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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5571_Библиотеки_им_академика_М_И_Перельмана
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considered in drug design because only the free drug concentration determin es the
pharmaceutical activity. Transport proteins are found in most organs involved in the
uptake and elimination of endogenous compounds and xenobiotics including drugs.
An attempt to predict binding to these proteins is very important. The volume of
distribution is a nonphysiological term that is a measure of drug distribution and
together with clearance determines the half-life of a drug and thus affects its dosing
regimen.
BBB Permeability Several recent reviews have been reported about BBB and
in silico modeling [28, 157–168]. CNS acting drugs have to overcome physical
barriers before reaching their drug target in the form of the blood–brain barrier. The
BBB is formed by the microvasculature of the brain that exhibits selective permeability for substances [169]. The brain capillary endothelium forms tight junctions
that surround the cell margin circumferentially. This results in the BBB being
essentially impermeable to hydrophilic compounds. The BBB has developed carrier
systems or transporters for the uptake of larger, hydrophilic, or charged compounds.
These transporters play a role in transporting amino acids, monocarboxylic acids,
peptides, and organic cations across the BBB. BBB transporters include the organic
cation transporters (OCT), organic anion transporters (OAT), and nucleoside transport system (Table 2.5). There are transporters located at the BBB interface also
involvedin the removal (efflux) of drugs from the CNS such as P-glycoprotein. P-gp is
a part of the multidrug resistance (MDR) gene and has a key role as a result of tight
junctions between the cells.
TABLE 2.5 Blood–Brain Barrier Transporters
BBB Transporters [165]
Energy GLUT1
MCT1
CRT
Amino acids LAT1, CAT1,
EAAT, TAUT
System beta
ASCT2
System T
Neurotransmitters GAT/BGT
SERT
NET
Organic cation and anion OCTN2
CHT1
OA T3
Oatp
ABC ABCB1
ABCC1
ABCG2
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In silico methods have been developed to establish predictive models for the uptake
of drugs into the CNS. Observations of simple descriptors underline that molecules
with MW < 300 have a favorable brain/blood ratio as compared to drug with
MW > 700 (analysis of 3059 diverse molecules with CNS penetration data) [145].
Ionization state is also critical with basic molecules typically more CNS penetrant
than neutral molecules, followed by zwiterrions. Acidic molecules are the least CNS
penetrant compounds [145].
Multiple linear regression or partial least squares methods have been used to
determine brain permeation models (log BBB) [20]. From these studies, several “rules
of thumb” have arisen for passive BBB permeability (in the absence of active
transport), similar to Lipinski’s rule-of-5 for intestinal absorption. These include,
if the sum of the nitrogen and oxygen (N þ O) atoms in a molecule is less than five
(some studies report six) and if C log P (N þ O) > 0, then compounds are likely to
penetrate the BBB [170]. Additionally, the polar surface area should be less than 90 A
˚
2
(some studies suggest PSA < 70 A
˚
2
), the MW < 450, and the log D between 1 and 3.
The degree of hydrogen-bonding capacity of the molecule also plays a significant
role in facilitating BBB permeability. Generally, increasing the hydrogen-bonding
capacity of a molecule leads to decreased BBB permeability, as observed from highly
polar/strong hydrogen-bonding molecules that do not cross the BBB readily [164].
Using these rules and tuning software packages that compute rule-of-5 values, it is
possible to filter compound collections to determine which compounds are excluded
from the CNS due to low predicted BBB permeability. Most computational models
predicting BBB are based on experimental log BB values, the logarithmic ratio
between the concentrations of a substance in the brain to that in the blood (log ([brain]/
[blood])). Experimental log BB data are relatively time-consuming to obtain and
surrogate measures are often performed. Molecules that show and do not show
activity at a CNS target are CNSþ and CNS , respectively. The measure of
permeability (log PS) by vascular perfusion methods are relatively rapid to determine
and can also be used. In general, in silico log BB models have been derived assuming
passive diffusion and equilibrium conditions. In contrast to log P, log BB covers a
much narrower range, in general between 2 and 1. Compounds with log BB > 0.3
cross the BBB readily, while values <1 indicate minor penetration [17]. PLS,
k-nearest-neighbor, decision trees, neural networks, and SVM have been used for
qualitative classification of compounds, CNSþ for those that are readily available to
the CNS, and CNS , for the molecules that are unlikely to penetrate the brain [17].
