Central Nervous System (CNS)
In this case study we applied PharmScreen to explore a dataset of selected inhibitors for soluble Epoxide Hydrolase (sEH), a new target for relevant therapeutic strategy in Alzheimer Disease (AD). The data set contains 204 compounds (including hits and decoys), 41 of them identified as known inhibitors and 7 of them with a pIC50 > 9.4. The goal was to determine the maximum number of hits in the top ranked positions.
For the study, we analyzed the data set using 41 other various references. Among the different options of PharmScreen, the tool allows using different reference compounds in a single run, selecting the best ligand-reference similarity score (best similarity), and ranking the compounds in the library based on best similarities.
Reference 33 displayed the highest similarity scores in the final ranking. A closer look at the results of this reference compound revealed 14 active compounds were ranked in top 10%, including 5 of the 7 more active compounds.
Antibiotic Drug Resistance (AMR)
Filling in the pipeline of antibiotics with true innovative starting points and approaches in Antimicrobial resistance (AMR), has been an issue for several years. From the perspective of CADD approaches, there are several challenges such as the limited knowledge about receptors, multiple additional factors affecting the final activity, or the desired molecular properties which are different from other therapeutic needs.
The application of ligand-based approaches could provide helpful information in the identification of new antimicrobials. In this case study, we applied PharmScreen, and a custom library for antibiotic research to identify inhibitors described in the literature for Mex flux pump protein (MFP). MC-207,110, a known inhibitor of MFP protein, was used as a reference.
From the different compounds selected during the virtual screening, MBX3135 was chosen because of its properties. This compound is described as an inhibitor for AcrB, another multidrug efflux pump protein. Interestingly, MC-207,110 is also identified as an inhibitor of AcrB in literature, revealing a similar mechanism of action.
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Fragment-Based Drug Discovery (FBDD)
A research group at a Big Pharma was looking to apply new technologies for fragment growing in their internal Fragment-Based Drug Discovery (FBDD) projects and compare PharmScreen’s capabilities compared to other existing tools in identifying new hits.
PharmScreen used 80 datasets provided by the client (each set composed of around 50K compounds) and only one hit and a reference fragment. Researchers evaluated the results delivered, and they observed that PharmScreen was able to retrieve the hit among the first 1,000 ranked (top 2%) molecules in 55 out of the 80 datasets.
- PharmScreen outperformed the CADD market leader when applied for Fragment-Based Drug Discovery (FBDD).
- The teams have started working on a co-signed publication.
G Protein-Coupled Receptor (Gpcr)
Ligand-Based Virtual Screening (LBVS) experiments are an essential task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. However, most of the he descriptors used in LBVS are based on shape and atom connectivity. The application of these descriptors limits the identification of novel compounds as output.
In a case study on GPCRs, we validate the capability of PharmScreen, our LBVS tool, to explore alternatives chemotypes. PharmScreen is a strategy to compute 3D molecular similarity based on the molecular hydrophobicity derived from the quantum mechanical version of the Miertus − Scrocco − Tomasi (MST) continuum model.
A set of 21 GPCR targets, which only include active molecules with an alternative scaffold regarding the reference structure, were compiled. The results point out the suitability of our MST-based hydrophobic descriptors for exploring a new chemical space. Considering the initial 5% for all the sets, 62 confirmed hits were reported by PharmScreen and 22 by another commercial tool, with focus on shape-based approaches.
exploring different chemical space
Evotec performed a comparison between PharmScreen and a ligand- and shape-based tool from a different commercial vendor in terms of hit identification. The comparison between the two tools was executed by Evotec, using a challenging but real case. It was based on a known kinase inhibitor (reference compound) and two internal compound libraries that contain real activity data.
For the present comparison, Evotec used three similitude methods for the other tool. Dedicated scientist compared the scores with the default configuration of PharmScreen.
Evotec used its HTS library, with approximately 12,500 compounds. The library was divided in two data sets:
- Data set 1 (DS1): ~6,000 compounds (all compliant with Ro5) with 1.5% of experimentally active compounds
- Data set 2 (DS2): ~6,500 compounds (max. heavy atom = 23; LogP ≤ 3-3.5) with 0.8% of experimentally active compounds.
When comparing the hits retrieved in the first 1000 positions, PharmScreen finds 40% and 36% of DS1 and DS2 hits, respectively, slightly better than 34% and 30% obtained by the other tool. Interestingly, 75% and 67% of these hits were unique to PharmScreen and not found by the other tool. This highlights the fact that each tool tested, explored a different chemical space.