1. Assignment
On
Drug design & toxicology
Submitted To
Prof. Dr. Ehsanul Huq
Department of Pharmacy
Primeasia University
Submitted By
NAME :Arfia Chowdhury
ID:123-008-062
Semester: Fall, 2013
Course Name: Applied Medicinal Chemistry
Course Code : MPP-6201
Submission Date: 22 March, 2013

2. Drug Design & Toxicology Page 2
Content Page No.
Drug Design ………………………………………………………………………………………………………………………………………………………. 3
Drug Design Process …………………………………………………………………………………………………………………………………………. 3
Types of Drug Design ……………………………………………………………………………………………………………………………………….. 4
o Ligand-based …………………………………………………………………………………………………............................................ 4
o Structure-based ……………………………………………………………………………………………………………………………………… 5
o Active site identification …………………………………………………………………………………………………………………………. 5
Toxicology Testing in Drug Design and Development …………………………………………………………………………………….. 5
Safety/Toxicity Issues in Drug Design …………………………………………………………………………………………………………….. 5
o Emerging Safety Biomarkers ………………………………………………………………………………..................................... 6
o Establishing Human First-Dose Levels ……………………………………………………………………………………………………. 6
o Pathway Analysis …………………………………………………………………………………………………………………………………… 7
o Genomic Biomarker Usage ……………………………………………………………………………………………………………………. 7
o Markers for Predictive Toxicology …………………………………………………………………………………………………………. 8
o Biomarker-Based Nonanimal Toxicity Testing ………………………………………………………………………………………... 8
Dynamic QSAR Techniques: applications in drug design and toxicology ………………………………………………………….. 9
ADME and Toxicology ………………………………………………………………………………………………………………………………………. 10
The application of discovery toxicology towards the design of safer pharmaceutical lead candidate..………………… 10
Future of toxicology-metabolic activation and drug design: challenges and opportunities in chemical toxicology… 11
Reference …………………………………………………………………………………………………………………………………………………..………. 11

3. Drug Design & Toxicology Page 3
Drug Design & Toxicology
Drug Design
Drug design, sometimes referred to as rational drug design or more simply rational design, is the inventive process of finding new
medications based on the knowledge of a biological target.[1] The drug is most commonly an organic small molecule that activates or
inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic
sense, drug design involves the design of small molecules that are complementary in shape and charge to the biomolecular target with
which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques.[2]
This type of modeling is often referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the
three-dimensional structure of the biomolecular target is known as structure-based drug design.
The phrase "drug design" is to some extent a misnomer. What is really meant by drug design is ligand design (i.e., design of a small
molecule that will bind tightly to its target).[3] Although modeling techniques for prediction of binding affinity are reasonably successful,
there are many other properties, such as bioavailability, metabolic half-life, lack of side effects, etc., that first must be optimized before
a ligand can become a safe and efficacious drug. These other characteristics are often difficult to optimize using rational drug design
techniques.
Drug Design Process

4. Drug Design & Toxicology Page 4
Types of Drug Design
There are two major types of drug design. The first is referred to as ligand-based drug design and the second, structure-based drug
design.
Ligand-based
Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of
interest. These other molecules may be used to derive a pharmacophore model that defines the minimum necessary structural
characteristics a molecule must possess in order to bind to the target. In other words, a model of the biological target may be built
based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the
target. Alternatively, a quantitative structure-activity relationship (QSAR), in which a correlation between calculated properties of
molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to
predict the activity of new analogs.
Structure-based
Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target
obtained through methods such as x-ray crystallography or NMR spectroscopy. If an experimental structure of a target is not available,
it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the
structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be
designed using interactive graphics and the intuition of a medicinal chemist. Alternatively various automated computational procedures
may be used to suggest new drug candidates.
