Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5319_Библиотеки_им_академика_М_И_Перельмана.pdf
19
Chapter • From In Vitro to In Vivo: Optimization of ADME and Toxicology Properties
nistic effect of clozapine at these receptors is responsible
for its atypical action prole.
Many drugs are classied as “dirty drugs” because of
their multifaceted action on many completely different
receptors. From apharmacologist’s point of view, such
acharacterization is appropriate. A general statement
about the therapeutic value cannot be derived from this.
It may well be that many dirty drugs are optimal for therapy because of their balanced action on multiple receptors. Recently, these compounds have been termed “rich
in pharmacology” and they dene a“polypharmacology.”
The suitability or unsuitability of adrug is decided only
in clinical trials and later by the experience gained from
broad application in patients.
The differences between enzymes and receptors in
distinct species also offer the chance to achieve desired
selectivity therapeutically. Species differences play arole
when an unwanted organism is to be killed, for example
with antibiotics, antifungals, antivirals, and antiparasitic
drugs. To avoid side effects in humans, the metabolic
pathways of the bacteria, fungi, viruses, or parasites are
targeted either by adequate selectivity or by selecting
asite of action that is not present in higher organisms
(see Sects.23.7, 24.3, 27.2, or30.11).
19.11 Of Mice and Men: The Value
of Animal Models
Quantitative activity–activity relationships are used to
extrapolate from animals to humans, but they are also
valuable for comparing different biological models. From
the vast number of examples described in the literature,
some typical relationships will be mentioned.
Even before the characterization of the different dopamine receptors (Sect.19.10, . Table19.2), 25clinically
used neuroleptics were investigated to unravel correlations between the results of in vitro models, animal experiments, and the potency of these substances in humans. Two radioactively labeled ligands, dopamine and
haloperidol 19.2 (Sect.19.10, . Fig.19.9), one of which
prefers the D1-type and the other the D2-type dopamine
receptor, were used to characterize binding. It was shown
that the average clinical dose correlated signicantly with
the displacement of the D2-type ligand haloperidol 19.2.
Signicantly higher concentrations were required to displace the D1-type ligand dopamine. There is virtually no
correlation with these data. Not only clinical efcacy, but
also data from animal models used to test neuroleptic
effects correlate better with the displacement of haloperidol than with dopamine (. Table19.3). Retrospectively, the results suffer from alack of ligand specicity
for asingle receptor, and the preparations are affected by
receptor heterogeneity because the presence of different
receptor subtypes was not standardized in the calf brain
homogenates used. All compounds were tested with dirty
. Fig. 19.9 The agonist dopamine preferably binds to the D1-type
of dopamine receptors (. Table19.2). It was clear very early, however,
from binding studies on membrane homogenates that the potency of
clinically used neuroleptics correlated with the displacement of haloperidol (r = 0.87) rather than with dopamine binding (r = 0.27)
. Table 19.3 Correlation of the clinical efcacy
(. Fig.19.10) of 25different neuroleptics and their potency in different animal models that are typically used for the
evaluation of neuroleptic effects with the displacement of
dopamine or haloperidol 19.2. The clinical data and the results
of the animal models correlate conspicuously better with the
displacement of the D2-type ligand haloperidol than with the
displacement of the D1-type ligand dopamine (r=correlation
coefcient)
Model Correla-
tion with
dopamine
displacement(r)
Mean clinical dose in humans 0.27 0.87
Inhibition of the stereotypical
behavior after administration of
apomorphine (rat)
Inhibition of the stereotypical
behavior after administration of
amphetamine (rat)
Protection from apomorphine-induced emesis (dog)
0.46 0.94
0.41 0.92
0.22 0.93
Correlation with
haloperidol
displacement(r)
ligands in dirty test models. The prole of the compounds
can only be unambiguously assigned by using uniform
receptor subtypes produced by gene technology (see
. Table19.2).
In many cases, the relationship between different experimental models is strongly dependent on the species
used. Studies of isolated arteries and veins from rabbit,
sheep, pig, and human lungs indicate that rabbit and
. • Of Mice and Men: The Value of Animal Models
. Table 19.4 Binding of substanceP and displacement by
the antagonist CP96345 19.5 (tested as aracemate) on cells
of different origins
System Binding of
substanceP,
IC50 in nM
Human cell line U373 0.13 0.40
Human cell line IM9 0.22 0.35
Guinea pig brain 0.07 0.32
Guinea pig lung 0.04 0.34
Rabbit brain 0.16 0.54
Mouse brain 0.19 32
Rat brain 0.20 35
Chicken brain 0.26 156
Displacement of
substanceP by
19.5, IC50 in nM
human vascular preparations are similarly sensitive to
noradrenaline. Sheep and pig arteries are much less sensitive. Isolated pig veins cannot be stimulated at all with
comparable doses of noradrenaline. The experimental
results are even more heterogeneous and difcult to interpret with acetylcholine stimulation. It should not be
forgotten that the metabolism of humans and animals
differs and inuences the test results.
