Torrey Pines Institute (TPIMS) combines a unique set of tools in order to provide a robust and cost effective drug discovery program. These tools which include TPIMS' proprietary "high density" combinatorial libraries, ligand and target based computational methods, as well as traditional medicinal chemistry which allows the implementation of the assay or assays that are most relevant to the end goal. A key advantage to TPIMS' unique drug discovery tools comes from the fact they can be used effectively in conjunction with a large range of assays, from ultra high throughput screening centers through to multi end-point in vivo models.

Additionally the TPIMS platform accommodates a high degree of flexibility in the drug discovery process maximizing any level of knowledge related to the biological system under study. The figure below illustrates that TPIMS' goal is to discover clinically viable candidates by following a path that is flexible, iterative and state of the art.

Combinatorial Libraries

TPIMS' drug discovery program is centered on our proprietary "high density" combinatorial libraries, otherwise known as mixture-based combinatorial libraries^1,2. The utility of mixture-based combinatorial libraries has been demonstrated in more than 100 separate studies in which active compounds have been identified. These studies have been carried out by more than 50 separate research groups. Novel enzyme inhibitors^3,4, agonists and antagonists to specific receptors⁵, antimicrobial, antifungal and antiviral compounds⁶, and B and T cell epitopes⁷ have been identified from such libraries, and have been extensively reviewed^8,9.

For more information on TPIMS libraries click here

HTS and uHTS

TPIMS currently has 37 small molecule libraries representing approximately 31 million compounds aliquoted into 96-well polypropylene plates. Each plate contains 80 samples (100ul/well) at a concentration of 1 mg/ml. The entire small molecule library collection is made up of 5,915 samples, aliquoted into 75 96-well plates.

In Vivo and Other Low Throughput Assays

TPIMS has developed a tool, the scaffold ranking library, that provides initial biological information on over 5,000,000 small molecules derived from 30 different scaffolds, from the testing of just 30 samples. From the results of these studies, additional sets of between 30 to 100 samples are prioritized for testing. In previous studies with complex in vivo models, a series of active individual compounds were identified from the testing of only 150 samples¹⁰.

Future Libraries

TPIMS is continually exploring the development of additional high density combinatorial libraries. In developing new libraries consideration is given to several factors including: drug like characteristics, orientation of library in chemical space (Figure below), diversity of substituents, ease and reproducibility of synthetic scheme.

Figure shows a potential new library (red) in existing drug space (yellow) and known leads (blue).

Lead Discovery and Optimization

The screening and deconvolution of TPIMS' libraries leads to the rapid identification of lead individual compounds as well as information on structural analogs. Inherent in the deconvolution of the TPIMS libraries is a certain amount of structure activity relationship ("SAR") data. This information alone or in conjunction with prior knowledge of the target(s) and/or additional leads is used to optimize the current leads.

Ligand Based

Whether the leads and structural analogs are derived solely from the screening of TPIMS' libraries or additional information is known from others sources, the information can be used to identify and optimize the leads from a ligand perspective. For example, active leads can be overlayed on top of each other to identify key common features (First figure below). This information can be contrasted with "inactive" compounds, specifically structural analogs derived from the TPIMS' libraries, to identify key negative features. Combined this information provides a clear pharmacaphore model (Second figure below) that is used with medicinal chemistry approaches to remove unwanted side effects while maintaining and/or improving the efficacy of the leads¹¹.

Additionally ligand based computational approaches can be used to aid in scaffold hopping and in silico screening of libraries.

Target Based

When specific information is known about the target from crystallography or NMR studies, target based models can be used to optimize leads. For example, leads derived from our libraries can be "docked" into the model targets to both identify putative binding modes as well as identify opportunities for increasing activity (Figure below)^12,13. As an example, in previous studies using docking models obtained with small molecules and protein kinases specific modifications to leads were identified. These structural modifications were designed to improve activity by including nitrogen atoms at specific positions in order to make hydrogen bonds with the target.

 

Another use of docking approaches is the virtual or in silico screening of synthetic combinatorial libraries and other compound collections^14,15. The outcome of such calculation is to predict what molecules within a library or collection are most and least likely to be active with a given receptor. Docking different combinatorial libraries that differ in the core scaffold with the same target enables the prediction of what library is the most likely to have activity. This process can be seen as a docking-based scaffold ranking and can be used to complement our traditional approach.