As experimental methods such as X-ray crystallography and NMR develop, the amount of information concerning 3D structures of
biomolecular targets has increased dramatically. In parallel, information about the structural dynamics and electronic properties about
ligands has also increased. This has encouraged the rapid development of the structure-based drug design. Current methods for
structure-based drug design can be divided roughly into two categories. The first category is about ―finding‖ ligands for a given
receptor, which is usually referred as database searching. In this case, a large number of potential ligand molecules are screened to
find those fitting the binding pocket of the receptor. This method is usually referred as ligand-based drug design. The key advantage of
database searching is that it saves synthetic effort to obtain new lead compounds. Another category of structure-based drug design
methods is about ―building‖ ligands, which is usually referred as receptor-based drug design. In this case, ligand molecules are built up
within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual
atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be
suggested.

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Flow charts of two strategies of structure-based drug design
Active site identification
Active site identification is the first step in this program. It analyzes the protein to find the binding pocket, derives key interaction sites
within the binding pocket, and then prepares the necessary data for Ligand fragment link. The basic inputs for this step are the 3D
structure of the protein and a pre-docked ligand in PDB format, as well as their atomic properties. Both ligand and protein atoms need
to be classified and their atomic properties should be defined, basically, into four atomic types:
hydrophobic atom: All carbons in hydrocarbon chains or in aromatic groups.
H-bond donor: Oxygen and nitrogen atoms bonded to hydrogen atom(s).
H-bond acceptor: Oxygen and sp2 or sp hybridized nitrogen atoms with lone electron pair(s).
Polar atom: Oxygen and nitrogen atoms that are neither H-bond donor nor H-bond acceptor, sulfur, phosphorus, halogen,
metal, and carbon atoms bonded to hetero-atom(s).
Toxicology Testing in Drug Design and Development
The primary objective of toxicology studies in the drug discovery process is to evaluate the safety of potential drug candidates. This is
accomplished using relevant animal models and validated procedures. The ultimate goal is to translate the animal responses into an
understanding of the risk for human subjects. To this end the toxicologist must be aware of the international guidelines for safety
evaluation as well as traditional and nontraditional toxicology models. As described in this unit, the typical toxicology profile consists of
safety pharmacology, genetic toxicology, acute and subchronic toxicology, absorption, distribution, metabolism, and excretion (ADME)
studies, reproductive and developmental toxicity, and an evaluation of carcinogenic potential.
Safety/Toxicity Issues in Drug Design
As pressure increases on major pharma to bring new compounds into development, so too does pressure to make sure that these
compounds are safe to test in humans. As evidenced in the TGN1214 trial last year, no company can afford to skip steps in insuring
that the compounds tested in humans are safe. Ensuring safety and lack of toxicity during drug development were central themes to

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Cambridge Healthtech’s ―Trends in Drug Safety‖ conference, held in San Francisco, and The Oxford International ―Second Biomarkers
Discovery and Development Congress‖ in Manchester U.K.
Emerging Safety Biomarkers
―Safety-related problems continue to be one of the major causes of drug attrition in preclinical and clinical development,‖ said Rakesh
Dixit, senior director and global head of toxicology at MedImmune (www.medimmune.com). ―The search for biomarkers that can be
objectively linked to adverse effects and target organ toxicities, and can then be translated from preclinical to clinical development, is
becoming an urgent matter for academics, federal agencies, and pharmaceutical companies alike.‖
One of the key points, said Dixit, is that the safety of the compound and drug in question is absolutely crucial. ―In the preclinical stages,
you can look and see what effect a compound has in animals, but when you attempt to examine the effects in man, it is harder to do
because of the limited noninvasive tests.‖
One of the primary challenges in using biomarkers to examine safety is that the tools available to reveal that information are already
20–30 years old. ―With regard to validating safety, there is really nothing out there that is cutting-edge in regards to detecting low-level
adverse responses noninvasively,‖ stated Dixit.