Tachykinins are short peptides that trigger avariety
of physiological and pathological processes. Their central role in pain and asthma is well established. They act
via the NK1, NK2, and NK3 receptor subtypes, which
also bind specically to the three peptide agonists substanceP, neurokininA, and neurokininB (Sect.10.7).
CP 96 345, 19.5, a nonpeptide NK1 antagonist, displaces substanceP with high afnity in two human cell
culture models and in guinea pig and rabbit membrane
preparations. In membrane preparations from mouse,
rat, and chicken brain, to which substanceP binds with
quite comparable afnities, 19.5 has IC50 values that are
60–500 times higher (. Table19.4). It has been suggested
based on sequence-specic point mutations that the agonist substanceP and the antagonist CP96 345 may bind
to different regions of the receptor (see Sect.29.6).
The differences between humans and individual animal species are not surprising considering that the amino
acid sequence of the receptor proteins usually differs at
multiple positions. The use of human proteins in molec-
. Table 19.5 Inhibition of the renins of humans and other
animal species by remikiren 19.6 and aliskiren 19.7
Renin from: IC50 in nM,
Remikiren
Human 0.8 0.6
Monkey 1.0 1.72
Dog 107 7
Rat 3600 80
IC50 in nM,
Aliskiren
ular test systems is just as critical to the relevance of the
results obtained as it is for the determination of the 3D
structures (Chaps.13, 14). This can be seen very clearly
in the results for the aspartic protease renin (Sect.24.2).
The inhibitors remikiren 19.6 and aliskiren 19.7 were
tested on renin from different species. The renins of two
primate species and humans were inhibited at very low
concentrations. In contrast, renin from rat and dog, two
of the most commonly used species in cardiovascular
pharmacology, was inhibited only at signicantly higher
concentrations (. Table19.5). Remikiren would have
been found in aclassic antihypertensive test using these
animal models, but would probably have been rated as
much too weak. Acomparison of the crystal structure
analyses of murine and human renin also reveals aconserved binding mode in the main chain of the peptide
inhibitors that is common to the other aspartic proteases.
However, there are subtle differences at the rim of the
binding pocket that can be attributed to sequence differences between the two species.
More than 90% of the amino acid sequences of the
5-HT1B and 5-HT1Dß subtypes of the human and rat
serotonin receptors are identical. When the relationships
between the individual amino acids are considered, the
homology is as high as 95%. Despite these similarities,
19
Chapter • From In Vitro to In Vivo: Optimization of ADME and Toxicology Properties
. Fig. 19.10 Different serotonin receptor ligands and the β-blockers
propranolol and pindolol show very different binding afnities on the
highly similar 5-HT receptors from rats and humans. The red circles
refer to the wild-type human receptor. They are irregularly scattered
over the diagram (correlation coefcient r = 0.27). If one amino acid
in the human receptor is exchanged for the corresponding amino acid
in the rat receptor, the binding prole changes. Relative to the afnity of the ligands, the human receptor becomes very close to the rat
receptor. The gray circles refer to this Asn 355 mutant (correlation
coefcient now r = 0.98)
anumber of drugs bind to these two receptors with very
different afnities. The difference is due to asingle amino
acid: the exchange of threonine 355 for an asparagine
(. Fig.19.10). This mutation converts the human receptor into the rat receptor in terms of afnity! After the
exchange of this amino acid, the β-blockers propranolol
and pindolol (Sect.29.3, . Fig.29.1) bind with approximately three orders of magnitude higher afnity. The
afnities of many other ligands are signicantly reduced.
19.12 Toxicity and Adverse Effects
One of the most difcult tasks in preclinical research is
to estimate the toxicity of asubstance, especially human
toxicity, from data obtained in other species. Such considerations must be made in order to assess the potential
danger of the substance before it is introduced into the
clinic. Are there any drugs without toxicity and without
side effects? Paracelsus recognized in the sixteenth century that:
“Everything is poison and nothing is without poison, it is
»
the dose alone that makes athing nonpoisonous.”
Friedrich Schiller had his Fiesko say:
“Adesperate evil needs abold remedy [medicine].”
»
And the pharmacologist Gustav Kuschinski formulated:
“Whenever it is proclaimed that asubstance has no side
»
effects, the urgent suspicion ensues that there is also no
main effect.”
Determination of acute toxicity in several species and
determination of chronic toxicity in at least two species
is routine prior to entering phaseI clinical trials, which
are tolerability studies in healthy volunteers. It is common practice to select species for chronic toxicity studies primarily on the basis of which species most closely
resembles humans in pharmacokinetics and metabolism
for the substance of interest.
