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15.2.2 Ligand-based methods
The shapes, electrical characteristics, and conformations of inhibitors, substrates, or
metabolites are used by ligand-based methods to determine the active sites of pro-
teins; however, this information is predicated on the idea that a compound’smeta-
bolic properties stem solely from its chemical structure and features. Pharmacophore
modeling is one of the most popular techniques in this field [14]. By building a phar-
macophore model that covers the structures or features of ligands in three dimen-
sions and then simulates the chemical and spatial characteristics of binding sites, the
relationships in ligands and receptors are able to be predicted. As a result, ligand data
availability is crucial for building pharmacophore models. Using pharmacophore
models to screen for promising compounds with exceptional ADMET properties has
been a common practice in recent years [15]. For instance, a pharmacophore model
was developed and validated by Nandekar and their group (2016) to screen anticancer
drugs that act through cytochrome P450 1A1 (CYP1A1) [16]. In the end, nine compounds
with desirable pharmacophore properties and the ability to produce reactive metabo-
lites were chosen for additional research. Rawat and Verma (2020) created a pharma-
cophore model in order to find novel, dual target inhibiting agents of Plasmodium
falciparum dihydroorotate dehydrogenase (PfDHODH) as well as cytochrome bc1 com-
plex (PfCytbc1) for the treatment of malaria. The effective multitarget inhibitor mole-
cule MMV007571 was employed to separate characteristics out of the binding data in
order to build the model. Over 40,000 molecules in a library were screened using the
model. Two compounds with the appropriate binding interest and pharmacokinetic
characteristics were created after a number of trials [17]. If different shapes are avail-
able, more models should be built to cover a wider range of chemical spaces, espe-
cially for highly flexible proteins. Chen et al. [18], for instance, described a shape-
based virtual screening method to identify novel cores for the acetylcholinesterase
(AChE) inhibitor design. To find new possible inhibitors, the form of the commercial
inhibitors tacrine was utilized. Finally, two hit compounds possessed superior AChE
inhibitory activity over tacrine along with good ADMET profile [18].
By simulating drug-macromolecule binding mechanisms throughout the ADMET
process at the atomic or molecular level, the molecular modeling approach signifi-
cantly aids in the rationalization of chemical metabolism. Structure-based approaches
are getting more precise and predictive as pharmacokinetic computation and struc-
tural elucidation techniques advance quickly. Nevertheless, there are still issues with
the molecular modeling approach such as the need to precisely assess the structural
adaptability of proteins [19]. Furthermore, our ability to accurately predict the drug’s
metabolic fate will be enhanced by high-resolution experimental structural informa-
tion on the target. It is undoubtedly possible to generate synergistic effects in meta-
bolic prediction by combining enhanced structure-based and ligand-based methods,
allowing for a deeper analysis of metabolic processes [20, 21].
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15.2.3 Data optimization and modeling
To forecast ADMET-related properties, two popular data modeling techniques are
PBPK modeling and QSAR modeling as discussed in Figure 15.4. Numerous molecular
descriptors, such as topological, geometrical, physicochemical, or electronic descrip-
tors, are key factors in ADMET analysis and prediction in QSAR.
The QSAR method can be used to predict a wide range of properties including the
blood-brain barrier (BBB), clinical adverse effects, percent protein binding, lipophilic-
ity (log P), preclinical toxicological endpoints, and metabolic rate of pharmaceutical
substances. Because most drugs are taken orally, the PBPK modeling always predicts
parameters regarding the dose size and frequency, such as the extent of dose that
Figure 15.4: Various strategies to predict ADMET profile.
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reaches the portal vein (F
abs
), total drug clearance (CL), and volume of distribution at
steady-state (V
ss
) [22, 23].
15.2.4 Quantitative structure activity relationship (QSAR)
Since the 1960s, pharmaceutical chemistry has made use of QSAR, which uses mathe-
matical representations to explain the connections among structures of molecules
and their biological functions (Hansch Analysis, 1981). It is primarily predicated on
the idea that molecules that are similar will have similar characteristics. Therefore,
the following two prediction methods are mainly taken into consideration: (1) molecu-
lar similarity-based prediction (pharmacophore- and molecular fragment-based meth-
ods) and (2) property similarity-based prediction (log P,logD, and others) [24]. The
workflow to use QSAR, toxicity, pharmacodynamic, and pharmacokinetic databases to
build in silico model is described in Figure 15.5.
