# Presentation: 847577

## Drug Design / drug discovery

- Jerome Baudry
- Assistant Professor BCMB
- UT/ORNL Center for Molecular Biophysics

- 2 previous incarnations:
- Research faculty at UIUC
- Research scientist at Transtech Pharma, Inc.

## Drug Design / drug discovery

_*What’s a drug? *_
- A substance that treats/cure a disease.
- A small molecule that interacts with a target, (often protein involved in the disease process; activator/inhibitor)
_*Drug discovery:*_
- The process of finding such a small molecule – combination of approaches
_*Drug discovery or drug design?*_ In principle: “Design” is more rational and targeted, and “discovery” is more serendipitous. But *design* and *discovery* share a lot and are ~ synonymous in a pharmaceutical context.

- Hoopkins, Groom, Nat Rev Drug Discov. 2002 1(9):727-30.

- 5% of human genome is “druggeable”

## Drug discovery market

- 25 new molecules /year

- _Intense scientific activity_: very interdisciplinary approach

- > $340 billion

| in millions US$ | Revenue | R&D | income  |
| --- | --- | --- | --- |
| Johnson & Johnson | 53,324 | 7,125 | 11,053 |
| Pfizer | 48,371 | 7,599 | 19,337 |
| GlaxoSmithKline | 42,813 | 6,373 | 10,135 |
| Novartis | 37,020 | 5,349 | 7,202 |
| Sanofi-Aventis | 35,645 | 5,565 | 5,033 |
| Hoffmann–La Roche | 33,547 | 5,258 | 7,318 |
| AstraZeneca | 26,475 | 3,902 | 6,063 |
| Merck & Co. | 22,636 | 4,783 | 4,434 |
| Abbott Laboratories | 22,476 | 2,255 | 1,717 |
| Wyeth | 20,351 | 3,109 | 4,197 |

- http://en.wikipedia.org/wiki/List_of_pharmaceutical_companies

## The drug discovery and design workflow:

- Chemistry: synthesis

- Discovery and design (hit/lead/optimisation)

- Biology: assay (binding/activity; in vitro / in vivo,)

- Target identification

- drug development:
- Pharmacology / testing

## The long and winding road to drug discovery

- Computational chemistry /Molecular modeling
- useful across the pipeline,
-  but
- very different techniques

- aim for success,
- but if not:
- fail early, fail cheap

## Structure-based

- know receptor,
- don’t known ligands

- Two pathways to drug discovery / drug seign

- ?
- What will be happy in there?

- don’t know receptor,
- known ligands

- Protein/ligand interactions
- structure/biophysics
- docking

- Statistical analysis of what group(s) are important for biological activity

## Structure-based approaches

- structure modeling
- (homology/experimental X-ray/NMR/neutron)
- Get a structure

- high-throughput docking/screening
- Get a “hit” (anything at all)

- Use knowledge of structure to find something that
- 1) binds, and 2) does the desired biological activity

- focused library docking
- fragment-based growth
- ‘individual’ molecules simulations

## Structure-based library screening 

- What do we need:
- 1) Compounds libraries
- 2) Protein target
- 3) Binding site in the protein
- 4) Docking: generate different (many) possible conformations of the compounds in the binding site
- 5) Scoring: evaluate the strength of the protein/ligand interactions (score).
- 6) Select preferred ligands to propose a list of prioritized compounds for experimental screening.

## Structure-based approaches

- PDB: http://www.rcsb.org/pdb/
- - experimental collection of (49 295) structures, ~18 000 non-redundant sequences
- - X-Ray & NMR,
- - nucleic acids, proteins, carbohydrates

- Structure modeling

## that’s ~1% of the 5.5 million protein sequences in swissprot (http://www.ebi.ac.uk/swissprot/sptr_stats/index.html)

- and < ~0.00007% of earth’s proteins, (5E6 organisms, 5K genes/genome, low-end estimate.)

- ~50,000 non-redundant protein structures in the PDB: is that a lot?

**Structure-based drug discovery = “Post genomics challenge”:**
**structural biology, functional genomics, chemical biology...**

*...*AQRTEVYTYRRS*...*

**protein**
**sequence**

**protein**
**structure**

- Must do for new pharmaceutical target

- (homology, ab-initio folding...)

- Structure-based approaches
- Structure modeling

## Structure-based approaches

- Structure modeling

**If no available experimental structure – work on that , and in the meantime: Homology modeling**: use structure of close (sequence-wise) proteins to build, by analogy, a new protein.

## Structure-based approaches

- http://blaster.docking.org/zinc/

- _Databases of compounds_
- - vendors
- - literature
-  corporate/laboratory
-  virtual compounds
- A priori anything, but we can be smarter than that

- http://nihroadmap.nih.gov/molecularlibraries/

- _Library designed against protein target, _- based on hits from previous database screening

- Millions of cmpds’ structures are available from public databases.
- Major NIH effort to fund & develop libraries:

- Compound selection

## Structure-based approaches

- When site is not known, eraser/flooding techniques

- binding site (3D)

- Or...make your life easier and build the site around a co-crystallized ligand
- If available...