Cats and guinea pigs are extremely sensitive to cardiac
glycosides. For this reason, they have traditionally been
used as models for the effect on humans. Rats are much
less sensitive. The hallucinogen lysergic acid diethyl amide
(LSD 2.21, Sect.2.5, . Fig.2.8) shows markedly different toxicity in several species. An experiment to test
the hallucinogenic effects of LSD on an elephant ended
in adisaster. Ahallucinogenic but nontoxic dose was
desired. Despite careful dose estimation, the elephant
died within minutes of being given 0.3 g of LSD (equivalent to 0.06 mg/kg). Compared to the mouse, which is
relatively insensitive (. Table19.6), the elephant was at
least 1000 times more sensitive. This experiment has not
been repeated! The discoverer of LSD, Albert Hofmann,
took 0.25 mg of LSD in his rst controlled self-experiment. In retrospect, his choice of dose was about three
to ve times the effective dose in humans. With about
0.0035 mg/kg he was well below the dose that killed the
elephant. Nevertheless, it can be assumed that LSD is
less toxic for humans than for elephants. Direct fatalities
due to LSD are not known, only mortality as aresult of
accidents or suicides while in apsychotic state.
The toxicity of the poisons that end up in our environment is studied in great detail. Chlorinated dibenzodioxins and furans are formed by the uncontrolled chemical decomposition of the corresponding substituted
chlorophenols. The Seveso accident is attributed to such
an incident. Toxic chlorinated dioxins and furans are also
. Table 19.6 Acute toxicity of lysergic acid diethyl amide
(LSD, 2.21, Sect.2.5, . Fig.2.8) in different species and in
humans (LD50=dose that was lethal for 50% of the animals)
Species Toxicity, LD50 (in mg/kg)
Mouse 50–60
Rat 16.5
Rabbit 0.3
Elephant
Human
≪ 0.06
≫ 0.003
. • Toxicity and Adverse Eects
formed during many combustion processes. Tetrachlorodibenzodioxin 19.8 (TCDD, “Seveso Dioxin”) is one of
the best-studied substances in terms of its toxicity. Here,
too, different species react differently (. Table 19.7).
There is adifference of three orders of magnitude regarding toxicity between the two relatively closely related
species, the hamster and the guinea pig. It is, therefore,
difcult to draw conclusions about toxicity in humans.
If extrapolated between primates and humans, TCDD
would be classied as relatively nontoxic. In the context
of humans, the denition of an acute LD50 is completely
inappropriate. In order to exclude one fatality per one
million people, an “LD
calculated. Because of its pronounced mutagenic effects,
long-term damage is the primary concern with TCDD. In
this case, it is questionable whether an absolute no-effect
level, i.e., the lowest ineffective dose, can be dened. The
assessment of the hazard potential of environmentally
relevant chemicals looks quite different when compared
with toxic natural products, natural radioactivity, cosmic radiation, etc., or even with socially tolerated substances of abuse such as alcohol and nicotine. This puts
into perspective some things that are very controversially
discussed in public forums.
When discussing structure–activity relationships, it
is important to recognize the difculty of using in vitro
studies to estimate the mutagenicity and carcinogenicity
of asubstance. While such tests provide valuable indications that must be carefully reviewed, they are not conclusive in either apositive or negative sense in individual
cases. It is extremely difcult to provide theoretical models for estimating toxicity and carcinogenicity with suf-
cient reliability and predictive power. The mechanisms
. Table 19.7 Acute toxicity of tetrachlorodibenzodioxin
19.8 in different animal species
Species Toxicity (LD50 in μg/kg)
Mouse 114–280
Rat 22–320
Hamster 1150–5000
Guinea pig 0.5–2.5
Mink 4
Rabbit 115–275
Dog > 100–< 3000
Monkey < 70
Human ?
” must be determined or
0.00001
responsible for the effects are too diverse and multifaceted, and the chemical structures and structure–activity
relationships applicable to different classes of substances
are too different.
Today, testing for toxic, carcinogenic, and teratogenic
side effects has reached ahigh standard. The pharmaceutical disasters of earlier decades, such as the following,
would be almost impossible with today’s standards:
Early childhood brain damage and death of many
-
premature and mature newborns by the sulfonamides
in the late 1930s,
Over 100 fatalities in the USA because of the use of
-
diethylene glycol as asolvent for sulfanilamide (this
incident led to the foundation of the Food and Drug
Administration, FDA),
The SMON (subacute myelo-optic neuropathy) ill-
-
ness of thousands of Japanese, caused by the pro-
longed and too-frequent use of an antidiarrheal med-
icine, and
The severe birth defects of approximately 10,000
-
children worldwide that were caused by thalidomide
(Contergan®) in the late 1950s.
However, criminal intrigue and the uncontrolled distribution of fake drugs from internet-based vendors, or the
unscrupulous pursuit of economic gain, can still cause
such disasters today. Acase in point is the melamine-contaminated infant formula (melamine makes the protein
content of inferior or diluted milk appear higher) in
China in September 2008, which sickened many thousands of toddlers and babies and even caused some
deaths.
In addition to the much stricter testing guidelines for
medicines that are now in place in most countries, there is
areporting system that records and investigates adverse
drug reactions. The slightest suspicion of acausal relationship can result in anything from public announcement or warning all the way to withdrawal of the marketing license.