However, there are newer and noninvasive ways to look at biomarkers, such as analyzing whole blood, plasma, urine, and limited
human biopsy samples (e.g., skin, muscle, buccal mucosal cells), which are ―probably the best line of analysis in humans right now,‖
reported Dixit. ―The next line would be to do a genetic profile, and that’s easier to do, too. But if you’re doing a clinical trial, repeat
muscle biopsies, for example, are harder to get.‖
Dixit also discussed the difference in definition between safety biomarkers and toxicity biomarkers. ―Some people use these terms
interchangeably, but there are subtle differences,‖ explained Dixit. ―The key thing to keep in mind is that toxicology is about how high
you can push the dose before things go wrong. Safety is how low you can go in dose response before subtle, low-grade toxicities may
appear.
―Safety biomarkers have a higher bar to reach than efficacy biomarkers. It’s harder to evaluate. But, the best way to validate is to
conduct exploratory safety biomarker analyses and see what you experience in the clinic along with conventional biomarkers. However,
the burden of proof is high, and the key challenge facing newer biomarkers is obtaining that proof,‖ Dixit said.
Establishing Human First-Dose Levels
Christopher Horvath, senior director of toxicology at Archemix (www.archemix.com), noted that while one can evaluate what are
considered classic toxicologic endpoints, those traditional measures of what might or might not be safe are not appropriate for some
biologics. ―For example, with biologics there are issues of superpharmacology—too much of the desired effect—being responsible for
the observed adverse effects. In contrast, for small molecules, the observed toxicity is often related to chemical metabolites, so you
need to look at secondary (or safety) pharmacology when you get different results from what you first expected from a compound. A
critical distinction in the selection of relevant species for nonclinical safety evaluation is that the chosen species should have
comparable metabolic profiles for a drug and comparable pharmacologic activity for a biologic,‖ Horvath said.
Currently, there is regulatory guidance available for establishing a maximum recommended starting dose (MRSD) for a first-in-human
(FIH) study. ―These parameters focus on the no adverse effect dose level (NOAEL) and toxicity and/or exposure algorithms to arrive at
the FIH MRSD,‖ said Horvath. ―But, it’s important to note that the extent to which the information generated during nonclinical
development of biologics is relevant to subsequent clinical development depends chiefly on the degree of pharmacologic relevance of
the test systems. A NOAEL achieved in a species incapable of displaying either appropriate pharmacologic activity or relevant toxicity
is not a good starting place for human safety predictions.‖

7. Drug Design & Toxicology Page 7
Horvath pointed out that one needs only to look at the results of the TGN1412 trial last year, wherein six volunteers were administered
a dose of 0.1 mg/kg—a dose 500 times lower than the NOAEL dose in monkeys (50 mg/kg)—and all six suffered catastrophic
multiorgan failure. ―I think all of us are saying the same thing—that if you blindly apply mathematical algorithms, the likely effect is that
you leave yourself open to wholly unpredictable results. Unfortunately, many view the role of nonclinical development as telling clinical
what they cannot do with respect to toxicity in FIH studies.
―Rather, we should be able to tell people in clinical development what they can do or expect to see in humans, to be able to make
better use of animal studies, to better inform and educate your clinicians, and to help guide them in setting pharmacologically relevant
doses and concentrations for FIH studies,‖ Horvath said.
Pathway Analysis
Another area of analysis that was covered at the Cambridge confab is hepatotoxicity. ―Drug-induced liver toxicity is a major issue, not
only for current health care but also drug development,‖ said Philip Hewitt, head of toxicogenomics for Merck KGaA (www.merck.de) of
Darmstadt, Germany.
