reading

Cover
Title Page
Preface
Companion Website Directions
1 Introduction
1. 1.1 Introduction
2. 1.2 Drugs and drug nomenclature
3. 1.3 Law of mass action
4. 1.4 Ionization
5. 1.5 Partition coefficients
6. 1.6 Further reading
2 Drug Administration and Distribution
1. 2.1 Introduction
2. 2.2 Drug transfer across biological membranes
3. 2.3 Drug administration
4. 2.4 Drug distribution
5. 2.5 Plasma protein binding
6. 2.6 Further reading
7. 2.7 References
3 Drug Metabolism and Excretion
1. 3.1 Introduction
2. 3.2 Metabolism
3. 3.3 Excretion
4. 3.4 Further reading
5. 3.5 References
4 Single‐compartment Pharmacokinetic Models
1. 4.1 Introduction
2. 4.2 Systemic clearance
3. 4.3 Intravenous administration
4. 4.4 Absorption
5. 4.5 Infusions
6. 4.6 Multiple doses
7. 4.7 Non‐linear kinetics
8. 4.8 Relationship between dose, and onset and duration of effect
9. 4.9 Limitations of single‐compartment models
10. 4.10 Further reading
11. 4.11 References
5 Multiple‐compartment and Non‐compartment Pharmacokinetic Models
1. 5.1 Multiple‐compartment models
2. 5.2 Non‐compartmental models
3. 5.3 Population pharmacokinetics
4. 5.4 Curve fitting and the choice of most appropriate model
5. 5.5 Further reading
6. 5.6 References
6 Kinetics of Metabolism and Excretion
1. 6.1 Introduction
2. 6.2 Metabolite kinetics
3. 6.3 Renal excretion
4. 6.4 Excretion in faeces
5. 6.5 Further reading
6. 6.6 References
7 Clearance, Protein Binding and Physiological Modelling
1. 7.1 Introduction
2. 7.2 Clearance
3. 7.3 Physiological modelling
4. 7.4 Further reading
5. 7.5 References
8 Quantitative Pharmacological Relationships
1. 8.1 Pharmacokinetics and pharmacodynamics
2. 8.2 Concentration–effect relationships (dose–response curves)
3. 8.3 Time‐dependent models
4. 8.4 PK‐PD modelling
5. 8.5 Further reading
6. 8.6 References
9 Pharmacokinetics of Large Molecules
1. 9.1 Introduction
2. 9.2 Pharmacokinetics
3. 9.3 Plasma kinetics and pharmacodynamics
4. 9.4 Examples of particular interest
5. 9.5 Further reading
6. 9.6 References
10 Pharmacogenetics and Pharmacogenomics
1. 10.1 Introduction
2. 10.2 Methods for the study of pharmacogenetics
3. 10.3 N‐Acetyltransferase
4. 10.4 Plasma cholinesterase
5. 10.5 Cytochrome P450 polymorphisms
6. 10.6 Alcohol dehydrogenase and acetaldehyde dehydrogenase
7. 10.7 Thiopurine methyltransferase
8. 10.8 Phase 2 enzymes
9. 10.9 Transporters
10. 10.10 Ethnicity
11. 10.11 Pharmacodynamic differences
12. 10.12 Personalized medicine
13. 10.13 Further reading
14. 10.14 References
11 Additional Factors Affecting Plasma Concentrations
1. 11.1 Introduction
2. 11.2 Pharmaceutical factors
3. 11.3 Sex
4. 11.4 Pregnancy
5. 11.5 Weight and obesity
6. 11.6 Food, diet and nutrition
7. 11.7 Time of day
8. 11.8 Posture and exercise
9. 11.9 Further reading
10. 11.10 References
12 Effects of Age and Disease on Drug Disposition
1. 12.1 Introduction
2. 12.2 Age and development
3. 12.3 Effects of disease on drug disposition
4. 12.4 Assessing pharmacokinetics in special populations
5. 12.5 Further reading
6. 12.6 References
13 Drug Interactions and Toxicity
1. 13.1 Introduction
2. 13.2 Drug interactions
3. 13.3 Toxicity
4. 13.4 Further reading
5. 13.5 References
14 Perspectives and Prospects
1. 14.1 Drug disposition and fate
2. 14.2 Pharmacodynamics
3. 14.3 Quantification of drugs and pharmacokinetics
4. 14.4 The future
5. 14.5 Postscript
6. 14.6 Further reading
7. 14.7 References
Appendix 1: Mathematical Concepts and the Trapezoidal Method
1. A1.1 Algebra, variables and equations
2. A1.2 Indices and powers
3. A1.3 Logarithms
4. A1.4 Calculus
5. A1.5 Calculating AUC values: the trapezoidal method
Appendix 2: Dye Models to Teach Pharmacokinetics
1. A2.1 Introduction to the dye model
2. A2.2 Arrangement for i.v. bolus or zero‐order infusions
3. A2.3 Arrangement for first‐order absorption
Appendix 3: Curve Fitting
1. A3.1 Graphical solution: method of residuals
2. A3.2 Iterative curve‐fitting
3. A3.3 Reference
Appendix 4: Pharmacokinetic Simulations
1. A4.1 Simulations for Chapter 4
2. A4.2 Simulations for Chapter 5
3. A4.3 Simulations for Chapter 7
Index
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List of Tables

Chapter 01
1. Table 1.1 Differences in rINN and USAN nomenclature
Chapter 02
1. Table 2.1 Selected examples of SLC and ABC transporters
2. Table 2.2 Examples of the effects of food and drugs on the oral availability of drugs
3. Table 2.3 Examples of apparent volumes of distribution
Chapter 03
1. Table 3.1 Some examples of drug‐metabolizing cytochromes
Chapter 04
