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Numerous types of toxicities occur including hepatic, hematological, cardiovascular,
carcinogenicity, teratogenicity, reproductive toxicity, cytotoxicity, and phospholipidosis.
Another possible classification is to break down toxicity into the following:
.
pharmacophore-induced toxicity (CYP inhibition and drug–drug interaction or
hERG binding);
.
structure-related toxicity (structural features and physicochemical properties
of the compound or metabolite allowing interactions at sites distinct from the
intended target);
.
metabolism-induced toxicity (drug altered to a reactive metabolite such as
electrophiles can react with nucleophilic functions in proteins, Cys, Lys,
Ser, His, and nucleic acids causing organ toxicity including carcinogenicity
(epoxides, quinine imines, thiophenes, thioureas, chloroquinolines) [20, 21].
Toxicity is a subtle issue since most chemicals become toxic at high doses. To
minimize patient risk during the drug discovery process, a compound may be admitted
as drug only if its efficacy and nontoxicity are demonstrated by pharmacological
and clinical trials. During preclinical development, toxicologists calculate the
“Therapeutic index” (the ratio of toxic dose to the therapeutic dose), the “safety
window” (the range between the effective concentration and the toxic concentration),
and the “maximum tolerated dose” (MTD) or “no observable adverse effect level”
(NOAEL) (the maximum concentration of a drug at which no toxics effects are
observed) [54].
TOXICITY DEFINITIONS [54]
Acute toxicity is the toxic effects from a substance resulting from a single exposure
or several exposures during less than 24 h from a single dose, occurring within
14 days after administration.
Carcinogenicity is toxicity due to agents that promote cancer and/or increase its
propagation.
Clastogenicity refer to a form of mutagenicity due to chromosome breakage.
Chronic toxicity is the toxic effects from repeated exposures to a substance over
a long-term dosing (months or years).
Cytotoxicity is toxicity against the cells promoting cell death, which could
drive to the whole organ toxicity (e.g., hepatoxicity).
Drug–drug interaction is the fact that a pharmaceutical or dietary agent affects
the metabolism, clearance, or safety of another molecule.
Genotoxicity is toxicity capable to induce DNA damage and thus inducing
mutations and cancer.
Idiosyncratic toxicity is an adverse reaction that occurs rarely (less than 1 in
1000) and unpredictably among the population, caused by reactive metabolites.
Patient enzyme irregularities or polymorphisms may be implicated.
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2.2.11.1 Toxicity Mechanisms In general, toxicity mechanisms are classified into
five categories [194]:
.
on-target
.
hypersensitivity and immunological reactions
.
off-target
.
biological activation
.
idiosyncratic toxicity
Statins are a well-known example of on-target toxici ty (Figure 2.20). These
molecules are employed to regulate synthesis of cholesterol. Extensive data support
the use of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins) for
both primary and secondary prevention of myocardial infarction, revascularization
procedures, stroke, and peripheral vascular disease. Yet, toxicity may occur because
statins inhibit the target HMG-CoA reductase in muscles leading to myopathy and
kidney damage.
Immunotoxicity is toxicity due to an immune reaction initiated by the drug
administered or its metabolites, which react as an antigen.
Metabolite-mediated toxicity is toxicity due to the formation of reactive
metabolites or electrophilic metabolites that could bind covalently to proteins
and DNA, driving to genotoxicity, target organ toxicity, and idiosyncratic
toxicity.
Mutagenicity is DNA damage that is considered to initiate carcinogenicity.
Organ toxicity is an organ-specific form of toxicity that alters an organ (e.g.,
liver, kidney, heart) in its integrity and then its function.
Phospholipidosis is an adverse drug reaction in response to cationic amphiphilic drugs that drive to a lipid storage disorder due to the accumulation of polar
phospholipids in cells.
Primary pharmacology or target-based toxicity is toxicity directly from modulation of the drug target.
Reproductive toxicity is adverse effects on sexual function and fertility,
including developmental toxicity in the offspring.
Safety pharmacology studies the effects of a compound on normal physiological functions. Well-known studies are the assessment of hERG potassium channel
binding and/or hERG blockade. Blocking this channel may be predictive for QT
interval prolongation and ultimately to potential life-threatening drug-induced
ventricular tachyarrhythmia.
Teratogenicity is embryo toxicity conducting to abnormalities of physiological
development and birth defects.
Toxicogenomics is a combination of toxi cology and genomics. It represents the
study of structure and function of the genome, in addition to interindividual
variations across the genome depending on responses to xenobiotic exposures.
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Hypersensitivity, immunological reactions, and allergic reactions may be immediate (IgE antibodies), cytotoxic and immune complex reactions (IgG or IgM), or
delayed (T-lymphocytes). It has also been reported that covalent binding of reactive
metabolites to tissue proteins can play a role in this toxicity. Penicillins and other
beta-lactam antibiotics are examples of these autoimmune responses.
