this all about rational drugs production and designs

1. RATIONAL DRUG
DESIGN
Prepared by:
Dr Saima Najm
PhD Pharm Chem
Assistant Professor
L.C.P.S

2. Tableofcontents
 Introduction
 Traditional drug design
 Rational drug design
 Concept of rational drug design
 Types of rational drug design
 Structure based drug design
 Ligand based drug design
 Pharmacophore based drug design
approach

3. INTRODUCTION
Drug design is the inventive process of finding new medications based
on the knowledge of the biological target.
In the most basic sense, drug design involves design of small
molecules that are complementary in shape and charge to the bio-
molecular target to which they interact and therefore will bind to it.
Drug design frequently but not necessarily relies on computer
modeling techniques. This type of modeling is often referred to as
computer-aided drug design.

4. Modeling techniques for prediction of binding affinity are
reasonably successful.
However 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.

5. Traditional drugdesign
Traditional drug discovery involves the origin of drug discovery that evolved in
natural sources, accidental events.
It was not target based and not much systemised as today.
Improved and advancements in pharmaceutical science and technology made
it revolutionized to much more systemize modern drug discovery
Traditional methods of drug discovery (known as forward pharmacology),
which rely on trial-and-error testing of chemical substances on cultured cells
or animals, and matching the apparent effects to treatments.

6. Methodsfor traditional drug design
Random screening
Trial and error method
Ethnopharmacology approach
Serendipity method
Classical pharmacology
Chemical structure based drug
discovery

7. Random screening
 It includes random screening of synthetic compounds or
chemicals or on natural products by bioassay procedures.
Involves two approaches:
1.Screening for selected class of compounds like alkaloids,
flavonoids, etc
2. Screening of randomly selected plants for selected bioassays

9. Contribution of randomscreening
• Later, the National Cancer Institute (NCI) of National Institute of
Health, USA, studied about 35,000 plant species for anticancer
activity, spending over two decades from 1960 to 1980.
• It resulted in proving two success stories, which were those of
paclitaxel and camptothecin.

10. Trialand errormethod
Trial and error method includes berries, roots, leaves and barks
could be used for medicinal purposes to alleviate symptoms of
illness.
Examples :Willow bark –contains salicin –fever reducing in
general
• Cinchona bark – contains quinine – fever associated with
malaria
• Chinese herbal remedies – used to treat many illness.

11. Ethnopharmacologyapproach
• Depends on empirical experiences related to the use of botanical drugs for the
discovery of biologically active New Chemical Entity.
• This process involves the observation, description, and experimental investigation
of indigenous drugs.
• It is based on botany, chemistry, biochemistry, pharmacology, and many other
disciplines like anthropology, archaeology, history, and linguistics
• In history several examples are present.
• Andrographis paniculata was used for dysentery in ethnomedicine and the
compounds responsible for the activity were isolated as andrographolide.
• Morphine from Papaver somniferum,
• Berberine from Berberis aristata,
• Picroside from Picrorrhiza kurroa.

12. Contributions ofEthnopharmacology
•Discovery of artemisinin from Artemesia alba for malaria,
• Guggul sterones from Commiphora mukul (for hyperlipidemia),
boswellic acids from Boswellia serrata (anti-inflammatory)
• Based on the leads from these codified systems of medicine
prevailing in China and India.

13. Serendipitymethod
• “Serendipity” refers to “an accidental discovery;” i.e, “finding one thing while
looking for something else.
• No scientific discovery has ever been made by pure luck.
• All happy accidents in science have one point in common: “each was
recognized, evaluated and acted upon in the light of the discoverer's total
intellectual experience.”
• The serendipitous discovery of penicillin in 1928 by Alexander Fleming
occurs.

14. • Fleming was engaged in research on influenza when one of his
staphylococcus culture plates had become contaminated and developed
a mold that created a bacteria-free circle
• Fleming recognized the possible significance of the bacteria-free circle and
by isolating the mold in pure culture Serendipity method.
• He found that it, produced a substance that has a powerful destructive effect
on many of the common bacteria that infect man.
• He named the antibacterial substance liberated into the fluid in which the
mold was grown “penicillin” after Penicillium notatum, the contaminant of the
staphylococcus colony that led to the discovery.

15. Classicalpharmacology
• Also known as function based approach.
• Anciently, drug discovery programmes were often based-successfully-
on measuring a complex response in vivo.
• Such as prevention of experimentally induced seizures, lowering of blood
sugar, or suppression of an inflammatory response.
• Without the prior identification of a drug target.