1. Table 4.1 Proportion of the dose eliminated or remaining as a function of the number of half‐lives elapsed after an i.v. bolus dose of drug
2. Table 4.2 Calculated rates of ethanol metabolism as a function of concentration
Chapter 05
1. Table 5.1 Results from i.v. injection of dye into a two‐compartment model
2. Table 5.2 Duration of action and tissue localization of thiopental at four doses in humans (adapted from Curry, 1980)
3. Table 5.3 Non‐compartment approach to determine CL and V_area from the data of Figure 5.6
4. Table 5.4 SMT approach to calculating pharmacokinetic parameters
Chapter 06
1. Table 6.1 A compilation of pharmacokinetic data for examples of drug pairs used in patients where there is a relation within each pair of precursor and metabolite
2. Table 6.2 Elimination half‐lives derived from the faecal excretion data of Figure 6.14
Chapter 07
1. Table 7.1 Effect of systemic clearance on kinetic parameters
2. Table 7.2 Values of systemic clearance, apparent volume of distribution and elimination half‐life for selected drugs in a 70 kg subject
3. Table 7.3 Physiological parameters for several species (Bischoff et al., 1971)
Chapter 09
1. Table 9.1 Some examples of peptides, proteins and other large molecules used as drugs
2. Table 9.2 Apparent volumes of distribution of selected large molecules
Chapter 10
1. Table 10.1 Paired plasma half‐life values (days) for the decline in phenylbutazone in seven pairs of identical and non‐identical twins (Vesell & Page, 1968)
2. Table 10.2 Examples of substances used to phenotype individuals for drug-metabolizing activity
3. Table 10.3 Examples of CYP2C9 substrates
Chapter 11
1. Table 11.1 Examples of the factors leading to inequivalence in drug formulations
2. Table 11.2 Selected physiological properties in men and women
3. Table 11.3 Examples of changes in enzyme activity during pregnancy.
4. Table 11.4 A representative selection of examples of apparent volumes of distribution in obesity
5. Table 11.5 A representative selection of examples of clearance and half‐life in obesity
6. Table 11.6 Examples of pharmacokinetic parameters showing time‐dependent changes
Chapter 12
1. Table 12.1 Development of phase 2 enzymes in human liver
2. Table 12.2 Some examples of the influences of GI pathology on drug absorption
3. Table 12.3 Pharmacokinetic parameters in normal subjects (N) and patients with cirrhosis (LD) after intravenous administration
4. Table 12.4 Changes in mRNA levels for selected transport proteins in hepatocytes from patients with hepatitis C virus and liver cirrhosis. Mean ± SD, all the differences are significant, p < 0.05
5. Table 12.5 Pharmacokinetic data relevant to drug elimination in anuric patients and patients with normal function

List of Illustrations

Chapter 01
1. Figure 1.1 Typical concentration–time curves after oral administration of a drug: (a) single dose of drug; (b) a single dose twice the size of the previous one; (c) the same drug given as divided doses. The dose and frequency of dosing for (c) were calculated to ensure the concentrations remained in the therapeutic window.
2. Figure 1.2 Illustration of the rate‐determining step in sequential reactions. The thinnest arrow represents the smallest rate constant and is therefore the rate‐determining step.
3. Figure 1.3 Curves for first‐order decay plotted as (a) C versus t and (b) ln C versus t.
4. Figure 1.4 Curves for zero‐order decay plotted as (a) C versus t and (b) ln C versus t. Note how for an initial concentration of 100, t_½ = 25, but for an initial concentration of 50, t_½ = 12.5.
5. Figure 1.5 Examples of ionizable groups in selected drug examples. Note how the pK_a values range (strong to weak) from 2.3 to 11.6 for the acids and 12.4 to 1.7 for the basic groups. Acidic hydrogen atoms are shown in red and basic nitrogen atoms in blue.
6. Figure 1.6 (a) Partitioning of chloral hydrate is unaffected by buffer pH. (b) Partitioning of non‐ionized amfetamine remains constant, 63:1. However, the ratio of ionized to non‐ionized is affected by buffer pH and as a consequence affects the apparent partition coefficient (APC) and the proportion extracted (inset). Note how when pH = pK_a, x = 1 and there are equal concentrations of ionized and non‐ionized amfetamine in the aqueous phase.
Chapter 02
1. Figure 2.1 General scheme showing the relationship between the various events involved in drug disposition and fate (see text for details).
2. Figure 2.2 Schematic representation of some of the mechanisms by which substances are transferred across lipid membranes.
