HTS AND QSAR BASED DRUG DISCOVERY AND DEVELOPMENT.

1. DRUG DEVELOPMENT
APPROACHES-
QSAR AND HTS
SUBMITTED TO Dr R. N.
LAKHSMIKANT
BY VIOLINA KALITA
M.Sc 2nd year

2. INTRODUCTION TO DRUG
DISCOVERY
• In the past most drugs have been discovered either by identifying the active
ingredient from traditional remedies or by serendipitous discovery.
• But now we now know diseases are controlled at molecular and
physiological levels.
• Also shape of the molecule at atomic level is well understood.
• Information of human genome .

3. HISTORY

4. METHODS
• Random Screening
• Molecular manipulation
• Molecular Designing
• Drug Metabolites
• Serendipity

5. Drug Discovery Cycle

8. Registration Of Drugs
• NDA must be submitted to DCGI(Drug Controller General of India)
• Phase III study reported to CDL(Central Drugs Laboratory) Kolkata
• Package inserted approved by DCGI
• Marketing approval from FDA

9. NOVEL DRUG
DEVELOPMENT• A Novel Drug or a New Molecular Entity (NME) is an active
compound, complex, molecule that previously has not been
approved by the FDA.
• This is different from a previously approved drug that has received
approval for an different but new condition. This is also different
from a generic drug which is a generic (typically) off-patent
formulation of the same NME but produced by an alternative
company.
• NMEs will also include drugs that use the same mechanism of
action as a previously approved drug. They are still considered a
Novel Drug since the extensive characterization of the
pharmacological properties of that drug were carried out.

10. APPROACHES
• QSAR- Quantitative Structure-Activity
Relationship.
• HTS- HighThroughtput Screening.

11. QSAR
• A QSAR is a mathematical relationship between a
biological activity of a molecular system and its
geometric and chemical characteristics.
• QSAR attempts to find consistent relationship
between biological activity and molecular properties,
so that this ‘rules’ can be used to evaluate the activity
of new compounds

13. • The biological activity can be expressed quantitatively as the
concentration of a substance required to give a certain
biological response. Additionally, when physiochemical
properties or structures are expressed by numbers, one can find
a mathematical relationships, or quantative structure activity
relationships, between the two. The mathematical expressions,
if carefully validated can be used to predicts the modeled
response of other chemical structures.
ACTIVITY=f (physiochemical properties and/or structural
properties) + error(model error and observational variability)

14. STATISTICAL CONCEPTS
• The problem of QSAR is to find coefficients C0,C1……….Cn such that
BIOLOGICALACTIVITY= C0+(C1+P1)+……+(CnPn) and the
prediction error is minimized for a list of m compounds.
• Partial Least Squares (PLS) is a technique used for computation of the
coefficients of structural descriptors.

16. SAR AND SAR PARADOX
• The basic assumption for all the molecule based hypothesis is that similar
molecules have similar activities. This principle is called Structure
Activity Relationship (SAR).
• The SAR paradox refers to the fact that it is not the case that all similar
molecules have similar activities.
E.g.: To define a small difference on a
molecular level like reaction ability, solubility etc might depend on another
difference

17. TYPES
• 1) FRAGMENT BASED (Group Contribution)
• 2) 3D QSAR
• 3) CHEMICAL DESCRIPTOR BASED.

18. FRAGMENT BASED QSAR
• Also known as GQSAR
• Allows flexibility to study various molecular fragments of
interest in relation to the variation in biological response.
• An advanced approach on fragment or group based QSAR
based on the concept of pharmacophore similarity is
developed.
• Proves to be a promising strategy for fragment library design
and in fragment to lead identification endeavors.

