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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series on Pharmaceutical Science
- Sprache: Englisch
Specifically geared to personnel in the pharmaceutical and biotechnology industries, this book describes the basics and challenges of oral bioavailability - one of the most significant hurdles in drug discovery and development. * Describes approaches to assess pharmacokinetics and how drug efflux and uptake transporters impact oral bioavailability * Helps readers reduce the failure rate of drug candidates when transitioning from the bench to the clinic during development * Explains how preclinical animal models - used in preclinical testing - and in vitro tools translate to humans, which is an underappreciated and complicated area of drug development * Includes chapters about pharmacokinetic modelling, the Biopharmaceutics Drug Disposition Classification System (BDDCS), and the Extended Clearance Classification System (ECCS) * Has tutorials for applying strategies to medicinal chemistry practices of drug discovery/development
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Seitenzahl: 842
Veröffentlichungsjahr: 2017
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TABLE OF CONTENTS
COVER
WILEY SERIES ON PHARMACEUTICAL SCIENCE AND BIOTECHNOLOGY: PRACTICES, APPLICATIONS, AND METHODS
TITLE PAGE
COPYRIGHT
DEDICATION
CHAPTER 1: DRUG PHARMACOKINETICS AND TOXICOKINETICS
1.1 INTRODUCTION
1.2 TOXICITY ASSESSMENT IN DRUG DISCOVERY AND DEVELOPMENT
1.3 PARAMETERS THAT DEFINE PHARMACOKINETIC PROFILE
1.4 TOXICOGENOMICS AND BIOMARKERS
1.5 SPECIES DIFFERENCE IN DRUG DISPOSITION
1.6 MIST (METABOLITES IN SAFETY TESTING)
1.7 PHARMACOLOGICALLY ACTIVE METABOLITES
1.8 REACTIVE METABOLITES
1.9 ENABLING TECHNOLOGIES
1.10 CONCLUSION
1.11 CHAPTER 1 TUTORIAL
1.12 CHAPTER 1 TUTORIAL ANSWERS KEY
REFERENCES
CHAPTER 2: GIT ANATOMY AND PHYSIOLOGY AND DRUG ORAL BIOAVAILABILITY: IMPACT OF SPECIES DIFFERENCES
2.1 INTRODUCTION
2.2 PHYSIOLOGICAL FACTORS THAT IMPACT ORAL DRUG ABSORPTION
2.3 MECHANISM OF ORAL ABSORPTION
2.4 PHYSIOLOGICAL FACTORS THAT IMPACT DRUG METABOLISM
2.5 CONCLUSION
2.6 CHAPTER 2 TUTORIALS
2.7 CHAPTER 2 TUTORIALS KEY
REFERENCES
CHAPTER 3: DRUG ROUTES OF EXCRETION
3.1 INTRODUCTION
3.2 RENAL ELIMINATION
3.3 HEPATOBILIARY ELIMINATION
3.4 CONCLUSION
3.5 CHAPTER 3 TUTORIALS
3.6 CHAPTER 3 TUTORIAL ANSWERS KEY
REFERENCES
CHAPTER 4: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT AFFECT DRUG ABSORPTION OF COMPOUNDS ABSORBED BY PASSIVE DIFFUSION
4.1 INTRODUCTION
4.2 MECHANISM OF ORAL ABSORPTION VIA PASSIVE DIFFUSION
4.3 PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT INFLUENCE PASSIVE PERMEABILITY
4.4 SOLUBILITY
4.5 PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT INFLUENCE SOLUBILITY
4.6 APPROACHES TO INCREASE DRUG DISSOLUTION
4.7 PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT INFLUENCE DISSOLUTION
4.8 THE USE OF PRODRUGS TO INCREASE SOLUBILITY AND PERMEABILITY
4.9 BIOPHARMACEUTICS CLASSIFICATION SYSTEM (BCS) AND DRUG
4.10 CONCLUSION
4.11 CHAPTER 4 TUTORIALS
4.12 CHAPTER 4 TUTORIALS ANSWERS KEY
REFERENCES
CHAPTER 5: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL FACTORS AFFECTING HEPATIC/INTESTINAL FIRST-PASS EFFECT
5.1 INTRODUCTION
5.2 PHYSICOCHEMICAL APPROACHES TO INCREASE METABOLIC STABILITY
5.3 CONCLUSION
5.4 TUTORIAL CHAPTER 5
5.5 TUTORIAL CHAPTER 5 ANSWER KEY
REFERENCES
CHAPTER 6: IMPACT OF INTESTINAL EFFLUX TRANSPORTERS ON ORAL ABSORPTION
6.1 INTRODUCTION
6.2 INTESTINAL EFFLUX TRANSPORTERS
6.3 CONCLUSION
6.4 CHAPTER 6 TUTORIALS
6.5 CHAPTER 6 TUTORIAL ANSWER KEY
REFERENCES
CHAPTER 7: IMPACT OF INFLUX TRANSPORTERS ON DRUG ABSORPTION
7.1 INTRODUCTION
7.2 INTESTINAL DRUG TRANSPORTERS
7.3 DRUG ABSORPTION AND CONCENTRATION GRADIENT
7.4 CONCLUSION
7.5 CHAPTER 7 TUTORIALS
7.6 CHAPTER 7 TUTORIALS ANSWERS KEY
REFERENCES
CHAPTER 8: EXTENDED CLEARANCE CLASSIFICATION SYSTEM (ECCS) AND ITS UTILITY IN PREDICTING CLEARANCE RATE-DETERMINING STEP IN DRUG DISCOVERY
8.1 INTRODUCTION
8.2 HEPATIC CLEARANCE
8.3 RENAL CLEARANCE
8.4 PROPOSAL OF EXTENDED CLEARANCE CLASSIFICATION SYSTEM (ECCS)
8.5 CONCLUSIONS
8.6 CHAPTER 8 TUTORIALS
8.7 CHAPTER 8 TUTORIAL ANSWER KEY
REFERENCES
CHAPTER 9: IN VITRO AND IN SITU APPROACHES TO MEASURE INTESTINAL PERMEABILITY AND EFFLUX TRANSPORTERS
9.1 INTRODUCTION
9.2 PARALLEL ARTIFICIAL MEMBRANE PERMEABILITY ASSAY
9.3 C-2 AND MDCK IMPLEMENTATION IN DRUG DISCOVERY AND DEVELOPMENT
9.4 SINGLE-PASS INTESTINAL PERFUSION
9.5 MISCELLANEOUS PERMEABILITY MODELS AND TECHNOLOGIES REVIEW
9.6 CONCLUSION
REFERENCES
CHAPTER 10: IN SILICO APPROACHES TO PREDICT INTESTINAL PERMEABILITY
10.1 INTRODUCTION
10.2 PREDICTION OF HUMAN INTESTINAL PERMEABILITY AND ABSORPTION BASED ON
I
PERMEABILITY INPUTS
10.3 CONCLUSION
REFERENCES
CHAPTER 11: IN VIVO PRECLINICAL APPROACHES TO DECONVOLUTE THE CONTRIBUTION OF FIRST-PASS EFFECT FROM ORAL ABSORPTION
11.1 INTRODUCTION
11.2
IN VIVO
ESTIMATION OF ORAL BIOAVAILABILITY AND ITS COMPONENTS
11.3 FRACTION OF THE DOSE ABSORBED INTO THE PORTAL BLOOD AFTER ORAL ADMINISTRATION (
F
A
·F
G
)
11.4 APPROACHES TO DIFFERENTIATE THE POOR ABSORPTION FROM FIRST-PASS EFFECT CONTRIBUTION TO LOW ORAL BIOAVAILABILITY
11.5 APPROACHES TO ASSESS THE PHARMACODYNAMICS ACTIVITY OF NME WITH POOR ORAL BIOAVAILABILITY
11.6 CONCLUSION
11.7 TUTORIAL CHAPTER 11
11.8 CHAPTER 11 TUTORIAL ANSWER KEY
REFERENCES
