Key Pharmacokinetic Concepts – Single Dose and Steady State Drug

AdministrationPankaj B. Desai. Ph.D.

Professor of Pharmacokinetics and Biopharmaceutics

Director, Drug Development Graduate Program

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

ADME ISSUES IN ANTIssues I-CANCER DRUG DEVELOPMENTADMEADMEADMEADME

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

Drug Input & Different Routes of Administration1. I.V. and I.A. injections:

• Bolus dosing

• Zero-Order Input (Infusions)

2. Extravascular Administration

• First Order (mostly passive diffusion)

• Zero Order (active transport and controlled release systems)

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)

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 kgSulfisoxazole 0.16 11.2 Phenytoin 0.63 44.1 Phenobarbital 0.55 38.5 Diazepam 2.4 168 Digoxin 7 490

Apparent Volume of Distribution

Conc = 2 mg/mlVd = 50 ml

Beaker without Charcoal

100 mg

Conc = 0.2 mg/mlVd = 500 ml

Beaker with Charcoal

100 mg

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)

Major Drug Elimination Pathways (Coordinated defense mechanism)

Renal Biliary

Biotransformation Excretion

HEPATIC Extra-Hepatic

Phase I Phase II

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 %)

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)

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

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

Representation of drug metabolism and excretion by the hepatocyte

Biliary Excretion is Transporter Mediated

Phase I and Phase II Drug Metabolizing Enzymes

Phase I enzymes: Predominantly cytochrome P450 (CYP)

Drug Metabolism by CYPs

CYP2D6 (25%)Includes: Tricyclic antidepressants,SSRI's, haliperidol, propanolol, atomoxetineDetxromethorphan,

CYP3A (50%)

CYP2E1(Chlorzoxazone)CYP1A2

5%

CYP2A6 (Coumarin)

Includes:lovastatincyclosporinnifedipinemidazolamethinylestradiolRitonavirMidazolamtestosterone

CYP2C9(15%)Includes:warfarinphenytointolbutamideLosartan

Theophylline, caffeine, Olanzapine

CYP2C8PaclitaxelRosiglitazonecerivastatin

CYP2B6 bupropion, tamoxifen, efavirenz

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

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 4-hydroxytamoxifen

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)

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 Subfamily Isoform

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

Examples of reactions catalyzed by cytochrome P450:

Hydroxylation of aliphatic carbon

Examples of CYP mediated Oxidative Metabolism

Examples of reactions catalyzed by cytochrome P450:

Heteroatom dealkylationExamples of CYP mediated Oxidative

Metabolism

Clearance Concepts

Compartmental Modeling

One-Compartment Open Model

I.V. bolus DB1 Cp1 Vd

k10

K10 = overall EliminationRate Constant

I.V. Bolus

tp

10p

keCC

Vd

DpC

Two-compartment Open model

tλCtλCCp Z1z1

1- hybrid rate constant (distribution)

z- hybrid rate constant (terminal)

Cp1 VC

Dp

I.V. bolus Dt

Ct

Vt

k12

k21

TissueCentral or Plasma

Two-compartment Open Model

Elimination only

Blood flow to human tissuesTissue Percent Body

WeightPercent Cardiac

OutputBlood Flow

(ml/100 g tissue/min)

Adrenals 0.02 1 550

Kidney 0.4 24 450

Liver 2.0 25

Hepatic

Portal

5 20

20 75

Brain 2.0 15 55

Skin 7.0 5 5

Muscle (basal)

40.0 15 3

Connective Tissue

7.0 1 1

Fat 15.0 2 1

Extravascular dose

DpCpVd

k10

kaSite of absorption

e.v. dose

0

4

8

12

16

0 5 10 15Time(hrs)

Co

nc

(ug

/ml)

Cp

Cp'

Cp'-Cp

F.Dose.Ka Cp=

V(ka-k) (e-k.t- e-ka.t)

NCA

Used to estimate• AUC• Bioavailability• Clearance• Volume of Distribution• Average Steady State

Concentration

AUC

Trapezoidal Rule

AUC= ½(t3-t2)(C2+C3)

AUC

ExampleTime (hr)

Conc (ug/ml)

AUC(ug.hr/ml)

0 0 0 0.25 2.025 0.25 0.5 3.53 0.69 1 6.07 2.40 2 8.75 7.41 4 9.36 18.11 6 8.1 17.46 8 6.41 14.51

10 5 11.41 12 3.71 8.71 14 2.75 6.46

19.38 AUC(0-) 106.80

Conc Time Profile (Oral Dose)

y = 20.245e-0.1419x

R2 = 0.9981

1

10

0 2 4 6 8 10 12 14 16

Time (hr)

Co

nc

(ug

/ml)

Cp(last)= 2.75/0.1419

0

2

4

6

8

10

0 2 4 6 8 10 12 14 16

Time(hr)

Co

nc(

ug

/ml)

Bioavailability

• Absolute Bioavailability

• Relative Bioavailability

F= [AUC]e.v/[DOSE]e.v [AUC]i.v/[DOSE]i.v

F= [AUC]e.v/[DOSE]e.v [AUC]std/[DOSE]std

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.