3. Figure 2.3 Equilibrium distribution of aspirin (pK_a = 3.4) between two solutions of pH 1.4 and pH 7.4 separated by a simple membrane. Relative concentrations are shown in [ ].
4. Figure 2.4 Representation of three types of capillary.
5. Figure 2.5 Absorption of uracil and salicylic acid from the rat small intestine as function of initial drug concentration as (a) percentage and (b) amount absorbed (mmol). Data for uracil were fitted to a hyperbola and those for salicylic acid to a straight line.
6. Figure 2.6 Examples of quaternary ammonium compounds. Quaternary nitrogen atoms are shown in blue, the tertiary amine in tubocurarine is shown protonated because that is how it is at physiological pH values.
7. Figure 2.7 Plasma concentrations of chlorpromazine after three routes of administration and the areas under the curves after normalization for dosage (inset).
8. Figure 2.8 Some of the factors that may reduce oral availability. Other factors include GI motility, absorptive surface area and splanchnic blood flow.
9. Figure 2.9 Scheme illustrating the key role of plasma in transporting drugs to sites of action, other storage sites and sites of elimination.
10. Figure 2.10 Approximate composition of the body.
11. Figure 2.11 Simplified diagram for the equilibrium distribution of an ionizable drug of small relative molecular mass. The drug readily filters into interstitial fluid so the unbound concentrations are equal to those in plasma.
12. Figure 2.12 Concentrations of thiopental (logarithmic scale) in the plasma and various tissues of a dog after intravenous administration of 25 mg kg^–1. (a) 0–3.5 h; (b) 0–24 h.
13. Figure 2.13 (a) Concentrations of thiopental in plasma water and cerebrospinal fluid (C.S.F.) of a dog given 25 mg kg^–1 intravenously. (b) Thiopental concentrations in the brain and plasma of a dog after intravenous administration of 40 mg kg^–1.
14. Figure 2.14 (a) PET scan of [¹¹C]‐triamcinolone (see text for details). (b) Time course of [¹¹C]‐triamcinolone in selected regions. Each point is the mean from three volunteers plotted at the midpoint of each PET scan.
15. Figure 2.15 PET scans of the cortex and striatum after administration of [¹⁸F]‐fluoroDOPA, the precursor of [¹⁸F]‐fluorodopamine.
16. Figure 2.16 Three approaches for solving protein binding data.
17. Figure 2.17 Fraction bound to human plasma protein for desipramine, chlorpromazine and thiopental (^†two individuals, Borga et al., 1969; *blood bank plasma, Curry, 1970).
18. Figure 2.18 Binding of phenylbutazone to plasma protein as a function of phenylbutazone concentration. The percentages indicate the binding at 100, 150, 225 and 250 mg L^–1.
Chapter 03
1. Figure 3.1 Preparation of a microsomal pellet containing mixed function oxidases.
2. Figure 3.2 The final stage of incorporation of molecular oxygen into drug (DH) showing the various oxidation states of iron in the haem‐containing cytochrome.
3. Figure 3.3 Examples of esters that are hydrolysed by plasma esterases. Esters are coded in red. The nitrogen of the carbamate ester of physostigmine is shown in blue.
4. Figure 3.4 Examples of amides and hydrazides that are hydrolysed.
5. Figure 3.5 Examples of nitro and azo reduction.
6. Figure 3.6 Oxidation of ethanol and methanol by alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH). Inhibitors are shown in purple‐edged boxes.
7. Figure 3.7 Metabolism of diazepam. Aliphatic hydroxylation gives temazepam, whilst N‐desmethylation produces nordazepam and formaldehyde. Desmethylation of temazepam, or hydroxylation of nordazepam, gives oxazepam. The 3‐hydroxy metabolites are conjugated with glucuronic acid and excreted in the urine.
8. Figure 3.8 Examples of S‐ and N‐oxidation metabolites of thioridazine.
9. Figure 3.9 Deamination of amfetamine.
10. Figure 3.10 Comparison of metabolism of malathion and comparative toxicity of malathion and malaoxon in insects and mammals.
11. Figure 3.11 Examples of some of the pathways by which chlorpromazine is metabolized. Red: introduced oxygen atoms; blue: deamination; green: dehalogenation; red/brown: desmethylation.
12. Figure 3.12 Glucuronidation of salicylic acid produces the β‐glucuronide.
13. Figure 3.13 Sulfation of minoxidil.
14. Figure 3.14 Examples of arylhydrazines and arylamines acetylated by human NAT2. Acetylated nitrogen are atoms highlighted in blue.
15. Figure 3.15 Formation of salicyluric acid.
16. Figure 3.16 Metabolism of paracetamol (acetaminophen).
17. Figure 3.17 Diagrammatic representation of the principal processes involved in the renal excretion of drugs.
18. Figure 3.18 Simplified diagram of transporters of kidney proximal tubular cells. Secretion into the cell is largely by facilitated diffusion whilst secretion into the tubular fluid is mediated by active transport. Inhibitors are shown in purple‐edged boxes.
