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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5849_Библиотеки_им_академика_М_И_Перельмана
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PHARMACOKINETICS
Pharmacokinetics is the study of the time course of a drug within the body and it
incorporates the ADME processes. The study of absorption, distribution, metabolism, and excretion of a molecule results in the generation of a number of
pharmacokinetic parameters that characterize that compound. The PK parameters
are based on the measurement of drug concentrations in blood or plasma. Several
important PK terms are investigated such as volume of distribution, clearance, and
half-life.
Effective intestinal permeability, P
eff
, measures how fast a compound crosses
apical membrane of the intestine and enters the cytosol. The fraction or percentage
of the dose absorbed (A or FA, fraction absorbed) is a measure of extent of oral
absorption (includes all processes from the dissolution to its transport across the
apical membrane of the epithelial barrier of the intestine). Bioavailability (F) is the
fraction or percentage of the dose available in the systemic circulation and is
equal to FA only if metabolism terms are zero. Drugs that are targeted to the central
nervous system (CNS) also need to cross the blood–brain barrier (BBB). The
volume of distribution (V
d
) is a theoretical concept that relates the administered
dose with the initial concentration (C
0
) present in the circulatory system.
V
d
¼ Dose=C
0
Clearance (Cl) of the drug from the body mainly takes place via the liver (by
hepatic clearance or metabolism, and by biliary excretion) and the kidney (by renal
excretion). When plotting the plasma concentration against time, the area under
the curve (AUC) relates to dose, bioavailability, and clearance.
AUC ¼ F Dose=Cl
The combined parameter half-life (t
1/2
), the time taken for the drug concentration in the plasma to reduce by 50%, is a function of the clearance and volume of
distribution, and reflects how often a drug needs to be administered.
Human t
1=2
¼ 0:693 Vd=Cl
METABOLISM
Most drugs cannot be eliminated from the body without previous biotransformation to metabolites. The key sites in the body for metabolism are the gut wall and
considered to be detrimental for the progression of a compound toward a drug.
Antitargets involve, for instance, hERG, CYPs, some GPCRs, P-gp, and PRX,
among others.
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2.2.3 In Silico ADME/Tox Methods and Modeling Approaches
ADME/Toxproperties are very difficult to predict, that is, human intestinal absorption
or metabolic stability arises from multiple physiological mechanisms difficult to
model. To manage this biological complexity, simplifications have to be introduced
and numerous methods have been developed during these past years in an attempt to
predict some of these properties.
In silico modeling of ADME/Tox properties can be performed using many
different approaches. These range from the so-called rules of thumb (e.g., rule-of-5,
polar surface area) to quantitative prediction approaches: QSAR and quantitative
structure–property relationship (QSPR) up to classification models, and similarity
searches, molecular modeling (struc ture-based approaches such as ligand–protein
docking, pharmacophore modeling, substructure, quantum mechanics) and physiologically based pharmacokinetic (PBPK) modeling [74, 75, 81–83] (Figures 2.9–2.11).
While PBPK models have received a lot of attention because they may provide
valuable information on how various factors influence PK, they are not be discussed
because these methods usually need experimental data and cannot be developed
solely from the molecular structures of the compounds (see for instance Ref. 84).
The assumption with many of these approaches is that there is always a function that
correlates biological properties with chemical structure.
Molecular modeling approaches can be used when the underlying ADME/Tox
mechanisms are relatively well understood (e.g., docking into a CYP experimental
structure or docking compounds into an hERG, human ether-a-go-go channel, 3D
structure built by comparative modeling methods). QSAR modeling includes several
steps (e.g., see Figure 2.9): data collection (a training set), descriptor generation and
selection (e.g., descriptors to charact erize compounds such as molecular properties,
fingerprints, etc.), a statistical model (multiple linear regression, neural networks,
etc.) to relate the target property (e.g., solubility) to the descriptors, generation of the
model and finally, validation of the model on a test set. To illustrate this point, one can
take a theoretical example of QSAR modeling. In this situation, several descriptors
the liver. Metabolic alteration of xenobiotics is traditionally subdivided into two
main phases: Phase I (oxidation, often via cytochrome P450 enzymes, reduction,
and hydrolysis) results in the introduction of new functional groups, while in
Phase II (conjugation reactions), highly hydrophilic moiety such as sulfate or
glucuronide is attached to make the compounds more water-soluble and to prepare
for excretion through urine and bile. The most important enzymes involved in
metabolism in humans are the cytochrome P450s: CYP3A4, CYP2D6, CYP2C9,
and CYP2C19. Metabolism is species-dependent and theref ore animal studies
may not be fully predictive for humans. Today, it is generally accepted that prior to
Phase I, Phase 0 takes place (uptake), mediated by transport proteins belonging to
the SLC (solute carrier) transporter superfamily. Phase II is followed by Phase III
(export); this efflux is mediated mostly by the members of the ABC (ATP-binding
cassette, e.g., P-gp) transporter superfamily.
