Drug discovery is the subject where a person can find how to develop a drug and a drug design

2. • Introduction to Drug Discovery and Design
• Methods of Drug Discovery and Target
validation.
• Combinatorial Chemistry
• Quantitative – Structure Activity and relationship
(QSAR)
• Computer Aided Drug Design (CADD)

3. • Drug discovery is the process through which potential new therapeutic entities are identified,
using a combination of computational, experimental, translational, and clinical models
• Drug development and discovery includes preclinical research on cell-based and animal models
and clinical trials on humans, and finally move forward to the step of obtaining regulatory approval
in order to market the drug
• .
• Modern drug discovery involves the identification of screening hits, medicinal chemistry and
optimization of those hits to increase the affinity, selectivity
• Once a compound that fulfills all of these requirements has been identified, it will begin the
process of drug development prior to clinical trials.
o This Special Issue “Drug Design and Discovery: Principles and Applications” was focused on the
basic principles of modern drug design and discovery and the potential applications
o It covered seventeen research articles and one communication contributed from experts all
around the world, as briefed below.

5. 1. Steps 1 – Discovery and Development-
• Research for a new drug begins in the laboratory and existing
treatments that have anticipated effects.
2. Steps 2 – Pre-Clinical Research-
• Drugs undergo laboratory & animal testing to answer basic
questions about safety.
3. Step 3 – Clinical Research
• Drugs are tested on humans to make sure they are safe &
effective.
4. Step 4 – FDA Review –
• FDA review teams thoroughly examine all of the submitted data
related to the drug or device & make a decision to approve or
not to approve it.
5. FDA Post Marketing –
• FDA monitors all drug & device safety once products are
available for use by the public.

7. 1. Target Identification:- it is the process of identifying the direct molecular
target.
eg. protein or nucleic acid
2. Target Validation:- it ensures that the engagement of target has potential
therapeutic effect. This is a critical step in drug development. If the target can’t
be validated, then it will not proceed in drug development.
3. Lead Identification:- a chemical lead is defined as synthetically stable, feasible,
and drug like molecule active in primary & secondary essays.
4. Lead Optimization:- it is the process by which a drug candidate is designed
after an initial lead compound is identified.

8. • High Throughput Screening (HTS) is identification of
one or more positive candidates.
• It extracted from a pool of possible candidates
based on specific criteria.
• It is a drug-discovery process widely used in the
pharmaceutical industry.
• It allows automation to quickly assay the biological
or biochemical activity of a large number of
compounds.
• It is a useful for discovering ligands for receptors,
enzymes, ion channels or other pharmacological
targets, or pharmacologically profiling a cellular or
biochemical pathway of interest

9. • 1st stage screening
o To Test the optical clarity, abrasion resistance, and adhesion
o It Eliminates ~ 90% of samples
• 2nd stage screening
o It Test whether the ability, integrity, gloss, and surface smoothness
o It eliminates the ~10% of the samples
• Rapidly identify coating samples with desired Properties
• Candidates for scale up
o Test according to the specification
• There are 4 steps in HTS
o Preparation of samples and compound libraries
o Establishment of a method suitable for lab automation
o Configuration of a robotic workstation
o Acquisition and handling of data

10. Detection method in HTS
• Spectroscopy: - It is used as a tool for studying the structures of atoms and molecules
• Mass spectrometry: - it also called mass spectroscopy, analytic technique by which
chemical substances are identified by the sorting of gaseous ions in electric and magnetic
fields according to their mass-to-charge ratios.
• Chromatography:- it is a technique for separating the components, or solutes, of a
mixture on the basis of the relative amounts of each solute.
• Calorimetry: - it enables the continuous monitoring of samples for a prolonged period.
• X ray diffraction: it powders diffraction (XRD) is a rapid analytical technique primarily
used for phase identification of a crystalline material and can provide information on unit
cell dimensions
• Microscopy: - it is a technical field of using microscopes to view samples & objects that
cannot be seen with the unaided eye.
• Radioactive methods: -Radioactive methods of analysis are particularly useful for the
detection and determination of trace quantities of materials and for the measurement of
larger quantities in complicated systems.

