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growing numbe r of in silico models were created to predict more complex ADMET
parameters as experimental data increased [56]. These parameters included the oral
bioavailability, BBB penetration level, Caco-2 permeation, human intestinal absorp-
tion, intera ction with drugs, P-glycoprotein, plasmaprotein binding rate, CYP meta-
bolic enzymes, and kidney clearance as shown in Figure 15.2 (Section 1). A number of
studies have detailed these in silico models and their predicted properties, and at-
tempts have also been made to bring together these models to predict ADMET param-
eters concurrently [57]. Furthermore, software that simultaneously predicts ADMET
parameters has been developed by integrating these models (as shown in Table 15.3).
The Swiss Institute of Bioinformatics created the hybrid web server known as
SwissADME. It can forecast and analyze the ADME properties of many compounds in
groups submitted from around the world and support a variety of input formats.
Water solubility, lipophilicity, physicochemical properties, drug likeness, pharmacoki-
netics, and medicinal chemistry are among the various drug physicochemical proper-
ties that this software generates [58]. These can be directly exported, saved as a
comma-separated values (CSVs) data file, and viewed by applications like WordPad
and Excel. Furthermore, it provides a bioavailability radar map that enables nonex-
perts despite professional knowledge to swiftly and easily assess the drug likeness of
tiny molecules [58, 59].
It is currently thought that in vivo toxicology, while still the gold standard for de-
termining drug side effects, will not assist in lowering the high rate of consumption in
late clinical development. Numerous computational tools have been created to fore-
cast drug toxicity, which helps to shorten the time and cost of drug development as
well as low er the attrition rate of molecular compounds in drug discovery [60]. The
ability of these toxicology prediction systems to forecast more complex toxicological
endpoints, like hepatotoxicity, teratogenicity, nephrotoxicity, and carcinogenicity, has
significantly increased in the recent years. There are numerous web-based toxicity
predictors available at the moment, both free and commercial. Examples include
Lazar and Toxtree [61].
Lazar is a tool for predicting toxicological endpoints like carcinogenicity, repro-
ductive toxicity, and long-term toxicity that was developed by in silico toxicology
GMBH. It is based on OpenTox, a comprehensive interface for a compatible prognostic
toxicology framework. It makes predictions about new compounds’ toxicity using
data mining algorithms by using the experimental training results [62]. To create a
local QSAR model, users only need to enter the compound’s structure into Lazar,
which afterward searches the database for a number of comparable compounds and
related experimental data. A graphical interface that presents structural characteris-
tics, compounds that are similar to the query compounds, and the toxicity properties
for each fragment will be used to display the prediction results obtained from the
model [62–64].
The European Chemicals Agency, a joint research Centre of the European Com-
mission, commissioned the free software program Toxtree. Its original purpose was to
394 Bhupender Nehra et al.
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facilitate the Cramer decision tree’s efficient development. With an entire set of 14
functional modules, the most recent version of Toxtree included extra projects like
the Verhaar scheme, corrosion rules, and BfR/SICRET skin irritation [65, 66]. Com-
pounds are categorized using physiochemicalexclusionrulesaswellasstructural
alert inclusion rules, among others. It includes no training set in contrast to Lazar.
Since structural filters are the foundation of its prediction, there is no relevance do-
main. It uses a decision tree framework for risk assessment when handling molecular
structure information. It is available at http://toxtree.sourceforge.net/, allowing users
to access the same [33, 66–68]. Also, involvement of artificial intelligence to predict
toxicity is shown in Figure 15.6.
According to Dong et al. [56], ADMETlab is a platform that facilitates systematic
ADMET estimation by utilizing an extensive collection of ADMET databases. The plat-
form consists of four functional modules that are used for database/similarity search,
ADMET endpoint estimation, system evaluation, and drug resemblance assessment
(based on Lipinski’s rule of five as well as the drug likeness model) [56]. By using the
SMILES, submitting a file in SDF format, or using the integrated JME editor to draw
the chemical structure, users may search one or more molecules with the platform.
Following the upload of the compound, the platform will make vast estimations of its
ADMET features by utilizing a variety of pharmacokinetics models constructed by the
various integrated data sets [69, 70].
