New Anti-Chemokine Oral Drug XC8 in the Treatment of Asthma Patients with Poor Response to Corticosteroids: Results of a Phase 2A Randomized Controlled Clinical Trial.

INTRODUCTION: A significant number of patients with moderate asthma remain symptomatic despite treatment with inhaled corticosteroids (ICS). These patients do not yet meet the criteria for oral corticosteroids (OCS) and monoclonal antibodies. The new anti-chemokine oral drug XC8 could represent an alternative treatment option for these patients. The objective of this trial was to evaluate the effect of different doses of the XC8 in patients with partly controlled asthma in a phase 2a clinical trial. METHODS: A double-blind, parallel-group, randomized, multicenter, phase 2a trial was conducted at 12 sites in Russia. Patients with asthma were randomized into four groups (n = 30 each) to receive XC8 at 2 mg, 10 mg, 100 mg or placebo once-daily for 12 weeks in addition to low-dose ICS with or without LABA. Efficacy and safety parameters were evaluated at weeks 0, 2, 6, and 12. RESULTS: No statistically significant difference between the treatment arms in the number of patients with adverse events was observed. The primary endpoint, improvement of forced expiratory volume in 1 s (FEV1) % predicted over 12 weeks compared to placebo, was not statistically significant. The treatment of patients with XC8 (100 mg) resulted in statistically and clinically significant improvements in FEV1 compared to baseline (7.40% predicted, p < 0.001). Patients with elevated peripheral blood eosinophil count (PBEC, > 300 cells/µl) or serum interferon-γ (IFN-γ) level (> 100 pg/mL) treated with XC8 (100 mg) achieved a statistically significant improvement in FEV1 (11.33% predicted or 8.69% predicted, respectively, p < 0.05) as compared to the baseline versus the placebo. The strongest effect was observed in patients with both high PBEC and IFN-γ level. Pharmacodynamic engagement was demonstrated through the reduction of serum levels of C-C motif ligand 2 (CCL2) and C-X-C motif chemokine 10 (CXCL10). Treatment with XC8 (100 mg) alleviated resistance to maintenance ICS therapy in patients with elevated IFN-γ level. CONCLUSIONS: Given the high safety, oral route of administration, and efficacy, XC8 may provide a promising treatment option for patients with mild-to-moderate asthma. TRIAL REGISTRATION: 795-30/12/2015 (Ministry of Health Russian Federation), NCT03450434 (ClinicalTrials.gov).

Docking Finds GPCR Ligands in Dark Chemical Matter.

High-throughput screening has revealed dark chemical matter, a set of drug-like compounds that has never shown bioactivity despite being extensively assayed. If dark molecules are found active at a therapeutic target, their extraordinary selectivity profiles make excellent starting points for drug development. We explored if ligands of therapeutically relevant G-protein-coupled receptors could be discovered by structure-based virtual screening of the dark chemical matter. Molecular docking screens against crystal structures of the A2A adenosine and the D4 dopamine receptors were carried out, and 53 top-ranked molecules were evaluated experimentally. Two ligands of each receptor were discovered, and the most potent had sub-micromolar affinities. Analysis of bioactivity data showed that the ligands lacked activity at hundreds of off-targets, including several that are associated with adverse effects. Our results demonstrate that virtual screening provides an efficient means to mine the dark chemical space, which could contribute to development of drugs with improved safety profiles.

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson's Disease.

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A2A adenosine receptor (A2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A2AAR antagonist with nanomolar affinity ( Ki = 19 nM) and inhibited MAO-B with an IC50 of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.

Placental transfer of tyrosine kinase inhibitors used for chronic myeloid leukemia treatment.

Both favorable pregnancy outcomes and fetal abnormalities have been associated with the use of tyrosine kinase inhibitors (TKIs) during pregnancy. The placental transfer of TKIs in humans is poorly understood. We observed women with chronic myeloid leukemia who used imatinib or nilotinib during the late pregnancy stages. The newborns had no birth abnormalities. We evaluated the drug concentrations in maternal blood, umbilical cord blood, and placental samples collected during labor. We found limited placental transfer of the TKIs. The fetal/maternal concentration ratio ranged from 0.5 to 0.58 for nilotinib and from 0.05 to 0.22 for imatinib. The placental/maternal ratio was higher for imatinib than for nilotinib. Theoretical pharmacokinetic modeling of passive placental crossing was insufficient to predict the in vivo data because the calculated fetal/maternal ratio was close to 1 for both drugs. We propose that active placental transport contributes to fetal protection against TKI exposure during pregnancy.

Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library.