CHAPTER 12: IN VITRO APPROACHES TO ASSESS HEPATIC METABOLISM AND FIRST-PASS EFFECT
12.1 INTRODUCTION
12.2
IN VITRO
TOOLS AVAILABLE TO MEASURE METABOLIC CLEARANCE
12.3 STEPS TO MEASURE
IN VIVO
HEPATIC CLEARANCE
12.4 CONCLUSION
12.5 CHAPTER 12 TUTORIALS
12.6 CHAPTER 12 TUTORIAL ANSWER KEY
REFERENCES
CHAPTER 13: THE UTILITY OF ECCS AS A ROADMAP TO IMPROVE ORAL BIOAVAILABILITY OF NEW MOLECULAR ENTITIES: INDUSTRIAL PERSPECTIVE
13.1 INTRODUCTION
13.2 ECCS CLASSIFICATION
13.3
f
a
MODULATION
13.4
f
g
13.5 HEPATIC CLEARANCE
13.6 CONCLUSION
13.7 CHAPTER 13 TUTORIALS
13.8 CHAPTER 13 TUTORIAL ANSWERS KEY
REFERENCES
INDEX
END USER LICENSE AGREEMENT
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Guide
Cover
TABLE OF CONTENTS
Begin Reading
list of Illustrations
CHAPTER 1: DRUG PHARMACOKINETICS AND TOXICOKINETICS
Figure 1.1 The contribution of various factors to the overall attrition of NCEs in year 2001.
Figure 1.2 The relationship between drug oral bioavailability and interindividual variability reported as coefficient of variation (%).
Figure 1.3 Estimation of the area under the plasma concentration–time curve (AUC).
Figure 1.4 Various routes/mechanisms of eliminations that contribute to drug CL
total
.
Figure 1.5 Volume of distribution and its relation with the extent of drug distribution in blood and tissues.
Figure 1.6 Tissue and blood binding and their impact on drug volume of distribution.
Figure 1.7 Plasma concentration–time profiles for drugs with half-lives of 6, 36, or 99 h administered once daily. Simulations were performed using Berkeley Madonna Software
®
. (a) Half-life is 6 h (e.g., atenolol); (b) half-life is 99 h (e.g., phenobarbital).
Figure 1.8 The semilog plot of plasma profile versus time of a compound that follows one compartment model with first-order input and first-order output.
Figure 1.9 The semilog plot of residual versus time.
Figure 1.10 The impact of changes in
k
a
values on the oral plasma profile of a compound.
Figure 1.11 Oral bioavailability is a multiplicity parameter and a product of
f
a
,
f
g
, and
f
h
.
Figure 1.12 The relationship between drug elimination/transport rate and free drug concentration for a Michaelis–Menten kinetics complying biological process.
Figure 1.13 The relationship between dose and AUC is indicator of presence or absence of drug linearity.
Figure 1.14 One-compartment model.
Figure 1.15 Two-compartment model.
Figure 1.16 Physiologically based pharmacokinetic (PBPK) model incorporating physiological compartments depending on the drug's distribution.
Figure 1.17 Known drug and system parameters used to build PBPK models.
Figure 1.18 Perfusion- and permeability-limited distribution.
CHAPTER 2: GIT ANATOMY AND PHYSIOLOGY AND DRUG ORAL BIOAVAILABILITY: IMPACT OF SPECIES DIFFERENCES
Figure 2.1 Plot of oral bioavailability (
F
) in animal species versus oral bioavailability in humans (in percentage). Diamonds are for mouse, circles for rats, triangles for dogs, and squares for nonhuman primates (NHP).
Figure 2.2 Comparative bulk pH across the gastrointestinal tract of rats, dogs, monkeys, and humans [25, 30].
Figure 2.3 The chemical structures of taurocholic acid and deoxycholic acid, respectively.
Figure 2.4 Possible routes of drug absorption across intestinal enterocytes.
Figure 2.5 Correlations of P-gp binding affinity (αKa) between human MDR1 and rhesus monkey Mdr1 (a) and between human MDR1 and beagle dog Mdr1 (b).
Figure 2.6 Uptake of [
3
H]estradiol-17β-d-glucuronide (1 μM), [
3
H]cholecystokinin octapeptide (0.1 μM) and [
3
H]estrone-3-sulfate (1 μM) into mock, cynomolgus OATP2B1, and human OATP2B1 expressing HEK-293 cells was determined following 1.5 min of incubation.
Figure 2.7 Correlation of percent of oral dose absorbed (
f
a
) between humans versus rats (a), monkeys (b), and dogs (c).
Figure 2.8 Major UDP-glucuronosyltransferases expressed in human liver and intestine based on protein level using proteomic approaches.
Figure 2.9 Expression of UDP-glucuronosyltransferase mRNAs in cynomolgus monkey large intestine, liver, and small intestine.
Figure 2.10 Chemical structures of FK3453 and M4. Zwisler et al. 2010. [192]. Reproduced with permission of John Wiley & Sons.
CHAPTER 4: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT AFFECT DRUG ABSORPTION OF COMPOUNDS ABSORBED BY PASSIVE DIFFUSION
Figure 4.1 The relationship between PSA and human
f
a
for a set of 309 molecules.
Figure 4.2 The relationship between lipophilicity descriptor parameters and human
f
a
using a set of 309 compounds (a)
c
log
P
and (b)
c
log
D
7.4
. Varma et al. 2010 [4] with permission.
Figure 4.3 Schematic diagram to illustrate the relationship between the fraction of drug unionized, and pH and p
K
a
of (a) bases and (b) acids.
Figure 4.4 The relationship between compound ionization state and human
f
a
(
n
= 309). Varma et al. 2010 [4] with permission.
Figure 4.5 Relationship between number of violations of RO5 and bioavailability and individual processes. “
n
” is the number of compounds in each bin [4] with permission.
Figure 4.6 The solubility ratio for polymorphs (
n
= 81).
Figure 4.7 N-alkylation of thalidomide alters the highly ordered crystal lattice and increases its solubility by fourfold.
Figure 4.8 Dissolution of a salt form of organic acid in gastric fluid.
Figure 4.9 The chemical structures of α-cyclodextrin, β-cyclodextrin, and γ-cyclodextrin.