Acomplication in estimating toxicity is the formation
of toxic and especially reactive metabolites, even at low
levels. As discussed in Sects.9.1 and19.6, an ideal drug
should contain predetermined cleavage and/or conjugation sites in addition to nely tuned pharmacodynamics
and pharmacokinetics. The more these requirements are
met, the lower the risk that the compound will exert toxic
effects.
Some toxicity studies suffer from the fact that the
results extrapolated to humans reect ahigher toxicity
than is actually the case due to the unphysiologically high
doses used in the studies. On the other hand, even the
most comprehensive study cannot eliminate the risk of
serious adverse events occurring in extremely rare cases
once the drug is in widespread use. An adverse event rate
of1 in 10,000 or less may go undetected in even the most
carefully conducted preclinical and clinical studies.
Chapter • From In Vitro to In Vivo: Optimization of ADME and Toxicology Properties
19
Toxic side effects in humans are particularly common after chronic drug abuse. The lifetime consumption
of large quantities of painkillers adds up to kilograms.
In the case of phenacetin (Sect.2.1), this resulted in an
effective and generally well-tolerated drug having to
be withdrawn from the market due to kidney damage
caused by inappropriate (abusive) use.
Gradually, humanity is becoming more concerned
about what happens to the products we release into the
environment. Pharmaceuticals are for consumption by
patients. However, much of what goes into the body also
comes out of the body. Many pharmaceuticals enter the
environment through wastewater. Often they remain unchanged, but in some cases they are metabolically modied. Once in the environment, they can be taken up by
other organisms and have similar or different effects.
In general, these effects are unknown and unstudied.
In spite of high dilutions, active substances can build
up locally to high concentrations or exceed therapeutically critical limits through specic accumulation. In the
case of antibiotics, the emergence of resistant bacteria is
known to occur in inadequately treated wastewater, e.g.,
after carelessly operated pharmaceutical factories, hospitals, or slaughterhouses. Hormones and contraceptives can interfere with the reproduction of sh populations. Lipid-lowering drugs such as statins (Sect.27.3)
interfere with cholesterol biosynthesis, a pathway that
is essential for fundamental processes in Nature. The
painkiller diclofenac (Sect. 27.9) has been found to
have adevastating effect on birds and sh, causing kidney damage. We need to develop amore responsible
approach to the disposal of excreted pharmaceuticals.
Presumably, the additional requirement that adrug be
toxicologically safe as adegradation product in the environment would raise the bar for an optimally effective therapeutic to heights that are almost impossible to
achieve. Nevertheless, there seems to be an urgent need
to think more carefully about the fate of excreted drugs.
Stricter requirements, especially for the environmentally
sound disposal of unused medicines, seem more than
necessary.
19.13 Animal Protection and Alternative
Test Models
As early as 1780, the philosopher Jeremy Bentham
discussed the rights of animals. The rst mass protests
against animal experimentation took place almost 150
years ago. In 1875, dedicated animal rights activist
Frances Power Cobbe founded the rst Society Against
Vivisection in England, and ayear later the demand for
anesthesia in animal experimentation led to the rst Animal Welfare Act. In Germany in 1879 the Internationale
Gesellschaft zur Bekämpfung der Wissenschaftlichen Thierfolter (International Society for the Abatement of Sci-
entic Animal Torture) was founded, followed in 1883
by the American Antivivisection Society. Anew militant
form of protest against animal experimentation, complete with the violent liberation of laboratory animals
and attacks on scientists, emerged in the 1970s. Peter
Singer’s book Animal Liberation was published in 1975
and became the bible of animal rights activists. The often-quoted story of animal trappers selling their prey
to the pharmaceutical industry was afantasy even in
the early days of drug discovery. Every pharmacologist
knows that any results obtained from such diverse animals would be completely useless without any knowledge
of their health history.
More in parallel with the development of the animal
welfare movement than inspired by it, alternative methods for pharmaceutical research were introduced in the
1960s, consisting mainly of binding studies on membrane
homogenates and cell culture studies. The number of ani-
mal experiments has been signicantly reduced in recent
decades due to the economic motivation arising from the
enormous costs of breeding and maintaining experimental animals, but also because of the rapid progress in gene
technology. As explained in Sect.7.5, models using lower
animals such as pinworms, fruit ies, or zebra sh are
increasingly being used for screening. Here, the ethical
threshold for animal testing is certainly lower.
More than 50% of all laboratory animals are used
for drug testing, with 12–15% each for basic research,
investigation of medical methods, and detection of environmental hazards. About half of all laboratory animals
are mice. The rest are rats and other rodents, and asmall
proportion are sh and birds. Only about 1.5% of the total number are cats, dogs, pigs, and other animals. Much
of the testing on the latter species is chronic toxicity test-
ing, which is required by law.
The decline in these numbers is remarkable because
pharmaceutical companies are investigating the biological activity of more compounds than ever before. Each
year, tens or even hundreds of thousands of compounds
are meticulously characterized, usually in automated in
vitro assays. Only afew of these compounds are ever
tested in animals. The number of animals used must
also be seen in the context of regulatory requirements to
demonstrate the efcacy and safety of new drugs, which
are increasing rather than decreasing. The vast majority
of these tests must still be performed on animals.
