 351-359 9. Emoto C, Murayama N, Rostami-Hodjegan A, Yamazaki H. Utilization of estimated physicochemical properties as an integrated part of predicting hepatic clearance in the early drug-discovery stage: Impact of plasma and microsomal binding, Xenobiotica 2009, 39(3): 227-235 10. Badwan A, Remawi M, Qinna N, Elsayed A, Arafat T, Melhim M, Hijleh OA, Idkaidek NM. Enhancement of oral bioavailability of insulin in humans, Neuro Endocrinology Letters, 30(1): 74-78 11. Grime KH, Bird J, Ferguson D, Riley RJ. Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods, European Journal of Pharmaceutical Sciences 2009, 36(2-3): 175-191 IN CONFIDENCE © 2001-2009
  61. 61. Publications: Growing Awareness Referring to Simcyp  Espie P, Tytgat D, Sargentini-Maier Maria-Laura, Pogessi I, Watelet JB. Physiologically based pharmacokinetics (PBPK), Drug Metabolism Reviews 2009, 41(3): 391-407  Peters SA, Ungell AL, Dolgos H. Physiologically based pharmacokinetic (PBPK) modeling and simulation: Applications in lead optimization, Current Opinion in Drug Discovery & Development 2009, 12(4): 509-518  Grimm SW, Einolf HJ, Hall SD, He K, Lim HK, Ling KH, Lu C, Nomeir AA, Seibert E, Skordos KW, Tonn GR, Van Horn R, Wang RW, Wong YN, Tang TJ, Obach RS. The conduct of in vitro studies to address time-dependent inhibition of drug- metabolizing enzymes: a perspective of the Pharmaceutical Research and Manufacturers of America (PhRMA), Drug Metabolism & Disposition, 37(7): 1355-1370  Chu V, Einolf HJ, Evers R, Kumar G, Moore D, Ripp S, Silva J, Sinha V, Sinz M. In vitro and in vivo induction of cytochrome p450: a survey of the current practices and recommendations: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective, Drug Metabolism & Disposition 2009, 37(7): 1339-1354  Summerfield S, Jeffrey P. Discovery DMPK: changing paradigms in the eighties, nineties and noughties. Expert Opinion on Drug Discovery 2009, 4(3): 207-218  Bouzom F, Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling, Fundamentals of Clinical Pharmacology 2008, 22(6): 579-587 Book Chapters  Zhao P, Zhang L and Huang SM, Complex Drug Interactions: Significance and Evaluation, in “Enzyme and Transporter Based Drug-Drug Interactions” Eds. Sandy K. Pang, David A. Rodrigues and Raimund M. Peter) , Springer, 2009, In press  Prakash C and Vaz ADN, Drug Metabolism: Significance and Challenges, in “Nuclear Receptors in Drug Metabolism” (Ed. W Xie), John Wiley & Sons, 2009, 1-42 IN CONFIDENCE © 2001-2009
  62. 62. Thanks for Your Attention Any Questions? IN CONFIDENCE © 2001-2009