Dawid Warszycki

A linear combination of pharmacophore hypotheses as a new tool in search of new active compounds--an application for 5-HT1A receptor ligands.

This study explores a new approach to pharmacophore screening involving the use of an optimized linear combination of models instead of a single hypothesis. The implementation and evaluation of the developed methodology are performed for a complete known chemical space of 5-HT1AR ligands (3616 active compounds with K i < 100 nM) acquired from the ChEMBL database. Clusters generated from three different methods were the basis for the individual pharmacophore hypotheses, which were assembled into optimal combinations to maximize the different coefficients, namely, MCC, accuracy and recall, to measure the screening performance. Various factors that influence filtering efficiency, including clustering methods, the composition of test sets (random, the most diverse and cluster population-dependent) and hit mode (the compound must fit at least one or two models from a final combination) were investigated. This method outmatched both single hypothesis and random linear combination approaches.

Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction

Fingerprints, bit representations of compound chemical structure, have been widely used in cheminformatics for many years. Although fingerprints with the highest resolution display satisfactory performance in virtual screening campaigns, the presence of a relatively high number of irrelevant bits introduces noise into data and makes their application more time-consuming. In this study, we present a new method of hybrid reduced fingerprint construction, the Average Information Content Maximization algorithm (AIC-Max algorithm), which selects the most informative bits from a collection of fingerprints. This methodology, applied to the ligands of five cognate serotonin receptors (5-HT2A, 5-HT2B, 5-HT2C, 5-HT5A, 5-HT6), proved that 100 bits selected from four non-hashed fingerprints reflect almost all structural information required for a successful in silico discrimination test. A classification experiment indicated that a reduced representation is able to achieve even slightly better performance than the state-of-the-art 10-times-longer fingerprints and in a significantly shorter time.

Rational design of 5-HT6R ligands using a bioisosteric strategy: synthesis, biological evaluation and molecular modelling

A bioisosteric strategy was successfully implemented with a screening protocol for new, potent 5-HT6R ligands. Initially, 2-[5-(4-methylpiperazin-1-yl)-2-nitrophenyl]-1,2,3,4-tetrahydroisoquinoline (9) was found in commercial databases using a bioisosteric query (screening 5-HT6R Ki = 128 nM). Then, the hit compound was bioisosterically modified (ring alteration) leading to a novel, high affinity (Ki = 6 nM) 5-HT6R ligand (10). Extensive docking studies followed by structural interaction fingerprint analysis supported by single-crystal X-ray structures of the investigated ligands suggest different binding modes with 5-HT6R models for compounds with varying activity. An alternative anchoring point for protonated amine (D7.36) that has not been previously reported was identified.

Ligand-Based Virtual Screening in a Search for Novel Anti-HIV-1 Chemotypes

In a search for new anti-HIV-1 chemotypes, we developed a multistep ligand-based virtual screening (VS) protocol combining machine learning (ML) methods with the privileged structures (PS) concept. In its learning step, the VS protocol was based on HIV integrase (IN) inhibitors fetched from the ChEMBL database. The performances of various ML methods and PS weighting scheme were evaluated and applied as VS filtering criteria. Finally, a database of 1.5 million commercially available compounds was virtually screened using a multistep ligand-based cascade, and 13 selected unique structures were tested by measuring the inhibition of HIV replication in infected cells. This approach resulted in the discovery of two novel chemotypes with moderate antiretroviral activity, that, together with their topological diversity, make them good candidates as lead structures for future optimization.

Asymmetric clustering index in a case study of 5-HT1A receptor ligands.

The automatic clustering of chemical compounds is an important branch of chemoinformatics. In this paper the Asymmetric Clustering Index (Aci) is proposed to assess how well an automatically created partition reflects the reference. The asymmetry allows for a distinction between the fixed reference and the numerically constructed partition. The introduced index is applied to evaluate the quality of hierarchical clustering procedures for 5-HT1A receptor ligands. We find that the most appropriate combination of parameters for the hierarchical clustering of compounds with a determined activity for this biological target is the Klekota Roth fingerprint combined with the complete linkage function and the Buser similarity metric.

From Homology Models to a Set of Predictive Binding Pockets–a 5-HT1A Receptor Case Study

Despite its remarkable importance in the arena of drug design, serotonin 1A receptor (5-HT1A) has been elusive to the X-ray crystallography community. This lack of direct structural information not only hampers our knowledge regarding the binding modes of many popular ligands (including the endogenous neurotransmitter-serotonin), but also limits the search for more potent compounds. In this paper we shed new light on the 3D pharmacological properties of the 5-HT1A receptor by using a ligand-guided approach (ALiBERO) grounded in the Internal Coordinate Mechanics (ICM) docking platform. Starting from a homology template and set of known actives, the method introduces receptor flexibility via Normal Mode Analysis and Monte Carlo sampling, to generate a subset of pockets that display enriched discrimination of actives from inactives in retrospective docking. Here, we thoroughly investigated the repercussions of using different protein templates and the effect of compound selection on screening performance. Finally, the best resulting protein models were applied prospectively in a large virtual screening campaign, in which two new active compounds were identified that were chemically distinct from those described in the literature.