19.14 Synopsis
Apart from potent and selective binding to atarget
-
protein, a successful drug candidate must exhibit
favorable pharmacokinetics. This comprises all pro-
cesses that affect the absorption, distribution, me-
tabolism, and excretion along with minor toxic side
effects.
. • Synopsis
Due to high costs and enormous experimental effort,
-
full pharmacokinetics and toxicity studies can only
be carried out on afew drug development candidates.
Aplethora of test methods have been developed to
relate chemical structure with ADME and toxicology
properties in the lead-optimization phase to reduce
the chances of failure due to insufcient pharmacokinetics at alate stage.
An active substance has to penetrate multiple lipid
-
membrane barriers and aqueous compartments on
its way from the site of application to the locus of
the target protein. To achieve sufcient distribution,
adequate lipophilicity must be present. This is described by the partition coefcient between the lipid
and aqueous phases. In the simplest model, the distribution between octanol and water is measured.
Rather sophisticated models have been established to
-
relate chemical structure with penetration properties.
Considerations about the release of the water solvation shell around adrug molecule and its potential to
form hydrogen bonds upon crossing lipid membranes
are particularly important.
Many drugs are either weak acids or bases. Depend-
-
ing on the pH used, they exist through dissociation
equilibria in either amore lipophilic neutral or more
polar ionized form. Membrane penetration of such
species will, therefore, depend very much on the local
pH conditions.
Because of the progressively changing pH conditions
-
in the stomach and intestines, appropriate pH conditions exist at some place along the gastrointestinal
tract that allow sufcient penetration of the neutral
form of weakly acidic or basic drug molecules.
Because of established equilibria, small amounts of
-
the neutral form of an acidic or basic drug molecule
are the intermediate over which membrane penetration occurs. Constant removal of the neutral species
from the aqueous phase into the membrane is quickly
replenished by the dissociation equilibrium.
The adjustment of the lipophilicity of adrug is cru-
-
cial for pharmacokinetics. Usually the more lipophilic
acompound is, the better it will be absorbed; however,
limited solubility in the aqueous phase restricts lipophilicity. Relevant test models have been developed
by using thin layers of human colon cells. These also
allow the absorption by transporters to be studied.
A
ctive substances are initially tested in simple in vitro
-
test models. Testing is gradually moved into animal
models via cellular assays. Relevant activity–activity
relationships must be established to correlate response
in animal models. At best, results from appropriate in
vivo testing allow the therapeutic effects in humans to
be predicted. They standardize test data and reduce
the number of animal experiments that are required.
Nature works with two orthogonal principles upon
-
release of its native substances: the specicity of the
biological effect and apronounced spatial compart-
mentalization. Some compounds are highly specic
and travel long ways through the organism to exert
their action. Others are locally synthesized and stim-
ulate their target protein in the immediate vicinity.
Here, high specicity and selectivity are not required.
Drugs administered orally or intravenously act
-
systemically on the entire organism; no organ- or
cell-specic compartmentalization can be achieved.
This has to be compensated for by sufcient specic-
ity and selectivity. Whether high isoform selectivity or
protein-family-wide promiscuity is required depends
very much on the mode of action and biological func-
tion of the target protein.
Prior to administration in humans, clinical candidates
-
are tested in animals. To draw conclusions about hu-
mans from animals, it must be considered that test
models strongly depend on the species used. Even me-
tabolism can be very different in humans and various
animal species.
Deviating therapeutic responses in animals and hu-
-
mans are also related to small differences in the amino
acid composition of the target proteins in various
species.
Estimates of human toxicity must be made from data
-
obtained in other species. Chronic toxicity is routinely
determined in two species and must be evaluated in
the animal species with the closest pharmacokinetic
and metabolic similarity to humans.
Different species, however, react differently to active
-
substances and frequently show several orders of
magnitude differences in toxicity.
Today, testing for toxic, carcinogenic, and terato-
-
genic adverse effects has reached ahigh standard.
The pharmaceutical catastrophes of earlier decades,
which were mostly caused by these adverse effects,
will hopefully be almost impossible nowadays.
Even the most comprehensive toxicity studies cannot
-
eliminate the risk of severe adverse effects occurring
in extremely rare cases once anew drug is adminis-
tered broadly.
The animal experiments of the past have been re-
-
placed by more conclusive binding studies on mem-
brane homogenates and cell cultures. Whole-animal
testing has shifted towards lower animals such as pin-
worms, fruit ies, or zebra sh. The toxicity studies
required by law make up alarge part of the animal
experiments that are done today.
