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In Silico Design of Selective Estrogen Receptor Modulators from Triazoles and Imines Joey Salisbury Dr. John C. Williams Small Molecules/Large Molecules.

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Presentation on theme: "In Silico Design of Selective Estrogen Receptor Modulators from Triazoles and Imines Joey Salisbury Dr. John C. Williams Small Molecules/Large Molecules."— Presentation transcript:

1 In Silico Design of Selective Estrogen Receptor Modulators from Triazoles and Imines Joey Salisbury Dr. John C. Williams Small Molecules/Large Molecules Seminar Summer 2008 How to Design Drugs Using a PlayStation 3

2 Estrogen Receptor α regulates gene expression depending on cell type

3 4-hydroxytamoxifen (a selective estrogen receptor modulator) Estradiol (natural estrogen receptor ligand) Estradiol

4 Why look for selective estrogen receptor modulators (SERMs)? Some are good: -- Postmenopausal osteoporosis responds favorably to SERMs. -- All SERMs decrease breast cancer risk, and tamoxifen is mainly used for its ability to inhibit growth in estrogen receptor-positive breast cancer. -- Cholesterol and triglycerides levels respond favorably. Some are bad: -- Deep venous thrombosis - the risk may be elevated in at least some SERMs. -- Hot flashes are increased by all SERMs. -- Tamoxifen may increase endometrial carcinoma risk The more SERMs we know, the better we can understand their action and develop more positive ones.

5 The Beginning of Future SERMs?

6 Estradiol

7

8 SMILES (Simplified Molecular Input Line Entry Specification) c4ccc(c2nnn(c1ccccc1)c2c3ccccc3)cc4

9 Combinatorial Chemistry Consider taking 21 different functional groups and placing them in every possible combination at R1, R2, and R3. Creating this library of chemicals, we see there are: 21 3 = 9,261 combinations

10 Combinatorial Chemistry Not impressed? Ok, well take those compounds and add a second functional group somewhere on the first one. With 21 possible functional groups, that means (21 3 ) 2 = 85,766,121 combinations

11 ADME/Tox Filter Eliminate compounds in our library which have poor ADME/Tox qualities Lipinski’s rule of 5 Not more than 5 hydrogen bond donors Not more than 10 hydrogen bond acceptors A molecular weight under 500 g/mol A partition coefficient log P less than 5 Done online, this step is fast 10,000 compounds take <1 min 10-90% of compounds eliminated

12 Going 3D Before we do our next step, we must convert our file containing SMILES formula into 3D structures To do this, you can download for free online the program MarvinBeans from ChemAxon

13 Docking with eHiTS (Electronic High-throughput Screening) Estradiol Estrogen Receptor α

14

15 Arylphosphonium salts complexed with AChE

16 What about PlayStation 3? All of this could be done on a PS3 (you just have to install Linux onto it) SimBioSys, the company that makes eHiTS, has made a special version of eHiTS which takes advantage of the PlayStation 3’s Cell Broadband Engine processor. Can run 30x faster than on a traditional processor! What would take us a month could be done in a day!

17 Conclusions Large numbers of small molecules can be generated and stored on a computer in SMILES format and then converted to 3D structures with free online applications. X-ray structures of large molecules can be found on the Protein Data Bank. We can attempt to predict the interaction between small molecules and a protein using docking programs such as eHiTS in order to design new drugs.

18 THE END THANK YOU


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