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higher MW, greater number of H-bond acceptors/donors, rotatable bonds and rings
than orally available molecules.
The rule-of-5 concept was recently reinvestigated, but this time, with the goal of
designing drugs that will be administered via the inhaled/intranasal routes. After
analysis of 81 marketed respiratory drugs, Ritchie et al. found that these molecules
have significantly higher hydrogen bonding and polar area, significantly higher MW
(yet, the significance disappears if glucocorticoids are excluded), a trend toward lower
lipophilicity but no difference in rotatable bonds and total ring count [114]. Overall,
the rule-of-5 descriptors are intuitively appealing. For instance, log P is an important
physicochemical parameter for oral absorption, since it relates to solubility and
influences the ability of a compound to permeate through cell membr anes including
those of the intestinal epithelial cells. Too hydrophilic compounds (negative log P) are
not able to pass through membranes, as they hardly enter the hydrophobic interior
(mimicked by the octanol phase in the octanol/water system) of the lipophilic bilayer.
Too lipophilic compounds (high log P) tend to be insoluble and also poorly permeate
through membranes, as they get stuck in the lipophilic bilayer. Molecular weight is
an important parameter indicating the size of the molecule. Too large molecules have
obviously difficulties to passively diffuse through membranes. A recent analysis of
antibacterial compounds was reported and indicates that these molecules occupy a
unique property space different as compared to drugs of other therapeutic areas. The
rule-of-5 do not apply to these molecules [115].
Several opposing arguments have been given against these rule-based counting
methods. For example, one argument against the rule-of-5 was that the property
profile of existing successful drugs only highlights a historical artifact. Analysis of
reported drugs provide information about properties of more “simple drugs of the
past” aimed at acting on more tractable targets that are not representative of the greater
complexity of modern drugs, required to interact with more challenging biological
targets. Also, an ambiguity that fueled further the debate against the rule-of-5 was
that the Lipinski’s dataset contained compounds that did make it to the market along
with compounds that failed in Phase 2 or 3. Further, do preclinical and Phase 1 or 2
studies truly eliminate compounds with poorer ADME characteristics? Also, the work
assumed that passive diffusion was the dominant process for passage across membranes, while in many cases, specific transporters play a role. Although these simple
rules can be criticized, they still tend to be supported today as the descriptors used to
profile the compounds capture some of the essential futures of the ADME/Tox
issues [60, 66]. However, the relationship between log P and ADME/Tox properties
may be the result of false perceptions, lack of knowledge, or oversimplifications [77].
Therefore, these rules are guidelines to help selecting molecules with desirable
physical properties. They are not intended to form a dogma and have to be tailored
according to the project, target and therapeutic area, and enhanced with new
approaches and concepts as our awareness over ADME/Tox matters increas es. Yet,
with our present knowledge about formulation, it does seem, independent of the
underlying molecular mechanisms, that the fate of compounds with excessive
molecular weight (>500) or log P (>5) tends to be heavily disfavored in the latter
stages of clinical development [9].
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Using simple counting methods to predict drug-likeness is often used in the
literature (e.g., see a recent review by Ritchie and Macdonald about counting aromatic
rings [116]). The structural and physicochemical properties of a compound may
determine its pharmacokinetic and metabolic behavior in the body (Table 2.2). For
example, the REOS system is a hybrid computer method that combines a set of
functional group filters (see Sections 2.2.11 and 2.3) with some simple counting
schemes similar to those used in the rule-of-5 [108]. Several parameters result from
modifications of the rule-of-5, while others, such as formal charges, come from
observations that benzamidine moiety (e.g., positively charged) tends to prevent
absorption, thus formal charges are often investigated. Number of heavy atoms can
also be important and the value range usually comes from the analysis of drugs present
on the market.
Several computer packages perform similar investigations (see Section 2.3)
including FAF-Drugs2 [117], Screening-Assistant [118, 119], or Filter (OpenEye
Scientific Software).
It is important to note that different authors are using the term “drug-like” slightly
differently [35]. Drug-likeness literally means similarity to known drugs. Considering
the diversity of mechanisms of action and properties of the known drugs and the
different administration routes, it is not surprising that the drug-like term is difficult to
define precisely and that there are multiple approaches to its assessment. Walters and
Murcko define “drug-like” compounds as “molecules that contain functional groups
and/or have physical properties consistent with the majority of known drugs” [24].