Bioisosteric Matrices for Ligands of Serotonin Receptors

The concept of bioisosteric replacement matrices is applied to explore the chemical space of serotonin receptor ligands, aiming to determine the most efficient ways of manipulating the affinity for all 5‐HT receptor subtypes. Analysis of a collection of over 1 million bioisosteres of compounds with measured activity towards serotonin receptors revealed that an average of 31 % of the ligands for each target are mutual bioisosteres. In addition, the collected dataset allowed the development of bioisosteric matrices—qualitative and quantitative descriptions of the biological effects of each predefined type of bioisosteric substitution, providing favored paths of modifying the compounds. The concept exemplified here for serotonin receptor ligands can likely be more broadly applied to other target classes, thus representing a useful guide for medicinal chemists designing novel ligands.

Ligand-guided homology modelling of the GABAB2 subunit of the GABAB receptor

γ-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system, and disturbances in the GABAergic system have been implicated in numerous neurological and neuropsychiatric diseases. The GABAB receptor is a heterodimeric class C G protein-coupled receptor (GPCR) consisting of GABAB1a/b and GABAB2 subunits. Two GABAB receptor ligand binding sites have been described, namely the orthosteric GABA binding site located in the extracellular GABAB1 Venus fly trap domain and the allosteric binding site found in the GABAB2 transmembrane domain. To date, the only experimentally solved three-dimensional structures of the GABAB receptor are of the Venus fly trap domain. GABAB receptor allosteric modulators, however, show great therapeutic potential, and elucidating the structure of the GABAB2 transmembrane domain may lead to development of novel drugs and increased understanding of the allosteric mechanism of action. Despite the lack of x-ray crystal structures of the GABAB2 transmembrane domain, multiple crystal structures belonging to other classes of GPCRs than class A have been released within the last years. More closely related template structures are now available for homology modelling of the GABAB receptor. Here, multiple homology models of the GABAB2 subunit of the GABAB receptor have been constructed using templates from class A, B and C GPCRs, and docking of five clusters of positive allosteric modulators and decoys has been undertaken to select models that enrich the active compounds. Using this ligand-guided approach, eight GABAB2 homology models have been chosen as possible structural representatives of the transmembrane domain of the GABAB2 subunit. To the best of our knowledge, the present study is the first to describe homology modelling of the transmembrane domain of the GABAB2 subunit and the docking of positive allosteric modulators in the receptor.

Halogen bonding enhances activity in a series of dual 5-HT6/D2 ligands designed in a hybrid bioisostere generation/virtual screening protocol

A novel hybrid bioisostere generation/virtual screening method combined with narrowing of chemical space through similarity to compounds that are active at the second target was successfully applied for the development of structurally new dual 5-HT6/D2 receptor ligands. Consequently, a series of derivatives of the found hit 1d (N-[2-(dimethylamino)ethyl]-N-(2-phenylethyl)aniline) was synthesized. The most active 5-HT6/D2 ligands also showed affinity for 5-HT7R and 5-HT2AR. The para-chloroaniline derivative was identified as a potent dual 5-HT6/5-HT7 receptor antagonist (Ki = 24 nM and Kb = 30 nM, Ki = 4 nM and Kb = 1.4 nM, respectively). In the case of halogen-containing compounds, interesting structure–activity relationships were observed at 5-HT6, D2 and 5-HT7 receptors, and the ligand–receptor complexes were subsequently examined using a molecular modelling approach that combined quantum-polarized ligand docking (QPLD) and Molecular-Mechanics-Generalized-Born/Surface Area (MM/GBSA) free-energy calculation, which permitted the identification of putative halogen binding pockets.

Soloxolone methyl inhibits influenza virus replication and reduces virus-induced lung inflammation

Highly pathogenic influenza viruses pose a serious public health threat to humans. Although vaccines are available, new antivirals are needed to efficiently control disease progression and virus transmission due to the emergence of drug-resistant viral strains. In this study, we describe the anti-viral properties of Soloxolone methyl (SM) (methyl 2-cyano-3,12-dioxo-18βH-olean-9(11),1(2)-dien-30-oate, a chemical derivative of glycyrrhetinic acid) against the flu virus. Anti-flu efficacy studies revealed that SM exhibits antiviral activity against the H1N1 influenza A virus in a dose-dependent manner causing a more than 10-fold decrease in virus titer and a reduction in the expression of NP and M2 viral proteins. In a time-of-addition study, SM was found to act at an early stage of infection to exhibit an inhibitory effect on both the attachment step and virus uptake into cells. Also, in infected cells SM downregulates the expression of the inflammatory cytokines IL-6 and TNF-α. In infected mice, SM administered intranasally prior to and after infection significantly decreases virus titers in the lung and prevents post-challenge pneumonia. Together, these results suggest that Soloxolone methyl might serve as an effective therapeutic agent to manage influenza outbreaks and virus-associated complications, and further preclinical and clinical investigation may be warranted.