15.2.5 PBPK models
Empirical models are the most common types of traditional drug pharmacokinetic
prediction models. To predict PK properties, PBPK models have been developed with
an in-depth knowledge of the pharmacokinetic processes of drugs [25]. In order to
replicate the PK profile of a drug in plasma and tissues, the PBPK model combines
arithmetical drug data (such as drug concentration and clearance rate) with species
physiology parameters. This model aims to lay out in vivo drug pharmacokinetics that
are influenced by tissue volume, administration routes, blood flow, biotransformation
pathways, and connections to body tissues or organs [26, 27]. Teorell’s work serves as
the foundation for the PBPK models. Teorel l presented a model with multiple com-
partments to replicate. Recent advances in PBPK modeling have expanded its applica-
bility to include drug research and development [28]. Furthermore, the development
of PBPK models and simulations in the development of drugs has been aided by the
growth of preclinical data, particularly in vitro data [28, 29]. By breaking down organ-
isms into individual organs, PBPK modeling explains the physical and biological state
of each compartment. Among the most frequent processes are those pertaining to
blood transport and penetration, circu lation between blood and organ tissue, and
metabolic excretion [30].
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Figure 15.5: Procedure for using toxicity, pharmacodynamic, and pharmacokinetic databases and models.
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15.3 Databases
A multitude of pertinent databases preserving pharmacokinetic parameters have come
into existence in the last 10 years due to their rapid development. We gathered a few of
the most popular databases and divided them into auxiliary and ADMET-related data-
bases [31]. Tables 15.1 and 15.2 provide a brief overview of these databases including
descriptions, data scales, and website links. Users can use the corresponding modules to
submit data about the compounds they wish to query in the ADMET-related databases.
Subsequently, pharmacophore or shape screening will be carried out to find more tar-
gets or details on the bioactivity of ligands that are similar to the query molecule. Fur-
ther, the query result also yields the properties related to ADMET. The primary purpose
of the auxiliary databases is to offer structural details about compounds, and while the
search results do include some ADMET-related information, it is not comprehensive,
and not all compounds are linked to such information [32, 33].
15.3.1 ADMET-related databases
Currently, a large number of in silico approaches are used to figure out ADMET, but
their construction requires enormous amounts of data. Trustworthy experimental data
are essential for successful prediction because the quantity and quality of the data are
directly correlated with the model’s prediction accuracy [34]. As of right now, the
ADME database [35], SuperToxic [36], PKKB [37], and DSSTox [38] are among the data-
bases that can aid in ADMET prediction. Users can get valuable information sets for use
in external algorithms to create prediction models by utilizing these databases. Through
search functions like similarity search or prediction, the databases may also be em-
ployed directly for prediction. Furthermore, these models are updatable [39].
A commercial database with a focus on pharmacokinetics information, the ADME
database was created in 2004 by Fujitsu and Zagreb University. A comprehensive infor-
mation about human-specific drug transporters and enzymes involved in drug metabo-
lism is provided. Drug research and development has made extensive use of the data
for things like ADME forecasting and drug-drug interactions [40]. By structure or sub-
structure, users can search for information about compounds’ classification, metabolic
processes, and kinetics. Large-scale downloads of user data and the public release of
some models are currently restricted by the database [33, 40].
SuperToxic gathers toxins from various sources, such as plants, animals, and syn-
thetic materials, and then gathers approximately 60,000 compounds along with their
structures. Additionally, it incorporates information on chemical characteristics and
commercial availability. These substances are categorized according to over 2 million
measurements of their toxicity. In order to assess the risk associated with their use,
the values can be used to investigate the relationship between the chemical composi-
tions and functions of toxins [41]. Using a structure, name, or property search, users
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Table 15.1: Various ADMET-related databases.
Database
name
Availability Description URL
DSSTox Free A freely available source of chemical
structure data combined with current
toxicity data to enable more accurate
predictive toxicology
https://www.epa.gov/chemical-re
search/distributed-structure-
searchable-toxicity-dsstox-database
AurSCOPE
ADME
Commercial Designing prediction models and
identifying possible medication
interactions can be done with a fully
annotated, organized knowledge
source
http://www.aureus-sciences.com/
BRENDA Free Informational database on enzymes
and enzyme–ligand combinations
http://www.brenda-enzymes.org/
DIDB Free To evaluate drug safety and
interactions based on human PK
https://www.druginteractioninfo.