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Figure 2.10 Typology of the learning methods (asterisk denotes methods interpretable in
terms of the impact of input descriptors over the model or output values).
Figure 2.9 In silico ADME/Tox modeling. Top: Key concepts to model ADME/Tox prop-
erties. In this case, information regarding molecular structures and biological responses are
known and relationships between the two are derived. Bottom: Diagram of the main elements
used to build in silico models.
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describing the molecular structure (denoted X, input variable) are related to the
observed activity (denoted Y, output variable) [75]. The parameters of the model
relating Y to X are fitted from a set of molecules for which experimental data are
available. This is commonly referred to as the training set. In general, models can only
reliably predict for molecules similar to those in the training set. This is sometimes
referred to as either the chemical space of the model or domain of applicability.
A statistical regression tool is used to derive linear models between X and Y. Multiple
linear regression model (MLR) and PLS approaches are commonly used but nonlinear
classification techniques tend to be used more and more at present. For instance,
multiple biological mechanisms usually contribute to a single ADME/Tox property
but the relations are seldom linear, and in this situation, it might be more appropriate to
use nonlinear methods (see below).
Classification modeling operates similar to QSAR modeling. Thus reliable data
that translate in the field of ADME/Tox to a set of molecules for which ADME/Tox
properties have been determined experimentally are needed. There are over 6000
molecular descriptors that can beused for in silico modeling [85–87]. Some descriptors
contain information about the conformation of a compound and can be defined by
the dimensionality of the structural representation. Descriptors fall into a number of
classes such as physicochemical, geometrical, topological, electropological, quantum
Figure 2.11 Overview of a fivefold cross-validation scheme. Partition of a sample of data into
subsets, iteratively, one part becomes a test set (black) the others are learning sets (gray).
Analysis is performed on the training/learning sets and test sets.
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chemical, and molecular fingerprints. One challenge is to use descriptors that are
capable of mechanistic interpretation and that can be translated to the bench [46].
Molecular descriptors are grouped according to their dimensionality as 0D,
1D, 2D, 3D, and 4D [85]. Zero-dimensional-descriptors are independent from
molecular connectivity and conformations and refer to atom and bond type counts.
1D-descriptors contain information about fragment counts, and their calculation is
independent of information on molecule structure. 2D-descriptors, called graph
invariants or topologic descriptors, are derived from molecular graphs, and are
conformationally independent. In contrast, 3D-descriptors depend on the geometrical coordinates of the atoms of molecules (quantum chemistry, molecular surface,
volumes, et c.). 4D-descriptors are energetic descriptors obtained by computing the
energy of interaction between the compound and m olecular probes. The definition
of descriptors, according to Roberto Todeschini and Viviana Consonni, is the
following: “The molecular descriptor is the final result of a logic and mathematical
procedure which transforms chemical information encoded within a symbolic
representation of a molecule into a useful num ber or the result of some standardized
experiment.”
Once all the descriptors are computed for the studied dataset, they are used in a
model aiming at obtaining information concerning ADME/Tox properties. The
general workflow for the modeling and process conception is presented below and
schematically represented in Figure 2.9.
To perform an efficient analysis, preliminary processing of the data, such as
selecting molecular descriptors (X) most related to the output variable Y in a large
dataset, has a large effect on the quality of the final solution. Analyses such as
principal component analysis can assist the process to reduce the dimensionality of
the data, to check the diversity of molecules in the training dataset, or to eliminate
outliers. Then, the challenge is the selection of the learning technique(s) that is most
suitable for modeling the property under investigation. The “learning problem” can be
roughly categorized as either supervised or unsupervised (Figure 2.10 and Section 2.3
of this chapter).
