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Director, Drug Development Graduate Program

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1 Director, Drug Development Graduate Program
Key Pharmacokinetic Concepts – Single Dose and Steady State Drug Administration Pankaj B. Desai. Ph.D. Professor of Pharmacokinetics and Biopharmaceutics Director, Drug Development Graduate Program

2 Morning Agenda: Wake Up and Smell the Coffee (Cytochrome P450 1A2 Substrate)
Overview of ADME principles Important PK Parameters First Pass Metabolism Compartmental & Non- Compartmental Analyses Single Dose Kinetics Multiple Dose Kinetics Drug-Drug Interactions Inter-Subject Variability CYP1A2 Substrate


4 Clinical Pharmacology
First in Human -Pharmacokinetically Guided Dose Escalation/ Drug Tolerance Study Pharmacokinetics-Pharmacodynamics Drug Metabolism Mass Balance with Radiolabeled Compounds Bioequivalence:Generic compounds Single and multiple doses Conventional versus controlled release formulations Bioavailability of metabolites Drug-Drug/Drug Dietary Product Interactions Special Populations

5 Drug Input & Different Routes of Administration
I.V. and I.A. injections: Bolus dosing Zero-Order Input (Infusions) Extravascular Administration First Order (mostly passive diffusion) Zero Order (active transport and controlled release systems)

6 Factors Affecting Drug Distribution
Phyisco-chemical properties of the drug Small vs. Large mol.wt. Compounds Hydrophilic vs. Lipophilic compounds pH of the milieu and pKa of the drug Perfusion rate (blood flow/min/g tissue) Protein binding Anatomical restrictions CNS- protected by the blood brain barrier Transport across placenta Salivary Drug Excretion (S/P ratios) Excretion of the drug in milk (M/P ratios)

7 Apparent Volume of Distribution
Mathematical term to correlate amount & concentration Merely a tool to understand the EXTENT of drug distribution- not a real physiological volume Compare to the volume of body waters Best calculated from I.V. Dosing as I.V. Dose/Cpo Drug L/Kg L/70 kg Sulfisoxazole 0.16 11.2 Phenytoin 0.63 44.1 Phenobarbital 0.55 38.5 Diazepam 2.4 168 Digoxin 7 490

8 Apparent Volume of Distribution
Total body Water 40 L, ~55 % body wt (w/w) TBW Plasma Water-3.5 L, ~4.5 % body wt (w/w) Total extracellular water - 15 L, 20 % body wt (w/w) ECW Total Intracellular water –20 L, 30 % body wt (w/w) Conc = 2 mg/ml Vd = 50 ml Beaker without Charcoal 100 mg Conc = 0.2 mg/ml Vd = 500 ml Beaker with Charcoal 100 mg

9 Major Drug Elimination Pathways (Coordinated defense mechanism)
Biotransformation Excretion Extra-Hepatic HEPATIC Renal Biliary Phase I Phase II



12 Glomerular Filtration
Kidney receives 1.1 L of blood (20 – 25%) of cardiac output 10 % is filtered at the glomerulus Compounds with Mol.wt < 20,000 filtered GFR = 120 ml/min CLR of Inulin - a measure of GFR Filtered freely into the tubule Not influenced by protein binding and neither secreted nor reabsorbed Rate of filtration = Fu. Cp.GFR Not a very effective drug extraction process (maximal ~ 0.11 or 10 %)

13 Active Secretion Detected when the overall rate of urinary drug excretion exceeds the rate of filtration Secretory processes (proteins) located predominantly within the proximal tubules Mechanisms exist for secreting acids (anions) and bases (cations) from plasma into the tubular lumen Energy-dependent Saturable processes Subject to competitive inhibition Effect of Protein-Binding Depends upon secretion efficiency and contact time at the secretory sites Restrictive (dependent on the Fub) vs. Non-Restrictive (perfusion-rate limited)

14 Reabsorption Must occur when CLR < fu.GFR
Reabsorption occurs all long the nephron, associated with reabsorption of water; majority however occurring from the proximal tubules Predominantly a passive diffusion process Driven by concentration-gradient across the tubular lumen Active secretion occurs for many endogenous compounds such as vitamins, electrolytes, glucose and amino acids Urine-Plasma Ratio (U/P) based on Henderson-Hasselbalch equation Influence of pKa and pH of urine

15 Major Tissues Involved in Drug Metabolism
Liver Small intestines Kidney Lung Other portals of entry into the body and protected organs. -e.g. nasal mucosa