16. Examples of FUNCTION based drug discovery
 Usually the Natural sorce derived drug comes in
this approach.
 Some of them enlisted in chart.

18. Chemical structure based drugdiscovery
In 1891: Paul Ehrlich – coined the term chemotherapy, used synthetic chemicals to try
and cure disease.

19. Concept of rational drugdesign
Rational drug design refers to the development of medications based
on the study of the structures and functions of target molecules.
The role of rational drug design is to use a methodological approach to
coming up with a new drug, as opposed to blindly hoping some stroke of
luck helps design a new drug, or instead of randomly testing hundreds of
drug molecules in hopes that one of them binds to a receptor and exerts
a therapeutic effect.

20. Rational drug design , involves three general steps to create a new drug:
Step 1. Identify a receptor or enzyme that is relevant to a disease they are
going to design a drug for.
Step 2. Elucidate the structure and function of this receptor or enzyme.
Step 3. Use the information from step two in order to design a drug
molecule that interacts with the receptor or enzyme in a therapeutically
beneficial way.

21. Basicrequirement
 Typically a drug target is a key molecule involved in a particular metabolic or
signaling pathway that is specific to a disease condition or pathology, or to the
infectivity or survival of a microbial pathogen.
 Some approaches attempt to inhibit the functioning of the pathway in the diseased
state by causing a key molecule to stop functioning. Drugs may be designed that bind
to the active region and inhibit this key molecule. Another approach may be to
enhance the normal pathway by promoting specific molecules in the normal
pathways that may have been affected in the diseased state.
 In addition, these drugs should also be designed in such a way as not to affect any
other important "off-target" molecules or anti targets that may be similar in
appearance to the target molecule, since drug interactions with off-target molecules
may lead to undesirable side effect. Sequence homology is often used to identify
such risks.

23. Types of Rational drugdesign
Rational drug design can be broadly divided into two categories:
 STRUCTURE BASED DRUG DESIGN- Relies on finding new
medication based on the knowledge of the target. Also known as
DIRECT DRUG DESIGN.
 LIGAND/PHARMACOPHORE BASED DRUG DESIGN- Relies on
knowledge of other molecules that bind to the biological target of
interest. Also known as INDIRECT DRUG DESIGN.

25. STRUCTURE BASED DRUGDESIGN
 Structure based drug design (direct drug design) relies on knowledge of
the three dimensional structure of 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 affinity and selectivity to the target may be designed
using interactive graphics and the intuition of a medicinal chemist

26.  Structure based design is one of the first techniques to be used in
the drug design.
 Structure based drug design that has helped in discovery process of
new drugs .
 In parallel , information about the structural dynamics and electronic
properties about ligands are obtained from calculations .
 This has encouraged the rapid development of the structure based
drug design

27. Stepsinvolvedinstructurebaseddrugdesign
1. In structure guided drug design, a known 3D structure of a target
bound to its natural ligand or a drug is determined either by X-ray
crystallography or by NMR to identify its binding site.
2. Once the ligand bound 3D structure is known, a virtual screening
of large collections of chemical compounds.
3. screening enables the identification of potential new drugs by
performing docking experiment of this collection of molecules. To
enhance binding and hence to improve binding affinity/specificity, a
group of molecules with similar docking scores is generally used for
potency determination; this is High-Throughput Screening (HTS).

28. 4. After the determination of biological potency, several properties such as
relationships (QSAR, QSPR, between potency and docking scores)
including statistical analysis can be performed to establish the potential
molecule(s) for lead drug discovery

29. Structure based drugdesign

30. Protein structuredetermination
 For structure-based drug design, a priority before investigating receptor–
ligand relationship is to obtain the target structure. There are some
major methods for protein structure determination by physical
measures, X-ray diffraction and NMR.
 The solved protein structures can be readily found at Protein Data
Bank. However, for proteins that have not been solved or are difficult to
isolate, modeling approach can be used such as Homology modeling,
folding recognition, Ab initio protein modeling, hot spot prediction.

31. Homologymodeling
 Homology modeling also known as comparative modeling of protein, refers
to constructing an automatic-resolution model of the “target” protein from
its amino acid sequence and an experimental three –dimensional
structure of a related homologous protein ( the template).
 Homology modeling is a fast method to obtain protein structures that can
not only be used in studying rational drug design but also for protein–
protein interaction and site-directed mutagenesis.
 Proteins lacking structural information could be constructed if they have
over 30% sequence identify with their related homologous proteins
(templates).

