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Fragment-Based HTS: Integrating FBLD Informatics into the HTS Workflow Pierre Baillargeon , Timothy P. Spicer, and Louis Scampavia Lead Identification Division, Translational Research Institute, The Scripps Research Molecular Screening Center, Jupiter, FL 33458 Results & Discussion Overview High Throughput Screening (HTS) and Fragment-Based lead discovery (FBLD) represent two different paradigms toward drug discovery, each with their respective advantages and disadvantages. A critical advantage found in HTS is its ability to quickly ascertain and advance hits for lead optimization when it is successful. However, the costs of the infrastructure and compound libraries can be prohibitive; and HTS campaigns can fail especially against orphan and/or nontraditional drug targets leaving investigators with poor prospects. FBLD’s key advantage is its ability to create de novo hits for otherwise intractable targets using smaller subset of fragment compounds; albeit needing substantial medicinal chemistry support post screening and at protracted and uncertain timelines. Fragment Base assisted HTS is a novel screening technique that can leverage the advantages of FBLD in a HTS environment and provide investigators with options for lead development. In principal, an initial HTS pilot screen is performed against a fragment subset (Rule of Three compounds); ideally representative as substructural components of the HTS library makeup. For high-risk projects the screening size is a fraction ( < 10%) of a full campaign, mitigating risk and costs associated with HTS. Fragments hits discovered are processed in silico for their superstructure counterparts within the HTS library and can be selectively cherry-picked to be re-screened in a primary mode. The second screening is now performed with full sized molecules (e.g. Rule of Five) representing an enhanced subset of the full deck ( < 10%). This two-step screening process provides investigators with select HTS hits that can be advanced (i.e. lead optimization) and/or fragment leads that can be redeveloped through traditional FBLD methods. Fragment Base assisted HTS is well suited for high-risk projects by providing reduced screening costs, an expedient pipeline toward optimization or option for pursing FBLD development. Presented is the informatics and pilot work used for formulating Fragment-Based assisted HTS. To enable this hybrid workflow, the structural relationships between compound fragments and superstructures must be identified and cataloged. Scripps has performed this work on the NIH’s MLPCN compound library and compared the results of this fragment analysis against several publicly available assays to determine if the fragments that naturally occur in the compound library are representative of the larger compound collection. To accomplish this, we added new descriptors to the Scripps compound database that identify naturally occurring fragments within our compound library and the related compounds that contain these fragments. The resulting Fragment-Based HTS data allows for an additional layer of Cheminformatic knowledge to be generated and presented to biologists and chemists. This poster can be viewed online by scanning the following QR code: Fragment Representation BCUT descriptors were generated for the NIH’s MLPCN compound library to examine the overlap in chemical space of fragments (red) compared to non-fragments (blue) which comprise the majority of the library. This analysis, visualized below, revealed that the fragments were distributed in a similar manner to the non-fragments. Conclusion Fragment-Based assisted HTS enables existing HTS infrastructure to be used with a subset of screening compounds to reduce costs for high-risk targets. While FB Assisted HTS retains many benefits of traditional HTS, it is also important to note potential limitations including restricted chemical space and compatibility with certain types of assays. Further studies are planned as we continue to develop these techniques. Integrating FBLD Informatics into HTS Workflow Assay ID Assay Name # compounds screened # fragments screened # superstructures (from fragment hits) screened # hits # fragment hits # superstructures hits % hits % superstructure hits % fragment hits 130.1.1.1 M1_AG_FLUO8_1536_1X%ACT 359484 15926 2754 1190 53 17 0.3% 0.6% 0.3% 130.1.2.1 M1_PAM_FLUO8_1536_1X%ACT 359484 15926 181 316 17 0 0.1% 0.0% 0.1% 130.1.3.1 M1_ANT_FLUO8_1536_1X%INH 359484 15926 1036 4560 91 59 1.3% 5.7% 0.6% 145.1.1.1 M4_AG_FLUO8_1536_1X%ACT 364131 16034 428 503 16 5 0.1% 1.2% 0.1% 145.1.2.1 M4_PAM_FLUO8_1536_1X%ACT 364131 16034 673 1450 69 3 0.4% 0.4% 0.4% 145.1.3.1 M4_ANT_FLUO8_1536_1X%INH 364131 16034 1350 2640 74 40 0.7% 3.0% 0.5% 141.1.1.1 M5_AG_FLUO8_1536_1X%ACT 364131 16034 1621 699 37 15 0.2% 0.9% 0.2% 141.1.2.1 M5_PAM_FLUO8_1536_1X%ACT 364131 16034 2436 1081 71 3 0.3% 0.1% 0.4% 141.1.3.1 M5_ANT_FLUO8_1536_1X%INH 364131 16034 966 2140 52 17 0.6% 1.8% 0.3% To determine whether or not Fragment-Based HTS produced results similar to traditional HTS efforts, data from a number of MLPCN screens was analyzed to compare Fragment-Based hit rates to traditional HTS hit rates. As seen in the table above, Fragment-Based hit rates matched the hit rates of the library as a whole. Further, superstructure hit rates appear to have enriched hit rates from both the full library and Fragment-Based subset within the library. Generating Fragment Metadata The existing Lead ID HTS informatics workflow at Scripps has been augmented by adding fragment and superstructure data to the corporate LIMS. With this data accessible via the LIMS database, it has been incorporated into the HTS Dashboard which provides scientists with summary HTS data in a unified web interface. From this interface, scientists can now also view fragment summary data and associated results from identified superstructures. The NIH’s MLPCN compound library was processed through Pipeline Pilot to identify fragments and then imported into JChem for overlap analysis to match fragments to superstructures. Instant JChem was used for structure database management, search and prediction. Instant JChem 14.7.28.0, 2014, ChemAxon (http://www.chemaxon.com/