11. 2. Drug design
• Drug design is the inventive process of finding new medications based on the
knowledge of a biological target.
• In the most basic sense, drug design involves the design of molecules that are
complementary in shape and charge to the molecular target with which they
interact and bind.
• The phrase "drug design" is to some extent a misnomer. A more accurate term is
ligand design.
• Although design techniques for prediction of binding affinity are reasonably
successful, there are many other properties, such as bioavailability, metabolic half-
life, side effects, etc.,
• These other characteristics are often difficult to predict with rational design
techniques. Nevertheless, due to high attrition rates, especially during clinical
phases of drug development.
• More attention is being focused early in the drug design process on selecting
candidate drugs whose physicochemical properties are predicted to result in fewer
complications during development and hence more likely to lead to an approved,
marketed drug

12. Drug design flowchart

13.  Drug designing:-
• Principles of drug designing
o The design Improving the binding of drugs
o It Increasing the selectivity
o It Reduce side effects
o It is Easy in synthesizable
o The Arrangement functional groups and identification
of a pharmacophore.
• Based on lead molecule
o Traditional
o Lead compound
o Analogue molecules designing new molecule
Eg; salicylic acid and aspirin
• Based on target structure
o By identifying the structure of drug target
o Designing by denovo drug designing
• Based on both leading compound and drug target
o Combination of both methods

14. 3. Combinatorial chemistry

15. Introduction Combinatorial chemistry
•Combinatorial Chemistry is a new method developed by
academics and researchers to reduce the time and cost of
producing effective, marketable and competitive new drugs.
•Scientists use Combinatorial Chemistry to create large numbers of
molecules that can be detected efficiently.
•This technique has captured the attention of many areas such as
Pharmaceutical chemistry, Biotechnology and Agro chemistry.
•This technique uses the same reaction conditions with the same
reaction vessels to produce a large range of analogues.
strategies
Conventional Combinatorial
1. One molecule at a time
2. Slower lead generation
3. Hundreds of molecules a
month
4. High risk of failure
1. Many molecules at a time
2. Faster lead generation
3. Thousands of molecules
month
4. Low risk of failure
Synergy Lead identification

16.  Application:
• Applications of combinatorial chemistry are very wide scientists use combinatorial chemistry
to create large populations of molecules that can be screened efficiently.
• By producing larger, more diverse compound libraries, companies increase the probability that
they will find novel compounds of significant therapeutic and commercial value.
• Provides a stimulus for robot-controlled and immobilization strategies that allow high-
throughput and multiple parallel approaches to drug discovery
 Advantages:
• Fast
o Combinatorial approach can give rise to million of compound in same time as it will take to
produce one compound by traditional method of synthesis.
• Economical
o A negative result of mixture saves the effort of synthesis, purification & identification of each
compound
• Easy
o Isolation purification & identification of active molecule from combinatorial library is relatively
easy.
• Drug Discovery
o Mixed Combinatorial synthesis produces chemical pool. Probability of finding a molecule in a
random screening process is proportional to the number of molecules subjected to the
screening process.

17. • Drug Optimization
o Parallel synthesis produces analogues with slight differences
which is required for lead optimization
 Disadvantages
o Efficiency is highly affected by compound's size, solubility and
function group.
o Compounds produced tend to be Achiral of Racemic
 Impact at lead discovery
• Traditionally lead drugs were found from natural products
•Synthetic custom crafted organic molecules made in small numbers
• Analogues of known actives (analogue me-toos)
 High Throughput screening (HTS) requires large numbers of
compounds to fuel the discovery process
 As an alternative to traditional synthesis many compounds rapidly
constructed was needed