32. • The modeled structures can be further modified in model refinement to
be consistent with the experiment data in covalent bonds, geometry,
and energy configuration.
• Force fields, such as CHARMM, AMBER, CVFF, CFF91, and GROMOS
can also be applied to molecules for calculating energy minimization,
which uses the function shown below:
E-total = E-stretching + E-bending + E-dihedral + E-
out-of-plane+ E-cross terms + E-VdW + E-
coulombic
• To ensure the rationality of the modeled structures, checks on
stereochemistry, energy profile, residue environment, and structure
similarity are often needed.

33. • Stereochemistry considers
1. the bond angles and lengths,
2. the dihedral angles of major chains, and
3. the non-covalent bonds of amino acid residues
within a protein.

34. Foldingrecognition
 Also known as ‘‘threading,’’ folding recognition was brought up in 1991
by Bowie and colleagues whom employed this method to describe the
environment of residues interactions.
 Folding recognition calculates the probabilities of the 3D structures
could form by given protein sequences. Both the environment of
residues interactions and the protein surface area are considered in the
threading protocol.
 Structure with the highest probability is recommended to construct the
protein model.

35. Ab initio proteinmodeling
The ab initio method is based on physical principles, residue interaction
center and lattice representation of a protein to build the target.
• This method is extremely useful when the other protocols fail to
predict an unknown protein structure. However, the identity and
accuracy given by ab initio modeling could be lower than other
approaches.
• Protein folding is not only a physical action, but also involves many
biochemical actions originated from inherent residues interaction
• Based on this concept, ab initio method hypothesizes that when a
protein folds, it would tend to achieve the most energetically
favorable state

36. Hot spotprediction
 Hot spot prediction in structure-based drug design is to determine the
ligand active site. While the active site may be determined via ligand
location in the crystal lattice after X-ray crystallography.
 This method is not possible for proteins that cannot be crystallized.
 The primary strategy of FTMAP utilizes small molecular fragments as a
probe for exploring protein surface. Spots where molecular fragments
clustered are predicted to be the favorable druggable sites. Significant
hydrogen bonds and non-bounded interactions can also be explored
between the probes and protein.

37. High throughtputscreening
 The pharmaceutical industry has adopted the experimental screening of large
libraries of chemicals against a therapeutically-relevant target (high-throughput
screening or HTS) as a means to identify new lead compounds.
 Through HTS, active compounds, antibodies or genes, which modulate a
particular biomolecular pathway, may be identified.
 These provide starting points for drug discovery and for understanding the
role of a particular biochemical process in biology.

38.  Although HTS remains the method of choice for drug discovery in
the pharma industry, the various drawbacks of this method, namely
1. the high cost,
2. the time-demanding character of the process as well as
3. the uncertainty of the mechanism of action of the active
ingredient have led to the increasing employment of rational,
structure-based drug design (SBDD) with the use of
computational methods.

39. Virtualscreening
 SBVS (structure based virtual screening) starts with processing the 3D target
structural information of interest. The target structure may be derived from
experimental data (X-ray, NMR or neutron scattering spectroscopy), homology
modeling, or from Molecular Dynamics (MD) simulations.
 There are numerous fundamental issues that should be examined when
considering a biological target for SBVS; for example, the druggability of the
receptor, the choice of binding site, the selection of the most relevant protein
structure, incorporating receptor flexibility, suitable assignment of protonation
states, and consideration of water molecules in a binding site.

40. In fact, the identification of ligand binding sites on biological targets
is becoming increasingly important.
 Another consideration for SBVS includes the careful choice of the
compound library to be screened in the VS exercise according to the
target in question, and the preprocessing of libraries in order to
assign the proper stereochemistry, tautomeric, and protonation
states.

41. 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.

42. 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).

43.  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.

44. Docking
 Docking refers to the ability to position a ligand in the active or a
designed site of a protein and calculate the specific binding affinities.
 Docking algorithms can be used to find ligands and binding
confirmation at a receptor site close to experimentally determined
structures.
 Docking algorithms are also used to identify multiple proteins to which a
small molecule can bind.
 Some of the docking programs are GOLD(Genetic optimization for
ligand Docking), AUTODOCK,LUDI,HEX etc.

45. • Docking attempts to find the “best” matching between two
molecules it includes finding the Right key for the lock.
• Given two biological molecules determine: Whether two
molecules “interact” If so, what is the orientation that maximizes
“interaction” while minimizing the total “energy” of the complex.
• GOAL: To be able to search a database of molecular
structures and retrieve all molecules that can interact with the
query structure.