Figure 4.10 The dissolution rate of ritonavir in 0.1 N HCl at 37 °C using crystalline (0.03 mg/cm
2
/min) and amorphous forms (0.3 mg/cm
2
/min). Law et al. 2004 [48] with permission.
Figure 4.11 The
C
max
(µg/mL) and AUC (µg*h/mL) of various ritonavir dispersions in beagle dogs.
Figure 4.12 Known functional groups on parent drugs that are pliable to prodrug design (shown in gray).
Figure 4.13 Bioconversion pathways of famciclovir to penciclovir.
Figure 4.14 The chemical structures of acyclovir and its commercial prodrug valacyclovir, respectively, with acyclovir bioavailability following oral dosing in humans for the two agents [80–82].
Figure 4.15 The chemical structures of gabapentin and its commercial prodrug XP13512 (Gabapentin enacarbil).
CHAPTER 5: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL FACTORS AFFECTING HEPATIC/INTESTINAL FIRST-PASS EFFECT
Figure 5.1 CYP isoforms relative content in (a) human liver and (b) intestine, respectively [13, 14].
Figure 5.2 A decision tree for human CYP450 substrates based on compounds
V
dss
, ionization, and planarity.
Figure 5.3 The mechanism of glucuronide conjugate formation using UDP-glucuronyl transferase. Nu is a nucleophilic site on a drug molecule.
Figure 5.4 The relationship between MDCK-LE permeability and human
f
a
. Human
f
a
> 80% (horizontal dotted line) where a permeability value of 5 × 10
−6
cm/s cutoff value would differentiate between low and high values.
Figure 5.5 The relationship between calculated lipophilicity (
c
log
D
) and drug fraction that escapes (a) intestinal and (b) hepatic first-pass effects, respectively.
Figure 5.6 The relationship between lipophilicity (log
D
) and affinity to CYP3A4 substrates corrected for microsomal protein binding for a set of CYP3A4 substrates [28–47].
Figure 5.7 The substitution of bifluorobenzyl group with ethyl group increased plasma exposure observed in monkeys at 7 h following 7 h oral dosing and maintained antiviral activity.
Figure 5.8 The relationship between the compound polar surface area (PSA) and the fraction that escapes hepatic first-pass effect (
f
h
).
Figure 5.9 Chemical structure of cholesterol absorption inhibitors including ezetimibe [56–58].
Figure 5.10 The rate of
para
position hydroxylation of phenobarbital was reduced by chlorination [7, 59].
Figure 5.11 The metabolic rate of O-dealkylation of metoprolol [8] was reduced by replacing the methoxy group with the bulky aliphatic cyclopropyl methoxy group to form betaxolol [9, 62].
Figure 5.12 Sensitivity analysis of the impacts of
f
u,p
and
f
u,mic
on human CL using SimCYP 13.1.
Figure 5.13 The relationship between
in vivo
rat clearance and human clearance.
Figure 5.14 The chemical structures of bambuterol and terbutaline.
Figure 5.15 The relationship between intrinsic
in vitro
intestinal clearance (CLu
int,g
), effective intestinal permeability (
P
eff
), and
f
g
for the prediction of intestinal extraction from
in vitro
data using
Q
gut
model.
CHAPTER 6: IMPACT OF INTESTINAL EFFLUX TRANSPORTERS ON ORAL ABSORPTION
Figure 6.1 Intestinal protein expression of intestinal efflux transporters (
ABCB1
,
ABCC2
, and
ABCG2
) in human jejunum and ileum (
N
= 4).
Figure 6.2 Protein expression of efflux transporters in human jejunum (
N
= 4) and Caco-2 (
N
= 4). (*Below limit of quantification.).
Figure 6.3 Physicochemical properties of known P-gp substrates: (a) measured log
D
pH 7.4
, measured log
P
, HBD, and HBA of known P-gp substrates; (b) polar surface area (PSA), molecular weight (
M
wt
), efflux ratio, and reported
K
m
of known P-gp substrates [17–93].
Figure 6.4 The relationship between measured log
P
and
K
m
values of P-gp substrates. Data obtained from Table 6.1 [17–93].
Figure 6.5 The relationship between efflux ratio and
K
m
of P-gp substrates. Data obtained from Table 6.1.
Figure 6.6 The
C
max
and AUC of talinololfollowing single doses of 100-mg talinolol immediate-release, 100-mg talinolol controlled-release.
Figure 6.7 The median changes in the AUC between pharmacogenetic variant and wild-type carrier (ABCB1) volunteers based on BDDCS Classes [156, 159–193].
Figure 6.8 Physicochemical properties of known BCRP substrates: (a) measured log
D
pH 7.4
, measured log
P
, HBD, and HBA of known BCRP substrates; (b) polar surface area (PSA), molecular weight (
M
wt
), efflux ratio, and reported
K
m
of known BCRP substrates [18, 25, 35, 60, 87, 223–249].
Figure 6.9 The relationship between
F
a
*
F
g
and the talinolol dose in humans. The triangles represent actual observed
F
a
*
F
g
at various oral doses. The lines represent fitted or simulated results based on changes in input parameters. Sensitivity analysis was run for (a)
K
m
, (b)
V
max
, (c)
P
eff
, and (d) solubility.
Figure 6.10 A proposed decision tree for predicting nonlinear pharmacokinetics of P-gp/CYP3A4 substrates.
CHAPTER 7: IMPACT OF INFLUX TRANSPORTERS ON DRUG ABSORPTION
Figure 7.1 Known proton-coupled/pH-dependent solute carriers expressed at the brush border membrane of small intestine.
Figure 7.2 Intestinal protein expression of clinically relevant uptake (OATP2B1, PepT1) transporters in human jejunum and ileum from four donors quantified using validated mass spectrometry-based targeted proteomic.
Figure 7.3 Protein expression of clinically relevant intestinal influx transporters in human jejunum (
N
= 4) and in Caco-2 cells (
N
= 4). Mean ± SD are given.
Figure 7.4 Chemical structures of estrone-3-sulfate and statins.
Figure 7.5 pH-Dependent uptake of E-3-S and statins by the mock-transfected and OATP2B1-transfected cells. Uptake by mock-transfected () and OATP2B1-transfected (Δ) cells was measured over 2 min, at various extracellular pH values in the range of 5.5 and 7.4. OATP2B1-mediated uptake () was obtained by subtracting the uptake in the mock-transfected cells from that by the OATP2B1-transfected cells.
Figure 7.6 The impact of grapefruit juice on atorvastatin acid, losartan, lovastatin, and simvastatin plasma exposure AUC
Δ
% in healthy volunteers [43–50].
Figure 7.7 The physicochemical properties of OATP2B1 substrates (a)
M
wt
and PSA, (b) cMDCK-LE and
c
log
D
.
Figure 7.8 Schematic representation of the single-nucleotide polymorphisms in structure of OATP2B1. Arrows indicate alteration of
in vitro
functional activity of the variant protein. ↓: Reduced activity, ↑: increased activity.
Figure 7.9 The AUC
(0→∞)
and
C
max
parameters of fexofenadine following oral administration of 60 mg orally with water in relation to the
SLCO2B1
c.1457C>T polymorphism.