Chapter • From In Vitro to In Vivo: Optimization of ADME and Toxicology Properties
Bibliography and Further Reading
General Literature
H. Kubinyi, Lipophilicity and Drug Activity, Progr. Drug Res., 23,
97–198 (1979)
J. K. Seydel and K.-J. Schaper, Quantitative Structure–Pharmacoki-
netic Relationships and Drug Design, Pharmac. Ther., 15, 131–182
(1982)
J. M. Mayer and H. van de Waterbeemd, Development of Quantitative
Structure–Pharmacokinetic Relationships, Environ. Health Perspect., 61, 295–306 (1985)
J. C. Dearden, Molecular Structure and Drug Transport, in: Quanti-
tative Drug Design, C. A. Ramsden, Eds., Vol. 4: Comprehensive
Medicinal Chemistry, C. Hansch, P. G. Sammes and J. B. Taylor,
Eds., Pergamon Press, Oxford, p. 375–411 (1990)
C. A. M. Hogben, D. J. Tocco, B. B. Brodie, and L. S. Schanker, On the
Mechanism of Intestinal Absorption of Drugs, J. Pharmacol. Exp.
Therap., 125, 275–282 (1959)
H. Kubinyi, QSAR: Hansch Analysis and Related Approaches, VCH,
Weinheim (1993)
C. Hansch and A. Leo, Exploring QSAR. Fundamentals and Appli-
cations in Chemistry and Biology, Vol. 1, American Chemical Society, Washington (1995)
R. L. Lipnick, Selectivity, in: General Principles, P. D. Kennewell,
Eds., Vol. 1: Comprehensive Medicinal Chemistry, C. Hansch,
P. G. Sammes and J. B. Taylor, Eds., Pergamon Press, Oxford, p.
239–247 (1990)
H. Kubinyi, Lock and Key in the Real World: Concluding Remarks,
Pharmac. Acta Helv., 69, 259–269 (1995)
C. A. Reinhardt, Hrsg., Alternatives to Animal Testing, VCH, Wein-
heim (1994)
R. Mannhold, Ed. Molecular Drug Properties, Wiley-VCH, Weinheim
(2008)
D. A. Smith, H. van der Waterbeemd and D. K. Walker, Pharmaco-
kinetics and Metabolism in Drug Design, Wiley-VCH, Weinheim
(2006)
B. Testa and H. van der Waterbeemd, (Eds)., ADME-Tox Approaches,
Vol. 5 of Comprehensive Medicinal Chemistry II, Elsevier (2007)
G. Orive, U. Lertxundi, T. Brodin, P. Manning, Greening the pharmacy.
New measures and research are needed to limit the ecological impact of pharmaceuticals, Science, 377, 259–260 (2022)
C. A. Lipinski, Lead- and drug-like compounds: the rule-of-ve revo-
lution, Drug Discov. Today, Technol., 1, 337–341 (2004)
L. Z. Benet, C. M. Hosey, O. Ursu, T. I. Oprea, BDDCS, the Rule of
5 and drugability, Adv. Drug Deliv. Reviews, 101, 89–98, (2016)
D. C. Doak, J. Zheng, D. Dobritzsch, J. Kihlberg, How Beyond Rule
of 5 Drugs and Clinical Candidates Bind to Their Targets, J. Med.
Chem., 59, 2312–2237, (2016)
D. G. Jimenez, V. Poongavanam, J. Kihlberg, Macrocycles in Drug
Discovery─Learning from the Past for the Future, J. Med. Chem.,
66, 5377–5396, (2023)
A. Tsuji, E. Miyamoto, N. Hashimoto and T. Yamana, GI Absorption
of β-Lactam Antibiotics II: Deviation from pH-Partition Hypoth-
esis in Penicillin Absorption through In Situ and In Vitro Lipoidal
Barriers, J. Pharm. Sci., 67, 1705–1711 (1978)
P. Seeman and H. H. M. Van Tol, Dopamine Receptor Pharmacology,
Trends Pharm. Sci., 15, 264–270 (1994)
B. D. Gitter etal., Species Differences in Afnitites of Non-Peptide
Antagonists for Substance P Receptors, Eur. J. Pharmacol., 197,
237–238 (1991)
J.-P. Clozel and W. Fischli, Discovery of Remikiren as the First Orally
Active Renin Inhibitor, Arzneim.-Forsch., 43, 260–262 (1993)
V. Dhanaraj etal., X-Ray Analyses of Peptide–Inhibitor Complexes
Dene the Structural Basis of Specicity for Human and Mouse
Renins, Nature, 357, 466–472 (1992)
E. M. Parker, D. A. Grisel, L. G. Iben and R. S. Shapiro, A Single
Amino Acid Difference Accounts for the Pharmacological Dis-
tinctions Between the Rat and Human 5-Hydroxytryptamine-1B
Receptors, J. Neurochem., 60, 380–383 (1993)
D. J. Hanson, Dioxin Toxicity: New Studies Prompt Debate, Regula-
tory Action, Chem. Eng. News, 69,7–14 (1991)