18.  Tools
1. Solid Phase Techniques
2.1 Advantages
2.2 Requirements
2.3 Examples of Solid Supports (2 slides)
2.4 Anchor or linker
o Merrifield resin for peptide synthesis (chloro-methyl
group)
o Wang resin (2 slides)
o 3Rink resin (2 slides)
o Dihydropyran resin (2 slides)
2. Parallel Synthesis
3.1. Houghton’s Tea Bag Procedure
3.2. Automated parallel synthesis (2 slides)
3.3. Automated parallel synthesis of all 27
tripeptides from 3 amino. (2 slides)
3. Mixed Combinatorial Synthesis
4. Solution phase synthesis

33. • Quantitative structure-activity relationship (QSAR) is a computational or
mathematical modeling method to reveal relationships between biological
activities and the structural properties of chemical compounds.
• QSAR modeling helps prioritize a large number of chemicals in terms of
their desired biological activities as an in silica methodology
• QSAR modeling has served as an inevitable process in the pharmaceutical
industry, but many constraints are involved.
• it has become difficult for QSAR-based model prediction to achieve a
reliable prediction score
 Essential steps in QSAR studies
• The principal steps of QSAR/QSPR include:-
1) Selection of data set and extraction of structural/empirical descriptors
2) variable selection,
3) model construction
4) validation evaluation

34.  SAR and the SAR paradox
• The basic assumption for all molecule-based hypotheses is that similar molecules have similar
activities. This principle is also called Structure–Activity Relationship (SAR).
• The underlying problem is therefore how to define a small difference on a molecular level, since
each kind of activity, e.g. reaction ability, biotransformation ability, solubility, target activity, and
so on, might depend on another difference.
• The SAR paradox refers to the fact that it is not the case that all similar molecules have similar
activities.
 Types of QSAR
1. Fragment based (group contribution)
o Analogously, the "partition coefficient"—a measurement of differential solubility and itself a
component of QSAR predictions—can be predicted either by atomic methods (known as
"XLogP" or "ALogP") or by chemical fragment methods (known as "CLogP" and other variations).

35. 2. 3D-QSAR
o The acronym 3D-QSAR or 3-D QSAR refers to the
application of force field calculations
requiring three-dimensional structures of a
Given set of small molecules
with known activities (training set).
3. Chemical descriptor based QSAR
o In this approach, descriptors quantifying
various electronic, geometric,
or steric properties
of a molecule
are computed and used to develop a QSAR.

36.  String based QSAR
o It has been shown that activity
prediction is even possible based purely
on the SMILES string.
 Graph based
o Similarly to string-based methods, the
molecular graph can directly be used as input
for QSAR models, but usually yield inferior
performance compared to descriptor-based
QSAR models.

37.  Modeling
o In the literature it can be often found that chemists have a preference for partial least squares
(PLS) methods, since it applies the feature extraction and induction in one step.

38. 5. Computer-aided drug design (CADD)
• The primary objective of CADD is to screen, optimize and evaluate the activity of the
compound against the target. It forms the multi-disciplinary approach for both academic
and major pharmaceutical companies for better efficacy with no/fewer side effects.
• The advancement in CADD is based on similarity, target identification, structure prediction,
binding site/ cavity, validation, understanding the protein-ligand interaction along with
screening vast set of compounds, grasping the molecular dynamics simulations.
• Based on physiological conditions, Tallying with ADMET properties, estimating biological
activity using QSAR.

39. Drug Disease Target
Oxymorphone Opioid analgesic
Agonist of mu- opiniod
receptor
Saquinavir AIDS
Inhibits proteases of HIV1
and HIV 2
Captopril Hypertension or high BP
Inhibits conversion of
angiotensin-converting
enzyme
Zanamivir
Affects influenza A and
influenza B
Inhibits neuraminidase
Dorzolamide
Glaucoma and ocular
hypertension
Inhibits carbonic anhydrase
 List of the drugs based on the computer-aided drug designing

40.  Types of CADD
1. Structure-Based Drug Designing (SBDD)
• The most efficient and powerful process which governs entire drug discovery is structure-based
drug designing (SBDD).
• The information about the target of small molecules, genetic information with their sequences,
binding information, cytotoxicity, absorption, metabolism, excretion(ADMET) data, and other
important biological information serve as the most efficient sources for accelerating the drug
discovery process.
• This has become a promising computational technique used by varied R&D, pharmaceutical
companies.
• Steps or Process of Structure-Based CADD
o Determination of protein structure
o Comparative modeling
o Identification of binding pocket
o Scoring function
o Knowledge-based scoring
o Force-based scoring function
o Protein-ligand docking algorithms
o Virtual screening
o Visualization of the protein-ligand diagram