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

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 Cp

min consideration

• Practical Considerations in Decision Making

Drug Accumulation Depends on Frequency of Administration

Multiple I.V. Dosing

The AUC within a dosing interval at steady state is equal to the total AUC of a single dose.

Peak, Trough and Css Average

Accumulation Index - Cssmax/Cmax

1

AUC at Steady State = AUC0 ∞

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

Goals of the Dosing Regimen

Dosing Regimen: Loading and Maintenance Doses

Constant Rate Regimens

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

Sources of VariabilitySources of Variability

Therapeutic Class

Anti-epileptic Drugs

Anti-InfectiveAgents

Anti-Cancer Drugs

Miscellaneous

CarbamazepinePhenobarbital

PhenytoinTopiramateFelbamate

RifampicinRifabutin

RifapentineClotrimazoleSulfadimidine

SuflinpyrazoneEfavirenz

AmprenavirNelfinavir Ritonavir

Capravirine

PaclitaxelDocetaxel

CyclophosphamideIfophosphamide

Tamoxifen4-hydroxy-tamoxifen

SU5416

LovastatinTroglitazone OmeprazolePrednisolone

ProbencidPhenylbutazone

Diazepamfexofenadine

Hyperforin

Examples of CYP3A InducersExamples of CYP3A Inducers

Induction of CYP1A2 (Ethoxyresorufin O-deethylase) by SU5416 in Primary Human Hepatocytes

Induction of CYP1A2 (Ethoxyresorufin O-deethylase) by SU5416 in Primary Human Hepatocytes

Stopeck et.al. Clin. Cancer Research, 2002

Salzberg et.al, Investigational New Drugs 24: 299–304, 2006)

Example of Auto-Induction – SU5416 Example of Auto-Induction – SU5416

Oral Treatment AUC Day 8 AUC Day 15 AUC Day

21/22Induction 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 et.al. Clin. Cancer Research, 2002

Salzberg et.al, Investigational New Drugs 24: 299–304, 2006)

Letrozole Alone

Letrozole + Tamoxifen ( 6 weeks & > 4 months)

Dowsett, M. et al. Clin Cancer Res 1999;5:2338-2343

Effect of Tamoxifen (TAM) Mediated CYP3A4 Induction

Effect of Tamoxifen (TAM) Mediated CYP3A4 Induction

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Pharmacogenomics. 2008 November; 9(11): 1695–1709.

PXRPXR

Midazolam Plasma Conc. ProfileMidazolam Plasma Conc. Profile

3 7 3 7

3 7

10

30

10

30

10

30

10

30

ID: 1 ID: 3 ID: 4 ID: 5

ID: 6 ID: 7 ID: 8 ID: 9

ID: 10 ID: 11 ID: 12 ID: 14

ID: 15

Time(hrs)

Mid

azo

lam

Co

nc.

(n

g/m

l)

Day 0Day 1Day 42

64

Effect of CYP3A/PXR Genotypes on CYP3A InductionEffect of CYP3A/PXR Genotypes on CYP3A Induction

Inhibition of Drug Metabolizing

EnzymesInhibitor absent

Active drug

CYP3A

Inactive drug

Inhibitor present

Active drug

CYP3A

Inactive drug

Inhibitor

Saquinavir +

Ritonavir

SaquinavirAIDS. 1997 Mar

15;11(4):F29-33

Plasma Rosuvastatin concentration-time profile in the absence and presence of

Darunavir/Ritonavir

Before DRV/RTV After DRV/RTV

• Graduate Students- Rucha Sane

– Niresh Hariparsad– Fang Li

– Ganesh Mugundu

• 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

-

• Funding Sources- Aventis Pharmaceutical, Eli Lily & Co, Bristol Myers Squibb

- Womens Health (UC), American Cancer Society

- NIH, Susan G. Komen Breast Cancer Foundation

•Former Student/Post-Doc Srikanth Nallani, Ph.D., FDA

Desai Lab with the UC PresidentDesai Lab with the UC President