A comprehensive and free tool for predicting ADMET properties is called Admet-
SAR in w hich ADMET-related information is gathered from published literature. A
customizable tool called ADMET-Simulator is also included in AdmetSAR [71]. It c an
predict approximately 50 ADMET endpoints and combines superior and predictive
QSAR models in a chemical informatics-based toolbox. With AdmetSAR, users can
quickly look up ADMET properties by searching the structure, the common name, or
CASRN [70, 72]. Version 2.0 of admetSAR is primarily concerned with chemical ADMET
property in silico prediction [73].
Table 15.3: Some commonly used freely accessible ADMET tools.
Database name Access Log S Log P Log D Sol TPSA BBB V
d
Met CL TOX
SwissADME Free Y Y N Y Y Y N Y N N
Lazar Free N N N N N N N N N Y
ADMETlab Free Y Y Y Y Y Y Y Y Y Y
ACD/I-lab Free Y Y Y Y Y Y Y N N Y
ToxTree Free N N N N N N N Y N Y
admetSAR Free N N N S N Y N Y N Y
Here, Y is yes and n is no.
Log S, water solubility; log P, octanol–water partition coefficient; log D, octanol–water distribution
coefficient; Sol, solubility; TPSA, topological polar surface area; BBB, blood–brain barrier; V
d
, volume of
distribution; Met, metabolism; CL, clearance; Tox, toxicity.
15 Developing safer therapeutic agents through toxicity prediction 395
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15.4 Conclusions
In this chapter, we gave a thorough overview of the methods and resources currently
in use for predicting ADMET properties, covering their fundamentals, categorization,
and uses. Furthermore, we gather relevant applications from articles that have been
published in the last years and examine the trends in the various application aspects.
This chapter’sgoalistofacilitatereaders’ rapid understanding of the methods and
features of the relevant tools (software and databases). It might also help readers
Figure 15.6: Role of computational strategies in toxicity predictions.
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comprehend how to apply currently available tools for pharmacokinetic predictions.
We are confident that the importance of in silico ADMET prediction in pharmacoki-
netics will rise as a result of users’ familiarity with current online services, leading to
more accurate predictions. Moreover, we anticipate a shorter timeframe from re-
search and development to market, lower costs during the most recent stage of devel-
opment, and a lower failure rate in drug development and recalls in the future.
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Bhupender Nehra, Manoj Kumar, Pooja A. Chawla
✶
, Viney Chawla
✶
,
and Sarita Pawar
16 Identifying prominent molecular targets
in the fight against drug resistance
Abstract: Drug resistance is a complex concern that can develop in cancer cells or in-
fections through a variety of pathways. Developing effective solutions to treat drug
resistance requires an understanding of the molecular targets involved in the phe-
nomenon. The wide spectrum of clinical diseases can be linked to many resistance
mechanisms, such as upregulated DNA repair processes, destruction of biofilms, mu-
tations in target proteins, efflux pumps, and increased drug metabolism by enzymes.
These molecular targets and tactics demonstrate the variety of ways that drug resis-
tance is being studied. In particular, combination therapies and novel drug delivery
systems hold great promise for defeating the adaptive mechanisms that cancer cells
or infections use to resist the effect of medicines. This book chapter summarizes the
various molecular targets that may have been observed in the pathophysiology of var-
ious diseases, together with the related mechanisms. By keeping these mechanisms in
mind, scientists may systematically identify the molecular targets and pathways that
contribute to medication resistance, which will help them to build more potent and
focused treatment plans to address resistance in a variety of illnesses.
Keywords: Targets, efflux, molecules, resistance, drug
16.1 Introduction
Drug resistance is one of the major threats overwhelming worldwide today. Drug re-
sistance is a significant chal lenge in the field of medicine, particularly in the treat-
ment of infectious diseases and cancer [1]. To combat drug resistance, researchers
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Corresponding author: Pooja A. Chawla, University Institute of Pharmaceutical Sciences and
Research, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India,
e-mail: pvchawla@gmail.com
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Corresponding author: Viney Chawla, University Institute of Pharmaceutical Sciences and Research,
Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India
Bhupender Nehra, Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science
and Technology, Hisar 125001, Haryana, India
Manoj Kumar, Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and
Technology, Hisar 125001, Haryana, India, email: drmanojmedal@gmail.com
Sarita Pawar, Department of Pharmaceutical Chemistry, Sanjivani College of Pharmaceutical Education
and Research (Autonomous), Kopargaon, Ahmednagar 423603, Maharashtra, India
https://doi.org/10.1515/9783111207117-016
https://t.me/med1917
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