Comparison of relevant descriptors in QSAR equations for log BB to those in
CNSþ/ classification algorithms is complicated. It has been suggested that CNSþ
agents should contain less than three H-bond donors and no carboxylic acids unless
they mimic neurotransmitters. Overall, compared to non-CNS drugs, CNSþ
molecules tend to be more lipophilic, more rigid, have fewer HBD, fewer formal
charges, and lower PSA (<80 A
˚
2
) (Figure 2.14).
Little structural knowledge is available for many BBB transporters, limiting the
design of compounds that will interact with these transporters as well as understanding mechanisms for increasing BBB permeability of neuroactive compounds. Thus
the use of docking/scoring, comparative modeling, and related approaches is difficult.
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Ligand-based methods can be used but are indirect since the transporter is inferred
from a series of ligands with experimentally derived affinity for a certain target.
These methods included QSAR, and techniques such as pharmacophore modeling
[172–174] using programs such as Catalyst (Accelrys, San Diego, CA) and DISCOtech
(Tripos). Also the popular 3D-QSAR technique of the SYBYL program (Tripos, St.
Louis, MO), comparative molecular field analysis (CoMFA) [175] is used. Homology
modeling has been applied to predict the structure of transporters such as the
P-glycoprotein structure, which was modeled by using the MsbA lipid tra nsporter
experimental structure. The resulting 3D model of the protein has been criticized
because it is certainly not very accurate and therefore should have low predictive
value [176]. A dopamine transporter (DAT), serotonin (SERT), and noradrenalin
(NET) models were defined using the crystal structure of Aquifex aeolicus LeuT(Aa)
[177]. Cocaine was docked into one binding pocket of DAT (corresponding to
the leucine-binding site in LeuT(Aa)), which involved several transmembrane
helices (TMHs). Clomipramine was docked into another binding pocket of DAT,
corresponding to the clomipramine-binding site in the crystal structure of a
Figure 2.14 Trifluoroperazine is a good brain permeator (log BB ¼ 1.44) with calculated
properties slightly within the guidelines for CNS compounds (MW ¼ 407.5, PSA ¼ 16,
log D ¼ 4.04, N þ O count ¼ 3). Alprazolam partitions equally between the brain and the
blood, with a log BB ¼ 0.04 (MW ¼ 308, PSA ¼ 49.5, log D ¼ 2.5, N þ O count ¼ 4) possibly
because the balance between hydrogen bonding and lipophilicity is not ideal. Indomethacin
is a poor brain permeator, with a log BB ¼1.26 (MW ¼ 357, PSA ¼ 78, log D ¼ 0.3,
N þ O count ¼ 5). The lack of penetration seems to be due to the carboxylic moiety, while
the hydrogen-bonding/lipophilicity balance is also against brain penetration [164, 171].
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LeuT(Aa)–clomipramine complex. The structures of the proposed cocaine- and
tricyclic antidepressant-binding sites were of particular interest for the design of novel
DAT interacting ligands.
Some packages for the prediction of BBB are listed in Table 2.6.
