Pharmacokinetic Modeling & Simulation in Discovery and non-clinical Development

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1 Pharmacokinetic Modeling & Simulation in Discovery and non-clinical Development Where do we stand?

2 Disclaimer I am not a bioinformatician, mathematician or biomedical engineer. I am a simple minded pharmacist, who enjoys the challenge of pharmacokinetics and pharmacodynamics.

3 Pharmacokinetic M&S in Discovery and non-clinical Development Desktop Drug Discovery Pharma companies turn to computer simulations to complement experimentation and trial design. A. Constans in Scientist 18 (2004) 4, 33 Imagine being able to discover the latest blockbuster drug using nothing but a PC and some highly sophisticated software. It is not as far-fetched as it sounds...????

4 In-silico / Physicochem Non-clinical DMPK Technologies Lipophilicity pka Solubility PSA Others Physicochemical descriptors In-vitro DMPK Metabolism Transporter Binding Microsomes Hepatocytes Recombinant isozymes Enzyme induction (Nu) Membrane vesicles Hepatocytes Cell lines CaCo2 In-vivo DMPK ADME QWBA SDPK (dogs, rat, and others) Mass balance studies Tissue distribution Metabolite Profiling MDPK (dogs, rat, and others) Plasma protein binding Erythrocyte / Plasma partition DDI studies Special binding (e.g. phospholipids) Special mechanistic DMPK Body fluid stability Ussing chamber Mechanistic animal models Portal vein (rat, others) Lymphatic duct (rat, rabbit) Bile duct cannulated rats Bile duct cannulated dogs (PA) Knockouts / Transgenics Mechanistic Tox Isolated perfused rat liver (IPRL)

5 Potential DMPK data - in-silico, in-vitro, in-vivo data Protein binding In-vitro Metabolism pka Solubility Solubility PK/PD model Permeability Animal TOX In-vitro PD Animal PK In-vivo PD Ultimate Goal: To predict the concentration-effectrelationship(s) in man, Ideally in the target population

6 Suggested integrated PBPK modeling (Theil et. al. Toxicol. Letters 138 (2003) 29-49) Solubility Lipophilicity Ionization Permeability Protein binding Absorbability Metabolic stability Plasma / Blood Tissue Absorption Metabolism (CLh) Elimination Distribution (Kp) Concentration-time profile(s) in plasma and tissue prior to in-vivo experiments (PBPK modeling)

7 Suggested integrated PBPK modeling (Theil et. al. Toxicol. Letters 138 (2003) 29-49) Absorption Distribution Metabolism Excretion Efficacy Rate and extent of absorption (Fabs) Vss, Tissue:Plasma partition coefficient Km, Vmax, CL Km, Vmax, CL IC50, EC50 Prediction with in-vitro data A priori PBPK model simulations {Cp-time profile; animal} Estimation of PK/PD Validation with preclin in-vivo data Preclinical in-vivo PK Preclinical in-vivo PK/PD Extrapolation to human Simulation of human PK prior to clin studies Predicted PK/PD in human

8 Partition coefficients of volatile organic chemicals The left Plateau: Water_fraction in tissue / Water_fraction in plasma The right Plateau: Lipid_fraction in tissue / Lipid_fraction in plasma J. dejongh et al. Arch. Toxicol. 72 (1997) 17

9 Distribution Tissue composition models Specific binding sites Lipids Water Proteins Tissue Lipids Water Proteins Plasma / Blood P T: P = Free/Solubilized K K vo: w vo: w ( V nt ( V np + 0.3V + 0.3V pht php ) + 1 ( V wt ) + 1 ( V wp Bound + 0.7V + 0.7V pht php ) ) fu fu p t + P Vss : = Vp Ti P VT i Poulin and Theil. J. Pharm. Sci. 89 (2000) 16

10 Vss Modeling and Validation with an internal data set (n=21) Vss pred - tissue composition models 10 1 Input: Physiology: Tissue composition data Compound information: Lipophilicity, protein binding, pka Output: P T:P, Volume of distribution (Vss) Vss exp. from {t, cp} single dose PK

11 Vss Modeling and Validation with an external data set (n=123) PREDICTED Input: Physiology: Tissue composition data Compound information: Lipophilicity, protein binding, pka 0,1 0, OBSERVED Output: P T:P, Volume of distribution (Vss) RAT ACIDS RAT BASES RAT NEUTRALS HUMAN ACIDS HUMAN BASES HUMAN NEUTRALS LINE OF IDENTITY x2 x0.5 Poulin, Theil J. Pharm. Sci. 2002