19. Figure 3.19 Effect of urine pH on amount of metamfetamine (methamphetamine) excreted in a single subject.
20. Figure 3.20 Concentrations of clonidine in plasma, submaxillary gland tissue (L = left, R = right) and submaxillary gland saliva in a cat after 250 mg kg^–1, intravenously.
21. Figure 3.21 Steady‐state venlafaxine and O‐desmethylvenlafaxine concentrations in plasma and milk in a single subject.
22. Figure 3.22 Enterohepatic cycling. The orally administered drug enters the intestine from the stomach and is absorbed into the hepatic portal vein, carried to the liver and converted to the glucuronide, which is secreted into the bile. Intestinal bacteria hydrolyse the glucuronide, liberating the parent drug, which can be reabsorbed.
Chapter 04
1. Figure 4.1 Model curve showing a theoretical perfect relationship between drug effect (intensity) and drug concentration following a single dose by any route other than intravenous injection.
2. Figure 4.2 Model curves showing a theoretical perfect relationship between drug effect (intensity) and drug concentration during a series of eight doses, with a dose interval 0.5 × t_½ of the drug. The broken line following the fifth dose represents the decay if that had been the last dose. Also shown are the concentrations after an infinite number of doses.
3. Figure 4.3 (a) Single‐compartment models are based on the concept of a well‐stirred solution of drug. (b) Typical representation of a single‐compartment model.
4. Figure 4.4 The concept of systemic clearance. The extraction ratio, E, is the proportion of drug removed as it flows through the eliminating organs and clearance equals the flow rate, Q, multiplied by the extraction ratio.
5. Figure 4.5 (a) Concentrations of phenylbutazone in one jugular vein of an anaesthetized sheep given a rapid intravenous dose in the other jugular vein. (b) Back‐extrapolation of a ln C versus t plot to obtain a value of ln C₀ and hence C₀.
6. Figure 4.6 Prednisolone concentrations in a kidney transplant patient after an i.v. dose of 20 mg.
7. Figure 4.7 Simulated curves for first‐order input into a single‐compartment model: (a) linear scales and (b) semilogarithmic scales. C₀^′ = 25 arbitrary units, k_a = 0.5 min^–1, λ = 0.1 min^–1.
8. Figure 4.8 Effect of lag time. The curve is moved to the right but the other parameters remain unchanged so the curves intercept on a line representing t_lag.
9. Figure 4.9 Comparison of plasma concentration–time curves for various values of F. (a) F = 1. At later times the concentrations after the oral dose must be higher than after i.v. (b) For F = 0.7 concentrations at later times are superimposed, whereas for F = 0.5 all the oral concentrations are less than the i.v. ones. C₀ is the initial plasma concentration after the i.v. injection.
10. Figure 4.10 Principle of sustained release: k_a < < λ so the overall rate is determined by k_a.
11. Figure 4.11 Fluphenazine enanthate was developed so that the rate of release from muscle is rate determining. (a) Linear scales: the areas under the curve are similar after the two preparations. (b) Logarithmic scales: resolution of the data into its two exponentials makes λ appear to be the rate constant of absorption, but the decline after injection of fluphenazine (red, open circles) confirms it is the elimination rate constant.
12. Figure 4.12 Attainment of steady‐state concentrations is a function of the elimination half‐life. Over 99% of steady state is achieved in a time equivalent to 7 × t_½.
13. Figure 4.13 Amikacin infusion: simulated curves derived using Equations 4.33, 4.34 and 4.35.
14. Figure 4.14 Simulation of an intravenous infusion when an appropriately sized loading dose is injected simultaneously at the start of the infusion.
15. Figure 4.15 Modelled curve showing repeated i.v. injections at intervals equivalent to the half‐life. Note the steeper decline in the curves as the concentration increases.
16. Figure 4.16 Multiple dosing at regular intervals with a drug with a short elimination half‐life.
17. Figure 4.17 Multiple dosing at regular intervals with a drug with a long elimination half‐life (red), also showing the effect of a loading dose (blue). The shaded area represents the therapeutic window.
18. Figure 4.18 Calculation of lithium steady‐state serum concentrations from the data for the first oral dose.
19. Figure 4.19 Comparison of non‐linear and linear kinetics. A: saturation of elimination; B: linear kinetics; C: saturation of absorption when it may be necessary to increase the dose to reach therapeutic concentrations. With increasing doses curve A asymptotes to V_max. The shaded area represents the therapeutic window.
20. Figure 4.20 (a) Steady‐state serum concentrations of phenytoin in five patients. The shaded area is the therapeutic window. (b) Phenytoin plasma concentrations in a single subject given three dosage regimens, each lasting 3 days (Gerber & Wagner, 1972).
21. Figure 4.21 Simulated decay curve for ethanol (C₀ = 2 g L^–1, V_max = 8 g h^–1, Km = 0.1 g L^–1). (a) Concentration plotted on a linear y‐axis. (b) Concentration plotted on a logarithmic y‐axis.
22. Figure 4.22 (a) Doubling the dose increases the time to reach the minimum effective concentration, C_eff by one elimination half‐life. (b) Multiple doses given when the plasma concentration or effect has fallen to some arbitrary level. The duration of effect after the first dose is less than that after the second and subsequent doses. After the second dose the maximum concentration/effect is the same.