46. • Docking works by
generating a molecular
surface of proteins
• Cavities in the receptor
are used to define
spheres (blue), the
centres are potential
locations for ligand
atoms.
• Sphere centres are
matched to ligand atoms
, to determine possible
orientations for the ligand.

47. Scoring Method
1. The basic assumption underlying structure-based drug design is that a good
ligand molecule should bind tightly to its target. Thus, one of the most
important principles for designing or obtaining potential new ligands is to
predict the binding affinity of a certain ligand to its target and use it as a
criterion for selection. A breakthrough work was done by Böhm to develop a
general-purposed empirical function in order to describe the binding energy.

48. • The concept of the “Master Equation” was raised. The basic idea is
that the overall binding free energy can be decomposed into
independent components which are known to be important for the
binding process.
• Each component reflects a certain kind of free energy alteration
during the binding process between a ligand and its target receptor.
The Master Equation is the linear combination of these components.
• According to Gibbs free energy equation, the relation between
dissociation equilibrium constant, Kd and the components of free
energy alternation was built.
• The sub models of empirical functions differ due to the consideration
of researchers. It has long been a scientific challenge to design the
sub models. Depending on the modification of them, the empirical
scoring function is improved and continuously consummated.

49. Binding freeenergy
Information on the energy status of the protein–ligand complex, free
ligands and unbound protein must be pre-determined. The energy is
calculated using the formula
Energy of binding = energy of complex + energy of ligand
+ energy of receptor.

50. De novoevolution
 After docking program, we can modify
ligands by two method
 The first method is based on active site
features to identify functional groups that
can establish strong interactions with the
receptor. Then, the functional groups can
be linked or attached to the original ligand
scaffolds.
The second method uses the
original ligand scaffolds to develop
derivatives that can complement the
receptor.

51. De Novo DrugDesign
 De Novo Drug Design De novo is a Latin expression meaning "from
the beginning". Active site of drug targets when characterized from
a structural point of view will shed light on its binding features.
 This information of active site composition and the orientation of various
amino acids at the binding site can be used to design ligands specific to
that particular target.
 The computer aided ligand design methods and distinguished them as
six major classes:
 Fragment location methods: To determine desirable locations of atoms
or small fragments within the active site.

52.  Site point connection methods: To determine locations (“site points”)
and then place fragments within the active site so that those locations
are occupied by suitable atoms.
 Fragment connection methods: Fragments are positioned and “linkers”
or “scaffolds” are used to connect those fragments and hold them in a
desirable orientation.
 Sequential buildup methods: Construct a ligand atom by atom, or
fragment by fragment.
 Whole molecule methods: Compounds are placed into active site in
various conformations, assessing shape and/or electrostatic
complementarity.
 Random connection methods: A special class of techniques
combining some of the features of fragment connection and
sequential buildup methods, along with bond disconnection strategies
and ways to introduce randomness.

55. Ligand based drugdesign
 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 which
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.

56. LIGAND BASED DRUG DESIGN
QSAR
SCAFFOLD
HOPPING
PHAEMACOPHOR
E APPROACH
PSEUDO
RECEPTORS
2D 3D
CoMF
A
CoMSI
A

57. Quantitative structure–activityrelationship
 Quantitative structure–activity relationship is a widely used
technique in drug designing process.
 It employs statistics and analytical tools to investigate the
relationship between the structures of ligands and their
corresponding effects.
 Hence, mathematical models are built based on structural
parameters to
describe this structure–activity relationship.

58. 2D-QSAR
 2D-QSAR was widely used to link structural property descriptors
(such as hydrophobicity, steric, electrostatic and geometric effects)
to molecular biological activity.
 The results were often analyzed with multiple regression analysis.
One of the most commonly used 2DQSAR methods was proposed by
Hansch.
 2D-QSAR cannot accurately describe the correlation between the 3D
spatial arrangement of the physiochemical properties, and the
biological activities,so 3D-QSAR approaches have been adapted.

59. 3D-QSAR
Frequently applied 3D-QSAR methodologies:
Comparative molecular field analysis (CoMFA)
Comparative molecular similarity indices analysis
(CoMSIA).

60. CoMFA
 Comparative molecular field analysis (CoMFA) is established on the
concept that the biological activity of a molecule is dependent of the
surrounding molecular fields, such as steric and electrostatic fields.
 The steric and electrostatic fields were calculated by CoMFA using
Lennard–Jones potential, and coulombic potential, respectively. Although
this method has been widely adopted, it has several problems.
It could cause large changes in CoMFA results. Moreover, in order to
examine both fields in the same PLS analysis, a scaling factor needs to be
added to the steric field.

