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Kallikrein-related peptidase 6 (KLK6) is a secreted serine protease that belongs to the family of tissue kallikreins. Aberrant expression of KLK6 has been found in different cancers and neurodegenerative diseases, and KLK6 is currently studied as a potential target in these pathologies. We report a novel series of KLK6 inhibitors discovered in a high-throughput screen within the European Lead Factory program. Structure-guided design based on docking studies enabled rapid progression of a hit cluster to inhibitors with improved potency, selectivity and pharmacokinetic properties. In particular, inhibitors 32 and 34 have single digit nanomolar potency against KLK6, with over 25-fold and 100-fold selectivity, respectively, against the closely related enzyme trypsin. The most potent compound, 32, effectively reduces KLK6-dependent invasion of HCT116 cells. The high potency in combination with good solubility and low clearance of 32 make it a good chemical probe for KLK6 target validation in vitro and potentially in vivo.

We have recently developed a 2P2IHUNTER learning machine tool for filtering potential orthosteric PPI modulators. In the present research article we applied our algorithm to 8.3 million compounds representing the main chemical providers commercially available to design a PPI-focused library, 2P2IREF, composed of 143 218 small molecules. Compounds corresponding to medicinally important privileged structures identified as core structures in numerous therapeutics were prioritized in a medicinal oriented version of this database (2P2IPRIV – 51 476 compounds). A diverse chemical library was generated using 2D molecular fingerprints (2P2IDIV – 8217 diverse compounds). The carbon bond saturation index (Fsp3) was used as a final filter (Fsp3 ≥ 0.4) to escape from flatland, another hurdle in the long path to the gold mine. We analyzed the resulting chemical space of this final library of 1683 compounds, 2P2I3D and discuss the chemical moieties proposed to the community. This library will now be tested to evaluate its ability to enhance hit rates in general screening campaigns and is open to academic and private companies for collaborative prospects.

Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse 'PPI-like' modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using Homogeneous Time-Resolved Fluorescence (HTRF®) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with non-oriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.

Over the last 10 years, protein–protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this ‘high-hanging fruit’ is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database ( http://2p2idb.cnrs-mrs.fr/ ) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard D ragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns . This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.

Phenylahistin is a fungal diketopiperazine derived from isoprenylated (Phe-DeltaHis) cyclodipeptide. The (-)-enantiomer is a cell cycle inhibitor, which can be potentially used as an antitumor agent. By contrast, the (+)-enantiomer exhibits no antimicrotubule activity. To better understand the differences that could arise from a difference of bioavailability, we investigated the interaction and metabolism of both enantiomers with mammalian cytochromes P450 (P450s). We found that both enantiomers were metabolized by various isoforms of mammal P450 with a noticeable activity for the (+)-enantiomer. P450 3A isoforms were mainly responsible for this metabolism, the bioactive (-)-enantiomer being 1.5 to 8 times less metabolized than the (+)-enantiomer. Spectral analysis of the interaction with P450s revealed that (-)-phenylahistin led to a hydrophobic type I signature, whereas the (+)-isomer yielded a Fe-N type II one. Structural analysis of metabolites by liquid chromatography-tandem mass spectrometry allowed us to characterize two major metabolites (P1 and P3) for both enantiomers. In human liver microsomal preparations, P1 was predominant in the (-)-phenylahistin metabolic profile. In contrast, (+)-phenylahistin mainly produced P3 in human microsomes and CYP3A human expressed P450s. (-)-Phenylahistin proved to be less toxic on P450-rich hepatocytes than on P450-deprived KB lines. The slower metabolism of this enantiomer could account for its higher toxicity. This is strengthened by the fact that isolated metabolites of (-)-phenylahistin showed no toxic effects toward KB lines. Finally, differences of metabolism and interaction mode between both phenylahistin enantiomers and CYP3A4 were supported by in silico molecular docking calculations.