16 Representation of drug metabolism and excretion by the hepatocyte

17 Biliary Excretion is Transporter Mediated


19 Phase I and Phase II Drug Metabolizing Enzymes
Phase I enzymes: Predominantly cytochrome P450 (CYP)

20 Drug Metabolism by CYPs
Theophylline, caffeine, Olanzapine CYP2A6 (Coumarin) CYP2E1 (Chlorzoxazone) CYP1A2 CYP2B6 bupropion, tamoxifen, efavirenz 5% CYP2C8 Paclitaxel Rosiglitazone cerivastatin CYP2C9 (15%) Includes: warfarin phenytoin tolbutamide Losartan CYP3A (50%) Includes: lovastatin cyclosporin nifedipine midazolam ethinylestradiol Ritonavir Midazolam testosterone CYP2D6 (25%) Includes: Tricyclic antidepressants, SSRI's, haliperidol, propanolol, atomoxetine Detxromethorphan,

21 Phase II Reactions Also known as Synthetic (conjugation) reactions
Major reaction: Transfer of the conjugating moiety to the drug Enzymes involved are “transferase” Glucuronosyl transferase Sulfotransferases N-acetyltransferase Methyltransferase Glycine transferase Glutathione-S-transferase

22 Drug Biotransformation Reactions
Active Drug to Inactive Metabolite Amphetamine Phenylacetone Phenobarbital Hydroxyphenobarbital Taxol 6-hydroxytaxol Active Drug to Active Metabolite Codeine Morphine Procainamide N-acetylprocainamide tamoxifen hydroxytamoxifen

23 Drug Biotransformation Reactions
Inactive Drug to Active Metabolite Hetacillin Ampicillin Sulfasalazine Sulfapyridine + 5 ASA Cyclophosphamide Nitrogen mustard Active Drug to Reactive Intermediates Acetaminophen Reactive metabolites (hepatic necrosis) Benzo(a)pyrene Reactive metabolite (carcinogenic)



26 Nomenclature Basis: Amino acid sequence
Families: Less than 40 % a.a. sequence assigned to different gene families (gene families 1, 2, 3, 4 etc.) Subfamilies: 40 – 55 % identical sequence (2A, 2B, 2C, 3A etc.) CYP3A4 Family Isoform Subfamily

27 CYP Nomenclature (Contd.)
Cytochrome P450 Nomenclature, e.g. for CYP2D6 CYP = cytochrome P450 2 = genetic family D = genetic sub-family 6 = specific gene NOTE that this nomenclature is genetically based: it has NO functional implication


29 Examples of CYP mediated Oxidative Metabolism
Examples of reactions catalyzed by cytochrome P450: Hydroxylation of aliphatic carbon

30 Examples of CYP mediated Oxidative Metabolism
Examples of reactions catalyzed by cytochrome P450: Heteroatom dealkylation Examples of CYP mediated Oxidative Metabolism

31 Clearance Concepts


33 Compartmental Modeling

34 One-Compartment Open Model
I.V. bolus DB1 Cp1 Vd k10 K10 = overall Elimination Rate Constant

35 I.V. Bolus

36 Two-compartment Open model
Central or Plasma Tissue k12 Cp1 VC Dp I.V. bolus Dt Ct Vt k21 1- hybrid rate constant (distribution) z- hybrid rate constant (terminal)

37 Two-compartment Open Model
Elimination only

38 Percent Cardiac Output Blood Flow (ml/100 g tissue/min)
Blood flow to human tissues Tissue Percent Body Weight Percent Cardiac Output Blood Flow (ml/100 g tissue/min) Adrenals 0.02 1 550 Kidney 0.4 24 450 Liver 2.0 25 Hepatic Portal 5 20 75 Brain 15 55 Skin 7.0 Muscle (basal) 40.0 3 Connective Tissue Fat 15.0 2

39 Extravascular dose Dp Cp Vd k10 ka Site of absorption e.v. dose


41 NCA Used to estimate AUC Bioavailability Clearance
Volume of Distribution Average Steady State Concentration

42 AUC Trapezoidal Rule AUC= ½(t3-t2)(C2+C3)

43 AUC

44 Example Cp(last)= 2.75/0.1419

45 Bioavailability Absolute Bioavailability Relative Bioavailability

46 Bioequivalence Two products are considered to be bioequivalent if the concentration time profiles are so similar that they are likely to produce clinically relevant differences in either efficacy or toxicity. Common measures used to assess differences are Tmax, Cmax and AUC.