M. J. Mateld, Animal Liberation or Animal Research? Trends Pharm.
Sci., 12, 411–415 (1991)
CLOGP program: https://www.daylight.com/products/pcmodels.html
(Last accessed Nov. 19, 2024)
ACD/pKa program: https://www.acdlabs.com/products/percepta-
platform/physchem-suite/pka/ Last accessed Nov. 19, 2024)
Pallas/pKa program: http://www.ccl.net/ccl/pallas.html (Last accessed
Nov. 19, 2024)
19
Special Literature
B. C. Lippold and G. F. Schneider, Zur Optimierung der Verfügbarkeit
homologer quartärer Ammoniumverbindungen, 2. Mitteilung:
In-vitro-Versuche zur Verteilung von Benzilsäureestern homologer
Dimethyl-(2-hydroxyäthyl)-alkylammoniumbromide, Arzneim.Forsch., 25, 843–852 (1974)
H. Kubinyi, Drug Partitioning: Relationships between Forward and
Reverse Rate Constants and Partition Coefcient, J. Pharm. Sci.,
67, 262–263 (1978)
R. C. Young, R. C. Mitchell, T. H. Brown, C. R. Ganellin, R. Grifths,
M. Jones, K. K. Rana, D. Saunders, I. R. Smith, N. E. Sore, T. J.
Wilks, Development of a new physicochemical model for brain
penetration and its application to the design of centrally acting H2
receptor histamine antagonists, J. Med. Chem., 31, 656–671 (1988)
H. van de Waterbeemd, P. van Bakel and A. Jansen, Transport in Quan-
titative Structure–Activity Relationships VI: Relationship between
Transport Rate Constants and Partition Coefcients, J. Pharm.
Sci., 70, 1081–1082 (1981)
C. Hansch, J. P. Björkroth and A. Leo, Hydrophobicity and Central
Nervous System Agents: On the Principle of Minimal Hydrophobicity in Drug Design, J. Pharm. Sci., 76, 663–687 (1987)
H. van de Waterbeemd and M. Kansy, Hydrogen-Bonding Capacity
and Brain Penetration, Chimia, 46, 299–303 (1992)
Protein Modeling and
Structure-Based Drug Design
Contents
20.1 Pioneering Studies in Structure-Based Drug Design – 310
20.2 Strategies in Structure-Based Drug Design – 311
20.3 Search Tools for Databases of Experimentally
Determined Protein Complexes – 312
20.4 Comparison of Protein-Binding Pockets – 312
20.5 High Sequence Identity Facilitates Model Generation – 312
20.6 Secondary Structure Prediction and Amino Acid
Replacement Propensities Support Model Building
at Low Sequence Identity – 314
20.7 Ligand Design: Seeding, Expanding, and Linking – 316
20.8 Docking Ligands into Binding Pockets – 316
20.9 Scoring Functions: Ranking of Constructed
Binding Geometries – 318
20.10 De Novo Design: From LUDI to the Automated
Assembly of Novel Ligands – 318
20.11 The Feasibility of Designing Ligands In Silico – 319
20.12 Synopsis – 320
Bibliography and Further Reading – 320
© The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2024
G. Klebe, Drug Design, https://doi.org/10.1007/978-3-662-68998-1_20
Chapter • Protein Modeling and Structure-Based Drug Design
20
Structure-based drug design focuses on the search, design, and optimization of asmall molecule that ts well
into the binding pocket of atarget protein to form energetically favorable interactions. First, adetailed analysis of the target protein is performed. All information
about its structure and that of related proteins is evaluated. Next, the properties of the binding pocket are
thoroughly explored, looking for areas where optimal
binding is expected. Experimental and computational
methods are used to discover a lead structure from
ascreening library (Chap.7). Alternatively, approaches
are used that start with asmall-molecule “seed” (or
“fragment”) in the binding pocket that is then allowed
to “grow” into apotent ligand using stepwise iterative
design. This approach uses fast docking techniques that
suggest relevant binding geometries. The geometries are
evaluated with ascoring function that estimates whether
they are energetically favorable.
However, acrucial prerequisite for the use of structure-based drug design is the knowledge of the spatial
structure of the target protein. Impressive progress in
the eld of protein structure determination (Chaps.13
and14) has led to the fact that the 3D structures of
almost all therapeutically relevant proteins are known
today or will be determined at the beginning of anew
project. Nevertheless, it should not be overlooked that
for some target proteins of interest there is still no experimentally determined three-dimensional structure available, and in these cases models have to be built.
Thanks to the sequencing of the human genome,
the blueprints for all the proteins of our species are now
known at the sequence level. The genomes of many
pathogens have also been sequenced, and new ones
are being discovered weekly. How can this enormous
advance in information be used to design new drugs?
Unfortunately, the step from the primary structure, this
means the amino acid sequence, to the 3D structure is
very difcult and, until now, has usually been followed
by experimental methods of structure determination
(Chap.13). Methods for reliable ab initio prediction of
the spatial structure of proteins based on theoretical
concepts have not yet been successfully developed. Increasingly, however, the situation arises where the structure of the protein of interest is unknown, but the structure of another related protein has been determined. In
such asituation, amodel of the unknown protein can be
constructed from the spatial coordinates of the already
characterized biopolymer. Recently, however, there has
also been abreakthrough in the structural prediction
of protein models from their primary sequence. Successful methods learn from the wealth of experimental
structural data using articial intelligence techniques
and are, thus, trained to model protein architectures
(Sect.20.6).