41.  Ligand-based drug designing (LBDD)
• The absence of a 3D macromolecular structure leads to very systematic target recognition
using advanced computational tools.
• The present pharmaceutical researchers have faced a tougher situation and major
challenge in this regard.
• The hypothesis of high-throughput screening(HTS) has been strengthened with better
emerging strategies.
• The indirect mode of drug designing or ligand-based drug designing has supported widely
this tougher challenge. This can be approached in three different ways such as:-
o Machine learning approach
o Similarity Search methods
o Pharmacophore models mapping
 Structure based drug design
(SBDD and ligand based drug design
(LBDD) are the two general types of
computer-aided drug design) n (CADD)
approaches in existence.

42.  Applications of Computer-aided drug design (CADD)
• The whole background in CADD draws much attention relaying on three
major factors:-
1) screening of large set of molecules on target structure, which is evaluated
by computationally as well as experimentally.
2) Guiding the optimization of lead compounds depending on their affinity,
pharmacokinetic parameters, as well toxicity.
3) Designing the novel compounds depending on their structure to improve
their functionality as a drug compound.
4) The applicability of CADD for modeling of the drug considers combinatorial
chemistry and bioinformatics which address the major issues including cost
and time duration
 Limitations of Computer-aided drug design (CADD)
• Although the SBDD approach has proved as a pioneer in drug designing, it
has to cross the challenges that the community has to look upon.
• This enhancement includes screening methods, chemo genomic
compounds, data improvement, quantity, quality of various tools and
databases, modifying in multi-target drug structures, toxicity predicting
algorithms.
• Integrating the approach for better efficacy and compatibility.

43.  Pharmacophore models mapping
• This approach is used in lead optimization in medical and computational chemistry.
• It is built upon using structural alignment and prediction activity and 3D database searching.
• The amount of known/unknown data set used for developing the molecular structure along
with its activity towards any target. This can be implied in fields like ADMET profiling,
estimation of side effects, off-target prediction.
• Taking the knowledge of pharmacophore, simulation studies were been improved by virtual
screening.
 Fundamental steps used in pharmacophore modeling
1.Diverse set of structures with properly defined experimental activity
2.Generation of conformations
3.Employing 3D pharmacophore models to a training set of compounds in three phases
• Constructive Phase: Pharmacophore models were built on common active molecules
• Phase of Subtraction: Subtracts unlike models of pharmacophore
• Phase of optimization: Generate the hypothesis by optimizing a model
4.Quality assessment: The selection of pharmacophore is laid on basis of the cost function
5.Validation
6.Fisher’s randomization test is performed for a validated set of compounds
7.Finally, mapping is used for the test set of compounds.

44.  Tools used for Pharmacophore modeling and applications
S.No
Tools Description Links
1.
Pharmer
This tool uses compounds for
virtual screening
Welcome to ZINCPharmer
(pitt.edu)
2.
Pharmapper
It implies knowledge of more
than 7000 target-based
pharmacophore models and
provides the best match for
input as a ligand against
pharmacophore-based
models.
Welcome to our lab (lab-
request.cn)
3. pharmacist
This tool searches from a set
of ligands in the absence of
the target
BioInfo3D Group (tau.ac.il)
4. Pharmit tool
This relies on drug monitoring
on Pharmacokinetics
pharmit: interactive
exploration of chemical space
(pitt.edu)
5.
Discovery Studio
A tool used for therapeutics
and drug monitoring
Free Download: BIOVIA
Discovery Studio Visualizer –
Dassault Systèmes (3ds.com)
6. Ligandscout
It models 3D
pharmacophores from
structural data either as
training/test set of molecules
Linux
Schrödinger | Schrödinger is