- Locate cavities in a protein

- Binding Site

## Structure-based approaches

- HIGH-THROUGHPUT  	OR 	LOW-THROUGHPUT ?
**fast** (initial) 			**accurate** (on best cmpds from initial)

- Choices based on the desired throughput
- from 10 seconds to 10 minutes / compound
- 650,000 cmpds library, on 10 processors: from 3 days to 6 months

- Most time-consuming part (by far)

- YES

- NO

- OK

- BETTER

- docking

## Structure-based approaches

- _Scoring functions. Quantify the energy of protein/ligand interactions such as: _
- hydrogen bond
- electrostatics
- van der Waals
- hydrophobic
- etc ...

- _Several scoring functions exist, more/less specialized, fast etc..._

- PROTEIN

- LIGAND

- scoring

## Future: force-field based / even QM-based

**Force-field based**: (CHARMM, AMBER etc). MMFF: very popular one because of “modular parametrisation”: easy to derive parameters from functional groups, well adapted to organic molecules.
- Physically ‘accurate’ but slow, parametrisation issues.
**Empirical** – count the number of interactions and assign a score based on the # of occurrences. E.g. :
- H-bonds, ionic interactions (easy because very directional and well quantified)
- Hydrophobic interactions (more difficult to assess and quantify)
- Number of rotatable bonds frozen (link to entropic cost of binding, quite difficult to estimate)
**Knowledge-based** – observe known protein/ligand structures, and favor interactions and geometries that are seen often. Idea: directly link to free energy because “real life” distribution (potential of mean force).
- But: based on small # of entries.
- Intense competition “my scoring function is better than yours”

- Different approaches depending on size

## Structure-based approaches

- scoring

- Enrichment factor = (5/30) / (30/ 1000000) = 166 HUGE SUCCESS

- Often: consensus scoring: choose the few molecules that are ranked consistently well among many docking function

- 1,000,000 molecules, 30 actives.  1000 selected, 5 actives

## Structure-based approaches

- 1,000,000 molecules, 30 actives.  1000 selected, 3 actives

- scoring

- COMPUTATIONAL DOCKING:
- GENERATE TESTABLE IDEAS

- Chemistry: synthesis

- Discovery and design (hit/lead/optimisation)

- Biology: assay (binding/activity; in vitro / in vivo,)

- Possible to start next round of iteration (or do ‘traditional’ modeling). Redock with improved accuracy (e.g QMMM)

## Examples (low-throughput)

- Reproduce know xtal structure HIV protease and inhibitor

- Works great  ... in most publications

**crystal structure**
**first round of docking (shape only)**
**final result (after rigid-body minimizations: energetics taken into account)**

- Ligand-based site

- Flood-based site

- Venkatachalam, et al.; J. Mol. Graph. Model.  2003, 289-307

## Examples (low-throughput)

- But also... fails miserably (rarely in publications !)

**crystal structure**
**final results (rigid-body minimizations)**
**Illustrate issues with binding site’s shape (there are workarounds)**

- Venkatachalam, et al.; J. Mol. Graph. Model.  2003, 289-307

## Example II): discovery of ligand/function for a new P450

- Ke et al, Archives of Biochemistry and Biophysics 436 (2005) 110–120

## high-throughput docking

- Development of a database of bio and agrochemical compounds of relevance for P450 (currently ~ 14,000 structures). In-house compounds, KEGG database: (http://www.genome.jp/kegg/ligand.html), Compendium of Pesticide Common Names: (http://www.alanwood.net/pesticides/index.html).

- Development of CYP120A1 model from CYP107A template (23.6% identity)

- HT-docking (LigandFit).
- identify **99** compounds consistently predicted to be good binders. Confirmed: retinoic acid

-        ~14,000
-       structures

- Ke et al.. *Arch. Biochem. Biophys. *2005

- Get a “hit” (anything at all)

## HT-DOCKING SUCCESS IF:

- In-silico combinatorial library design & structure-based screening:
- fast, efficient and inexpensive tool to :
-  	discover new possible ligands against a macromolecular target
-  	test library design ideas
-  	identify most promising scaffolds and R groups prior to synthesis

|  | Baudry, J.; Hergenrother, P. J. "Structure-based Design and In-Silico Virtual Screening of Combinatorial Libraries.  A Combined Chemical/Computational Laboratory Assignment" J. Chem. Ed. 2005, 82, 890-894. http://www.scs.uiuc.edu/~phgroup/pdfs/2005PJHchemed.pdf |
| --- | --- |

- i) FIND A FEW MOLECULES OF INTEREST
- ii) MUCH QUICKER AND CHEAPER THAN “real” screening

## Comparison model / crystal structure 

**Comparison model / crystal structure **

- residues within 4 Å of heme

- Green/blue: model,   red/orange:crystal

## Residues around the ligand’s -ionone ring are very close in both structures

- Residues around the ligand’s -ionone ring are very close in both structures

- (phe182 & Trp76 same pharmacophore)

- Green/blue: model,   red/orange:crystal

**Comparison model / crystal structure **

## De novo design

- Fragment-based “inside-out” approach

- Put functional groups in binding site (docking or manually, or combination)

- Link these groups (docking or manual, or combination):  *must* be able to synthesize it – no molecular monsters

- Caflish, Miranker, Karplus * ****J *****.Med.Chem.** 36, 2142-2167 (1993)
- Eisen, Wiley, Karplus, Hubbard **Proteins Structure, Function and Genetics** 19, 199-221 (1994).

- i)dock functional groups

- ii)keep low energy groups
- link with scaffolds

- iii) correct binding site, but ≠ too;
- “lead hopping”