Lipinski defines drug-like “as those compounds that have sufficiently acceptable
ADME properties and sufficiently acceptable toxicity prope rties to survive through
the completion of human Phase 1 clinical trials.” In addition to favorable physical
properties that should translate into good absorption, distribution, metabolism,
excretion, and toxicity profiles of drug-like molecules, the synthetic accessibility
of the compound and its analogs is also considered an important aspect of drug-like
molecules. Further, the filtering techniques described above provide a convenient
and efficient means for identifying compounds that an experienced medicinal chemist
would tend to avoid. However, these methods do not necessarily identify molecules
that could be considered as interesting hits/leads. Thus, for numerous projects, in
addition to eliminating molecules containing undesirable fragments, it is also
TABLE 2.2 Default Values for the REOS Filtering Tool
Property Minimum Maximum
MW 200 500
log P 55
Hydrogen-bond donors 0 5
Hydrogen-bond acceptors 0 10
Formal charge 22
Number of rotatable bonds 0 8
Number of heavy atoms 15 50
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important to ensure that molecules in a screening collection contain functionality
known to impart biological activity. One example of this concept reported by Muegge
and coworkers [120] involves the idea of privileged structures. In this work, each
molecule was assigned a score on the basis of the presence of structural fragments
typically found in drugs; the fragments used in this study are shown in Figure 2.12.
A molecule is given one point for each nonoverlapping fragment. Molecules with a
score between two and seven are classified as drugs, otherwise they are classified as
nondrugs.
Lead-likeness versus drug-likeness is another key concept. Drug-likeness is a
property that is most often used to characterize compound libraries that are screened
to find novel hit/lead chemicals. Yet drugs are typically the endpoints of a medicinal
chemistry optimization program. Teague et al. pointed out that hits/leads may be
classified into three categories: (i) low-affinity compounds with low MW and log P,
(ii) high affinity and high MW including peptides and natural products that need
improved pharmacokinetic profiles, and (iii) low affinity with MW between 300 and
500 and log P between 3 and 5 [61]. Most of the HTS hits belong to category (iii).
Optimization of these hits/leads is difficult because most often, hydrophobic groups
are added during the optimization to increase potency of the compounds. Thus, in
general, lead compounds become larger and more lipophilic during the optimization
process. Teague et al. estimate that the MW increases by up to 200 Da while the log P
increases by 0.5–4 U during the optimization. Taking these observations into account,
it may be more desirable to bias libraries toward “lead-like” compounds rather than
toward fully optimized drug-like compounds [121–125]. These authors suggested to
bias screening libraries to have MW < 350 Da and computed log P between 1 and 3.
Congreve et al. [62] suggested the following values to profile a compound collection
toward “hit discovery,” MW < 300, HBD 3; HBA 3, calculated log P ¼ 3 with
some authors suggesting a polar surface area around 60 A
˚
2
and a few rotatable bonds.
There are differing philosophies as to the introduction of filtering methods
(drug-like, lead-like, ... etc.) in the drug-discovery process. One idea suggests
Figure 2.12 Chemical groups commonly found in drugs.
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that as compounds undergo significant changes during the lead-optimization
process, it might be premature to use predictive ADME/Tox in compound selection
at the screening stage. However, analyses of the progress of a number of drugdiscovery programs have indicated that drug candidates are typically larger and
more lipophilic than the initial lead. On the basis of these observations, it may be
beneficial to select screening compounds that are predicted to be soluble and orally
bioavailable.
As discussed above, properties implicit to the drug-likeness of molecules such as
oral bioavailability or membrane permeability have often been connected to simple
molecular descriptors such as log P, MW, or the counts of hydrogen-bond acceptors
and donors. Other simple counting criterion for drug-likeness is an atom filter.
Typically, compounds with atoms other than C, N, O, S, H, P, Cl, Br, F, I, Na, K,
Mg, Ca, Li are removed from a compound selection (some authors tend also to avoid
Br). This criterion discriminates, for example, against antineoplastics, antiacids, or
vitamins that contain Pt, As, Al, Si, Fe, or Co. Some authors combine several of the
rules mentioned above; these are then generally referred to as the extended rule-of-5.