20. 3D-QSAR
• Application of force field calculations requiring three
dimensional structures of a given set of small molecules
with known activities.
• Structural descriptors are of immense importance in every
QSAR model. Common structural descriptors are
pharmacophores and molecular fields.
• 3D data has to be converted to 1D in order to use
PLS(Partial Least Square).
• Software- Tripos- ComFa, VolSurf

22. Chemical Descriptor Based
• Various electronic, geometric, or steric properties of a
molecule are computed and used to develop a QSAR.
• Different from fragment based in that the descriptors
are computed for the system as a whole rather than
from the properties of individual fragments.
• And from 3D QSAR in that the descriptors are
computed from scalar quantities, rather than from 3D
fields.

24. MODELING
Data Mining approach Matched molecular pair analysis
• uses support vector machines,
decision trees, neural networks for
inducing a predictive learning model.
•Molecular mining approach, a special
case of structured data mining
approaches, apply a similarity matrix
based prediction
•Derived from non linear machine
learning.
•New concept, also called prediction
MMPA which is coupled with QSAR
model in order to identify activity
cliffs.

25. QSAR PROTOCOL

26. VALIDATION
Internal
validation/Cross
validation
External validation Blind external
validation
Data
randomization/Y
scrambling
•While extracting
data ,cross
validation is the
measure of model
robustness, the
more the model is
robust, the less
data extraction
perturbs the
original model.
•By splitting the
available data set
into training set
for model
development and
prediction set of
model
productivity
check
•By application
on new external
data
•For verifying the
absence of chance
correlation
between the
response and
modeling
descriptors

27. ADVANTAGES
• Quantifying the relationship between structure and activity
provides an understanding of the effect of structure on
activity, which may not be straightforward when large amounts of
data are generated.
• There is also the potential to make predictions leading to the
synthesis of novel analogues. Interpolation is readily justified, but
great care must be taken not to use extrapolation outside the range
of the data set.
• The results can be used to help understand interactions between
functional groups in the molecules of greatest activity, with those
of their target. To do this it is important to interpret any derived
QSAR in terms of the fundamental chemistry of the set of
analogues, including any outliers.

28. DISADVANTAGES
• False correlations may arise through too heavy a reliance being
placed on biological data, which, by its nature, is subject to
considerable experimental error.
• Frequently, experiments upon which QSAR analyses depend, lack
design in the strict sense of experimental design. Therefore the
data collected may not reflect the complete property space.
Consequently, many QSAR results cannot be used to confidently
predict the most likely compounds of best activity.
• Various physicochemical parameters are known to be cross-
correlated. Therefore only variables or their combinations that have
little covariance should be used in a QSAR analysis; similar
considerations apply when correlations are sought for different sets
of biological data.

29. APPLICATIONS
• CHEMICAL-First QSAR applications was to predict boiling
points.
• BIOLOGICAL-Identification of chemical structures that
could have good inhibitory effects on specific targets and have
low toxicity.
Prediction of partition coefficient log P, which is used
to know the drug likeness.
• Used for risk assessment. Suggested by REACH regulation-
Regulation, Evaluation, Authorization and Restriction of
chemicals

30. HTS
Commonly used terms in drug discovery
• High throughput screen: an optimized, miniaturized assay format that
enables the testing of > 100,000 chemically diverse compounds per day.
• Assay: a test system in which biological activity can be detected
• Hit: a molecule with confirmed concentration-dependent activity in a screen,
and known chemical structure.
• Progressible hit: a representative of a compound series with activity via
acceptable mechanism of action and some limited structure-activity
relationship information
• Lead: a compound with potential (as measured by potency, selectivity,
physico-chemical properties, absence of toxicity or novelty) to progress to
a full drug development programme

31. What is High throughput
Screening ?
• High throughput screening (HTS) is a tool for early-
stage drug discovery.
• Definition : HTS is process by which large number of
compounds are rapidly tested for their ability to
modify the properties of a selected biological target.

33. HISTORY

35. Why High throughput screening
need arises ?
• FACT 1: recent understanding of disease mechanisms has
dramatically increased no. of protein targets for new drug
treatment.
• FACT 2: new technologies have increased the no. of drugs that
can be tested for activity at these targets.