20.1 Pioneering Studies
in Structure-Based Drug Design
Considering that most of the structures of therapeutically relevant proteins have been determined in the last
20years, it is even more impressive that the rst work
in structure-based drug design was already done in the
1970s. The pioneers in this eld were Chris Beddell and
Peter Goodford, who began developing methods for ligand design at the Wellcome Research Laboratories in
1973. Hemoglobin was chosen as the target protein because, at the time, it was the only example with aknown
3D structure that had some relevance to pathophysiology. The goal of this work was to nd aligand that
would exert an allosteric modulating effect, analogous
to the natural ligand diphosphoglyceric acid 20.1 (DPG;
. Fig.20.1). The hope was to nd a therapeutic ap-
proach that could help homozygous patients with lethal
sickle cell anemia (Sect.12.13). DPG is synthesized in
red blood cells. It binds to hemoglobin and reduces its
afnity for oxygen. This allows oxygen absorbed in the
lungs to be released to other tissues.
The portion of hemoglobin that binds to DPG contains alarge number of positively charged amino acids
(. Fig.20.1). An optimal ligand should, therefore, contain anegatively charged group to form multiple salt
bridges to hemoglobin, just as DPG does. However, such
compounds cannot penetrate the membrane of a red
blood cell. Therefore, the Wellcome group considered
structures that interact with hemoglobin in other ways.
Compounds were selected that contained reactive groups
that could be attached to the amino groups of the lysines
in the binding pocket or to the N-terminus. The idea was
to design acompound containing two correctly spaced
reactive groups that could form Schiff bases with two
of these amino groups. Dibenzyl-4,4′-dialdehyde 20.2
(. Fig.20.2) was chosen as the parent structure. The
putative binding mode of this compound is shown in
. Fig. 20.1 Schematic binding mode of diphosphoglyceric acid 20.1
(DPG) to the allosteric binding site of hemoglobin. The ligand is
bound through multiple charge-assisted hydrogen bonds (N-terminal
amino groups, His2, Lys82, and His 143) from the β1 and β2 subunits
. • Strategies in Structure-Based Drug Design
. Fig.20.3. Compound 20.2 was synthesized but proved
to be too insoluble for testing. Sufcient solubility was
achieved by introducing an additional carboxyl group in
20.3. In addition, this compound with its carboxyl group
should provide an additional favorable interaction with
the lysine side chain of the protein. Compounds 20.4 and
20.5 are the bisulte adducts of the corresponding aldehydes. These compounds were tested and indeed showed
the desired allosteric effect. However, they bind to the
oxy form of the protein and increase its oxygen afnity.
They proved to be potent inhibitors of the erythrocyte
deformation that occurs in sickle cell disease by stabilizing the oxy form. The deformation begins with the
aggregation of the desoxy form. The targeted design of
these dibenzyldialdehydes is the rst example of rational,
structure-guided protein–ligand design.
. Fig. 20.2 Structures of the diphosphoglyceric acid competitive
hemoglobin ligands 20.2–20.5 that were developed by Beddell and
Goodford
20.2 Strategies in Structure-Based
Drug Design
In order to design aligand for aprotein with aknown
3D structure, it is necessary to analyze the structure of
the protein. What does the binding pocket of the protein
look like? Where are the hot spots of binding, this means
where can functional groups of aligand bind particularly
well to the protein? For such analyses, experimental data
can be used or computer programs are available. They
search the surface of aprotein for suitable binding sites
for different functional groups, such as acarbonyl group,
acarboxylic acid function, or an amino group. Aselection of experimental methods for nding hot spots using
X-ray structural analysis and NMR spectroscopy were
presented in Sects.7.8 and7.9, and some computational
methods are described in Sect.17.10.
. Fig. 20.3 Postulated binding mode of the hemo-
globin ligands 20.2 and 20.5 after chemical reaction to
the Schiff base or the bisulte addition product. It is
assumed for both compounds that they bind covalently to the
β
and β2 subunits of hemoglobin through
subunit
1
1
their N-terminal amino acids. Compound 20.5 should
also be able to form ahydrogen bond with its charged
groups to the side chains of amino acids His2 and His
143 of the β1 and β2 subunits, as well as Lys82 of the
β
To design new drugs, an attempt is made to nd
new ideas for potential ligands, either by using docking
methods through virtual screening (Sect.7.6). Or, alternatively, amolecule that has been discovered using one
of the techniques described in Chap.7 can be modied
and progressively increased in size in astepwise fashion.
Modication of a known structure has the advantage
that potent and selective protein ligands can be obtained
relatively quickly. In addition, if the 3D structure of the
protein is known, clear structure–activity relationships
will usually emerge. However, there is arisk that the proposed structures will remain close to the original lead
structure. For example, when an initial 3D structure of
an enzyme complexed with apeptidic ligand is solved,
the resulting design proposals are often very similar
to peptides (Chaps.23, 24, 25). The path to an orally
available drug can then be quite long (Chap.10). Another approach is de novo design, or fragment-based lead