―Toxicogenomics is gaining importance as a tool for toxicity prediction and supports classic toxicity tests for rapid and early toxicity
screening.‖
Hewitt reported that his group has been looking at both global arrays (of 20,000–24,000 genes) and focused arrays (550 liver-specific
genes), concentrating on well-known model compounds. ―We have good in vivo data using these technologies, with accurate
classification of hepatotoxic compounds using gene-expression signatures. However, we are still having problems with in vitro
technologies. This is the area we are currently focusing on.‖
One model hepatotoxicant Hewitt’s lab has been studying is lipopolysaccharide (LPS), using pathway analysis tools to aid
interpretation of the mechanisms of toxicity. In vivo and in vitro comparisons in gene deregulation after exposure to LPS have been
compared. ―We see similar responses in vivo and in vitro, with an excellent correlation between the systems, giving us confidence that
we are going in the right direction,‖ said Hewitt. ―One of our aims is to eventually be able to predict drug toxicity at low drug levels and
after long-term treatment of rat and human hepatocytes.‖
Genomic Biomarker Usage
―Our lab is heavily involved with translation research in oncology,‖ said Hans Winkler, senior director of functional genomics at
Johnson and Johnson Pharmaceutical Research and Development (www.jnjpharmarnd.com). ―We focus on the pharmacodynamic
work on all projects that are in preclinical testing, looking closely at targets and effects in vivo and in vitro.‖
Winkler noted that with current targeted therapies in oncology, the response rates are relatively low, about 10–15%. ―Obviously this is
not a good response rate,‖ Winkler said. ―So larger trials are needed to show efficacy.‖
One approach to tackle this is gene-expression analysis and profiling. ―And once we have the gene-expression profile,‖ said Winkler,
―we can make comparisons and identify those genes that contribute to a response. What we are researching depends on the
compound and the clinical activity. In principle, you follow the compound. In research, every one is going after the major tumors and
cancers. But, there are also the hematological indications to pursue.‖
In addition, Winkler focused on developing proof of principle and proof of concept. ―One important aspect of research is to understand
the molecular aspect—the mechanism of action at the molecular level. We do a lot of compound profiling, and in that, we learn how
those gene expressions are changed by a compound, which genes work with a compound, and which genes interfere with a
compound.‖

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However, Winkler noted that the way most companies do safety and efficacy studies—usually broken up in three steps by three
departments doing three different kinds of studies (pharmacology, pharmacokinetics, and safety)—often results in important information
going by the wayside.
―If we want to do safety and efficacy studies effectively, we need to do these three studies at once, and we need to generate more
effective data. Right now, we start with 100 compounds and maybe one makes it to development. If we are able to improve success
rates, improve ways of assessing clinical safety, and do so in a more integrated fashion, it would be the start of a strong clinical
program.‖
Markers for Predictive Toxicology
Hugh Salter, associate director of pharmacogenomics at AstraZeneca (www.astrazeneca.com), said, ―We’re examining potential
hepatotoxicity markers using microarray data taken from in vitro samples. My talk focused on looking at the complexity of microarray
data analysis techniques, the building of predictive models, and some of the limitations.‖
In general terms, it is possible to build promising predictive models from quite naive data. ―We’ve developed an interesting method by
which we can describe a set of compounds that allows you to make predictions in a set of independent models. It allows you to form
clusters in the data, and you can see how the chemical information overlaps with the biology,‖ Salter said.
Salter noted that his group does have a route to examine a set of compounds in the in vitro space, but to see how that is relevant to
behavior in the in vivo space is a key issue. ―One problem is that with microarray data, you are dealing with 30,000 to 40,000 signals,
and we need to find the 300 to 400 most important genes to profile—in other words, large numbers of variables, small numbers of
samples. We need to flip this equation around so that vastly more samples can be screened.