org/
ChemTunes Free The safety and risk assessment
process for chemical substances is
supported and facilitated by a special
cheminformatics platform and a
knowledgeable, quality-controlled
database
https://www.mn-am.com/products/
chemtunes
ToxBank Commercial For thorough toxicological data
analysis and alternate detection of
repeated dose toxicity tests
http://toxbank.net/
ADME
database
Commercial A paid database that is updated every
months to research drug
interactions and ADME
https://www.fujitsu.com/jp/group/
kyushu/en/solutions/industry/
lifescience/admedatabase/
Knowitall Commercial Fast and precise spectrum
identification is possible with the aid
of the largest mass spectrometry
library in the world
http://www.bio-rad.com/
CTD Free An open-access repository housing
scientific information regarding the
connections between genes,
substances, and human illnesses
http://ctdbase.org/
ACD/
Percepta
Commercial Used for toxicity endpoint calculations,
ADME characteristics, and
physicochemical parameters
calculations
https://www.acdlabs.com/products/
percepta/index.php
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Table 15.1 (continued)
Database
name
Availability Description URL
ACToR Free Investigating and displaying intricate
computational toxicology data
https://actor.epa.gov/actor/home.
xhtml
CEBS Free A toxicological tool that can be used to
simulate, forecast, examine, and
evaluate how time and dose affect an
experiment’s outcome
https://manticore.niehs.nih.gov/
cebssearch
ToxRefDB Free Save information from animal toxicity
tests conducted in vivo and supply
toxicity endpoints for use in predictive
modeling
https://catalog.data.gov/dataset/tox
cast-toxrefdb
Metabolism
and
Transport
Database
Free A database on the metabolism of
small molecules transported by
transport that can be utilized for
modeling and computational analysis
http://www-metrabase.ch.cam.ac.
uk/
SuperToxic Free Able to conduct risk assessment, link
to other databases, and perform
similarity screening
http://bioinformatics.charite.de/
supertoxic/
Liceptor
Database
Free A ligand database that includes
information on D structures,
associated molecular characteristics,
and bioactivity, including assays,
functions, and potential therapeutic
uses
http://www.evolvus.com/
NTP Free Investigated at any chemical that
might have an effect on health
https://sandbox.ntp.niehs.nih.gov/
neurotox/
TDB Free Explain the relationship between
toxins and targets as well as the toxic
action mechanism, which can be used
to predict targets that are toxic or
toxins themselves
http://www.tdb.ca/
RTECS Free It includes supplementary data on the
chemical industry and workplace
health and safety that can be utilized
to evaluate the chemical exposure of
employees
https://www.atsdr.cdc.gov/substan
ces/index.asp
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can quickly obtain the structure and toxicology of any compound with matching prop-
erties. Additionally, SuperToxic lets users explore the d ata by presenting all entries
beginning with a selected alphabetic character or set of numbers. It is also possible to
record the available NSC or CASRN numbers in the database [42]. The dosage, type of
examination (toxicology evaluation, including LD50), and cell lines or organisms
which dictate the toxicity are a few of the toxicity details that were obtained from the
database. Additionally, the database incorporates software programs that are fre-
quently used in the creation of contemporary composite databases inc luding My-
Chem/OpenBabel (property calculation), JMol (visual inspection), and Marvin Sketch
(molecule drawing). In order to find possible targets in biochemical pathways and
look for compounds, SuperToxic was also linked to the Protein Data Bank, UniProt,
and KEGG databases [42, 43].
A few recently created ADMET-related databases, such as the Chemical Effects in
Biological Systems database (CEBS), the Toxicity Reference Database (ToxRefDB), and
the Comparative Toxicogenomics Database (CTD), should also raise concerns in addi-
tion to the databases mentioned above. CTD is a robust public database created to im-
prove knowledge of the effects of environmental exposures on human health. In
order to help develop hypotheses about the underlying mechanisms of diseases af-
fected by the environment, it provides data on chemical–gene/protein interactions,
chemical–disease, as well as gene–disease relationships that are put together with
pathway and function data [44]. Toxicological endpoints are gathered from in vivo an-
imal toxicity tests and made available by ToxRefDB for predictive modeling. A little
over 28,000 datasets originating from almost 400 endpoints have been created and
archived. The usefulness of toxicology prediction has been greatly increased by the
Table 15.1 (continued)
Database
name
Availability Description URL
TOXNET Free It consists of a collection of databases
covering toxicologically hazardous
chemicals and related fields as they
relate to environmental health
https://toxnet.nlm.nih.gov/
IDAPM Free Explain the relationship between
toxins and targets as well as the toxic
action mechanism, which can be used
to predict targets that are toxic or
toxins themselves
http://idaapm.helsinki.fi/
eChemPortal Free The following searches are available:
GHS, chemical substance, and
chemical property data
http://www.oecd.org/chemicalsaf
ety/risk-assessment/echemportal
globalportaltoinformationchemical
substances.htm
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latest update to ToxRefDB, which also includes links to other resources [45]. A compre-
hensive and easily accessible collection of animal data from 11,000 test articles and
over 8,000 studies covering all available carcinogenicity, genetic toxicity, and short-
term toxicity studies from the National Toxicology Programme (NTP) is provided by
CEBS, a toxicology resource. Building a more precise model for toxicity prediction is
made possible by the high-quality data in CEBS [46].