23. Figure 4.23 The proportionate change in the duration of effect (Δ) on doubling the dose depends on where the minimum effective concentration line is initially. (a) When the line is close to the maximum concentration, doubling the dose more than doubles the duration. (b) When the line is much lower, doubling the dose may have little effect on either the onset or duration of action.
Chapter 05
1. Figure 5.1 Blood concentrations of methylene blue (mean ± SEM, n = 7) after 100 mg intravenously. Data fitted using sum of three exponential terms (solid line).
2. Figure 5.2 Example of data that fits a two‐compartment model using (a) linear coordinates and (b) a semi‐logarithmic plot.
3. Figure 5.3 Diagrammatic representation of a two‐compartment model.
4. Figure 5.4 (a) Photographs taken following i.v. bolus injection of dye into the apparatus described in Appendix 2: A: immediately after the injection; B: 1 min after the injection; C: when the concentrations in the two compartments were equal; D: post equilibrium. (b) Dye concentrations measured in the central and peripheral compartments.
5. Figure 5.5 Dye simulation demonstrating the effect that changing CL has on V. Note how reducing CL increases AUC and reduces V_area. Data provided by medical students of the London Hospital Medical College.
6. Figure 5.6 Empirical demonstration of how the relative size of the absorption rate constant affects the shape of the plasma concentration–time curve. These curves were obtained using the dye model, with different sized Büchner flasks connected in series to provide first‐order input in the two‐compartment model (Appendix 2, Figure A2.2) so there can be no doubt that these curves are from the same two‐compartment model.
7. Figure 5.7 (a) Constant rate infusions into a two‐compartment model. (b) Semilogarithmic plot showing how the post‐infusion decay is influenced by the duration of the infusion.
8. Figure 5.8 Infusion into a two‐compartment model with different loading doses. (a) R₀/k₁₀ gives instantaneous steady‐state concentration, but this falls as drug is transferred to the peripheral compartment. (b) A loading dose R₀/λ₂ ensures the concentrations do not fall below C^ss, but the peak concentration may be unacceptably high.
9. Figure 5.9 Representation of tissues that might constitute the central and peripheral compartments for a lipophilic drug.
10. Figure 5.10 The model for thiopental requires three compartments.
11. Figure 5.11 Red line: time course of thiopental in plasma calculated from the composite data of Brodie after 0.5 g dose; the subject regained consciousness after 20 min. Blue line: calculated time course for 0.75 g dose, with predicted recovery at 2.8 h.
12. Figure 5.12 Modelled curves showing the effect of repeat injections (arrows) of thiopental at 20 min intervals using the parameters of Figure 5.11.
13. Figure 5.13 Model data for i.v. injection into a single compartment, showing C versus t and Ct versus t.
14. Figure 5.14 Statistical moment approach applied to the dye model data of Figure 5.2.
Chapter 06
1. Figure 6.1 (a) Amount of drug and metabolite in the body (as a fraction of dose given) when the half‐life of the metabolite is shorter than that of the drug following an i.v. dose of each. (b) Same drug and metabolite as in (a), but when only the parent drug is injected. (c) Situation when the metabolite has a longer half‐life than the parent drug.
2. Figure 6.2 A model of metabolism where two metabolites, M₁ and M₂, are produced in parallel. Unchanged drug, D, and the metabolites are excreted into the urine.
3. Figure 6.3 A model of excretion where the metabolites M₁, M₂ and M₃ are produced sequentially. Unchanged drug, D, and metabolites are excreted into urine.
4. Figure 6.4 Propranolol is oxidized to 4'‐hydroxypropranolol, which is conjugated to either the sulfate or glucuronide metabolite. Each of the three pathways is formation rate limited.
5. Figure 6.5 Plasma concentrations of 4′‐hydroxypropranolol and its sulfate and glucuronide conjugates (a) after i.v. injection of 4′‐hydroxypropranolol, 2 mg kg^–1 in dogs. The kinetics of the conjugates are formation rate limited, but because 4′‐hydroxypropranolol is metabolized by two routes in parallel its half‐life is less. (b) Plasma concentrations of 4′‐hydroxypropranolol sulfate after i.v. injection of 2 mg kg^–1 in dogs.
6. Figure 6.6 Mean plasma concentrations of propranolol and its 4′‐hydroxy metabolite after oral administration of propranolol (20 mg) to six normal volunteers. Error bars represent mean ± SEM.
7. Figure 6.7 Plasma concentrations of prednisone and prednisolone after a single oral dose of prednisone (red) or an i.v. dose of prednisolone hemisuccinate ester (blue) to a healthy male volunteer.
8. Figure 6.8 Sulindac (a sulfoxide) is reversibly reduced to pharmacologically active sulfide or oxidized to inactive sulfone metabolite.
9. Figure 6.9 Plasma diazepam (red) and nordazepam (blue) concentrations in a healthy female volunteer after 2 mg diazepam at night for 30 days.
10. Figure 6.10 Plasma nordazepam concentrations after dosing with nordazepam (3.0 mg kg^–1), diazepam (3.0 mg kg^–1) and clorazepate (4.5 mg kg^–1).