2.2.7 Transport Proteins
The exchange between body compartments often uses active transporters. Several
types have been identified and are not discussed in this chapter. About 40 proteins that
belong to the ABC and SLC superfamilies that influence absorption, distribution,
and excretion of drugs and xenobiotics are known. These transporters can modify PK
and PD parameters but also effect toxicity. In the ABC famil y (multidrug resistance,
ATP-binding cassette), P-gp is considered of significant importance. The multispecific solute carrier transporters are the other main superfamily of proteins that play
a key role in the human body. Transporters are integral membrane proteins with
primarily 12 transmembrane domains (some have some extra or less helices). The
classification also accounts for the type of energy source and the direction of the
transport. The energy source can be the hydrolysis of ATP or use of a voltage and/or
ion gradient to transport both ions and solutes. The coordination between transporters
and metabolic enzymes is important [43]. One of the best-studied transporters is P-gp,
encoded by human MDR1 gene, a member of the ABC superfamily of transport
proteins. The overexpression of P-gp is associated with a multidrug resistance
phenotype in various forms of cancer that cause a suboptimal efficacy of many
molecules. P-gp plays a significant role in the absorption, distribution, metabolism,
and excretion processes of a wide range of drugs [178]. For instance, P-gp has been
shown to limit oral absorption of paclitaxel and docetaxel, modulate hepatic, renal or
intestinal elimination, and restrict CNS entry of certain drugs (e.g., cyclosporin A,
digoxin, indinavir, ritonavir, saquinavir). Molecules can be substrates, inhibitors, or
inducers of P-gp. A better understanding of the relationships between the structure
of P-gp binders (substrate or inhibitor) has been obtained using QSAR, pharmacophore, and protein modeling, yet, data are still missing to fully capture the functioning
of this protein. Models are not sufficiently sophisticated to fully rationalize earlier
observations for well-known P-gp substrates in terms of MW, lipophilicity, hydrogen
bonding, presence of a basic nitrogen, but are useful to assist some of the drug design
TABLE 2.6 Packages for the Prediction of BBB
Software Package Website
ADME Boxes (P-gp) www.ap-algorithms.com
ACD/ADME suite www.acdlabs.com
ADMET Predictor www.simulationsplus.com
QikProp www.schrodinger.com
KnowItAll www.biorad.com
Volsurf www.moldiscovery.com
ChemSilico www.chemsilico.com
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steps [179, 180]. One study made use of the MolSurf program to generate descriptors
to build a PLS model to predict P-gp-associated ATPase activity [181]. This model
identified the main contributing descriptors for predicting ATPase activity as the size
of the molecular surface, polarizability, and hydrogen-bonding potential. In general,
as MW increases, the P-gp efflux ratio increases (MW > 400). As molecules become
larger, the permeability, on average, decreases, limiting oral and/or brain exposure,
and P-gp efflux becomes an additional complication-reducing exposure [145]. Ionization state plays only a minor role in determining the efflux when compared to MW.
Increasing the number of hydrogen bond acceptors appears to confer increasing
likelihood of P-gp efflux. Log P values have a weak nonlinear effect. Example of
programs able to predict P-gp binders are implemented in ADME Boxes (www.apalgorithms.com) and ACD/ADME suite (www.acdlabs.com). A machine learning
method, SVM, was explored for the prediction of P-gp substrates. About 120
substrates and 80 nonsubstrates were collected from the literature and 22 final
descriptors were used to develop the model [182]. Although the prediction accuracies
were reasonably good, the structural features responsible for discriminating the two
groups of molecules could not be identified. An unsupervised machine learning
approach based on the Kohonen self-organizing maps was explored to classify drugs
as P-gp substrates or inhibitors [183]. As for the SVM model, the P-gp SOM model
was unable to provide hints on structural modifications that should be pursued to
modify P-gp binding. The X-ray structure of multidrug efflux pump P-gp was recently
reported (mouse P-gp solved at 3.8 A˚resolution, PDB entry 3G60) by Aller et
al. [184]. The relatively low resolution and as a nucleotide-free state suggest that the
structure may represent a crystallization artifact or a nonfunctional conformation that
has only very transient existence, impeding its use for ADME/Tox prediction [185]
(Figure 2.15). Other transporters potentially involved in limiting the oral uptake of
drugs include the MDR-associated proteins MRP1 and MRP2, and the recently
discovered breast-cancer-resistance protein (BCRP). It will be important to expand
our knowledge about these transporters in order to understand their effects on
pharmacokinetics, pharmacodynamics, and toxicity. Currently, knowledge about
these transport systems is relatively poor compared to P-gp.