Functionally selective ligands stabilize conformations of G protein-coupled receptors (GPCRs) that induce a preference for signaling via a subset of the intracellular pathways activated by the endogenous agonists. The possibility to fine-tune the functional activity of a receptor provides opportunities to develop drugs that selectively signal via pathways associated with a therapeutic effect and avoid those causing side effects. Animal studies have indicated that ligands displaying functional selectivity at the D2 dopamine receptor (D2R) could be safer and more efficacious drugs against neuropsychiatric diseases. In this work, computational design of functionally selective D2R ligands was explored using structure-based virtual screening. Molecular docking of known functionally selective ligands to a D2R homology model indicated that such compounds were anchored by interactions with the orthosteric site and extended into a common secondary pocket. A tailored virtual library with close to 13 000 compounds bearing 2,3-dichlorophenylpiperazine, a privileged orthosteric scaffold, connected to diverse chemical moieties via a linker was docked to the D2R model. Eighteen top-ranked compounds that occupied both the orthosteric and allosteric site were synthesized, leading to the discovery of 16 partial agonists. A majority of the ligands had comparable maximum effects in the G protein and ß-arrestin recruitment assays, but a subset displayed preference for a single pathway. In particular, compound 4 stimulated ß-arrestin recruitment (EC50 = 320 nM, Emax = 16%) but had no detectable G protein signaling. The use of structure-based screening and virtual libraries to discover GPCR ligands with tailored functional properties will be discussed.

Quantifying Possible Routes for SpnF-Catalyzed Formal Diels-Alder Cycloaddition.

The Diels-Alder reaction is a cornerstone of modern organic synthesis. Despite this, it remains essentially inaccessible to biosynthetic approaches. Only a few natural enzymes catalyze even a formal [4 + 2] cycloaddition, and it remains uncertain if any of them proceed via the Diels-Alder mechanism. In this study, we focus on the [4 + 2] cycloaddition step in the biosynthesis of spinosyn A, a reaction catalyzed by SpnF enzyme, one of the most promising "true Diels-Alderase" candidates. The four currently proposed mechanisms (including the Diels-Alder one) for this reaction in water (as a first-order approximation of the enzymatic reaction) are evaluated by an exhaustive quantum mechanical search for possible transition states (728 were found in total). We find that the line between the recently proposed bis-pericyclic [J. Am. Chem. Soc. 2016, 138 (11), 3631] and Diels-Alder routes is blurred, and favorable transition states of both types may coexist. Application of the Curtin-Hammett principle, however, reveals that the bis-pericyclic mechanism accounts for ∼83% of the reaction flow in water, while the classical Diels-Alder mechanism contributes only ∼17%. The current findings provide a route for modeling this reaction inside the SpnF active site and inferring the catalytic architecture of possible Diels-Alderases.

An explicit account of solvation is essential for modeling Suzuki-Miyaura coupling in protic solvents.

We compared explicit and implicit solvation approaches in modeling the free energy profile of the final step of Suzuki-Miyaura coupling. Both approaches produced similar ΔG(≠) in all the studied solvents (benzene, toluene, DMF, ethanol, and water). Solvation free energies of individual reaction components reasonably correlated for explicit and implicit models in aprotic solvents (RMSE = 30-50 kJ mol(-1), R(2) > 0.71). However for ethanol and water the correlation was poor. We attributed this difference to the formation of the PdH-O hydrogen bond with Pd(PPh3)2 which was surprisingly observed in explicit modeling. Further QM calculations of the Pd(PPh3)2-H2O system confirmed the direction (PdH) and stability of this bonding. Therefore we stress the need for considering explicit solvation for modeling Pd-catalyzed reactions in protic solvents.

2,3-Dihydroxy-quinoxaline induces ATPase activity of Herpes Simplex Virus thymidine kinase.

2,3-Dihydroxy-quinoxaline, a small molecule that promotes ATPase catalytic activity of Herpes Simplex Virus thymidine kinase (HSV-TK), was identified by virtual screening. This compound competitively inhibited HSV-TK catalyzed phosphorylation of acyclovir with Ki=250 µM (95% CI: 106-405 µM) and dose-dependently increased the rate of the ATP hydrolysis with KM=112 µM (95% CI: 28-195 µM). The kinetic scheme consistent with this experimental data is proposed.

Alchemical FEP Calculations of Ligand Conformer Focusing in Explicit Solvent.

Slow rotational degrees of freedom in ligands can make alchemical FEP simulations unreliable due to inadequate sampling. We addressed this problem by introducing a FEP-based protocol of ligand conformer focusing in explicit solvent. Our method involves FEP transformations between conformers using equilibrium dihedral angle as a reaction coordinate and provides the cost of "focusing" on one specific conformational state that binds to a protein. The calculated conformer focusing term made a considerable difference of 5-10 kJ/mol in computed relative binding free energies of studied Syk inhibitors and significantly improved the resulting accuracy of predictions.

Lead Finder docking and virtual screening evaluation with Astex and DUD test sets.