These counting methods are generally used to filter or clean a collection and tend to
give a yes/no answer or binning as poor/medium/good. More complex models build
through mathematical modeling or docking/scoring and can be used at later stages of
the drug discovery process [108]. In most cases, the above-mentioned rules were
developed by translating the collected knowledge of scientists involved in drug
discovery into a computer program. An alternative approach is to use two sets of
compounds—one labeled as drugs and the other as nondrugs—and allow the method
to learn to distinguish the two classes (see above the different classification methods
and Section 2.3). This method is less dependent on experts’ point of view, but the
outputs also have to be analyzed with care to ensure that the models are meaningful
(see below).
2.2.5 Lipophilicity
Assessing the pharmacokinetic structure–activity relationships throughout these
past 30 years identified several essential physicochemical properties to control in
drug development, including the numbers of hydrogen bond-donors and acceptors,
polar surface area, the number of rotatable bonds, molecular weight, and lipophilicity.
These parameters are often interrelated although arguably lipophilicity is the most
wide reaching [64].
The IUPAC (International Union of Pure and Applied Chemistry) definition of
lipophilicity is “Lipophilicity represents the affinity of a molecule or part of it for a
lipophilic environment. It is commonly measured by its distribution behavior in
a biphasic system, either liquid-liquid (e.g., partition coefficient in octanol/water).”
A small amount of lipophilicity is required in almost all drugs to facilitate their
passage from aqueous environments (e.g., gastric fluid, blood, extra-cellular fluid)
into tissue compartments and cells. Lipophilicity is implicated in numerous
biological events (e.g., decreased metabolic stability in liver microsomes when log
P is increasing).
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Lipophilicity for organic compound is often expressed as a partition or distribution
coefficient (P, log P, or log D) between octanol (usually octan-1-ol) and aqueous
phases. The partition coefficient P is a ratio of concentrations of unionized compound
between these two solutions. To measure the partition coefficient of ionizable solutes,
the pH of the aqueous phase is adjusted such that the predominant form of the
compound is unionized. The logarithm of the ratio of the concentrations of the
unionized solute in the solvents is called log P and is commonly used in drug
discovery.
The distribution coefficient D is defined as the overall ratio of a compound (ionized
and nonionized) between the two phases. This value log D is pH-dependent, and hence
one must specify the pH at which the log D is measured. For unionizable compounds,
log P ¼ log D at any pH.
Lipophilicity significantly impacts ADME/Tox properties and is widely used in
drug discovery for quantitative SAR and quantitative structure–property relationship
studies. Highly lipophilic compounds (log P > 5) tend to have high potency due to
nonspecific binding, but they are also more vulnerable to metabolism, leading to high
hepatic clearance, they have low solubility, erratic oral absorption and high plasma
protein binding, and they are more likely to bioaccumulate [56]. A compound with
moderate lipophilicity (log P between 0 and 3) has a good balance between solubility
and permeability and is optimal for oral absorption, cell-membrane permeation in
cell-based assays, is generally good for BBB penetration, and has usually low
metabolic liability. Hydrophilic compounds (log P < 0) have good solubility, but
poor permeability for gastroi ntestinal (GI) or BBB penetration, and are more
susceptible to renal clearance. Lipophilicity can be enhanced by increasing the
molecular size and decreasing the hydrogen-bonding capacity.
It is necessary to account for the ionic state of the compound when describing the
lipophilicity of a potential drug. Ionization results in decreased lipophilicity with
respect to the neutral state. The changing pH environment in the body indicates that
the compounds will often be found as a mixture of ionic species. While biochemical
mechanisms maintain the pH of blood at 7.4, the pH along the GI tract varies from the
stomach (fasted pH 1–2, fed pH 3–7) to the colon (pH 5–8). Oral drug absorption
occurs in the small intestine at pH around 5.5. As such, it has been suggested to use
log D in place of log P when filtering compounds via the rule-of-5 and other druglikeness tools [126].
In a recent review, Mannhold et al. benchmarked most of the existing in silico
methods to compute log P [127]. Numerous methods can be used, including
substructure-based methods in which molecules are cut into fragments (fragmental
methods) or down to the single-atom level (atom-based methods), and a final
summing of the substructure contributions gives the final log P (i.e., problems arise
when fragments are missing or when atomic contributions cannot be parameterized
properly). Property-based approaches can also be used. These methods utilize
descriptions of the entire molecule and comprise (i) empirical approaches and
methods that use 3D structure representation and (ii) methods based on topological
descriptors, using diverse types of statistical approaches. Some of these tools to
predict log P (and log D and log S (see below) [128]) are listed in Table 2.3.