Figure 7.10 The mean plasma concentration of cefadroxil in healthy volunteers in absence (-•-) and presence (-o-) of 5 mg/kg cephalexin (45 mg/kg).
Figure 7.11 Key features in compounds determining recognition as a peptide transporter substrate. The structural components that critically control affinity of key elements are presented on the backbone of a tripeptide as model compound.
Figure 7.12 Dose proportionality of gabapentin blood AUC
(0→∞)
following single oral doses of XP13512 immediate release capsules or oral gabapentin.
CHAPTER 8: EXTENDED CLEARANCE CLASSIFICATION SYSTEM (ECCS) AND ITS UTILITY IN PREDICTING CLEARANCE RATE-DETERMINING STEP IN DRUG DISCOVERY
Figure 8.1 The framework of extended clearance classification system (ECCS) for identifying the predominant mechanism that determines systemic clearance of drugs [10].
CHAPTER 9: IN VITRO AND IN SITU APPROACHES TO MEASURE INTESTINAL PERMEABILITY AND EFFLUX TRANSPORTERS
Figure 9.1 Schematic diagram of a PAMPA donor/acceptor compartments assembly.
Figure 9.2 Systematic design of the
in vitro
cell culture monolayer experiment.
Figure 9.3 The relationship between rat
P
eff
versus human
f
a
[48, 49].
CHAPTER 11: IN VIVO PRECLINICAL APPROACHES TO DECONVOLUTE THE CONTRIBUTION OF FIRST-PASS EFFECT FROM ORAL ABSORPTION
Figure 11.1 The chemical structure of 1-aminobenzotriazole.
Figure 11.2 The impact of ABT route of administration with 1-h pretreatment on the oral plasma exposure (AUC) of midazolam (10 mg/kg) [9].
Figure 11.3 The chemical structure of CP-100,356 [10].
Figure 11.4 The chemical structure of elacridar (GF120198).
Figure 11.5 The relationship of the AUC ratio between monkeys (with elacridar (EL)/without elacridar (UT)) and human (ABCG2 421AA/CC) after oral administration of rosuvastatin (•), pitavastatin (◼), fluvastatin (○), and sulfasalazine (Δ). The solid lines represent a 1:1 correspondence and the dotted lines represent the twofold difference.
Figure 11.6 The impact of 1000 mg of GF-120198 PO dose on the renal recovery and exposure of Topotecan (1 mg/m
2
PO dose).
Figure 11.7 The impact of formulation on the renal recovery (Xu%) and AUC following acyclovir 2.5 mg/kg PO dose [13].
Figure 11.8 The chemical structures of zosuquidar and Ko143, respectively.
Figure 11.9 The
f
a
·
f
g
of fexofenadine (5 mg/kg), sulfasalazine (5 mg/kg), and topotecan (0.3 mg/kg) following ZSQ (30 mg/kg) and/or Ko143 (10 mg/kg) in portal vein cannulated rats.
Figure 11.10 The relationship between rat and human clearance.
Figure 11.11 Routes of administration to assess biological activity in preclinical species.
Figure 11.12 Plasma [
S
,
S
]-reboxetine concentration–time profiles after intravenous, subcutaneous, intraperitoneal, and oral administration (5 mg/kg) of [
S
,
S
]-reboxetine to Sprague Dawley rats (Pfizer internal data).
CHAPTER 13: THE UTILITY OF ECCS AS A ROADMAP TO IMPROVE ORAL BIOAVAILABILITY OF NEW MOLECULAR ENTITIES: INDUSTRIAL PERSPECTIVE
Figure 13.1 The relationship between LE-MDCK permeability versus human
f
a
for set of compounds classified based on ECCS.
Figure 13.2 The median and average of
f
a
for compounds classified based on their ECCS.
Figure 13.3 The median, average, and range of
f
g
for compounds classified based on their ECCS.
Figure 13.4 The median, average, and range of CL
h
for compounds classified based on their ECCS.
Figure 13.5 The median, average, and range of
f
h
for compounds classified based on their ECCS.
Figure 13.6 The median, average, and range of
f
for compounds classified based on their ECCS.
List of Tables
CHAPTER 1: DRUG PHARMACOKINETICS AND TOXICOKINETICS
Table 1.1 Typical Body Weight and Hepatic Blood Flow for Various Preclinical Species and Human
Table 1.2 The Factors that may Contribute to Drug Nonlinear Kinetics
Table 1.3 Examples of Human Drug Metabolites that are known to be Responsible for General Toxicity and Potentially Idiosyncratic Drug Toxicity
CHAPTER 2: GIT ANATOMY AND PHYSIOLOGY AND DRUG ORAL BIOAVAILABILITY: IMPACT OF SPECIES DIFFERENCES
Table 2.1 Comparison of the Anatomical Lengths of the Intestinal Tract and Its Major Subdivision in Rats, Dogs, Monkeys, and Humans [9–12]
Table 2.2 Comparison of the Absolute Surface Areas and Surface Area of the Gastrointestinal Tract Relative to Total Body Surface Area in Rats, Dogs, Monkeys, and Humans [8–11]
Table 2.3 The Impact of Food Caloric Density and Meal Volume on the Gastric Emptying Rate in Humans
Table 2.4 The Absorption of a Set of Hydrophilic Drugs in Rats, Dogs, and Humans
Table 2.5 Relative Expression of mRNA for MDR1, MRP2, and BCRP in Small Intestine and Colon of Cynomolgus Monkeys and Humans Determined using RT-PCR
Table 2.6 The Absorption of a Set of PEPT1 Transporter Substrates in Rats, Dogs, Monkeys, and Humans [95–97]
Table 2.7 CYP Enzymes of Major Drug Metabolizing CYP Family in Human, Mouse, Rat, Dog, and Monkey
CHAPTER 3: DRUG ROUTES OF EXCRETION
Table 3.1 Partial List of Known OAT1 Transporter Substrates
Table 3.2 Partial List of Known OAT3 Transporter Substrates
Table 3.3 Partial List of Known OCT2 Transporter Substrates
Table 3.4 Partial List of Known Compounds that are Substrates for MATE1 Transporters
Table 3.5 Partial List of Known Compounds that are Substrates for MATE2K Transporters
Table 3.6 Partial List of Known Compounds that are Substrates for OATP1B1 Transporters
Table 3.7 Partial List of Known Compounds that are Substrates for OATP1B3 Transporters
Table 3.8 Partial List of Known Compounds that are Substrates for OCT1 Transporters
Table 3.9 Partial List of Known Compounds that are Substrates for MRP2 Transporters
CHAPTER 4: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL PROPERTIES THAT AFFECT DRUG ABSORPTION OF COMPOUNDS ABSORBED BY PASSIVE DIFFUSION
Table 4.1 Marketed Cyclodextrin-Containing Drug Products