In unsupervised learning, there is no output measure Y and the goal is to describe
the association and patterns among a set of input descriptors X. The features
are only observed and the task is to describe how the data are organized and
clustered. These methods are particularly useful for features extraction, data
clustering, and visualization. They include hierarchical clustering, k-means,
unsupervised neural networks, Kohonen self-organizing mapss (SOMs) methods, and principal component analysis (PCA).
In supervised learning, the goal is to predict the value of one or more output
measure(s) Y based on a number of input measures X. The methods are called
“supervised” because of the presence of the output variable(s) that guide the
learning process. The outputs vary in nature; they can be discrete (categorical or
qualitative) or continuo us (quantitative). This distinction in output types has led
to a naming convention for the prediction tasks: classification and regression
when one predicts qualitative and continuous outputs, respectively.
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Statistical regression is primarily a tool for deriving models between inputs X and
output Y, and has been applied in many ADME models, including the calculation of
whole molecules physicochemical properties. Regression methods correspond to
supervised methods. The most commonly used are multiple linear regression model,
partial least square (PLS), recursive partitioning or decision tree (DT), artificial neural
networks (ANN), and support vector machines (SVM). Classification methods,
employed to assign the property of a molecule to one of two or more classes, mainly
include linear discriminant analysis (LDA), k-nearest-neighbors, decision trees,
artificial neural networks, and support vector machine. Classification and regression
tasks have some commonality and methods, such as ANN, DT, SVM, can be applied
to both.
UNSUPERVISED
Hierarchical Clustering [88] and k-Means [89]
Cluster analysis, also called data segmentation, groups a collection of objects into
“clusters,” such that those within each cluster are more closely related to one
another than objects assigned to other clusters. The measure of similarity is central
to the cluster analysis and has to be appropriately chosen. For hierarchical
classification, objects then clusters are successively grouped between themselves
to form a hierarchical classification. For k-means, the number of clusters k is first
chosen and objects are clustered around k centroids.
PCA
The goal of principal component analysis [90] is to project data into a subspace
made of linear combinations of the original descriptors so that this subspace is
the best-simplified image, in a small dimension, of the original data in terms of
variation (see Section 2.3).
UNSUPERVISED AND SUPERVISED
Neural networks are based on neurons (computational elements) connected to a
framework. The two most important subtypes are Kohonen self-organizing maps
and artificial neural networks. SOM are examples of unsupervised NNs,
particularly useful for features extraction, data classification, clustering, and
visualization. Artificial neural networks is a pattern recognition approach [91],
fitting nonlinear function relating a discrete or continuous output Y and a set of
inputs for a training set of molecules. Once memorized, this network can then be
used to make predictions for molecules for which Y is unknown (see
Section 2.3).
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With this large range of available methods, it is not always easy to decide which
strategies to use [47]. The reliability of the methods will obviously depend on several
SUPERVISED
Classification
Linear Discriminant Analysis is conceptually close to PCA, but linear combinations of descriptors are performed to optimize the prediction of the discrete
outcome variable. It calculates discriminant functions or hyperplanes that partition
the space of chemical descriptors to give the best separation between classes [92].
k-nearest-neighbors (kNN) identifies the k molecules in the training set that
lies closest to the molecules for which the prediction is being made [93]. Measure
of structural similarity between molecules is assessed using Tanimoto index or
Euclidean distance in the space of descriptors. The unknown molecule is then
assigned by a voting procedure among the k-nearest-neighbors.
Regression
Multiple linear regression model is one of the oldest methods to find a linear
relationship betwee n the observed activities and a set of descriptors [94].
A problem with this approach is that it is generally considered as requiring more
molecules in the training set than descriptors (roughly five times more). Moreover,
correlated descriptors or descriptors with skewed distribution (i.e., low variance)
will result in poor regression models. MLR is close to partial least square but
concerns the prediction of only one quantitative output using the best linear
combination of descriptors and without any use of graphical representations and
thus poorer interpretation.
Partialleast square has become a standard in ADME/Toxas it overcomesmost of
the previously mentionedproblems [95]. A large number of descriptorscan be used,
even larger than the number of molecules in the training set, and the descriptors that
influence the most the model can be conveniently identified. More complex
nonlinear relationship can be treated by nonlinear PLS [96] (see Section 2.3).