61. CoMSIA
 Comparative molecular similarity index analysis (CoMSIA) is a method
developed recently as an extension of CoMFA.
 The CoMSIA method includes more additional field properties they are
steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen
bond acceptor.
 CoMSIA is insensitive to the orientation of the aligned molecules and
correlates to the grid by using Gaussian function.
 Furthermore, the improved function algorithm is least influenced by the
relative distance to the van der Waals surface.

62. Scaffoldhopping
 Scaffold hopping is to identify isofunctional molecular structure with
different molecular backbones having similar or improved properties.
It is used to discover structurally novel compound by modifying the
central core structure of the molecule.
.

63. Pseudoreceptors
 Pseudo receptor models combine the advantages of these two
strategies and represent a unifying concept for both receptor
mapping and ligand matching.
 They can provide an entry point for structure-based modelling in drug
discovery projects that lack a high-resolution structure of the target.

64. Pharmacophore basedapproaches
 A pharmacophore is the ensemble of steric and electronic features that is
necessary to ensure the optimal supramolecular interactions with a specific
biological target and to trigger (or block) its biological response.
 Thus a pharmacophore does not represent a real molecule or a set of
chemical groups, but is an abstract concept.
 “A pharmacophore is the pattern of features of a molecule that is
responsible for a biological effect”.

65.  These molecular patterns can be labeled as hydrogen bond donors
or acceptors, cationic, anionic, aromatic, or hydrophobic, and any
possible combinations.
 Different molecules can be compared at the pharmacophore level this
usage is often described as “pharmacophore fingerprints.”
 When only a few pharmacophore features are considered in a 3D
model the pharmacophore is sometimes described as a “query.”

66. Pharmacophore fingerprints
 A pharmacophore fingerprint typically explain a molecule as a unique data
string.
 All possible three-point or four-point sets of pharmacophore features
(points) are enumerated for each ligand.
 The distance between the feature points is counted in bonds or by
distance-binning when using 3D fingerprints.
 Such a fingerprint can be used to analyze the similarity between molecules
or among a library of molecules.

68. Pharmacophorequery
• A pharmacophore model
consists of a few features
organized in a specific 3D
pattern.
• Each feature is typically
represented as a sphere
(although variants exist)
with a radius determining
the tolerance on the
deviation from the exact
position.

69. Ligand based pharmacophoremodeling
 Ligand based pharmacophore modeling is usually carried out by extracting
common chemical features from 3D structures of a set of known ligands
representative of essential interactions between the ligands and a specific
macromolecular target.
In general, pharmacophore generation from multiple ligands (usually called
training set compounds) involves two main steps:
1.Creating the conformational space for each ligand in the training set to
represent conformational flexibility of ligands.
2.Aligning the multiple ligands in the training set and determining the essential
common chemical features to construct pharmacophore models.

70. • Currently, various automated pharmacophore generators have been
developed, including commercially available software – such as HipHop,
HypoGen DISCO,GASP, GALAHAD,PHASE, PROTEIN PLUS.
 Challenges in ligand based pharmacophore modeling:
• The first challenging problem is the modeling of ligand flexibility.
Two strategies have been used to deal with this problem:
• pre – enumerating method : In which multiple conformations for each
molecule are precomputed and saved in a database.
• on-the-fly method: In which the conformation analysis is
carried out in the pharmacophore modeling process.

71. • The Molecular alignment is the second challenging issue in ligand
based pharmacophore modeling.
The alignment methods can be classified into two categories in terms of
their fundamental nature:
point-based
property-based approaches

72. Structure-based pharmacophore
modeling
 Structure-based pharmacophore modeling works directly with the 3D
structure of a macromolecular target or a macromolecule– ligand complex.
 The structure-based pharmacophore modeling methods can be further
classified into two subcategories:
Macromolecule– ligand-complex based.
Macromolecule (without ligand)-based.

73. • The macromolecule–ligand-complex-based approach is convenient in
locating the ligand-binding site of the macromolecular target and
determining the key interaction points between ligands and
macromolecule.
• The structure-based pharmacophore (SBP) method implemented in
Discovery Studio is a typical example of a macromolecule-based
approach.

74. Applications
 Pharmacophore modeling is used in de novo design of ligands.
 Its also has its role in virtual screening and docking. Compared with
pharmacophore-based VS, pharmacophore-based de novo design
shows a unique advantage in building completely novel hit
compounds.
 Applications of pharmacophore have also been extended to lead
optimization, multitarget drug design, activity profiling and target
identification.