Off-target activities can be beneficial or detrimental and involve interactions with
a target that was not expected. Antitargets are targets that are detrimental to the
progression of a compound. Off-target toxicity such as binding to CYP or hERG is
related to pharmacophore-induced toxicity. Drug–drug interaction can occur when
patients receive several medications, at the same time potentially leading to a
competition for the same metabolizing enzyme such as CYP3A4. Consumption of
grapefruit or grapefruit juice inhibits the metabolism of statins, which increases the
risk of dose-related adverse effects including myopathy/rhabdomyolysis. Furanocoumarins in grapefruit inhibit the cytochrome P450 enzyme CYP3A4, which is
involved in the metabolism of most statins.
Cardiac arrhythmias characterized by a prolongation of the QT-interval [195] are
measured by electrocardiogram on the surface of the heart tissue. hERG channel
blockade can cause this adverse event. This reaction seems to be a consequence of a
drug binding within a water cavity inside the transmembrane region of the channel
presumably preventing required conformational changes [17] (Figure 2.21). Terfenadine, an antihistamine formerly used for the treatment of allergic conditions, is a
known example. It was marketed under various brand names and removed from the
market in the 1998 due to the risk of cardiac arrhythmia (Figure 2.22). When
considering safety margins for hERG and card iac issues, free plasma concentrations
and plasma protein binding are important (e.g., fluoroquinolone antibiotics and
antipsychotics) [195].
Biological activation includes structure-related toxicity (toxic structural featur es
and physicochemical properties) and metabolism-induced toxicity (CYP induction
[rifampicin, carbamazepine, nevirapine, probenicid] or inhibition [erythromycin,
Figure 2.20 Lovastatin, the first statin to be marketed.
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tamoxifen, ritonavir]), pregnane X receptor, and drug altered to a reactive metabolites.
The latter can react with nucleophilic functions in proteins and nucleic acids causing
organ toxicity, including carcinogenicity (e.g., epoxides, quinine imines, thiophenes,
thioureas, chloroquinolines). Over the years, several authors [33, 38, 39, 196, 197]
Figure 2.22 Drug withdrawn due to QT prolongation concerns. Terfenadine (antihistamine)
binds to hERG and removed from the market in 1998.
Figure 2.21 Putative interactions between a small compound and the hERG potassium
channel.
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have focused on identifying chemical compounds or moieties (structural alerts),
which have been associated with toxicity issues such as covalent binding, and
intercalation to DNA. Some of these known unacceptable moieties (toxicophores)
are categorized in Table 2.9.
In general, some drugs containing such groups can be considered safe if the dose
does not exceed 10 mg day
1
, but this observation has to be investigated on a case-
by-case basis.
Idiosyncratic toxicities are due in most situations to reactive metabolites. Subsequent the complex, protein–metabolite conjugate triggers an immune response.
This is difficult to reproduce and predict and seldom occurs [199]. Bromfenac, an
NSAID that was withdrawn from the market in 1998 due to reports of idiosyncratic
hepatoxicity, is one example (Figure 2.23). This molecule contains several toxicophores including an aniline ring that has the potential to form a reactive nitroso group.
The molecule appears to bind to endogenous protein, which is then recognized as
a nonself protein and initiates an immune response [200].
2.2.11.2 In Silico Models for Toxicity Overall, the existing packages to forecast
toxicity are categorized into two groups [20, 31, 201] (see Section 2.3). The first is
data-driven or structure-based that relies on the generation of descriptors from the
chemical structure and statistical analysis of the relationships between these descriptors and the toxicological effect. Gepp and Hutter have been using a decision
trees to predict putative hERG blockers [202]. Other classification models have also
been used to predict the cardiotoxicity of drug molecules [203] and one approach
TABLE 2.9 Known Unacceptable Toxicophore Moieties
a
Classification Reactivity Chemical Function
Electrophilic
reactive
molecules
React covalently with proteins
and biological nucleophiles
a-Haloketones, boronic acids,
aldehydes, and 1,2-dicarbonyls
Tight-binding or
metal-chelating
molecules
React with metalloproteases Hydroxamate, oxime, and thiol
chelators
Redox/thiol Michael acceptors,
undesirable functional
group which can alkylate
thiol groups of glutathione,
proteins, and DNA
Quinones
DNA intercalating
agents
Strong binding (intercalation)
causing mutations
Acridine
Others Aldehydes, soft electrophiles as
aliphatic ketones and
cyclohexanones
a
See Section 2.3 for examples of reactive groups, please see some other examples of chemical structures in
drugs that can become toxic metabolites in Nassar et al. [198].
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[202] is available online at http://www.cbs.dtu.dk/services/hERG/. The second
package is based on expert system approaches, which synthesize and formalize the
knowledge present in the scientific literature and of human experts.