36. GOALS
• Goal is to identify ‘hits’ or ‘leads’
- affect target in desired manner
- active at fairly low concentration (more likely to show specificity)
- new structure
-The greater the number and diversity of compounds screened, the
more successful screen is likely to be.
-HTS = 50,000-100,000 compounds screened per day!!!

37. • The majority of drug targets are :
• • a) G-protein coupled receptors
• • b) nuclear receptors
• • c) ion channels
• • d) enzymes

39. EXPLANATION
• It is a method for scientific experimentation especially used in drug discovery and is relevant
to biology and chemistry. This process in combination with robotics, data processing and
control software, liquid handling devices and sensitive detectors allows a researcher to
quickly conduct millions of chemical, genetic or pharmacological tests.
• It can rapidly identify active compounds, antibodies or genes which modulate a particular
bimolecular pathway. Considered as a process in which batches of compounds are tested for
binding activity or biological activity against target molecules.
• Is a process of screening more compounds against more targets per unit time, which should
generate more hits, which in turn will generate more leads, subsequently generating more
products.
• Defined by the number of compounds tested to be in the range of 10,000-100,000 per day,
ultra HTS is defined by screening more than 100,000 data point generated per day. These two
technologies play a vital role in drug discovery to find new chemical compounds.

42. PROCEDURE
• High-throughput screening in drug discovery is used to screen :
• Novel biological active compounds
• Natural products
• Combinatorial libraries (Ex: peptides; chemicals)
• Biological libraries
• DNA chips
• RNA chips
• Protein chips
• Main lab ware is the microtiter plate. Modern microplates are
performed in automation-friendly microtiter plates with a 96, 384,
1536 or 3456 well format. These wells contain experimentally
useful matter, often an aqueous solution of dimethyl sulfoxide
(DMSO).

43. Primary screen is designed to rapidly identify hits from compound libraries,
run in multiplets of single compound concentrations. Hits are then retested,
usually independently from the first assay.
If a compound exhibits the same activity, it is coined as confirmed hit, which
proceeds to secondary screens or lead optimization. The results from lead
optimization are used to decide which substances will make it on to clinical
trials.
In combination with bioinformatics, it allows potential drugs to be quickly and
efficiently screened.

44. • The key to high-throughput screening is to develop a test, or assay, in
which binding between a compound and a protein causes some visible
change that can be automatically read by a sensor. Typically the change is
emission of light by a fluorophore in the reaction mixture.
• One way to make this occur is to attach the fluorophore to the target protein
in such a way that its ability to fluoresce is diminished (quenched) when
the protein binds to another molecule.
• Assay technology in HTS-1)Cell growth test-cell based assays or
phenotypic assays,
2) Tissue response-targeted functional cell based
assay,
3) Enzyme test-biochemical test.

46. ADVANTAGES
• High sensitivity of assay(single molecule
detection)
• High speed of assay(automation)
• Minimization of assay(microtitre plate assay)
• Low background signal
• Low complexicity of assay
• Reproducibility
• Fast data processing of results
• Acceptable cost

47. DISADVANTAGES
• High cost
• Low data quality
• Local contamination
• Need of relatively pure product

48. APPLICATIONS

49. CONCLUSIONS
• HTS is became an effective technique and competitive with
the latest, upcoming related technologies in the market. The
growing importance of this process is cost effectiveness of
drug discovery and development, operating processes for
development of homogeneous, fluorescence-based assays in
reduced formats.
• The combination with robotics, data processing and control
software, liquid handling devices, TR-FRET, FRET,
Fluorescence polarization techniques has added a significant
valued to each data point generated by high throughput
screens.

50. REFERENCES
• Drug discovery and development-linkedIn
slide share
• HTS-Wikipedia
• QSAR on drug discovery and
development-Robert et.al
• Different approaches on drug discovery
and development-wikishare.com