Lead Finder is a molecular docking software. Sampling uses an original implementation of the genetic algorithm that involves a number of additional optimization procedures. Lead Finder's scoring functions employ a set of semi-empiric molecular mechanics functionals that have been parameterized independently for docking, binding energy predictions and rank-ordering for virtual screening. Sampling and scoring both utilize a staged approach, moving from fast but less accurate algorithm versions to computationally more intensive but more accurate versions. Lead Finder includes tools for the preparation of full atom protein and ligand models. In this exercise, Lead Finder achieved 72.9% docking success rate on the Astex test set when the original author-prepared full atom models were used, and 74.1% success rate when the structures were prepared by Lead Finder. The major cause of docking failures were scoring errors resulting from the use of imperfect solvation models. In many cases, docking errors could be corrected by the proper protonation and the use of correct cyclic conformations of ligands. In virtual screening experiments on the DUD test set the early enrichment factor of several tens was achieved on average. However, the area under the ROC curve ("AUC ROC") ranged from 0.70 to 0.74 depending on the screening protocol used, and the separation from the null model was not perfect-0.12-0.15 units of AUC ROC. We assume that effective virtual screening in the whole range of enrichment curve and not just at the early enrichment stages requires more accurate solvation modeling and accounting for the protein backbone flexibility.

Hit clustering can improve virtual fragment screening: CDK2 and PARP1 case studies.

Virtual fragment screening could be a promising alternative to existing experimental screening techniques. However, reliable methods of in silico fragment screening are yet to be established and validated. In order to develop such an approach we first checked how successful the existing molecular docking methods can be in predicting fragment binding affinities and poses. Using our Lead Finder docking software the RMSD of the binding energy prediction was observed to be 1.35 kcal/mol(-1) on a set of 26 experimentally characterized fragment inhibitors, and the RMSD of the predicted binding pose from the experimental one was <1.5 Å. Then, we explored docking of 68 fragments obtained from 39 drug molecules for which co-crystal structures were available from the PDB. It appeared that fragments that participate in oriented non-covalent interactions, such as hydrogen bonds and metal coordination, could be correctly docked in 70-80% of cases suggesting the potential success of rediscovering of corresponding drugs by in silico fragment approach. Based on these findings we've developed a virtual fragment screening technique which involved structural filtration of protein-ligand complexes for specific interactions and subsequent clustering in order to minimize the number of preferable starting fragment candidates. Application of this method led to 2 millimolar-scale fragment PARP1 inhibitors with a new scaffold.

TSAR, a new graph-theoretical approach to computational modeling of protein side-chain flexibility: modeling of ionization properties of proteins.

A new graph-theoretical approach called thermodynamic sampling of amino acid residues (TSAR) has been elaborated to explicitly account for the protein side chain flexibility in modeling conformation-dependent protein properties. In TSAR, a protein is viewed as a graph whose nodes correspond to structurally independent groups and whose edges connect the interacting groups. Each node has its set of states describing conformation and ionization of the group, and each edge is assigned an array of pairwise interaction potentials between the adjacent groups. By treating the obtained graph as a belief-network-a well-established mathematical abstraction-the partition function of each node is found. In the current work we used TSAR to calculate partition functions of the ionized forms of protein residues. A simplified version of a semi-empirical molecular mechanical scoring function, borrowed from our Lead Finder docking software, was used for energy calculations. The accuracy of the resulting model was validated on a set of 486 experimentally determined pK(a) values of protein residues. The average correlation coefficient (R) between calculated and experimental pK(a) values was 0.80, ranging from 0.95 (for Tyr) to 0.61 (for Lys). It appeared that the hydrogen bond interactions and the exhaustiveness of side chain sampling made the most significant contribution to the accuracy of pK(a) calculations.

CSAR scoring challenge reveals the need for new concepts in estimating protein-ligand binding affinity.

The dG prediction accuracy by the Lead Finder docking software on the CSAR test set was characterized by R(2)=0.62 and rmsd=1.93 kcal/mol, and the method of preparation of the full-atom structures of the test set did not significantly affect the resulting accuracy of predictions. The primary factors determining the correlation between the predicted and experimental values were the van der Waals interactions and solvation effects. Those two factors alone accounted for R(2)=0.50. The other factors that affected the accuracy of predictions, listed in the order of decreasing importance, were the change of ligand's internal energy upon binding with protein, the electrostatic interactions, and the hydrogen bonds. It appears that those latter factors contributed to the independence of the prediction results from the method of full-atom structure preparation. Then, we turned our attention to the other factors that could potentially improve the scoring function in order to raise the accuracy of the dG prediction. It turned out that the ligand-centric factors, including Mw, cLogP, PSA, etc. or protein-centric factors, such as the functional class of protein, did not improve the prediction accuracy. Following that, we explored if the weak molecular interactions such as X-H...Ar, X-H...Hal, CO...Hal, C-H...X, stacking and π-cationic interactions (where X is N or O), that are generally of interest to the medicinal chemists despite their lack of proper molecular mechanical parametrization, could improve dG prediction. Our analysis revealed that out of these new interactions only CO...Hal is statistically significant for dG predictions using Lead FInder scoring function. Accounting for the CO...Hal interaction resulted in the reduction of the rmsd from 2.19 to 0.69 kcal/mol for the corresponding structures. The other weak interaction factors were not statistically significant and therefore irrelevant to the accuracy of dG prediction. On the basis of our findings from our participation in the CSAR scoring challenge we conclude that a significant increase of accuracy predictions necessitates breakthrough scoring approaches. We anticipate that the explicit accounting for water molecules, protein flexibility, and a more thermodynamically accurate method of dG calculation rather than single point energy calculation may lead to such breakthroughs.