CHAPTER 5: PHYSICOCHEMICAL AND BIOPHARMACEUTICAL FACTORS AFFECTING HEPATIC/INTESTINAL FIRST-PASS EFFECT
Table 5.1 Classification of Hepatic Metabolic Stability Based on Human and Rat Liver Microsomes and Human Hepatocytes
Table 5.2
In Vitro t
1/2
, Intrinsic Clearance, Microsomal and Plasma Fraction Unbound, Blood to Plasma Ratio, and Blood Clearance of a Set of Drugs
Table 5.3 The Oral Bioavailability of Nalbuphine Following Oral Dosing of the Parent Drug and Its Acetylsalicylate and Anthranilate Esters in Dogs
Table 5.4 List of Drug Candidates That Have Dropped in Clinical Trials due to Poor Oral Pharmacokinetic Profiles or Toxicity Ascribed to AO Metabolism
CHAPTER 6: IMPACT OF INTESTINAL EFFLUX TRANSPORTERS ON ORAL ABSORPTION
Table 6.1 Partial List of Known P-gp Substrates, Their
In Silico
and Measured Physicochemical Properties, Renal Recovery (%),
In Vitro
Transporter Substrate Parameters [17–93]
Table 6.2 Partial List of Known P-gp Inhibitors and Their
In Vitro
Transporter Substrate Parameters [24, 37, 50, 54, 55, 58, 59, 97–151]
Table 6.3 Classification Scheme of the Drugs Based on the Relevance of P-gp-Mediated Efflux Transport on the Intestinal Drug Absorption
In Vivo
[201–203]
Table 6.4 Nonsynonymous Nucleotide Polymorphisms in the
ABCB1
Gene and Known Ethnic Differences in the Frequency of Genetic Variants
Table 6.5 Partial List of Known BCRP Substrates, Their
In Silico
and Measured Physicochemical Properties, Renal Recovery (%),
In Vitro
Transporter Substrate Parameters [18, 25, 35, 60, 87, 223–249]
Table 6.6 Partial List of Known BCRP Inhibitors and Their
In Vitro
Transporter Substrate Parameters [20, 37, 43, 60, 117, 130, 139, 140, 143, 148–150, 224, 225, 229, 230, 237, 251, 258–318]
CHAPTER 7: IMPACT OF INFLUX TRANSPORTERS ON DRUG ABSORPTION
Table 7.1 Major Uptake Intestinal Transporter Uptake Mechanism, Expression, GIT Segment Expression, Affinity, and Capacity
Table 7.2 Partial List of Known Drugs that are in Clinical Use and Substrates of OATP2B1 [38, 55–75]
Table 7.3 Partial List of Known Inhibitors of OATP2B1 [31, 58, 61–63, 65, 75–94]
Table 7.4 Nonsynonymous Nucleotide Polymorphisms in the
SLCO2B1
Gene [102–105]
Table 7.5 Partial List of Known Drugs that are in Clinical Use and Substrates of PepT1 Transporter [123–132]
Table 7.6 Partial List of Known Drugs that are in Clinical Use and Inhibitors of PepT1 Transporter [123–132, 136–141]
Table 7.7 A Partial List of Known Substrates for MCT1 Transporter [158, 159, 169–180]
Table 7.8 A Partial List of Known Inhibitors for MCT1 Transporter [170, 171, 184–186]
CHAPTER 8: EXTENDED CLEARANCE CLASSIFICATION SYSTEM (ECCS) AND ITS UTILITY IN PREDICTING CLEARANCE RATE-DETERMINING STEP IN DRUG DISCOVERY
Table 8.1 Representative Examples per ECCS Class and the Major Transporters and Metabolizing Enzymes Involved in Their Clearance
CHAPTER 9: IN VITRO AND IN SITU APPROACHES TO MEASURE INTESTINAL PERMEABILITY AND EFFLUX TRANSPORTERS
Table 9.1 Similar Correlation Between
P
eff
Values in Literature-Based Human and Rat Perfusion Values
CHAPTER 10: IN SILICO APPROACHES TO PREDICT INTESTINAL PERMEABILITY
Table 10.1 Common Molecular Descriptor Programs and Their Relevant Information [22, 23]
Table 10.2 Commercial Permeability and
In Silico
Models and Their Relevant Information [36–40]
CHAPTER 11: IN VIVO PRECLINICAL APPROACHES TO DECONVOLUTE THE CONTRIBUTION OF FIRST-PASS EFFECT FROM ORAL ABSORPTION
Table 11.1 Administration Volumes Considered Good Practice (and Possible Maximum Dose Volume)
Table 11.2 The Pharmacokinetic Parameters of [
S
,
S
]-Reboxetine Following 5 mg/kg IV, SC, PO, and IP Dosing to Sprague Dawley Rats (Pfizer Internal Data)
CHAPTER 12: IN VITRO APPROACHES TO ASSESS HEPATIC METABOLISM AND FIRST-PASS EFFECT
Table 12.1
In Vitro
and
In Vivo
Inputs Used to Scale CL
int,app,scaled
[13, 14]
Table 12.2 Enzyme Abundance and ISEF Correction Values for Known CYP Enzymes
CHAPTER 13: THE UTILITY OF ECCS AS A ROADMAP TO IMPROVE ORAL BIOAVAILABILITY OF NEW MOLECULAR ENTITIES: INDUSTRIAL PERSPECTIVE
Table 13.1 Molecular Weight, Ionization, ECCS Classification, CL
t
,
CL
h
, f
a
, f
g
, f
h
, f
, Main Enzymes, Uptake Transporters, and Efflux Transporters Affecting the Disposition of Molecules [4, 14]
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Ayman F. El-Kattan • Oral Bioavailability Assessment: Basics and Strategies for Drug Discovery and Development
ORAL BIOAVAILABILITY ASSESSMENT
Basics and Strategies for Drug Discovery and Development
AYMAN F. EL-KATTAN
This edition first published 2017
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Names: El-Kattan, Ayman F., author.
Title: Oral bioavailability assessment : basics and strategies for drug discovery and development / Ayman F. El-Kattan.
Description: Hoboken, NJ : John Wiley & Sons Inc., 2017. | Includes bibliographical references and index.
Identifiers: LCCN 2016055388 (print) | LCCN 2016056137 (ebook) | ISBN 9781118916698 (cloth) | ISBN 9781118916940 (Adobe PDF) | ISBN 9781118916933 (ePub)
Subjects: | MESH: Biological Availability | Drug Evaluation, Preclinical | Toxicokinetics | Administration, Oral
Classification: LCC RS403 (print) | LCC RS403 (ebook) | NLM QV 38 | DDC 615.1/9–dc23
LC record available at https://lccn.loc.gov/2016055388
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I love you
CHAPTER 1DRUG PHARMACOKINETICS AND TOXICOKINETICS
1.1 INTRODUCTION
Pharmacokinetics (PK) is the science that describes the time-course of drug concentration in the body resulting from administration of a certain drug dose. Similarly, toxicokinetics (TK) is the science that investigates how the body handles toxicants as illustrated by its plasma profile at various time points. In comparison, pharmacodynamics (PD) is the science that describes the relationship of the time-course of drug concentration and its effects in the body [1, 2].