11. Figure 6.11 Renal clearance as a function of urine flow rate: (a) ethanol, data are five independent estimates in one subject, and (b) glutethimide, data are 10 independent estimates.
12. Figure 6.12 Concentrations of ethanol in whole blood (•) and urine (°) after an oral dose of 64 g of ethanol with 116 mL of water. Note that the urine concentrations are plotted at the midpoint of the collection interval.
13. Figure 6.13 (a) Plasma concentrations and urinary excretion rate of fluphenazine after an intramuscular injection of 25 mg. (b) Urinary excretion rate of fluphenazine and conjugated fluphenazine in a patient receiving 50 mg fluphenazine decanoate (i.m.) weekly.
14. Figure 6.14 Faecal excretion of ¹⁴C‐label following temoporfin administration to BALB/c mice; solid lines are the least squares fit of the data to a two‐compartment model. Tissues samples were assayed after 2, 3 and 5 weeks.
Chapter 07
1. Figure 7.1 Effect of clearance on AUC, C_max and t_max. The curves (solid lines) were modelled using dose = 100, V = 140, F = 1, k_a = 0.1 and CL = 1–8. Broken line: CL = 1 and k_a =0.3, other parameters unchanged.
2. Figure 7.2 Relationship between observed extraction ratios in perfused rat liver and the values predicted using V_max and Km values from metabolism in rat liver homogenates. The solid line is the ‘line of identity’, that is, slope = 1.
3. Figure 7.3 Effect of plasma protein binding on the extraction of a highly extracted drug (propranolol), a poorly extracted drug (warfarin) and one with intermediate extraction (phenytoin).
4. Figure 7.4 Effect of plasma protein binding and elimination half‐life for four values of intrinsic unbound clearance. Calculated from Equation 7.23, using Q_H = 1.5 L min^–1, V = 70 L and intrinsic clearance = 100, 10, 1 and 0.1 times Q_H. Note that the proportionate changes in t_½ are much greater for capacity‐limited drugs.
5. Figure 7.5 Correlation between scaled in vitro clearance values from human hepatocytes and observed in vitro hepatic clearance for 26 drugs, r² = 0.61.
6. Figure 7.6 Hypothetical physiological model demonstrating how relevant organs are connected by arterial and venous blood flows, and how various routes of administration can be depicted as required by the model.
Chapter 08
1. Figure 8.1 (a) Fraction of receptors occupied (occupancy) as a function of drug concentration. K_D is numerically equal to the concentration at which half the receptors are occupied (inset). (b) Logarithmic transformation converts the hyperbola into a symmetrical sigmoid shape typical of log(dose)–response curves.
2. Figure 8.2 Log(agonist concentration)–response curves. Agonist A is more potent than B because it has a lower EC₅₀. The partial agonist cannot elicit the maximum response even when all the receptors are occupied.
3. Figure 8.3 (a) The influence of the Hill coefficient, γ, on the shape of the effect–concentration curve. All the curves have the same E_max = 100 and EC₅₀ = 10. (b) The same data with concentration plotted on a logarithmic scale.
4. Figure 8.4 Model quantal dose–response curve for psychopharmacology.
5. Figure 8.5 Dose–response relationship for phenobarbital concentrations (logarithmic scale) and sleeping time in mice. Each point is the mean of 6–8 mice ± SEM.
6. Figure 8.6 Types of concentration–effect–time relationship. (a) Type A: The effect parallels the concentration and the same effect occurs at the same concentration irrespective of the time at which it occurs. The concentration–effect curve may be linear (A₁) or saturated if it is an Emax model (A₂). The numbers represent the times at which the data were sampled. The closed symbols represent data on the rising phase and open symbols those on the declining phase. (b) Type B: The peak effect occurs before the peak plasma concentration. The concentration–effect curve shows a clockwise hysteresis (see text for details). (c) Type C: The effect lags behind the concentrations. The concentration–effect curve shows an anticlockwise hysteresis and the same effect may occur at different concentrations.
7. Figure 8.7 (a) Time course of the plasma concentrations of carebastine (red) and the histamine‐induced wheal area (blue) in a representative subject after a single oral dose of 10 mg of ebastine. (b) Relationship between plasma concentration and effect – the times of collection are shown.
8. Figure 8.8 Plasma concentrations of glutethimide and percentage suppression of smooth tracking.
9. Figure 8.9 (a) Mean plasma concentrations of LSD in five individuals following i.v. bolus doses of 2 μg kg^–1, together with calculated concentrations in the peripheral compartment. (b) Performance score based on arithmetic tests in the same experiment. The insert in (b) shows the performance score plotted against concentration in the peripheral compartment, indicating a straight line relationship with evidence of modest hysteresis.
10. Figure 8.10 Addition of an effect compartment to a single‐compartment model. The same approach is applied to two‐compartment models and the effect compartment is usually linked to the central compartment.
11. Figure 8.11 Relationship between predicted effect site concentration (C_e) of lorazepam ( □ ) and three psychomotor effects. Solid lines fitted to the PK‐PD model as a function of C_e; arrows show hysteresis.