12 Distribution - Mechanism based modeling Vp - Plasma volume Vt - Tissue volume Kp - Tissue:Plasma partition coeff fup - unbound fraction in plasma LogP - Octanol:buffer partition coefficient Poulin, Theil, J Pharm Sci Feb 2002

13 Available physiologically based ADME Modules Absorption Distribution Metabolism PBPK Model to predict Concentration-time profiles prior to in-vivo experiments

14 Generic PBPK modeling To describe ADME (Pharmacokinetics) in animals (validation) and in humans (prediction)

15 Modeling Input information (data) Physiology Tissue composition (water, proteins, lipids, neutral lipids, phospholipids), Tissue volumes, Organ 1 Organ 2 Drug-specific input Distribution Organ distribution governed by e.g.:hydrophobicity, LogD Phospholipid binding Protein binding Blood cell distribution Organ Blood Flows GIT compartmental volumes; Surface, ph, motility, transit time, Transporter activity Dose Splanchnic Blood Liver Gastrointestinal Tract Hepatic clearance Intrinsic clearance: km and Vmax, Protein binding Absorption LogD, Permeability, Transporter affinity Solubility, pka, particle size, dissolution rates, excipients

16 PBPK Modeling Conventional mass balance ODEs Q T, C art Q T, C ven Q T, C art Q T, C ven V T, C T, K p V V, C V PS T CL T V EV, C EV, Kp dct ( t) dt = V T QT K p C ( t) + T Q V T T C art ( t) CL EV dcv ( t) Q = dt T + CL V V V + PS T PST CV ( t) + V K V p C EV Q ( t) + V T T C Art ( t)

17 Absorption physiological advanced PBPK model with ACAT model (GastroPlus ) as input Physiological Disposition Model Physiological Absorption Model GastroPlus Venous blood Intravenous dose (Bolus or infusion) Oral dose Stomach Q Lung Q Adipose Q Bone Q Brain Q Heart Q Kidney Q Muscle Q Liver CL hepatic Q Skin Dissolved drug Lung Adipose Bone Brain Heart Kidney Muscle Liver Skin Precipitation Undissolved drug Release Unreleased drug Q Hepatic Absorption Q Portal Q Skin Dissolution Small Intestine Compartment Q Lung Q Adipose Q Bone Q Brain QHeart QKidney Q Muscle K a Spleen Gut Q Spleen Q Gut Colon Arterial blood Excretion Input: Physiology (tissue flows, tissue volumes) Compound information: Lipophilicity, pka, molecular weight, protein binding, in-vitro clearance Output: Blood, Plasma and Tissue concentrations Disadvantages: no enterohepatic circulation only perfusion limited PK no permeability limited PK no transporter functionality

18 PK-Sim - whole body PBPK simulation tool Input: Physiology (tissue flows, tissue volumes) Compound information: Lipophilicity, molecular weight, protein binding, in-vitro clearance Output: Blood, Plasma and Tissue concentrations Merits: Distinguishes between permeability and perfusionlimited PK based upon

19 Current status of PK M&S in discovery and non-clinical development In silico tools developed, validated and implemented to predict passive absorption and distribution processes Prediction of hepatic clearance requires in-vitro data Generic PBPK tools are available to predict primarily based on in-silico and some in-vitro data plasma and tissue kinetics Quantitative prediction of contributions of active transport for the disposition remains still a challenge First attempts attempt are made to incorporate variability and uncertainty information into the predictions

20 Modeling Strategy (Theil et. al. Toxicol. Letters 138 (2003) 29-49) Prediction of Distribution Tissue:plasma partition Prediction of Elimination hepatic clearance Exp. Plasma concentration-time profiles in rats after i.v. Predicted {cp;t} rat, i.v. If prediction fails -model refinement and/or exp Kp s Extrapolation to man Disposition Prediction of absorption Exp. Plasma concentration-time profiles in rats after p.o. Predicted {cp;t} rat, p.o.. Extrapolation to man oral PK

21 Iteration - Modeling & Experimentation Modeling & Simulation becomes the primary experiment. in-silico in-vitro (in-vivo) Decision Modeling & Simulation In-vivo Experiment The in-vivo experiment can be considered as confirmatory trial

22 Challenges in the field of PK M&S Prediction of sub-populations e.g. children, elderly, obese patients Prediction of regional distribution Incorporation of variability and uncertainty More relevant contribution with regards to modeling of dynamics (safety and efficacy information)

23 Acknowledgement Thierry Lavé Sami Haddad Neil Parrott Patrick Poulin