PK is considered a biomarker of drug exposure as well as marker of efficacy and safety. Key determinants of the pharmacokinetics of a drug include absorption, distribution, metabolism, and elimination (ADME) [3]. Discovering novel therapeutic agents is an increasingly time-consuming and costly process. Most estimates indicate that it takes approximately 10–15 years and more than $1.2 billion to discover and develop a successful drug product [4]. It is well established that poor drug PK is one of the leading causes of compounds failure in preclinical and clinical drug development [5]. For example, attrition due to poor pharmacokinetics contributed to 10% of the attrition reported for compounds developed by the pharmaceutical industry in 2001 (Figure 1.1) [6].
Figure 1.1 The contribution of various factors to the overall attrition of NCEs in year 2001.
Kola and Landis 2004 [6]. Reproduced with permission of Nature Publishing Group.
Compounds with poor PK profile tend to have low oral systemic plasma exposure and high interindividual variability, which limits their therapeutic utility (Figure 1.2) [7]. Therefore, a better understanding of the PK profile early on enables the discovery of compounds with drug-like properties [8]. In drug discovery settings, the main outcomes of PK/TK assessments are to
select compounds with the maximum potential of reaching the target;
determine the appropriate route of administration to deliver the drug (typically oral);
understand how the drug blood levels relate to efficacy or toxicity in order to choose efficacious and safe doses;
facilitate appropriate dose sections for rodent and/or nonrodent species in toxicology testing and drug safety evaluation;
decide on the frequency and duration of dosing in order to maintain adequate drug concentration at target for disease modification; and
accurately predict the PK in humans profile prior to clinical studies.
Figure 1.2 The relationship between drug oral bioavailability and interindividual variability reported as coefficient of variation (%).
Hellriegel et al. 1996 [7]. Reproduced with permission of John Wiley & Sons.
A PK/TK study involves dosing animals or humans with NCE and collect blood samples at predefined time points. After sample preparation and quantification, a concentration–time profile is generated (Figure 1.3). In drug discovery, preliminary PK studies are usually conducted in rodents to evaluate the extent of drug exposure in vivo. These rodent studies are commonly followed by studies in nonrodents such as dogs or monkeys to better characterize the PK profile of the compound and to support safety risk assessment studies. Pharmacokinetic scaling, also known as allometry, is a discipline that was extensively used in the past to predict human PK profile using preclinical data and in predicting the drug human half-life, dose, and extent of absorption. This approach is based on empirical observations that various physiological parameters are a function of body size. The allometric methods assume that the same metabolic and disposition processes in the species evaluated are correlated with those observed in humans. However, the cytochrome P450 enzymes in the rat are not the same as those in humans, and thus, may exhibit altered disposition of the compound or even produce different metabolite patterns (see Chapter 2) [9, 10]. Similarly, uptake and efflux transporters in the animal species may differ in substrate specificity or rate, as compared to humans, and thus may confound predictions of human PK [11]. Accurate prediction of human pharmacokinetic profile is imperative to minimize drug failure in development due to pharmacokinetic liability. More detailed description of methods in predicting human PK is beyond the scope of this chapter, but can be found in many excellent reviews [12–15]. An in-depth discussion of various PK concepts and their applications can be found in various references [16, 17].
Figure 1.3 Estimation of the area under the plasma concentration–time curve (AUC).
1.2 TOXICITY ASSESSMENT IN DRUG DISCOVERY AND DEVELOPMENT
Several toxicology studies are conducted during early drug discovery and all the way to the late stages of drug development before a new drug application (NDA) filing is made. In spite of comprehensive toxicity assessment in early- and late-stage discovery, attrition of NCEs in clinical studies is not uncommon owing to disconnect in predictions of risk in humans based upon preclinical data obtained from cell culture and animal models. Nevertheless, extensive preclinical assessment and appropriate scaling and modeling tools will improve predictions. In general, the correlation between human and animal toxicities is good for conditions such as cardiovascular, hematological, and gastrointestinal diseases and the poorest correlation for adverse drug reactions such as idiosyncratic reactions, skin rash, hypersensitivity, and hepatotoxicity. Toxicology testing in drug discovery is initiated by the high-throughput screening, which is followed up by definitive tests. Screening refers to the methods that yield rapid and comprehensive data often using in vitro tools. The origin of any toxicological or safety outcome is multifactorial and complex and thus demands for use of sophisticated systems for definitive assessment. Thus, many pharmaceutical companies are also introducing in vivo (i.e., animals) toxicology studies as early as possible, quite often in the lead optimization (LO) stage. Extensive and appropriate toxicology studies of varying duration ranging from acute, single dose to chronic, repeat dose in rodent and nonrodent species are needed to establish safe human clinical trials. Acute toxicity (single dose-ranging) studies in preclinical species are performed to support selection of a drug candidate for potential advancement to repeat-dose toxicology studies and ultimately to enable initial FIH clinical trials. The objective of such studies is to identify a dose at which the major adverse effects are observed. These studies are usually carried out in rodents, following a single dose up to a limit of 2000 mg/kg. The information obtained may be translated to select the dose levels for the first in-human studies and also to give an indication of potential effects of acute overdose in humans.
Early drug development starts with candidate compound selection. Repeat-dose toxicity studies (7–14 days in duration) in both rodent and nonrodent species are used to better refine safety margins, PK/PD modeling, and set appropriate dosages for the subsequent good laboratory practice (GLP) 1-month general toxicology and safety pharmacology (i.e., cardiovascular testing in a nonrodent; CNS and respiratory function tests in a rodent) studies that proceeds the investigational new drug (IND) application before starting FIH clinical trials. Toxicokinetic assessment is based on the multiple samples obtained throughout the duration of the study along with the PK data. Such data are critical to define a margin of safety between the no observed adverse effect level (NOAEL) and the projected plasma concentrations achieved in human. It is generally considered that a 100-fold safety factor (rodent-to-human exposure ratio) from the most sensitive species NOAEL provides good safety margin in clinical studies. However, our enhanced capability of understanding interspecies sensitivity and detecting more and more subtle effects may warrant a more flexible approach. The toxicology assessment profile includes, for example, the maximum tolerated dose (MTD), safety margins and therapeutic index, target organ toxicities, most sensitive preclinical species, and reversibility of an effect/toxicity. Biomarkers characterization and preclinical to clinical translation can also be investigated in these GLP toxicology studies.
Later drug development includes Phases I–IV. Phase I (FIH) starts with a single dose escalation, then multiple dosing in normal healthy subjects. These studies are used to establish human safety profile and MTD. Phase II defines the efficacy/safety of candidate profile in target patient population (e.g., rheumatoid arthritis), drug–drug interactions, and proof of concept (POC) before proceeding into Phase III. Several repeat-dose toxicology studies (general toxicology, embryo-fetal and developmental, fertility, juvenile, carcinogenicity) of longer duration (3 months and up to 2 years) in both rodent and nonrodent species are conducted to support clinical trials of longer duration in patients.
The purpose of this chapter is to introduce the fundamentals of PK and TK, and their applications to drug discovery and development. It also presents the fundamentals of computational analysis of the data derived from the estimated concentrations in the biological matrices such as plasma. Finally, the implications of species differences, genomics, and exposure of the metabolites in determining the safe dose in the first in human (FIH) clinical trials and further identification of clinical dosage regimen are discussed.