15.3.2 Auxiliary databases
For ADMET prediction, databases of biological responses, pathways, and adverse reac-
tions are crucial in alongside ADMET-related databases. The majority of these data-
bases, including PubChem [47], DrugBank [48], and ChEMBL [49], are accessible for
free. They may offer structural data to construct models or be accessed for informa-
tion about compounds, though they are rarely used to directly predict ADMET-related
properties. The anticipated compound structure can be downloaded by users and
used as an input file for additional software.
A comprehensive database called DrugBank combined the physical, biological,
chemical, and pharmaceutical data of thousands of well-researched drugs and drug
targets. The information on ADMET and other types of QSAR information were added
to DrugBank 4.0 [50]. The most recent version of DrugBank, 5.0, has further revised
this data [51]. Users can search DrugBank’s spectral library for exact or approximate
matches using mass-to-charge ratio (m/z) lists or chemical shifts. Additionally, Drug-
Bank allows users to query its database using simple text queries and consistently cat-
egorizes substances into distinct groups based on structural similarities and features
[50, 51].
The publicly accessible PubChem database comprises three interrelated compo-
nents: (1) compound, which houses over 102 million distinct chemical structures sub-
mitted by different depositors; (2) substance, which holds over 251 million records
encompassing complexes, extracts, combinations, and noncharacteristics; and (3) Bio-
Assay, which holds over 1,067,000 bioassays offering composite nearby substructures,
similar structures, bioactivity data, and additional search features [47, 52].
For many drug-like bioactive compounds, ChEMBL is an open data database that
includes two-dimensional structures, determined characteristics (molecular weight,
lipophilicity, etc.), and abstract biological functions (pharmacology and ADMET data)
[53, 54]. It is made up of three distinct datasets – DrugStore, CandiStore, and StARlite –
all of which were initially created by Inpharmatica. In order to satisfy user needs for
intelligently clustering pertinent information and integrating data throughout thera-
peutic studies and areas, ChEMBL’s data were taken from the scientific literature [55].
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15.3.3 Commonly used software to predict ADMET
In order to lower late failure and cost, favorable ADMET characteristics are crucial as
early criteria for drug candidates. Preclinical research on developing and discovering
drugs must si multaneously optimize several ADMET attributes since they are highly
interdependent. Nonetheless, the most challenging and unappeal ing step is concur-
rent optimization of multiparameter ADMET. Therefore, only a few basic properties –
such as log P, log D, and log S – were involved in the early prediction of ADMET. A
Table 15.2: Various types of auxiliary databases.
Database
name
Availability Description URL
ChEMBLdb Free A public, bioactive database that can show metabolite
pathways, provide links to enzymes that metabolize
metabolites, and provide information about the source
of document data
https://www.ebi.ac.
uk/chembldb/
DrugBank Free Preserving data regarding medications and associated
targets
https://www.drug
bank.ca/
ChemProt Free A tool for in silico small molecule estimation that
incorporates protein complexes is linked to disease,
cells, and molecules
http://potentia.cbs.
dtu.dk/ChemProt/
PubChem Free A freely accessible database holding small molecules’
biological characteristics
http://pubchem.ncbi.
nlm.nih.gov/
SIDER Free Includes data on pharmaceuticals that are marketed
and the side effects associated with them, which can be
utilized to quickly identify and trace the source of an
extracted side effect
http://sideeffects.
embl.de/drugs/
STITCH Free A resource and search engine for information on how
chemicals and proteins interact
http://stitch.embl.de/
KEGG Free A popular reference source for combining and
interpreting massive datasets is produced by high-
throughput experimental techniques like genome
sequencing
https://www.kegg.jp/
BindingDB Free An online database with experimental binding affinities
that mostly highlights interactions between drug-like
molecules and possible drug targets
http://www.bind
ingdb.org/bind/
index.jsp
TTD Free Gives information on the targeted disease, known and
researched therapeutic macromolecules, pathways, and
related medications that are aimed at each of these
targets
http://bidd.nus.edu.
sg/group/cjttd/
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