Chapter 09
1. Figure 9.1 Lymphatic and capillary networks. Drugs absorbed into the lymphatic system avoid the hepatic portal vein and escape first‐pass metabolism. Molecules too large to enter the capillaries are transported via the lymphatic system. Microfold cells can absorb antigens.
2. Figure 9.2 Formula of CCK8.
3. Figure 9.3 Structure of ciclosporin.
4. Figure 9.4 Representation of heparin. Heparin consists of repeating disaccharide units which may be sulfated to varying degrees.
5. Figure 9.5 Half‐life and clearance of trastuzumab as a function of dose.
6. Figure 9.6 Structures of vasopressin and desmopressin.
7. Figure 9.7 Space‐filled model of mipomersen showing the phosphorothioate linkages. Grey, carbon; blue, nitrogen; red, oxygen; purple, phosphorous; yellow, sulfur.
Chapter 10
1. Figure 10.1 Frequency distribution for plasma isoniazid concentrations 6 h after oral doses of 9.7 mg kg^–1 in 267 members of 53 families. Slow acetylators have the higher isoniazid concentrations.
2. Figure 10.2 Distribution for the percentage of N‐acetyl metabolite in plasma 6 h after a test dose of sulfadimidine (0.5 g, p.o.) in undergraduate medical students. Rapid acetylators have higher concentrations of the acetyl metabolite.
3. Figure 10.3 Duration of apnoea in adult male patients with normal and atypical cholinesterase.
4. Figure 10.4 Mean plasma concentration of nortriptyline after a single oral dose in subjects with varying numbers (0–13) of CYP2D6 genes, as indicated on the lines. There were five subjects in each group apart from n = 1 for the subject with 13 genes.
5. Figure 10.5 Simplified diagram of hepatic transporters. Organic anion transport polypeptides (OATP1B1, OATP1B2 and OAT2B1), organic anion transporter 2 (OAT2) and organic cation transporter 1 (OCT1) facilitate the uptake of solutes into hepatocytes. Multidrug‐resistance‐associated protein 3 (MRP3) is an efflux pump. The hepatic canalicular efflux proteins include multidrug resistance proteins (P‐gp and MDR2), MRP2, breast cancer resistance protein (BCRP) and bile salt export pump (BSEP), which utilize ATP (not shown), and multidrug and toxin extrusion protein 1 (MATE1).
6. Figure 10.6 Increase in exposure (as assessed by AUC) in subjects with OATP1B1*15/*15 variant genes when compared with ‘normal’ genes. Repeated studies in 32 volunteers.
7. Figure 10.7 Global distribution of selected SLCO1B1 haplotypes as shown by the key.
8. Figure 10.8 Effect of genotype on warfarin dosing. Individuals with variant CYP2C9*2/*3 and VKORC1 haplotypes A/A (see text for details) require the lowest dose.
Chapter 11
1. Figure 11.1 Influence of lactose and calcium sulfate as excepients on the concentrations of phenytoin in blood in a patient taking 400 mg per day.
2. Figure 11.2 Selected metabolic pathways for propofol. Direct conjugation produces propofol glucuronide. After phase 1 oxidation to 4‐hydroxypropofol, 1‐ or 4‐glucuronides are formed.
3. Figure 11.3 Comparison of areas under the curve, corrected for dose and weight, in male and female patients. Centre: Propofol showed no significant differences in AUC. Left: females had significantly higher AUCs of 4‐hydroxypropofol. Right: females had significantly higher AUCs of propofol glucuronide (see Figure 11.2 for structures).
4. Figure 11.4 (a) BMI as a function of total body weight (TBW) in a male, height 1.77 m, and a female, height 1.63 m. (b) Lean body weight (LBW) as a function of TBW for the same hypothetical male and female subjects as in (a), calculated using Equations 11.2 and 11.3. The difference between TBW and LBW gives an approximate estimate of the amount of fat, shown by the broken lines. Note the much greater proportion of fat in females.
5. Figure 11.5 (a) Plasma nifedipine concentrations following administration of 20 mg capsules while standing, lying on the right and lying on the left side. *Significantly different from standing; **significantly different from lateral right. (b) Changes from baseline for pulse rate (solid symbols) and diastolic blood pressure (open symbols) for lying on the right (, ) and left (, ). Significant differences in pulse rates between 0.25 and 2.5 h.
6. Figure 11.6 (a) Maximum concentration and (b) area under the curve for glyceryl trinitrate in two matched groups of healthy volunteers, one group lying down (face up) and the other sitting. Data are mean ± SEM.
Chapter 12
1. Figure 12.1 Relative distribution of lean body weight (LBW), total body water and fat as a function of age. Compiled from the data of Fomon et al. (1982) and Chumlea et al. (1999).
2. Figure 12.2 (a) Half‐life of diazepam as a function of age. There were no significant differences between smokers and non‐smokers. (b) Volume of distribution of diazepam as a function of age.
3. Figure 12.3 Age‐related variation of CYP2D6 protein and CYP2D6 mRNA in human liver.
4. Figure 12.4 (a) GFR in neonates and children. Values have been normalized to a standard body surface area of 1.73 m². (b) Inulin clearance, mean ± SEM in adults normalized to 1.73 m².