―The main point is that finding the markers is not as simple as we’d like it to be,‖ Salter said. ―It’s a set of techniques that certainly have
promise, and what we’ve done is work on improving the data readouts and technology.‖
Biomarker-Based Nonanimal Toxicity Testing
The keynote presentation at ―Biomarkers Discovery and Development‖ examined the identification, validation, and implementation of
biomarker-based, nonanimal toxicity tests in drug development. ―We’re not heavily geared toward genomic biomarker assessment—
we’re working with a reconstituted epidermis model,‖ said Stuart Freeman, director of worldwide toxicology, GlaxoSmithKline
(www.gsk.com). ―We’ll examine skin irritation, sensitization, and look at biomarkers that are predictive of toxicities that are seen in the
skin.‖
The presentation discussed the identification of key transit biomarkers and relevant end points, the validation of biomarker response to
toxic challenge, and demonstrating the effective use of validated tests in drug development. ―While we’re not currently using genomic
biomarkers in our skin model, it’s an area of tremendous promise and application,‖ Freeman said. ―As we increase our knowledge base
in this area, these techniques will become a main stream way of assessing risk and toxicity.‖
What Freeman’s group looks for is cytokines secreted by skin models. ―When it comes to sensitization, there is an immunological
response,‖ said Freeman.
―Secreted biomarkers, like cytokines, improve our ability to pick up sensitization events upstream. Toxicology high-throughput
screening can be enhanced by this technology. As soon as you discover a compound, the clock starts ticking,‖ said Freeman.
―Anything that increases the speed of drug development is good business.‖
The space inside the ligand binding region would be studied with virtual probe atoms of the four types above so the chemical
environment of all spots in the ligand binding region can be known. Hence we are clear what kind of chemical fragments can be put into
their corresponding spots in the ligand binding region of the receptor.

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Dynamic QSAR Techniques: applications in drug design and toxicology
Identification of non-toxic drug design is a major challenge in the field of drug design, most of the drug failure due to toxicity being
found in late development or even in clinical trials. Thus the use of predictive toxicology is called for. Keeping this problem in view,
several QSAR methods have been employed previously but we have started this study with latest dataset and apply different machine
learning classifiers including non-linear method implemented in WEKA and linear method (Multiple linear regressions (MLR)) using R-
package.
The basic principles of 3-D quantitative structure-activity relationships (QSARs) analysis are discussed in the light of the fuzzy logic
concept. According to that concept, the traditionally one chemical - one structure - one parameter value relationship in QSAR is
suggested to be modified into one chemical finite set of structures - range of parameter values principle. In this respect, two recently
developed techniques accounting for conformational flexibility in 3-D QSARs are reviewed. A basic assumption underlying both
methods is that chemical behavior in complex biological systems is context-dependent. A molecule can exist and interact in a variety of
conformations depending on the specificity of the endpoint under investigation and reaction media. It was demonstrated that selection
of active conformer(s) in QSAR studies is a task as important as the selection of relevant molecular parameters. Specifically selected
active conformers, rather than the lowest-energy states of the chemicals are suggested to be used in the correlative QSARs. The
method for recognition the common reactivity pattern (COREPA) of structurally heterogeneous compounds that elicit similar biological
behaviour is based on all energetically reasonable conformers of chemicals. The principle assumption of the method is that biologically
similar chemicals should possess a commonality in their stereoelectronic (reactivity) pattern. Originally developed algorithms for
conformer generation are presented in association with the QSAR methods accounting for conformational flexibility of chemicals.
Applicability of the QSAR technique for selection active conformers is illustrated by presenting QSAR models derived for Ah binding
affinity of PCBs and antimicrobial activity of rifamicin derivatives. Models for predicting estrogenic activity of structurally diverse
chemicals and ACE inhibition exemplified the applicability of the COREPA method. The model performance is analyzed by the 3D
screening exercise of large chemical inventories with subsequent experimental validation within the EDAEP project. Besides the impact
of conformational flexibility of chemicals in 3D QSAR the role of different molecular descriptors is discussed with respect to their ability
to describe molecular interactions with different specificity.

10. Drug Design & Toxicology Page 10
ADME and Toxicology
ADME and Toxicology testing has become one of the most important research activities related to new drug discovery. Ensuring that
the drugs make it to the market quicker with higher hit rates thereby saving cost, requires prior indication of human toxicity as well as
pharmacokinetics and therefore is of utmost importance. Since the pharmaceutical and biotechnology companies are utilizing the
newer technologies, ADME and toxicology screening at early stages of drug discovery and development process has become even
more imperative.