2.2.8 Plasma Protein Binding
The binding of drugs to plasma proteins and to human serum albumin (HSA) in
particular has ADME/Tox implications affecting clearance, volume of distribution,
and efficacy. Drugs can bind to a variety of particles in the blood, including red blood
cells, leukocytes, and platelets, as well as proteins HSA (particularly acidic drugs),
a1-acid glycoprotein (AAG) (basic drugs), lipoproteins (neutral and basic drugs),
erythrocytes, and a ,b,g -globulins. From a recent analysis of 2939 diverse drugs with
in vitro plasma protein binding data, it has been shown that as MW increases, plasma
protein binding on average increases (molecules with MW between 500 and 700 are
98.2% bound) [145]. In terms of ionization state, binding to plasma proteins follow
the trend acids > neutrals > zwiterrions > bases. Lipophilicity is a key contributor to
the extent of binding.
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HSA binding has been intensively investigated [41, 186, 187]. HSA is a singlechain protein that contains several binding sites for drugs, including site I and site II
(indole-benzodiazepine site) (Figures 2.16 and 2.17). Site I compounds seem to be
dicarboxylic acids and/ or bulky heterocyclic molecules with a negative charge
localized in the middle of the structure. The site appears to be large and can
accommodate large ligands such as bilirubin. Site II seems to be smaller, and ligands
are often aromatic carboxylic acids with a negatively charged acidic group at one end
of the molecular removed from a hydrophobic center.
Blood levels for plasma proteins are increased or decreased under different
conditions. The level of AAG is increased in neoplasms, while HSA is markedly
depressed in progressive malignancies [187].
The experimental structure of AAG has been solved recently at 1.8 resolution
(PDB file 3BX6) [188]. This protein belongs to the lipocalin family in which the
beta-barrel, built up from eight antiparallel b-strands encloses a central cavity
(Figure 2.18). The AAG cavity is divided into three distinct pockets—the central,
deep hydrophobic site and the two adjacent smaller and negatively charged sites. The
cavity allows for the binding of both apolar and basic ligands. AAG binds to more than
300 pharmaceutical agents. The majority are basic compounds such as beta-blockers,
but neutral and acidic molecules such as steroid hormones or the anticoagulant
coumarin molecules may also bind. Genetic variability of AAG adds even more
variability to its ligand-binding properties [187].
Figure 2.15 Ribbon drawing of mouse P-gp embedded in a membrane bilayer (PDB file
3G60). The inhibitor QZ59-SSS bound in the transmembrane region is shown as a stick model
(in circle).
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Figure 2.17 Examples of molecules binding to HSA. Site I (a) warfarin (anticoagulant);
(b) indometacin (nonsteroidal anti-inflammatory drug, NSAID). Site II (c) naproxen (NSAID);
(d) diazepam (valium).
Figure 2.16 Warfarin bound to the site I of HSA (PDB file 1H9Z).
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Several studies were reported that attempt to predict HSA binding using known
HSA binders and QSAR [189, 190]. Several software packages known to predict
binding to plasma proteins are listed in Table 2.7.
2.2.9 Metabolism
Drug metabolism is traditionally divided into Phase I and Phase II processes. This
classical division while useful is neither absolute nor definitive. Phase I enzymes
include cytochrome P450 monooxygenase (CYPs), azo and nitro group reductase,
monoamine oxidase, while Phase II enzymes involve N- and O-methyl transferases,
D-glucuronic acid transferase, glutathione transferase, and sulfate transferase. CYP
enzymes have been extensively investigated because they play a pivotal role in drug
Figure 2.18 Cartoon diagram of AAG in complex with 2-amino-2-hydroxymethyl-propane1,3-diol (tris) highlighting the relatively large binding cavity.