Individual Compounds Synthesis

TPIMS possesses the capability to rapidly synthesize, purify and characterize (by LCMS and NMR) any individual compound contained in the libraries as well as structural analogs in quantities needed for any secondary lead confirmation assays. This capability is due to the fact that the synthetic schemes have already been developed and optimized when the library was developed. Thus, this ability facilitates the opportunity to execute necessary experiments before entering clinical trials.

Summary

TPIMS' drug discovery platform is a novel and robust system that offers a high degree of freedom for use in a variety of biological systems. The approach provides for rapid results in a cost efficient and timely manner.

Contacts

TPIMS Outreach or Marc Giulianotti

Extensive Reviews

I.	Houghten, R. A.; Pinilla, C.; Giulianotti, M. A.; Appel, J. R.; Dooley, C. T.; Nefzi, A.; Ostresh, J. M.; Yu, Y.; Maggiora, G. M.; Medina-Franco, J. L.; Brunner, D.; Schneider, J. J. Comb. Chem 2008, 10, 3-19.
II.	Pinilla, C.; Appel, J. R.; Borras, E.; Houghten, R. A. Nat Med 2003, 9, 118-122.
III.	Houghten, R. A.; Pinilla, C.; Appel, J. R.; Blondelle, S. E.; Dooley, C. T.; Eichler, J.; Nefzi, A.; Ostresh, J. M. J. Med. Chem. 1999, 42, 3743-3778.

Cited References

1	Houghten, R. A.; Pinilla, C.; Blondelle, S. E.; Appel, J. R.; Dooley, C. T.; Cuervo, J. H. Nature 1991, 354, 84-86.
2	Vidal, A.; Nefzi, A.; Houghten, R. A. J. Org. Chem. 2001, 66, 8268-8272.
3	Apletalina, E.; Appel, J.; Lamango, N. S.; Houghten, R. A.; Lindberg, I. J. Biol. Chem. 1998, 273, 26589-26595.
4	Cameron, A.; Appel, J.; Houghten, R. A.; Lindberg, I. J. Biol. Chem. 2000, 275, 36741-36749.
5	Dooley, C. T. a. R. A. H. Biopolymers (Peptide Science) 2000, 51, 379-390.
6	Blondelle, S. E. a. K. L. Biopolymers (Peptide Sci.) 2000, 55, 85-87.
7	Borras, E.; Martin, R.; Judkowski, V.; Shukaliak, J.; Zhao, Y.; Rubio-Godoy, V.; Valmori, D.; Wilson, D.; Simon, R.; Houghten, R.; Pinilla, C. Journal of Immunological Methods 2002, 267, 79-97.
8	Houghten, R. A.; Pinilla, C.; Appel, J. R.; Blondelle, S. E.; Dooley, C. T.; Eichler, J.; Nefzi, A.; Ostresh, J. M. J. Med. Chem. 1999, 42, 3743-3778.
9	Pinilla, C.; Appel, J. R.; Borras, E.; Houghten, R. A. Nat Med 2003, 9, 118-122.
10	Houghten, R. A.; Pinilla, C.; Giulianotti, M. A.; Appel, J. R.; Dooley, C. T.; Nefzi, A.; Ostresh, J. M.; Yu, Y.; Maggiora, G. M.; Medina-Franco, J. L.; Brunner, D.; Schneider, J. J. Comb. Chem 2008, 10, 3-19.
11	J.Alvarez, B. S. Visual Screening in Drug Discovery CRC Press Boca Rotan FL 2005.
12	Kitchen, D. B.; Decornez, H. l. n.; Furr, J. R.; Bajorath, J. r. Nature Reviews Drug Discovery 2004, 3, 935-949.
13	Jorgensen, W. L. Science 2004, 303, 1813-1818.
14	Shoichet, B. K. Nature 2004, 432, 862-865.
15	Leach, A. R., Gillet, V.J. An Introduction to Chemoinformatics. ; Kluwer Academic Publishers: AA Dordrecht, The Netherlands., 2003.

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