47 Other Parameters CL = Di.v/AUC AUMC = ½(t2-t1)(C1t1 +C2t2)
MRT (Mean Residence Time) = AUMC/AUC or MRT = 1/K or CL/V Vss = CL. MRT

48 Multiple Dosing –Overall Aims
Key Concepts Principle of Superposition Drug Accumulation and Steady State Persistence Factor and Accumulation Factor Peak, Trough and Steady State Average Levels Applications Determination of drug concentrations and amounts following multiple i.v. and e.v. doses (Ka > > K10) max, min and during a dosing interval Determination of dosing regimens Doses (Maintenance and Loading) and Dosing Interval Cpmax consideration Cpmin consideration Cpmax and Cpmin consideration Practical Considerations in Decision Making

49 Drug Accumulation Depends on Frequency of Administration

50 Multiple I.V. Dosing The AUC within a dosing interval at steady state is equal to the total AUC of a single dose. 12

51 Peak, Trough and Css Average
Accumulation Index - Cssmax/Cmax1 AUC at Steady State = AUC0 ∞


53 Impact of Half-life and dosing interval Half-Life on

54 Goals of the Dosing Regimen

55 Dosing Regimen: Loading and Maintenance Doses

56 Constant Rate Regimens


58 Sources of Variability
Genetic factors Genetic differences within population Racial differences among different populations Environmental factors and drug interactions Enzyme induction Enzyme inhibition Physiologic considerations Age Gender Diet/nutrition Pathophysiology Drug dosage regimen Route of drug administration Dose dependent (nonlinear) pharmacokinetics

59 Examples of CYP3A Inducers
Therapeutic Class Anti-epileptic Drugs Anti-Infective Agents Anti-Cancer Drugs Miscellaneous Carbamazepine Phenobarbital Phenytoin Topiramate Felbamate Rifampicin Rifabutin Rifapentine Clotrimazole Sulfadimidine Suflinpyrazone Efavirenz Amprenavir Nelfinavir Ritonavir Capravirine Paclitaxel Docetaxel Cyclophosphamide Ifophosphamide Tamoxifen 4-hydroxy-tamoxifen SU5416 Lovastatin Troglitazone Omeprazole Prednisolone Probencid Phenylbutazone Diazepam fexofenadine Hyperforin

60 Stopeck Clin. Cancer Research, 2002
Induction of CYP1A2 (Ethoxyresorufin O-deethylase) by SU5416 in Primary Human Hepatocytes Stopeck Clin. Cancer Research, 2002 Salzberg, Investigational New Drugs 24: 299–304, 2006)

61 Example of Auto-Induction – SU5416
Oral Treatment AUC Day 8 AUC Day 15 AUC Day 21/22 Induction of clearance Once weekly (n=3) 156 ± 117 131 ± 140 141 ± 90 10% Twice weekly (n=3) 329 ± 187 117 ± 92 198 ± 321 40% Daily dosing (n=3) 412 ± 111 21 ± 36 9 ± 16 98% Stopeck Clin. Cancer Research, 2002 Salzberg, Investigational New Drugs 24: 299–304, 2006)

62 Effect of Tamoxifen (TAM) Mediated
CYP3A4 Induction Letrozole Alone Letrozole + Tamoxifen ( 6 weeks & > 4 months) Pharmacokinetic 24-h profiles of the mean ± SD phase letrozole levels after 6 weeks of treatment with letrozole alone (•), 6 weeks of treatment with letrozole plus tamoxifen ( ), and >4 months of treatment with letrozole plus tamoxifen ( ). Dowsett, M. et al. Clin Cancer Res 1999;5:

63 PXR Pharmacogenomics November; 9(11): 1695–1709.

64 Midazolam Plasma Conc. Profile
Effect of CYP3A/PXR Genotypes on CYP3A Induction Time(hrs) Midazolam Conc. (ng/ml) Day 0 Day 1 Day 42

65 Inhibition of Drug Metabolizing Enzymes
Inhibitor absent Active drug CYP3A Inactive drug Inhibitor present Active drug CYP3A Inactive drug Inhibitor Saquinavir + Ritonavir The most important hepatic enzyme involved in the metabolism of protease inhibitors is cytochrome P450 3A4 (CYP3A4). Ritonavir (RIT) is a potent inhibitor of CYP3A4 and inhibits saquinavir (SQV) metabolism in healthy volunteers. In this study we investigated the kinetics of SQV when administered alone and in combination with RIT in HIV-infected patients AT STEADY STATE. AIDS Mar 15;11(4):F29-33 Saquinavir

66 Plasma Rosuvastatin concentration-time profile in the absence and presence of Darunavir/Ritonavir
Before DRV/RTV After DRV/RTV

67 Desai Lab with the UC President
Collaborators Arthur Buckley, Ph.D., College of Pharmacy Julie Nelson, Ph.D., Department of Molecular Genetics, Biochemistry and Microbiology Elizabeth Shaughnessy, MD - Judith Feinberg, MD Brian Goodwin, Ph.D., GlaxoSmithKline Stephen Storm, Ph.D. University of Pittsburgh Graduate Students - Rucha Sane Niresh Hariparsad Fang Li Ganesh Mugundu Former Student/Post-Doc Srikanth Nallani, Ph.D., FDA Funding Sources Aventis Pharmaceutical, Eli Lily & Co, Bristol Myers Squibb Womens Health (UC), American Cancer Society NIH, Susan G. Komen Breast Cancer Foundation

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