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2.2.6 pK
a
pKais the ionization constant of a compound [129]. More than 60% of marketed drugs
are ionizable. pK
a
affects solubility, permeability, log D, and oral absorption by
TABLE 2.3 Examples of In Silico Tools to Predict log P (and log D) and Other ADME
Values: Solubility (Sol), HIA, Caco-2 (C2), Oral Bioavailability (OB)
Programs log P log D Sol HIA C2 OB URL
AB/log P Y
a
http://www.ap-algorithms.com
ABSOLV, LSER Y http://www.ap-algorithms.com
ACD/log P Y Y Y http://www.acdlabs.com
ADME Boxes Y Y Y Y Y Y http://www.ap-algorithms.com
ADMEWORKS Y Y Y http://www.fqs.pl/life_science/
admeworks_predictor
ALOGP Y Y http://www.talete.mi.it
Discovery Studio Y Y Y http://www.accelrys.com
ALOGPS Y Y http://www.vcclab.org
Chemaxon Y Y http://www.chemaxon.com
CLOGP Y http://www.biobyte.com,
http://www.daylight.com
COSMOFrag Y http://www.cosmologic.de
CSLogP Y http://www.chemsilico.com
HINT Y http://www.edusoft-lc.com
KowWIN Y http://www.syrres.com
KnowItAll Y Y Y Y Y http://www.knowitall.com
MiLogP Y http://www.molinspiration.com
MLOGP Y http://www.talete.mi.it
MLOGP(SR) Y http://www.simulations-plus.com
MolLogP Y http://www.molsoft.com
NCR þ NHET Y http://www.vcclab.org
OsirisP Y http://www.actelion.com
PreADME Y Y Y Y http://camd.ssu.ac.kr/adme
QikProp Y Y Y Y http://www.schrodinger.com
Quantum
Pharmaceuticals
Y Y Y Y http://q-pharm.com
SRlogP, ADMET
predictor
Y Y Y Y http://www.simulations-plus.com
SLIPPER-2002 Y http://camd.ipac.ac.ru
SPARC Y Y http://ibmlc2.chem.uga.edu
TLOGP Y http://www.upstream.ch
VEGA Y http://www.ddl.unimi.it
VLOGP Y http://www.accelrys.com
Volsurf/Volsurf þ Y Y Y http://www.moldiscovery.com
XLOGP2 Y ftp://ftp2.ipc.pku.edu.cn
XLOGP3 Y http://sioc-ccbg.ac.cn
a
Y means the tool performs this prediction.
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modulating the distribution of neutral and charged species. Acidic compounds tend to
be more soluble and less permeable at high pHs and basic compounds tend to be more
soluble and less permeable at low pH. pK
a
impacts biological activity and metabolism
through electrostatic interaction. For example, CYP2D6 typically metabolizes nitrogen-containing bases where the basic nitrogen is 5–7 A˚from the site of
metabolism.
Prediction can be done through applying a mechanistic perturbation method to
estimate the pK
a
value based on a number of models that account for electronic effects,
solvation effects, hydrogen-bonding effects, and the influence of temperature (e.g.,
SPARC). One of the most common techniques used in pK
a
prediction is quantitative
structure activity/property relationships deriving their fit equations from partial least
squares or multiple linear regression. Other methods include neural networks,
quantum mechanical continuum, solvation models, and anticonnectivity models
(see a recent review by Meloun and Bordovska [130]). Unfortunately, these authors
note that to date no reliable method for predicting pK
a
values over a wide range of
molecular structures has been made available, although some recent tools could be
more efficient but independent benchmarking is missing.
There are several available packages for the prediction of pK
a
values (Table 2.4).
Two of the software programs are freely usable online (SPARC and VCCLAB). A
decision tree model based on a novel set of SMARTS strings has also been recently
reported [132]. Protonation states can also be assigned using simple rules in the
OpenBabel open source package (http://openbabel.org/) and with SPORES [133].
A recent Web application for studying the protonation states of protein–ligand
complexes (and also the free energy of binding) has been reported (http://hinttools
.isbdd.vcu.edu/CT). This method should assist in investigating molecules and preparing files for ADME/Tox predictions and virtual screening computations [134].
