Overview of computer aided drug designing. Clinical and Pre-clinical trials. Prediction of properties and Drug-likeness. Advanced treatments of protein-ligand binding. Summary

1. 1

2. Outline
• Overview of computer aided drug designing.
• Clinical and Pre-clinical trials.
• Prediction of properties and Drug-likeness.
• Advanced treatments of protein-ligand
binding.
• Summary
2

3. Computer aided drug designing.
• Drug design with the help of computers may
be used at any of the following stages of drug
discovery:
– hit identification using virtual screening
(structure- or ligand-based design)
– hit-to-lead optimization of affinity and selectivity
(structure-based design, QSAR, etc.)
– lead optimization optimization of other
pharmaceutical properties while maintaining
affinity
3
http://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design

4. Computer aided drug designing.
4
• the classical project-collaboration approach between chemistry, biology
and drug metabolism (ADME) groups in the 1990s
• a much more automated world at the start of this millennium in which
combinatorial chemistry (Combi-Chem), high-throughput screening and
ADME studies are linked together in a streamlined fashion.
ADMET in silico modeling towards Prediction Paradise?, Nature

5. Computer aided drug designing.
• In order to overcome the insufficient prediction of binding affinity
calculated by recent scoring functions, the protein-ligand
interaction and compound 3D structure information are used to
analysis. For structure-based drug design, several post-screening
analysis focusing on protein-ligand interaction has been developed
for improving enrichment and effectively mining potential
candidates:
– Consensus scoring
• Selecting candidates by voting of multiple scoring functions
• May lose the relationship between protein-ligand structural information and
scoring criterion
– Geometric analysis
• Comparing protein-ligand interactions by visually inspecting individual
structures
• Becoming intractable when the number of complexes to be analyzed
increasing
– Cluster analysis
• Represent and cluster candidates according to protein-ligand 3D information
• Needs meaningful representation of protein-ligand interactions.
5
http://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design

6. Clinical Trials
• Sets of tests in medical research and drug development
that generate safety and efficacy data
• Only after satisfactory information has been gathered on
the quality of the nonclinical safety, and health
authority/ethics committee approval is granted
• The most commonly performed clinical trials evaluate
new drugs, medical devices (like a new catheter),
biologics, psychological therapies, or other interventions.
6
http://en.wikipedia.org/wiki/Clinical_trial

7. Clinical Trials
• Clinical trial may be designed to do:
– Assess the safety and effectiveness of a new medication or device on
a specific kind of patient
– Assess the safety and effectiveness of a different dose of a medication
than is commonly used
– Assess the safety and effectiveness of an already marketed
medication or device for a new indication
– Assess whether the new medication or device is more effective for the
patient's condition than the already used ("the gold standard" or
"standard therapy")
– Compare the effectiveness in patients with a specific disease (e.g.,
device A vs. device B, therapy A vs. therapy B)
http://en.wikipedia.org/wiki/Clinical_trial
7

8. Clinical Trials
• 5 different types
– Prevention trials
• find better ways to prevent disease in people (medicines, vitamins, vaccines,
minerals, or lifestyle changes)
– Screening trials
• test the best way to detect certain diseases or health conditions.
– Diagnostic trials
• find better tests or procedures for diagnosing a particular disease or condition.
– Treatment trials
• test experimental treatments, new combinations of drugs, or new approaches to
surgery or radiation therapy.
– Quality of life trials
• ways to improve comfort and the quality of life for individuals with a chronic
illness.
– Compassionate use trials
• provide partially tested, unapproved therapeutics to a small number of patients
who have no other realistic options.
http://en.wikipedia.org/wiki/Clinical_trial
8

9. Clinical Trials
• Clinical trials involving new drugs are commonly classified into
four phases.
– Phase 0: Pharmacodynamics and Pharmacokinetics
– Phase 1: Screening for safety
– Phase 2: Establishing the testing protocol
– Phase 3: Final testing
– Phase 4: Post approval studies
• Each phase has a different purpose and helps scientists
answer a different question:
• Before pharmaceutical companies start clinical trials on a
drug, they conduct extensive preclinical studies.
http://en.wikipedia.org/wiki/Clinical_trial
9