TABLE 2.7 Packages for the Prediction of Plasma Protein Binding
Software Package Website
ChemSilico www.chemsilico.com
ACD/Labs www.acdlabs.com
ADMET Predictor www.simulationsplus.com
ADME Boxes www.ap-algorithms.com
QikProp www.schrodinger.com
KnowItAll www.biorad.com
DSMedChem www.accelrys.com
q-Albumin QuantumLead, www.q-lead.com
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metabolism. CYP is a heme-containing superfamily of enzymes consisting of
isoenzymes and catalyze several oxidation reactions (Figure 2.19). The most important forms in man are CYP2D6, CYP2C9, CYP3A4, CYP1A1, CYP2C19, and
CYP2E1. More than 90% of all marketed drugs are subjected to metabolic reactions
by at least one of the CYP enzymes. The abundance of CYPs in many organs/tissues is
established in the gut and intestinal tissues. Genetic diversity issues have been
reported for this family usually causing decreased or missing enzymatic activity.
Gene duplication can occur and lead to increased activity [191]. Thus, it is becoming
imperative to recognize individuals who could have such genetic diversities. Certain
substances can induce increased expression of CYPs via binding to nuclear receptors.
For example, known inducers of CYP3A4 bind to RXR receptor tamoxifen. Conversely, xenobiotics may block specific CYPs and cause drug–drug interactions.
Numerous X-ray structures of CYPs have been reported and will assist to rationalize
compound binding to these proteins [17, 192].
Simple CYP alerts have been developed over the years and may assist the hitfinding stage but not during optimization. CYP3A4 usually binds lipophilic molecules
and has a large flexible binding site. CYP2D6 has bases with aromatic groups 5–7 A
˚
removed from the basic center similar to tricyclic antidepressants. CYP2C9 tend to
interact with amphipathic molecules and favor acidic compounds like some nonsteroidal anti-inflammatory agents. Numerous computer studies have been performed
on diverse CYP enzymes using small datasets of compounds [17, 145, 193].
Figure 2.19 CYP2C9 cocrystallized with warfarin (PDB file 1OG5). The substrate-binding
pocket is essentially hydrophobic and the compound is at about 10 A˚from the heme group. See
insert for color representation of this figure.
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Several in silico packages and databases to predict CYP metabolism are listed in
Table 2.8.
2.2.10 Elimination
Elimination of drugs usually occurs via the liver and the kidney but the lungs, skin, and
so on, also play a role. Typically, lipophilic compounds tend to be eliminated by the
liver, whereas hydrophilic molecules undergo renal clearance. There has been very
little in silico modeling or prediction of excretion. Passive excretion can theoretically
be predicted using some of the approaches described above for tissue distribution
since similar physicochemical and physiological properties (blood flow, protein
binding, lipophilicity, pK
a
) with different limits (glomerular filtration and molecular
weight) may be used. In silico packages to predict drug clearance with only molecular
structure input data are still under development.
2.2.11 Toxicity
In the pharmaceutical industry, there is a concern that close to 20% (and possibly
more) of drug attrition during preclinical and clinical development is due to toxicity
issues. Because of the relatively small number of patients enrolled in clinical trials,
it is statistically difficult to detect rare adverse reactions. Thus, all toxicity issues are
not noticed during clinical trials, thereby causing major problem in today’s drug
discovery endeavors [32, 53].
Toxicity effects can be divided in at least four categories according to the
pathological effect induced:
.
cell death (apoptosis and necrosis) and tissue injury, which is probably the most
common response;
.
cancer;
.
altered phenotype/function relating to cell alterations;
.
immunological hypersensitivity, causing downregulation of the immune system
or producing autoimmunity to native proteins [194].
TABLE 2.8 Packages for the Prediction of CYP Metabolism
Software Package Website
MetabolExpert www.compudrug.com
META multicase www.multicase.com
MetaSite www.moldiscovery.com
KnowItAll www.biorad.com
Meteor www.lhasalimited.org
MDL database Metabolite www.mdl.com
SimCYP www.simcyp.com
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