2.2.6.1 Absorption, Solubility, Permeability, Bioavailability The oral absorp-
tion of a drug depends on the free concentration in the gastric fluid and the subsequent
TABLE 2.4 Software Available for pKaValue Prediction
Software Website
ACD/Labs www.acdlabs.com
ADME Boxes www.ap-algorithms.com
MOE www.chemcomp.com
ADMET Predictor www.simulationsplus.com
ChemAxon www.chemaxon.com
CS pK
a
www.chemsilico.com
PALLAS www.compudrug.com
Pipeline Pilot www.scitegic.com
Epik www.schrodinger.com [131]
QikProp www.schrodinger.com
SPARC ibmlc2.chem.uga.edu/sparc
MoKa www.moldiscovery.com
VCCLAB www.vcclab.org
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membrane permeation to reach the blood. Overall, it is commonly accepted that
solubility, permeability, and bioavailability are all related and interdependent [50].
The definitions for intestinal absorption, permeability, fraction absorbed, and bioavailability are used interchangeably [135]. Several in silico models and computer
tools are available to predict oral absorption. These models can be relatively simple
and involve only a few descriptors, such as log P, log D, or polar surface area.
Alternatively, they can make use of complex mathematical modeling and even
docking/scoring into some specific receptors, while pharmacophore modeling,
ligand-based approaches, and simulation of diffusion across a biological membrane
at atomic resolution (Table 2.3) can also be applied. Many reactions/events have to be
considered, such as active transport (transporters), efflux pumps (e.g., P-gp), cell
permeability predictions (Caco-2, PAMPA), as well as physicochemical properties
such as pK
a
, size, polar surface area, rotatable bonds, and H-bond donors and
acceptors. Predictions are extremely difficult even at this stage. Computer packages
typically assume that the compounds are passively absorbed, while methods considering active transport or efflux are still under development.
The rule-of-5 is considered to relate to intestinal absorption and can thus be used
for this purpose. For instance, using QSPR approaches and 17 diverse drug compounds, the following equation was proposed (see reviews such as [20, 136, 137]):
log Permeability ¼½0:008ðMWÞ ½0:043ðPSAÞ 5:165
Absorption–simulation programs, such as GastroPlus (www.simulations-plus
.com), are valuable tools in lead optimization as they require only a limited number
of in vitro input data [20, 26, 138]. The OraSpotter program (www.zyxbio.com) uses
only information about the molecular structures, which are transformed into SMILES
strings. The descriptors are then evaluated from the SMILES data.
Solubility Solubility affects both in vitro assa y results and in vivo oral bioavailability. Compounds with poor aqueous solubility may precipitate during in vitro
assays and result in lower concentration than was anticipated. Insoluble compounds
tend to give erratic assay results. Poor aqueous solubility is also one of the major
causes for low systemic exposure and, consequently, lack of in vivo activity.
Therefore, screening of solubility early in drug discovery is of great importance
[56, 139]. Different methods can be used to model solubility such as fragment-based
models that sum substructure contributions and models based on log P, on solvation
properties, and hybrid models (e.g., neural network models using log P in combination with topological and quantum chemical descriptors [140]) (Table 2.3)
[141, 142]. Empirically, it appears that most drugs have a log S ranging from 1
to 5, reflecting a compromise between the polarity needed for reasonable aqueous
solubility and the hydrophobicity needed for acceptable diffusion across the membrane [142]. Possible ways to improve solubility involve adding ionizable groups,
hydrogen bonding/polar groups, and reduce log P and MW. A Web application [143]
to predict pH-dependent aqueous solubility of drug-like molecules (pHSol) is
available at http://www.cbs.dtu.dk/services/pHSol/.
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Permeability Permeability is an important factor for passage through cell membranes in cell-based assays, absorption through the GI tract, and penetration through
the blood–bra in barrier and through other physiological barriers (Figure 2.13). There
are several transport mechanisms involved for small molecules: transcellular passive
diffusion, paracellular passive transport, and active/carrier-mediated and efflux [144].