10. Preclinical Trials
• Before clinical trials, during which important feasibility,
iterative testing and drug safety data is collected
• To determine a product's ultimate safety profile
– Pharmacodynamics (what the drug does to the body) (PD)
– pharmacokinetics (what the body does to the drug) (PK)
– ADME
– toxicity testing through animal testing
• Both in vitro and in vivo tests will be performed
• Based on pre-clinical trials, No Observable Effect Levels
(NOEL) on drugs are established, which are used to determine
initial phase 1 clinical trial dosage levels on a mass API per
mass patient basis.
http://en.wikipedia.org/wiki/Pre-clinical_development
10

11. Prediction of Properties and Drug-likeness
• Credits and Thanks for raising the awareness on the
properties and structural features
– Lipinski, Murcko, co-workers at Pfizer and Vertex
• Main Goal = Apply MADE early in pre clinical
development to avoid late stage failures
• Analyses of classes of compounds are informative
• Avoidance of the extremes seems to be safe strategy
The Many Roles of Computation in Drug Discovery, Science
11

12. Prediction of Properties and Drug-likeness
• Why?
• Which?
• How?
• When?
• What?
– Predict
– Tools
12

13. Why computational ADME required?
• Traditional drug designing is a Multi step Time consuming
process.
• Adverse pharmacokinetic properties were investigated in
development stage.
ADMET in silico modeling towards Prediction Paradise?, Nature
13

14. • The rate at which biological screening data are obtained has
dramatically increased
• Combinatorial chemistry feeds these hit-finding machines
• Increased the demands for absorption, distribution,
metabolism, excretion and toxicity data early
Why computational ADME required?
• Attrition in the drug development
• Early decision
ADMET in silico modeling towards Prediction Paradise?, Nature
14

15. Why computational ADME required?
• The promising
compound went over
the line and was
abandoned in the later
stage due to its oral
bioavailability of only
1%.
Peptide like thrombin inhibitor
The Many Roles of Computation in Drug Discovery, Science
15

16. Which properties make drugs different
from other chemicals?
• Numerous studies
• Influential one = LIPINSKI’s “Rule of Five”
• Mol mass < 500 da
• Calc octanol/water
partition coefficient < 5
• H-bond donors <5
• H-bond acceptors <10
• Physicochemical and Structural Properties characteristic
of a good drug
The Many Roles of Computation in Drug Discovery, Science
16

17. Which properties make drugs different
from other chemicals?
• These properties = Build ADME models == Property based
design
• Similar molecules =~= Similar ADME properties
• Predict properties like
– Lipophilicity
– Solubility
– Amount absorbed
ADMET in silico modeling towards Prediction Paradise?, Nature
17

18. How are ADMET data obtained?
• Three ways
– Automated in vitro assays
– In silico selection of both the relevant assays and the compounds that go
through them
– Predictive models that can possibly replace in vitro or in vivo experiments
• The predictions come from regression equations or neural
networks
• QikProp – Fast and executed for large libraries
– Input is 3D structure
– Output is profile of
• Structural features (Surf Area and H bonding potentials)
• ADME properties
• Undesirable functionality
• Primary metabolites
• Comparison with other drugs
The Many Roles of Computation in Drug Discovery, Science
18

19. How are ADMET data obtained?
• The two drugs approach hydrophobic and Hydrophilic extremes
• Hydrophobic
– Poor solubility
– High serum protein binding
– Good Cell permeability
• The Opposite is true in case of hydrophilic compounds
• This is responsible for the solubility vs. permeability struggle
The Many Roles of Computation in Drug Discovery, Science
19

20. When is ADMET data needed?
Design of New
compounds
Need of
properties
Traditional
or Combo
Chemistry
• Predictions are not perfect at this point.
• series of molecules is focused around a lead and is further
optimized towards a clinical candidate, more robust mechanistic
models will be required.
ADMET in silico modeling towards Prediction Paradise?, Nature
20

21. What ADME properties do we want to
predict?
DOSAGE Amount DOSAGE Frequency
Volume of
Distribution
Volume of
Distribution
ClearanceClearance AbsorptionAbsorption
Oral
bioavailability
Oral
bioavailability
Half LifeHalf Life
ADMET in silico modeling towards Prediction Paradise?, Nature
21