5. Figure 12.5 Lidocaine plasma concentrations (mean ± SD) in seven patients with heart failure and in control subjects after i.v. bolus dose of 50 mg.
6. Figure 12.6 Desethylation of lidocaine to the less active monoethyl metabolite (MEGX), which accumulates in plasma, and to the inactive desdiethylmetabolite (GX), which is normally excreted via the kidneys.
7. Figure 12.7 Half‐life, apparent volume of distribution and clearance after i.v. injection of 40 mg (+)‐propranolol in normal subjects, patients with serum albumin >30 g L^–1 and patients with serum albumin <30 g L^–1. Data are mean + SD. * p < 0.05, **p < 0.001 relative to normal subjects.
8. Figure 12.8 Relationship between enzymatic hydrolysis of suxamethonium and duration of apnoea after 0.6 mg kg^–1 in patients with severe and moderate liver disease and normal livers. Data are mean ± SEM. After Birch et al. (1956) and Foldes et al. (1956).
9. Figure 12.9 Sequential progressive model of hepatic dysfunction: implications for clearance of drugs predominantly metabolized by individual CYP pathways in liver.
10. Figure 12.10 Effects of liver disease on conjugation reactions. Broken lines represent the mean value for each group.
11. Figure 12.11 (a) Effect of renal clearance on the elimination rate constant for a drug which is only eliminated via the kidneys (red), not eliminated by the kidneys (green), and eliminated by renal and non‐renal routes (blue). (b) The same data plotted as elimination half‐life versus CL_cr calculated using k_NR = 0.1 h^–1, α = 0.05.
Chapter 13
1. Figure 13.1 Effect of (a) food and (b) gastric pH on the absorption of ketoconazole. The increased acidity was achieved with glutamic acid hydrochloride. Cimetidine and sodium hydrogen carbonate were given to simulate achlorhydria.
2. Figure 13.2 Inhibition of first‐pass metabolism of terfenadine to fexofenadine increases plasma terfenadine concentrations, increasing the risk of toxicity.
3. Figure 13.3 Example of enzyme induction in a patient taking warfarin (5 mg d^–1). Addition of dichloralphenazone reduced warfarin concentrations and anticoagulant effects, both of which returned on cessation of the enzyme‐inducing drug.
4. Figure 13.4 Interaction of cimetidine and amitriptyline. (a) Co‐administration of cimetidine increased amitriptyline concentrations compared with placebo, whereas (b) the corresponding concentrations of the metabolite, nortriptyline, were reduced. Note that the area under the curve for amitriptyline was increased and that for nortriptyline was reduced.
5. Figure 13.5 (a) Isobologram depicting effects of diazepam, ethanol and a combination of the two on the digit substitution test. From the data of Curry & Smith (1979). (b) Isobologram showing differentiation of potentiation, addition and antagonism for homergic drug interactions.
6. Figure 13.6 Condensation of pyridoxal phosphate with isoniazid.
7. Figure 13.7 Proposed mechanism of hepatotoxicity of isoniazid and iproniazid.
8. Figure 13.8 Simple representation of oxyhaemoglobin. The porphyrin nitrogens are depicted and the globular protein that binds via a histidine nitrogen. Oxyhaemoglobin is diamagnetic, that is, there are no unpaired electrons, so it is postulated that one electron from iron (giving Fe³⁺) is paired with one from oxygen, giving superoxide.
9. Figure 13.9 Oxidation of acetanilide and phenacetin.
10. Figure 13.10 Postulated mechanisms of paracetamol‐induced hepatotoxicity. Hepatocytes, Kupffer cells and endothelial cells participate in production of reactive nitrogen and oxygen species. The relative levels of nitric oxide (NO) and superoxide (O^–₂) determine whether mechanism of hepatic necrosis is dependent on protein nitrosylation or lipid peroxidation.
11. Figure 13.11 Paracetamol concentrations after paracetamol self‐poisoning. Concentrations above the treatment lines indicate a high probability of hepatotoxicity occurring unless antidotal therapy is initiated. The high‐risk line is for patients with low GSH concentrations.
12. Figure 13.12 Oxidative debromination of halothane. Trifluoroacetyl chloride is hydrolysed or reacts with proteins to produce antigens capable of triggering an allergic reaction.
Appendix A
1. Figure A1.1 Straight line representation of A = kC + b.
2. Figure A1.2 (a) Differentiation allows the slope at any value of x to be calculated. (b) Integration between x = 2 and x = 6 allows the area depicted by the shading to be calculated (see text for details).
3. Figure A1.3 (a) Example of the trapezoid method of calculating the area under a concentration versus time curve. The rate constant is calculated from the slope of the terminal points of ln C versus time plot (inset). (b) Printout from an Excel spreadsheet showing the areas of individual trapeziums and the total, which is good agreement with the theoretical value of 100 for this example.
Appendix B
1. Figure A2.1 Apparatus configured for demonstrating intravenous injection into a two‐compartment, well‐stirred, open model.
2. Figure A2.2 Apparatus arranged for first‐order input into a two‐compartment model.
Appendix C
1. Figure A3.1 Method of residuals.