The two most important pathways for drug absorption are transcellular passive
diffusion and efflux transport by P-gp or multidrug-resist ant proteins. Transcellular
diffusion is driven by the concentration gradient, while efflux transport is energydriven. The paracellular route is only available in the small intestine, while transcellular absorption can take place throughout the length of the GI tract. The GI tract also
contains drug metabolism enzymes (e.g., CYP3A4, sulfotransferases) and efflux
pumps (in particular P-gp) that decrease the absorption of compounds. Investigation
of these mechanisms is complex, in vitro assays, such as Caco-2 cells (include
transporters) and PAMPA (parallel artificial membrane permeability assay analyzes
passive transports) have been developed for this purpose and can be somewhat
simulated in silico. P-gp-efflux is an important mechanism that nature uses to prevent
entrance of toxic substances. P-gp is abundant in cells with protective barriers. It plays
a key role in drug resistance in chemotherapy, BBB penetration, and oral absorption.
It also plays an important role in metabolism, where P-gp and CYP3A4 work in
concert to eliminate certain drugs. This is named “efflux-metabolism alliance” in
which both act on a wide variety of xenobiotics and overlap for substrate
specificity and tissue location. There is a strong relationship between solubility and
lipophilicity—when log P increases, solubility tends to decrease [145]. In general,
when one has to choose between improving solubility or permeability, preference
should be given to permeability, because solubility can often be improved through
formulation [23, 146, 147]. Permeability can be improved by modification of structure
or by prodrug approaches.
Prediction of effective permeability can be performed through the use of different
methods (Table 2.3). The rule-of-5 and different types of polar surface area models
Figure 2.13 Simplified view of some permeability mechanisms.
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can be used (e.g., well-absorbed compounds have a log P between 1 and 5.9 and a
polar surface area between 0 and 132 A
˚
2
) [144]. One study reported that
Permeability ¼2:546 0:011ðPSAÞ0:278ðHBDÞ½136
Models using both linear and nonlinear relationships have also been used to model
intestinal permeability [144]. Possible ways to improve permeability involve the
removal of ionizable groups, adding hydrophobic groups, reduce size, hydrogen
bonding, and polarity (high PSA suggests many hydrogen bonds to water and may
hinder facile passa ge into membranes).
Bioavailability The oral bioavailability of a molecule is a complex PK parameter
that comprises absorption (solubility, permeability) and clearance. For all these
factors, ionization of the molecules tends to play an important role, but this state
is difficult to predict in silico [50]. Many methods have been developed and reviewed
to predict solubility [137, 141, 142, 148], permeability [144, 149], and/or absorption [43, 84, 150–153] (Table 2.3). In 2002, Veber et al. reported the findings from a
study of rat availability data, acquired by GlaxoSmithKline, for 1100 drug candidates [112]. They found that molecules possessing fewer than 10 rotatable bonds and
having a polar surface area less than 140 A
˚
2
(or H-bond count less than 12) generally
showed oral bioavailability in rats exceeding 20%. In 2004, Lu and coworkers
examined the relationship of rotatable bond count and polar surface area (PSA) with
the oral bioavailability in rats for 434 compounds and found that the correlations were
dependent on the calculation methods [154]. Martin proposed a score to predict
bioavailability based on several molecular properties, including polar surface area,
rule-of-5, and molecular charged state [155]. Generally, the work of Veber, Lu, and
Martin support the same conclusion that bioavailability may be well predicted by
simple molecular properties. However, Hou et al. found that the prediction of
intestinal absorption was possible with “rule-based” and counting methods and
cautioned the prediction of human bioavailability with these approaches [156]. They
also suggested that statistical methods could provide better results. Often, prediction
models of oral bioavailability are based on QSAR/QSPR analysis [47]. A possible
method to evaluate bioavailability according to Van der Waterbeemd [20, 21] is
Oral bioavailability ¼ f ðlog D; molecular size; H-bond capacityÞ
Distribution Distribution of drugs throughout the body is important since it
determines whether the molecule will elicit a pharmacological response. Distribution
parameters that can be investigated in silico include blood–brain barrier permeability,
plasma protein binding (PPB), volume of distribution, and transporters. BBB is an
essential property for drugs that target the CNS such as anxiolytics and antidepressants. The protein human serum albumin is present in many tissues and is responsible
for unspecific drug binding (PPB). Yet, other proteins (low-density lipoprotein, highdensity lipopro tein, a1-acid glycoprotein, myosin, actin, etc.) are known to also bind
drugs in a nonspecific manner. Nonspecific protein binding should be carefully
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