Although complex, it is possible to attempt to predict toxicity [204] using the
in silico packages listed in Table 2.10. Development of new mathematical models and
better understanding of toxicity are greatly assisted by the storage of information in
databases.At present, severalkeypublicly available toxicity databases can be consulted
to gain insights about toxicity mechanisms and to assist decision making [205].
2.3 PREPARATION OF COMPOUND COLLECTIONS AND
COMPUTER PROGRAMS, CHALLENGING ADME/Tox PREDICTIONS
AND STATISTICAL METHODS
2.3.1 Preparation of Compound Collections and Computer Programs
The nature/composition of a compound collection has a significant impact in
determining both the quantity and quality of identified hits/leads and ultimately of
Figure 2.23 Bromfenac (NSAID) and idiosyncratic toxicity.
TABLE 2.10 Packages for the Prediction of Toxicity In Silico
Software Package Website
MCASE www.multicase.com
Discovery Studio TOPKAT www.accelrys.com
DEREK www.lhasalimited.org
Lazar http://lazar.in-silico.de
Leadscope Solutions www.leadscope.com
PreADMET http://preadmet.bmdrc.org
Q-Lead www.q-lead.com
Hazard-Expert www.compudrug.com
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the overall success of a screening project [206]. The different types of screening
collections and different ways to prepare them depending on the projects, targets, and
goals can be used for HTS campaigns or virtual screening. Available electronic
compound collections (over 40 million compounds in total in 2009) are categorized as
follows (some collections fit into several categories):
.
combinatorial chemistry libraries
.
collections for cherry picking
.
focused-libraries (eithe r bioactive collections containing compounds with wellcharacterized biological function or target-specific collections)
.
fragment or building block libraries
.
diversity sets
.
collections containing only marketed experimental drugs
.
collections containing natural products.
In general, the first step toward the preparation of a screening library suitable for hit
discovery projects is to assemble several public/commercial/proprietary collections.
At least two different scenarios occur, either the library is used without additional
intervention or it can go through series of gradual in silico filtering steps. For the latter,
there are no perfect filters and filters can be tailored according to the project and/or
stage of the project (according to target location in the human body, target types,
project stage [hit discovery or compound optimization, etc.]). Some scientists believe
that the screening library should be as large as possible to explore more chemical
space even if it becomes less “drug-like,” expecting that medicinal chemists will be
able to fix the problems later. Conversely, others might argue that the first hit
compounds must be as “clean” as possible to provide suitable development candidates
easier to handle by chemists avoiding difficult formulation strategies [24]. There are
still major debates about how to design a compound collection. Both the private sector
and academic groups increasingly agree that collections should be “filtered” in order
to remove undesirable molecules/groups [9, 33, 39, 56]. It is well documented that to
avoid costly failures in screening projects, ADME/Tox properties should be considered at an early stage [58, 63]. As a result, currently, most libraries are at least crudely
rule-of-5 compliant.
In the following paragraphs, we suggest possible avenues to design a generic
compound collection and also give examples of strategies implemented in pharmaceutical companies. We provide URL links to several compound collections, databases [207], and computer packages that should facilitate the work. There are several
types of computer methods helping to filter a collection including methods that are
rule-based (Lipinski’s rule-of-5) or knowledge-based. The process is better described
as “cleaning” a collection since the human body is immensely complex and the
available tools cannot yet handle this intricacy. The latter usually uses machinelearning approaches (e.g., neural networks, support vector machines) that can yield
models with higher predictive accuracy as compared to rule-based strategies. This
improvement is often costly since the chemical compound is retained (or rejected)
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in (from) the collection with little or no possibility of understanding why [25, 208].
This lack of interpretability hinders chemists’ efforts to identify the causes and
solutions of the problem. Thus, while statistical methods are of great value, they have
to be used with care. A good compound collection filtering or profiling model or
descriptor must represent a balance between accuracy and interpretabilit y. For
simplicity, we will focus on rule-based methods, while many reviews referring to
machine-learning approaches are provided throughout the chapter.