1.3 PARAMETERS THAT DEFINE PHARMACOKINETIC PROFILE
1.3.1 Area Under the Curve (AUC)
The first step in a pharmacokinetic experiment is to dose animals or humans with NCE and collect blood samples at predefined time points. Animals are generally dosed intravenously (IV) and/or orally (po). After sample preparation and quantification usually using LC/MS/MS, a plasma concentration–time profile is generated (Figure 1.3) [18].
Mathematically, area under the plasma (or blood) concentration–time curve (AUC) can be calculated from the obtained concentration–time profile by
AUC is a primary measure of the extent of drug availability to the systemic circulation (i.e., reflects the total amount of unchanged drug that reaches the systemic circulation following intravenous or extravascular administration). The unit for AUC is concentration per unit time (e.g., ng*h/mL). AUC is determined using simple integration method as shown in Equation 1.1 or a linear trapezoidal method, which is the most widely used approach (Figure 1.3).
The area of each trapezoid is calculated using the following equation:
The extrapolated area from tlast to is estimated as
where Clast is the last observed concentration at tlast and Ke the slope obtained from the terminal portion of the curve, representing the terminal elimination rate constant. The total AUC () is determined as
AUC is used in the calculation of clearance, apparent volume of distribution, and bioavailability (see Sections 1.3.2, 1.3.3, and 1.3.5) and reflects the general extent of exposure over time.
1.3.2 Mean Residence Time (MRT)
Mean residence time (MRT) is the average time for all drug molecules to exist in the body. MRT is another measure of drug elimination and its unit is time (e.g., hour). Following intravenous dosing, MRTiv is calculated as
where AUMC is the area under the first moment versus time curve from time t = 0 to and calculated using trapezoidal rule similar to AUC.
In some cases, MRT can be a better parameter to assess drug elimination compared to half-life (t1/2) This assessment can be attributed to the greater analytical sensitivity shown with various analytical systems such as LC/MS/MS, the lower drug concentrations measured following drug administration appeared to yield longer terminal half-life, which are not related to the drug's pharmacologically relevant half-life. In a case like this, it would be recommended to measure MRT rather than half-life to assess drug elimination.
1.3.3 Clearance (CL)
Clearance (CL) is a primary pharmacokinetic parameter that describes the process of irreversible elimination of a drug from the systemic circulation. CL is defined as the volume of blood or plasma that is totally cleared of its content of drug per unit time. Thus, CL measures the removal of drug from blood or plasma. However, CL does not indicate the amount of drug that is being removed, but instead represents the rate of drug elimination from blood. Therefore, CL unit is given as mL/min or mL/min/kg (normalized to body weight).
The most widely used approach to evaluate plasma (total) CL involves intravenous administration of a single dose and measuring its plasma concentration at different time points to calculate its AUC (Figure 1.3). In this manner, the calculated CL (Equation 1.6) will not be confounded by complex absorption and distribution phenomena, which is commonly observed during oral dosing [7].
In general, a drug is either eliminated unchanged through excretion in the urine and/or bile, or by metabolic conversion into more polar metabolite(s) that can be readily excreted in urine and/or bile. Therefore, total body clearance is an additive parameter and the sum of all clearances by various mechanisms. Mathematically, it is also expressed as shown in Equation 1.7 (Figure 1.4),
where CLtot is the total body clearance from all different organs and mechanisms, CLhep the hepatic blood clearance, CLren the renal clearance, and CLbil the biliary clearance.
Figure 1.4 Various routes/mechanisms of eliminations that contribute to drug CLtotal.
It is interesting to note that around three quarters of the top 200 prescribed drugs in the United States are primarily cleared by hepatic metabolism [19]. The hepatic extraction ratio (Eh) is a pharmacokinetic parameter that is widely used to assess the liver's ability to extract drug from the systemic circulation [17]. Eh is defined as the fraction of a drug in the blood that is cleared (extracted) on each passage through the liver and is a function of CLhep and the hepatic blood flow (Q) [17]:
Typical values for the hepatic blood flow in various preclinical species and human are summarized in Table 1.1.
Table 1.1 Typical Body Weight and Hepatic Blood Flow for Various Preclinical Species and Human
Species
Body Weight (kg)
Liver Blood Flow (mL/min/kg)
Mouse
0.02–0.025
90
Rat
0.25
55
Rabbit
2.5–3
71
Cynomolgus monkey
4–5
44
Dog
10–12
31
Human
70
21
If the predominant clearance mechanism for a compound is via hepatic metabolism, then it is reasonable to assume that the CLtot is equal to CLhep. Thus,
Compounds that undergo hepatic metabolism can be classified according to their Eh. Compounds with Eh > 0.7 are considered high extraction drugs, whereas, compounds with Eh < 0.3 are considered low extraction drugs. Eh has a major impact on oral drug bioavailability.
1.3.4 Plasma versus Blood Clearance
Calculation of Eh from drug clearance in blood requires the determination of drug concentration in whole blood. Since determination of drug concentration is usually performed in plasma or serum, knowledge of the blood/plasma concentration ratio is necessary to estimate the blood clearance. Blood clearance is calculated using this equation:
Tip
Various factors can lead to a total clearance of an investigated compound that is higher than hepatic blood flow (Table 1.1). For example, extrahepatic elimination pathways can play a key role in the elimination of xenobiotics, although hepatic clearance is commonly the main route of elimination [20]. Compounds with high blood to plasma ratio are preferentially distributed in red blood cells. Therefore, their plasma clearance would overestimate blood clearance. Furthermore, compounds with poor stability in blood/plasma tend to have high clearance. Overall, these factors should be considered and investigated when this trend is observed.
1.3.5 Apparent Volume of Distribution (Vd)
Volume of distribution is a proportionality factor that relates the amount of a drug in the body to its blood or plasma concentrations at a particular time,
Following intravenous dosing and at t = 0 h, the amount of drug in the body is equal to the administered intravenous dose. Vd at t = 0 is termed volume of the central compartment (Vc).
Tip
Need to always remember that volume of distribution has no physiological relevance. There are compounds that have a Vd that is significantly lower (e.g., acetyl salicylic acid Vd = 0.15 L/kg) than total body water (0.6 L/kg) and ones that are significantly higher (e.g., loratidine Vd = 120 L/kg). This question usually arises when Vd is smaller than total body water. The answer is simple: Vd is not physiologically relevant.
Similar to CL, Vd is a primary independent pharmacokinetic parameter and its unit is volume (e.g., L/kg). Vd is a mathematical constant that has no physiological relevance. Vd is used to assess the extent of drug distribution within or outside the total body water. In the literature, Vd ranges from 3 to more than 40,000 L per 70 kg human body weight. For example, if the drug has a Vd that is smaller than the total body water (human total body water = 42 L per 70 kg human body weight, which is equivalent to 0.6 L/kg), then the drug would be expected to have limited tissue distribution (e.g., acetyl salicylic acid has a Vd = 10.5 L per 70 kg human body weight, which is equivalent to 0.15 L/kg) [21]. On the other hand, if a drug has a Vd larger than the total body water, then the drug is likely able to distribute to body tissues (e.g., loratidine has a Vd = 8400 L per 70 kg human body weight, which is equivalent to 120 L/kg) (Figure 1.5) [22]. Therefore, the term apparent volume of distribution is usually used.