Our ADME & Toxicology 2013 is an international event being hosted in New Delhi, India. This event will focus on the vital role of ADME
and Toxicology in identification and design of a successful drug candidate. Our array of Keynote Speakers will give presentations on all
areas of ADME and Toxicology backed up by a panel of expert speakers speaking on highly relevant and ―hot‖ topics such as
Pharmacogenomics and Drug Safety, In-silico PK/PD, Toxicity and Genotoxicity Modeling, New Therapeutic Targets, Metabolomics
and ADME Optimization for DrugDesign/Discovery.
Also, co-located with our ADME & Toxicology 2013 conference will be our Nanomedicine meeting. Registered delegates will have
unrestricted access to both meetings ensuring a comprehensive learning and sharing experience as well as being financially beneficial
for attendees.
The application of discovery toxicology towards the design of safer pharmaceutical lead candidates
Toxicity is a leading cause of attrition at all stages of the drug development process. The majority of safety-related attrition occurs
preclinically, suggesting that approaches to identify 'predictable' preclinical safety liabilities earlier in the drug development process
could lead to the design and/or selection of better drug candidates that have increased probabilities of becoming marketed drugs. In
this Review, we discuss how the early application of preclinical safety assessment--both new molecular technologies as well as more
established approaches such as standard repeat-dose rodent toxicology studies--can identify predictable safety issues earlier in the
testing paradigm. The earlier identification of dose-limiting toxicities will provide chemists and toxicologists the opportunity to
characterize the dose-limiting toxicities, determine structure-toxicity relationships and minimize or circumvent adverse safety liabilities.

11. Drug Design & Toxicology Page 11
Future of toxicology-metabolic activation and drug design: challenges and opportunities in chemical
toxicology
The issue of chemically reactive drug metabolites is one of growing concern in the pharmaceutical industry inasmuch as some, but not
all, reactive intermediates are believed to play a role as mediators of drug-induced toxicities. While it is now relatively straightforward to
identify these short-lived electrophilic species through appropriate in vitro "trapping" experiments, our current understanding of
mechanistic aspects of xenobiotic-induced toxicities is such that we cannot predict which reactive intermediates are likely to cause a
toxic insult and which will be benign. Little is known about the identities of the macromolecular targets (primarily proteins) of these
electrophiles or the functional consequences of their covalent modification by reactive drug metabolites. As a result, several companies
have adopted approaches to minimize the potential for metabolic activation of drug candidates at the discovery/lead optimization phase
as a default strategy. However, research leading to a deeper insight into mechanistic aspects of toxicities caused by reactive drug
metabolites will aid greatly in the rational design of drug candidates with superior safety profiles and represents a challenging and
exciting opportunity for chemical toxicology.
References
1. Madsen, Ulf; Krogsgaard-Larsen, Povl; Liljefors, Tommy (2002). Textbook of Drug Design and Discovery. Washington, DC:
Taylor & Francis. ISBN 0-415-28288-8.
2. Cohen, N. Claude (1996). Guidebook on Molecular Modeling in Drug Design. Boston: Academic Press. ISBN 0-12-178245-X.
3. Tollenaere JP (April 1996). "The role of structure-based ligand design and molecular modelling in drug discovery". Pharm
World Sci 18 (2): 56–62. doi:10.1007/BF00579706. PMID 8739258.
4. Guner, Osman F. (2000). Pharmacophore Perception, Development, and use in Drug Design. La Jolla, Calif: International
University Line. ISBN 0-9636817-6-1.
5. Leach, Andrew R.; Harren Jhoti (2007). Structure-based Drug Discovery. Berlin: Springer. ISBN 1-4020-4406-2.
6. Wang R,Gao Y,Lai L (2000). "LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design". Journal of Molecular
Modeling 6 (7–8): 498–516. doi:10.1007/s0089400060498.
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