Classification or Regression
Decision tree creates a branching structure in which the branch taken at each
intersection is determined by a rule related to the descriptors splitting the local
molecule set into two more homogeneous subsets. Each “leaf ” of a tree is assigned
to a class or a value [97] (see Section 2.3).
Support vector machine is one of the most popular kernel methods, initially
introduced by Vapnik in 1995 [98]. Owing to its outstanding performance in
nonlinear structures, the application has spread rapidly over the past decade (see
Section 2.3).
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factors such as noise, size of the data, and output types. Performance of the models can
be quantified in terms of accuracy (such as good classification rate, sensitivity,
specificity for classification, and sum-of-squares errors for regression) but also in
terms of interpretability. Concerning accuracy of predictions, there is a need to find a
balance between oversimplified and complicated models. An oversimplified model
could ignore complicated features and noise, thus its predictive performance could be
poor. On the other hand, if one uses a complicated model that interpolates the data
exactly, the prediction error on the training data can become close to zero. However,
such complicated models tend to have poor generalization performance, that is, the
models make poor prediction on new test data points not similar to any of the existing
training data points. Such poor generalization performance is called overfitting. This
overfitting phenomenon can be avoided by using a cross-validation technique [99].
When training data are used for constructing the model and also for assessing its
performance, they tend to overfit the model. Hence, it is necessary to assess an
external validation data set that should be large and diverse enough, and which
overlaps as less as possible with the training set while remaining in the domain of
application. Cross-validation is an effective technique to avoid overlaps with the
learning set. First, the training data are partitioned into k parts, in which one part is
used for the test set and the remaining k 1 parts are used for modeling. The process
is iterated k times. This process is called k-fold cross-validation (Figure 2.9).
When k is equal to the number of molecules, it is called leave-one-out (LOO)
cross-validation. In general, the most unbiased estimates can be obtained by LOO, but
it often produces high variance and is computationally intensive [100]. A recommended approach to estimate true accuracy of a model, when sufficient data are
available, is to add an independent test set of molecules to which the model is never
fitted. If the model is not overfitted, the correlation between predicted and experimental values for the test should be comparable to that of the training set. Concerning
interpretability, some models tend to be “black boxes” (i.e., the chemical structure is
taken as input and a prediction is returned with little or no possibility of understanding
the connection between the two). This lack of interpretability is a problem for
chemists and drug designers as it is difficult to propose potential solutions. For
example, some nonlinear models are more appropriate for early stages of lead
identification, as they are often “black-box” techniques that derived little information
about their prediction. Linear correlation has to be preferred during lead optimization
when more comprehensible and interpretable models are desired. Therefore, a good
prediction must represent a balance between accuracy and interpretability. The
integration of different methods or consensus modeling can be a valid solution to
optimize the prediction and its interpretability, allowing to overcome the limitations
of a single method [101]. In fact, a combination of two or more models for the same
property, based on different principles, may provide higher confidence in the results
obtained for which they agree.
In practice, ADME/Tox filtering usually initially uses simple counting methods
(e.g., rule-of-5) combined with some structural alert checks (e.g., identification of
reactive groups). Subsequently, more CPU demanding and/or complex statistical
methods can be applied.
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2.2.4 Physicochemistry, Pharmacokinetics, Drug-Like
and Lead-Like Concepts
Drug molecules generally act on specific targets, and upon binding to the receptors,
hopefully exert a desirable alteration of the target/cellular activities. Before interacting with the receptor, drug molecules must travel from the entry point through the
body to reach the site of action at the desired concentration and with sufficient
duration. After executing their activities, drug molecules should be eliminated from
the organism. For most drugs, the preferred route of administration is by oral
ingestion. Compounds have to be soluble and possess an appropriate level of
lipophilicity. The latter is critical to achieve cell permeability, a prerequisite for oral
bioavailability. Such observations have prompted drug discovery researchers as
early as in the 1960s to search for properties (e.g., MW, log P [partition coefficient
determined by the logarithm of the concentration ratio of a nonionized solute in two
different solvents (generally water and octanol)], among others) that drug molecules
should display as compared to nondrugs. At about the same time, the quest for the
design of molecular descriptors that coul d correlate with the so-called drug-like
profile was initiated [40, 46, 52, 102–105]. If one examines the journey of a drug in the
human body, the entire process can be broadly described by several subevents such as
absorption, distribution, metabolism, and excretion. This area of research aiming
at understanding the fate a molecule in the body is often referred to in the literature as
DMPK drug metabolism and pharmacokinetics [52]. The key questions that this
research is trying to address involve, for example, the following:
.