22. What computational tools are used?
Molecular Modeling
• Protein Modeling that uses
Quantum mechanical
methods for interaction
study
• 3D structural info needed
• No structure available
– Homology modeling (related
structures)
– Pharmacophore modeling
(superposition of known
substrates)
Data Modeling
• QSAR and QSPR with
biological and
physicochemical data
– search for correlations
between a given property and
a set of molecular and
structural descriptors of the
molecules in question
• QSAR
– Mol size
– H bonding
– simple multiple linear regression
to modern MULTIVARIATE
ANALYSIS techniques
ADMET in silico modeling towards Prediction Paradise?, Nature
22

23. What computational tools are used?
ADMET in silico modeling towards Prediction Paradise?, Nature
23

24. What computational tools are used?
• UC – 781 is most hydrophobic. It is potent in vitro. It is
seen as microbicide than oral drug.
– Poor solubility and high serum binding
• Long BB predictions are also interesting from standpoint
of potential CNS penetration.
– Beneficial for attack on HIV reservoirs
– Concern CNS side effects
The Many Roles of Computation in Drug Discovery, Science
24

25. Prediction of Properties and Drug-likeness
• Good predictive models for ADMET parameters depend
crucially on
– selecting the right mathematical approach
– the right molecular descriptors for the particular
ADMET endpoint
– sufficiently large set of experimental data for the
validation of the model
• Several published ADME data sets are available for data
modeling, but the quality of the data and the number of
available training examples remain important issues.
ADMET in silico modeling towards Prediction Paradise?, Nature
25

26. Advanced treatments of protein-ligand
binding.
• For obtaining better accuracy and to match
computing demands
• Monte Carlo statistical mechanics or
Molecular Dynamics simulations are applied
– Classical force field is used
– Sampling all degrees of freedom of complexes
– Representation of water molecule in aqueous
surroundings
The Many Roles of Computation in Drug Discovery, Science
26

27. Advanced treatments of protein-ligand
binding.
• Free Energy Perturbation and Thermodynamic
Integration compute free energy changes.
• Perturbations are made to convert one ligand to another
using Thermodynamic cycles
• Δ Δ Gb = Δ GX – ΔGY = Δ GF – ΔGC gives the difference in
free energies of ligands binding X and Y
• Two series of mutations are performed to convert X to Y
unbound in water and complexed with biomolecule
which yield Δ GF and ΔGC.
• Same cycle can be performed with One inhibitor and 2
proteins
The Many Roles of Computation in Drug Discovery, Science
27

28. Advanced treatments of protein-ligand
binding.
• Inclusion of water makes the representation more realistic
• water molecules that form hydrogen-bonded bridges
between inhibitors and protein hosts are well illustrated
• Water molecules form specific clathrate-like networks
around nonpolar groups
• Optimization of the water structure is becoming a more
common part of inhibitor design
• Other Strengths
– rigor
– extensive sampling
– binding affinities.
• remains difficult to handle large structural differences
between ligands as in changing the core structure
The Many Roles of Computation in Drug Discovery, Science
28

29. Advanced treatments of protein-ligand
binding.
• Hybrid Methods
– Linear response theory
– free energy of interaction of a solute with its environment is given by one-half
the electrostatic energy plus the van der Waals energy scaled by an empirical
parameter
– For binding a ligand to a protein, the differences in the interactions between
the ligand in the unbound state and bound in the complex then provide an
estimate of the free energy of binding.
– Energy components are obtained from MC or MD simulations for inhibitors in
water and for the protein-inhibitor complexes in water.
• Advantages
– absolute free energies of binding can be approximated
– only simulations at the endpoints of a mutation are required
– computing demands are reduced to a few hours per compound.
– Easy to treat structurally diverse ligands
– Better accuracy
The Many Roles of Computation in Drug Discovery, Science
29

30. 30
ADMET in silico modeling towards Prediction Paradise?, Nature
SUMMARY

31. References
• William L. Jorgensen, et al., The Many Roles of
Computation in Drug Discovery, Science 303, 1813
(2004);
• Han van de Waterbeemd and Eric Gifford, ADMET in
silico modeling towards Prediction Paradise?, Nature
reviews/drugdisc, March 2003;
• Wikipedia
• The future of computation in drug discovery, By
Wavefunction on Monday, November 28, 2011
• anchorquery.ccbb.pitt.edu/class/lecture.pdf
• http://en.wikipedia.org/wiki/Pre-clinical_development
• http://en.wikipedia.org/wiki/Clinical_trial
• http://en.wikipedia.org/wiki/Drug_design#Computer-
aided_drug_design
31