2.3.2 Preparing a Compound Collection: Materials and Methods
One concept regarding ADME/Tox in silico filters is that the physicochemical
properties of a compound are expected to determine its pharmacokinetic and
metabolic behavior in the body. In general, molecules with inadequate initial
properties (ADME/Tox profile) usually increase the development costs and tend to
put significant burden to patients even if they do not fail in clinical trials. Further, a
compound collection may be prepared for a chemical biology project or for drug
discovery, and in these cases, one may need molecules with a more “lead-like” or
“drug-like” profile [45, 61, 209]. Experimental ADME/Tox measurements are still
very difficult since there are many different levels of complexity such as crossing
physiological barriers, group reactivity, and metabolism. Different experimental
assays have been developed over the years to attempt to assess/predict ADME/Tox
properties, but in silico ADME/Tox computations can also be carried out. These
calculations provide valuable information that can then be further investigated
experimentally [25]. Well-known methods for evaluating the drug-like or lead-like
properties of a compound were implemented in several library design programs
and are routinely used in the pharmaceutical industry. Such rules are the so-called
rule-of-5 [210], a set of four property values that were derived from classifying the key
physicochemical properties of drug compounds. These properties are defined by the
values of the log P (ratio of concentrations, at equilibrium, of a compound in the two
phases of mixture of two immiscible solvents, commonly octanol and water), the
molecular weight, the number of hydrogen bond donors (expressed as the sum of
OHs and NHs), and the number of hydrogen bond acceptors (expressed as the sum of
N and O). Furthermore, screening processes are often clouded if selected molecules
contain reactive functional groups [211] that interfere and aggregate in biochemical
assays [212, 213] or are frequent hitters (defined as compounds that are biologically
active across a range of targets and often causing serious problems in drug discovery
projects) [214]. Over the years, many additional rules have thus been proposed [112, 215] and can be smartly combined with “rule-of-5.” The polar surface
area, roughly defined as the surface sum over all polar atoms (O and N) and the number
of flexible bonds, can be proposed as extended rules (Section 2.2). Likewise, the
selection of compounds based on substructure features or similarity measures could
be managed by encoding molecules in a bit-string technology or “fingerprints” [104].
Their structural similarity can be evaluated using Tanimoto coefficient. Other
physicochemical properties, BBB, HSA binding, or toxicity predictions can be
carried out (Section 2.2).
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In order to design a compound collection, one needs to search and select
appropriate databases and computer programs. Several outstanding commercial
packages have been developed and can be used to perform this filtering. Free
computer tools are also available, either as standalone software or online. Compound
collections distributed by chemical vendors are typically in 2D. If 3D structures are
required to compute descriptors, commercial or open source packages are available.
Below are some major tools in the field and a practical approach to design a compound
collection.
2.3.2.1 Databases Chemical compounds are stored in databases in different
electronic formats. For chemists, the easiest representation of a molecule is a 2D
diagram. Since 2D representation is not fully adapted to computational operations,
several formats have been developed over the years. A commonly used format is the
atom–bond connection table [31] that mimics the chemical structure as a graph
containing a set of vertices (atoms) linked by edges (bonds). Examples include SDF
(structure-data-file) and MOL (created by MDL and now owned by Symyx) file
formats [216] (Figure 2.24). Other types of formats are often referred to as line format
since one molecule can be described by one line of strings. Examples include SMILES
(simplified molecular input line entry specification) [217], SMARTS (Smiles
ARbitrary Target Specification) [218] (Daylight Chemical Information Systems),
and the InChI (IUPAC International Chemical Identifier, provided by the IUPAC)
formats [219].
SMILES ASCII strings define a typographical standard representation of molecular structure of compound using common letters and numbers. Dependi ng on the
canonicalization algorithm used to obtain the string sequence for one molecule,
Canonical SMILES can be cited when this string sequence describe and ensure the
uniqueness of the compound. Finally, Isomeric SMILES term is employed when
structure, connectivity, and chirality properties are specified in the sequence.
SMARTS are also one-line notation but specify substructural features and atom
typing in molecule. IUPAC InChI describes the standard for formula representation
of a molecule but is more difficult to read.
2.3.2.2 Free and Open-Access Online Chemistry Databases Tables 2.11 and 2.12
list free compound libraries. DrugBank, PubCHem, and ZINC are briefly discussed
with more detailed information found at www.vls3d.com.
DrugBank DrugBank (http://www.drugbank.ca) [232] is hosted at the University of
Alberta, Canada, and supported by Genome Alberta & Genome Canada, a private,
nonprofit Corporation. In 2009, the database contained nearly 4800 drug entries
including around 1350 FDA approved drugs, and more than 3200 experimental drugs.
PubChem Currently, PubChem (http://pubchem.ncbi.nlm.nih.gov) [247] is the
largest freely available public molecular information database. PubChem consists
of three databases (PubChem Compound, PubChem Substance, and PubChem BioAssay) and contains more than 18 million unique chemical structures and more than
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38 million substances. It provides biological property information for each compound
and is hosted by the National Center for Biotechnological Information (NCBI). The
PubChem Compound database is a repository of individual compounds. The PubChem substance database contains descriptions of chemical samples. This database is
linked to the PubMed citation engines and with PubChem BioAssay information.
PubChem BioAssay is a collection of bioassay data from several HTS campaigns.
Figure 2.24 Nitrazepam represented in different formats: (a) 2D stick representation,
(b) chemical structure diagram, denomination, and molecular formula, (c) SDF file format,
(d) different line formats. See insert for color representation of this figure.
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