Figure 1.5 Volume of distribution and its relation with the extent of drug distribution in blood and tissues.
It should be emphasized that binding to both blood and tissue components such as lipids and proteins has a significant impact on the drug volume of distribution as outlined in the following equation:
where fu,blood is the free fraction of the drug in blood, fu,tissue the free fraction of the drug in tissue, Vblood the volume of drug in blood, and Vtissue the volume of drug in tissue. As depicted in Figure 1.6, an increase in fu,blood is associated with an increase in drug Vd, whereas an increase in fu,tissue is associated with a decrease in drug Vd. Furthermore, increasing drug lipophilicity is associated with a decrease in fu,tissue, which usually leads to an increase in the drug Vd.
Figure 1.6 Tissue and blood binding and their impact on drug volume of distribution.
1.3.5.1 Apparent Volume of Distribution at Steady State (Vdss)
Vdss is the volume of distribution that is determined when plasma concentrations are measured at steady state and in equilibrium with the drug concentration in the tissue compartment.
Although Vdss is a steady-state parameter, it can be calculated using non-steady-state data as
Furthermore, Vdss is used in the calculation of a loading dose as
Use of loading dose is important especially for those drugs in which it is desirable to immediately or rapidly reach the steady-state plasma concentration (Css) (e.g., anticoagulant, antiepileptic, antiarrhythmic, and antimicrobial therapy).
1.3.6 Half-life (t1/2)
t1/2 is the time that is required for the amount (or plasma concentration) of a drug to decrease by one half. It is calculated by the following equation:
t1/2 is a dependent pharmacokinetic parameter that is determined by both CL and Vd, which are independent primary pharmacokinetic parameters. Therefore, t1/2 is increased by a decrease in CL or increase in Vd and vice versa. t1/2 is the most widely reported pharmacokinetic parameter since it may constitute a major determinant of the duration of action after single and multiple dosing. The unit for t1/2 is time (e.g., h). In addition, t1/2 plays a key role in determining the time that is required to reach steady state following multiple dosing and the frequency with which doses can be given. In general, for a drug that follows one compartment kinetics, it takes five half lives for it to reach steady-state concentrations after multiple dosing and under linear conditions. For example, for a drug with a half-life of 6 h (e.g., atenolol), steady-state concentrations are reached in about 30 h regardless of its dose or dosage regimen. Similarly, a drug such as phenobarbital with a t1/2 of 99 h, would take 495 h to reach its steady-state concentrations (Figure 1.7).
Figure 1.7 Plasma concentration–time profiles for drugs with half-lives of 6, 36, or 99 h administered once daily. Simulations were performed using Berkeley Madonna Software®. (a) Half-life is 6 h (e.g., atenolol); (b) half-life is 99 h (e.g., phenobarbital).
If a drug follows one compartment model following intravenous dosing, then its t1/2 is calculated as follows:
where MRTiv is the mean residence time following intravenous dosing. This calculation assumes that t1/2 is proportional to MRTiv.
The elimination rate constant ke is a first-order rate constant that is used to describe drug elimination from the body. The ke can be calculated directly from the slope of the straight line or from biologic t1/2 using Equation 1.18.
It is interesting to note that in light of the major advancements realized in the field of drug analysis and as greater analytical sensitivity has been achieved, lower concentrations are being detected, therefore, using the t1/2 calculated from the terminal elimination phase resulted in significantly longer terminal t1/2. For example, a t1/2 of 120 h was calculated with indomethacin, whereas 2.4 h pharmacologically relevant t1/2 is reported. Therefore, scientists are recommended to determine the most biologically relevant t1/2 by using Equation 1.16 where t1/2 is defined by the drug clearance and volume of distribution.
Tip
Develop a habit of double-checking the t1/2 calculated from the terminal elimination phase following intravenous dosing by comparing it with that calculated using Equation 1.16. If the two numbers are similar, then this is the pharmacologically relevant t1/2. Otherwise, report the value determined using Equation 1.16.
1.3.7 Maximum Plasma Concentration (Cmax) and Time of Maximum Concentration (tmax)
Cmax is defined as the maximum observed drug concentration in the plasma concentration–time profile following intravenous or oral dosing. Most commonly, Cmax is obtained by direct observation of the plasma concentration–time profile (Figure 1.3). For some drugs, the biological effect is dependent on the Cmax. For example, aminoglycosides, which are widely used antibiotics, need to achieve a Cmax that is at least 8- to 10-fold higher than the minimum inhibitory concentration (MIC) to obtain a clinical response ≥90% [23, 24]. The unit of Cmax is concentration unit (e.g., ng/mL).
tmax is the time required to reach Cmax. As with Cmax, tmax is usually determined from direct observation of the plasma concentration–time profile and its unit is time (e.g., h) (Figure 1.3). As depicted in Equation 1.18, tmax is independent of drug dose, bioavailability, or volume of distribution and is only determined by the rate constants of absorption (ka) and elimination (ke).
1.3.8 Absorption Rate Constant (ka)
The ka for a drug administered by a route other than intravenous is the rate of absorption of a drug absorbed from its site of administration. The rate of absorption usually follows first-order kinetics. Many approaches are used to calculate this parameter. For example, rate of absorption can be calculated from the following equation:
The ka can also be calculated using the method of residuals also known as feathering. The calculation is made with the assumption that the pharmacokinetics of the compound tested follows one compartment model with first-order input and output and is described using Bateman equation (Equation 1.19). The shape of the compound plasma profile is described by ka and ke. In general, ka is larger than ke and suggests that the compound absorption is faster than its overall elimination rate:
The following steps can be used to calculate ka:
Graph measured plasma concentration in semilog scale plot.
If
k
a
>
k
e
, then
achieves zero faster than
. As a result, the plasma concentration (
C
′) is described by
Determine the intercept (
Equation 1.22
) and
k
e
(slope) of the terminal linear portion of the graph using either linear regression or graphically (
Figure 1.8
).
Calculate the difference between
C
′ that depicts the terminal phase of the oral plasma profile and
C
(Bateman equation).
Plot (
C
′ −
C
) values in the same semilog scale plot.
Calculate the
k
a
from the slope using either linear regression or graphically (
Figure 1.9
).
Figure 1.8 The semilog plot of plasma profile versus time of a compound that follows one compartment model with first-order input and first-order output.
Figure 1.9 The semilog plot of residual versus time.
Finally, ka can also be calculated using the moment method:
where MRTpo is the mean residence time after oral dosing and MRTiv the mean residence time after intravenous dosing.
1.3.9 Flip–Flop Kinetics
Tip
To determine if a drug undergoes flip–flop kinetics following oral administration, both intravenous and oral plasma profiles for the drug should be characterized. If observed, the cause, usually associated with poor solubility, dissolution, and/or permeability of the tested article, may need to be investigated [25].
Flip–flop kinetics is a phenomenon where the terminal phase of the plasma profile of a drug following its oral administration is determined by the drug absorption. Here, the drug ka is much slower than its ke. This condition is usually associated with sustained absorption characterized by a decrease in C