Is it possible to define some specific features that match with these subprocesses?
.
Is it possible to find specific characteristics in known drugs that are missing or
different in nondrugs?
.
How to mathematically model these reactions, which level of complexity, and
which stage of the drug discovery process one should act upon?
.
How reliable such predictions could be?
As mentioned above, one of the first attempts to address some of these questions is
the “rule-of-5” devised by Lipinski and coworkers at Pfizer [59]. The rule-of-5 is
specifically related to the permeability and solubility of the compounds and thus to
the absorption of therapeutic agents but has also other implications. Lipinski et al.
analyzed the physicochemical properties of over 2200 compounds from the World
Drug Index because it was presumed that they would have suitable solubility and
permeability (the compounds were supposed to have entered Phase 2 clinical trials).
They found that in a high proportion of compounds, a series of four rules was generally
true. These rules state that an orally active drug has in general no more than one
violation of the following criteria:
.
hydrogen-bond donors 5
.
hydrogen-bond acceptors 10
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.
molecular mass 500 Da
.
log P (the method used was C log P, see for instance www.daylight.com) 5.
If the molecule has more than one violation, it may be poorly absorbed. First, one
notes that all numbers are multiples of five, which is the origin of the rule’s name. In
that work, any oxygen and nitrogen atoms are defined as hydrogen-bond acceptors
and N–H and O–H groups are considered to be hydrogen-bond donors. Interestingly,
all the rule-of-5 descriptors can be derived from the structure of the compound while
the log P is in fact a QSAR-fitted descriptor method. The authors underlined that the
majority of drugs that violate the rule-of-5 are antibiotics, antifungal agents, vitamins,
natural products among others, yet these compounds are often orally available,
suggesting that they act as substrates for transpor ters [20, 106]. The rule-of-5 concept
has been extended along the following line, it describes molecular properties
important for a drug’s pharmacokinetics in the human body, not only absorption
but also distribution, metabolism, and excretion. However, the rule-of-5 does not
predict if a compound is pharmacologically active. The rule-of-5 can thus be
considered as necessary conditions but not sufficient for a molecule to become a
drug, it tends to avoid false negatives at the expense of false positives. Obviously,
simplicity may also lead to the lack of discrimination.
Sakaeda et al. have analyzed the rule-of-5 and reported similar exclusion criteria
(MW 500 and log P 5) that differentiated poorly absorbed drugs from good
drug candidates after investigation of over 200 marketed oral drugs [107]. Similar
investigations were carried out by several other groups [9, 60, 108] and usually
supported the view of Lipinski and collaborators. The rule-of-5 was also further
validated by Ghose and coworkers [109]. These authors examined the computed
physical properties of over 6000 molecules taken from the Comprehensive Medicinal
Chemistry Database. Ranges were established for log P values (computed with A
log P), molecular refractivity,MW,and number of atoms (e.g., most of the compounds
have MW values ranging from 200 to 400 and log P values running from 0 to 4).
A large percentage of high MW compounds were either antibacterials or antineoplastics. Many compounds with high log P values were from classes considered to be
CNS active (anti-Parkinsonian, antipsychotic, and antidepressant), consistent with
the fact that CNS compounds tend to be more lipophilic than other biologically active
small molecules [110, 111]. Veber and colleagues [112] investigated the concept
further. This group assembled a database of over 1000 drug candidates with extensive
animal data such as rat oral bioavailability, and, from many computed molecular
properties such as rotatable bond, H-bond acceptors and donor, log P, and molecular
weight. The authors found that reducing the number of rotatable bonds had the largest
impact on determining oral bioavailability in rats. They proposed that compounds
with 10 or fewer rotatable bonds and a polar surface area below 140 A
˚
2
will have a
higher chance of displaying good bioavailability. Vieth et al. have studied a large
collection of marketed drugs in order to uncover optimal physicochemical parameters
associated with compounds that are presumed to have good pharmacokinetic properties (e.g., those assumed to have poorer properties would be infused molecules or
topical drugs) [113]. They found that on average, intravenous drugs have significantly
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