Ákos Tarcsay

Impact of Lipophilic Efficiency on Compound Quality

Lipophilic efficiency indices such as LLE and LELP were suggested to support balanced optimization of potency and ADMET profile. Here we investigated the performance of LLE and LELP on multiple data sets representing different stages of drug discovery including fragment and HTS hits and leads, development candidates, phase II compounds, and launched drugs. Analyzing their impact on ADME and safety properties and binding thermodynamics, we found that both LLE and LELP help identifying better quality compounds. LLE is sensible for the development stages but does not prefer fragment-type hits, while LELP has an advantage for this class of compounds and discriminates preferred starting points effectively. Both LLE and LELP have significant impact on ADME and safety profiles; however, LELP outperforms LLE in risk assessment at least on the present data set. On the basis of the results reported here, monitoring lipophilic efficiency metrics could contribute significantly to compound quality and might improve the output of medicinal chemistry programs.

Contributions of molecular properties to drug promiscuity.

In contrast to designed polypharmacology that can result in efficient drugs for complex disorders, unintended drug promiscuity has detrimental contribution to side effects and toxicology. Characterization of promiscuous compounds enhances the understanding of complex interaction patterns and aids the design of compounds with broader selectivity against off-targets that has a major impact on medicinal chemistry outcome. In this Miniperspective we provide insights to the effect of physicochemical parameters on promiscuity. Information collected from recent, large-scale in vitro studies enabled us to discuss the relationships between physicochemical properties and promiscuity in detail. In light of these data, lipophilicity and basic character have the highest influence. On the basis of the accumulated knowledge, we propose the extensive use of pre- and postsynthesis metrics, as well as strict control of physicochemical properties during medicinal chemistry optimizations.

In silico site of metabolism prediction of cytochrome P450-mediated biotransformations.

Introduction: Preclinical research involves the in vitro monitoring of metabolic stability to deliver compounds with improved ADME profiles. Prediction of the metabolically vulnerable points can substantially help in analyzing CYP-mediated metabolism data and support optimization efforts in drug discovery programs. Moreover, fast and reliable in silico predictions could accelerate the characterization of in vitro/in vivo metabolites. Areas covered: This paper reviews in silico methods available for CYP-mediated site of metabolism (SOM) prediction. Comprehensive and practical knowledge in this field can guide the identification of best practice and may inspire ideas for the development of novel approaches. Expert opinion: Comparison of the efficacy of SOM prediction methodologies revealed the general dependency on the studied isoform and substrate set. Increasing knowledge on P450 X-ray structures, on biotransformations and on the mechanistic details of the catalytic cycle revolutionized the prediction of SOM. Although no ultimate solution exits, combined methods covering both steric and electronic effects are preferred on most of the pharmaceutically relevant isoforms.

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.

Is there a link between selectivity and binding thermodynamics profiles

Thermodynamics of ligand binding is influenced by the interplay between enthalpy and entropy contributions of the binding event. The impact of these binding free energy components, however, is not limited to the primary target only. Here, we investigate the relationship between binding thermodynamics and selectivity profiles by combining publicly available data from broad off-target assay profiling and the corresponding thermodynamics measurements. Our analysis indicates that compounds binding their primary targets with higher entropy contributions tend to hit more off-targets compared with those ligands that demonstrated enthalpy-driven binding.

Comparative evaluation of pKa prediction tools on a drug discovery dataset

Due to their impact on pharmacokinetic and pharmacodynamic properties the accurate prediction of dissociation constants is of outmost importance in drug discovery settings. The prediction accuracy, however, is typically assessed on public datasets most likely included in the training sets of the available tools. In this work we therefore tested five pK(a) prediction softwares such as ACD, Epik, Marvin, PharmaAlgorithm and Pallas on novel, never-published compounds. Our dataset consists of 177 pK(a) values of 95 structurally diverse in-house compounds prepared for real-life drug discovery programs. The thorough analysis of prediction accuracy allowed us identifying the best practice and exploring the limitations of the current methods. Mean absolute errors (0.86-1.28) obtained for this set of discovery compounds indicates the potential in the improvement of the available pK(a) prediction approaches. Limitations were further characterized by measuring and evaluating 39 pK(a) values of additional 28 commercially available compounds representing the most challenging chemotypes. We believe that these results would facilitate further developments and hopefully contribute to the necessary improvement of the prediction accuracy.

Demonstration of an intramitochondrial invertase activity and the corresponding sugar transporters of the inner mitochondrial membrane in Jerusalem artichoke (Helianthus tuberosus L.) tubers

Genetic evidences indicate that alkaline/neutral invertases are present in plant cell organelles, and they might have a novel physiological function in mitochondria. The present study demonstrates an invertase activity in the mitochondrial matrix of Helianthus tuberosus tubers. The pH optimum, the kinetic parameters and the inhibitor profile of the invertase activity indicated that it belongs to the neutral invertases. In accordance with this topology, transport activities responsible for the mediation of influx/efflux of substrate/products were studied in the inner mitochondrial membrane. The transport of sucrose, glucose and fructose was shown to be bidirectional, saturable and independent of the mitochondrial respiration and membrane potential. Sucrose transport was insensitive to the inhibitors of the proton-sucrose symporters. The different kinetic parameters and inhibitors as well as the absence of cross-inhibition suggest that sucrose, glucose and fructose transport are mediated by separate transporters in the inner mitochondrial membrane. The mitochondrial invertase system composed by an enzyme activity in the matrix and the corresponding sugar transporters might have a role in both osmoregulation and intermediary metabolism.

Quinolinyl- and phenantridinyl-acetamides as bradykinin B1 receptor antagonists

A new series of quinolinyl- and phenantridinyl-acetamides were synthesizer and evaluated against bradykinin B1 receptor. In vitro metabolic stability data were reported for the key compounds.The analgesic effect of compound 20 from the phenantridine series was proved in-vivo.

Homology modeling and binding site assessment of the human P-glycoprotein.

BACKGROUND Due to its impact on multidrug resistance and pharmacokinetics P-glycoprotein (P-gp) has been identified as an important anti-target in pharmaceutical research. Recent publication of the mouse P-gp structure prompted us to build a new model for human P-gp and investigate its binding-site characteristics. RESULTS We developed and validated the human P-gp model that was used for induced-fit docking of experimentally characterized P-gp substrates. Residues located in the binding pocket are in good correlation with the results of side-directed mutagenesis studies. However, enrichment studies aimed at discriminating inhibitors and substrates from decoys resulted in only limited enrichments. CONCLUSION A mouse P-gp-based homology model might be useful when analyzing protein-ligand interactions of known human P-gp substrates if induced-fit effects are considered.

Charting the chemical space around the (iso)indoline scaffold, a comprehensive approach towards multitarget directed ligands

Within the framework of orthosteric G protein coupled receptor (GPCR) polypharmacology herein we report the systematic elaboration and thorough evaluation of a data matrix generated by sampling the chemical space around a common 5,6-fused bicyclic heteroaromatic template applying characteristic pharmacophore elements of central nervous system (CNS) relevant aminergic GPCR ligands.