Peder Svensson

Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces

Abstract Structure-property relationships, central to many of today’s drug discovery strategies, are not straightforward to deal with when trying to predict drug efficacy, that is, the combined outcome of target affinity, pharmacodynamic behavior, pharmacokinetic properties, and metabolic fate. In this article, we discuss the handling of chemical property information in reagents-for-synthesis selection, enumeration, and virtual library construction. We describe the use of diversity assessment and/or experimental design in selection of compound-libraries-to-be-synthesized. Our overall objective was to identify good-quality drug candidates through reliable structure-activity relationship data, with the minimum number of compounds synthesized and tested. Chemical filters, property filters, scoring functions, and utilization of interactive visualization tools are discussed. The concept of chemical diversity and aspects of chemical space navigation employing a proprietary tool, Chemical Global Positioning System (ChemGPS), for mapping the drug-related chemical space are examined. Guidelines and workflow recommendations for the practicing medicinal chemist are proposed.

Computational Modelling of Inhibitor Binding to Human Thrombin

Thrombin is an essential protein involved in blood clot formation and an important clinical target, since disturbances of the coagulation process cause serious cardiovascular diseases such as thrombosis. Here we evaluate the performance of a molecular dynamics based method for predicting the binding affinities of different types of human thrombin inhibitors. For a series of eight ligands the method ranks their relative affinities reasonably well. The binding free energy difference between high and low affinity representatives in the test set is quantitatively reproduced, as well as the stereospecificity for a chiral inhibitor. The original parametrisation of this linear interaction energy method requires the addition of a constant energy term in the case of thrombin. This yields a mean unsigned error of 0.68 kcal/mol for the absolute binding free energies. This type of approach is also useful for elucidating three-dimensional structure-activity relationships in terms of microscopic interactions of the ligands with the solvated enzyme.

Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces11Color Plates for this article are on page 541.

Structure-property relationships, central to many of todays drug discovery strategies, are not straightforward to deal with when trying to predict drug efficacy, that is, the combined outcome of target affinity, pharmacodynamic behavior, pharmacokinetic properties, and metabolic fate. In this article, we discuss the handling of chemical property information in reagents-for-synthesis selection, enumeration, and virtual library construction. We describe the use of diversity assessment and/or experimental design in selection of compound-libraries-to-be-synthesized. Our overall objective was to identify good-quality drug candidates through reliable structure-activity relationship data, with the minimum number of compounds synthesized and tested. Chemical filters, property filters, scoring functions, and utilization of interactive visualization tools are discussed. The concept of chemical diversity and aspects of chemical space navigation employing a proprietary tool, Chemical Global Positioning System (ChemGPS), for mapping the drug-related chemical space are examined. Guidelines and workflow recommendations for the practicing medicinal chemist are proposed.

Heterodimer Formation within Universal Stress Protein Classes Revealed By an In Silico and Experimental Approach

Universal stress proteins (Usps) are found in all kingdoms of life and can be divided into four classes by phylogenic analysis. According to available structures, Usps exist as homodimers, and genetic studies show that their cellular assignments are extensive, including functions relating to stress resistance, carbon metabolism, cellular adhesion, motility, and bacterial virulence. We approached the question of how Usps can achieve such a variety of functions in a cell by using a new procedure for statistical analysis of multiple sequence alignments, based on physicochemically related values for each amino acid residue of Usp dimer interfaces. The results predicted that Usp proteins within a class may, in addition to forming homodimers, be able to form heterodimers. Using Escherichia coli Usps as model proteins, we confirmed the existence of such interactions. We especially focused on class I UspA and UspC and demonstrated that they are able to form homo- and heterodimers in vitro and in vivo. We suggest that this ability to form both homo- and heterodimers may allow for an expansion of the functional repertoire of Usps and explains why organisms usually contain multiple usp paralogues.

Selective Pharmacophore Models of Dopamine D1 and D2 Full Agonists Based on Extended Pharmacophore Features

This study is focused on the identification of structural features that determine the selectivity of dopamine receptor agonists toward D1 and D2 receptors. Selective pharmacophore models were developed for both receptors. The models were built by using projected pharmacophoric features that represent the main agonist interaction sites in the receptor (the Ser residues in TM5 and the Asp in TM3), a directional aromatic feature in the ligand, a feature with large positional tolerance representing the positively charged nitrogen in the ligand, and sets of excluded volumes reflecting the shapes of the receptors. The sets of D1 and D2 ligands used for modeling were carefully selected from published sources and consist of structurally diverse, conformationally rigid full agonists as active ligands together with structurally related inactives. The robustness of the models in discriminating actives from inactives was tested against four ensembles of conformations generated by using different established methods and different force fields. The reasons for the selectivity can be attributed to both geometrical differences in the arrangement of the features, e.g., different tilt angels of the π system, as well as shape differences covered by the different sets of excluded volumes. This work provides useful information for the design of new D1 and D2 agonists and also for comparative homology modeling of D1 and D2 receptors. The approach is general and could therefore be applied to other ligand–protein interactions for which no experimental protein structure is available.

Investigation of D1 Receptor–Agonist Interactions and D1/D2 Agonist Selectivity Using a Combination of Pharmacophore and Receptor Homology Modeling

The aim of this study was to use a combined structure and pharmacophore modeling approach to extract information regarding dopamine D1 receptor agonism and D1/D2 agonist selectivity. A 3D structure model of the D1 receptor in its agonist‐bound state was constructed with a full D1 agonist present in the binding site. Two different binding modes were identified using (+)‐doxanthrine or SKF89626 in the modeling procedure. The 3D model was further compared with a selective D1 agonist pharmacophore model. The pharmacophore feature arrangement was found to be in good agreement with the binding site composition of the receptor model, but the excluded volumes did not fully reflect the shape of the agonist binding pocket. A new receptor‐based pharmacophore model was developed with forbidden volumes centered on atom positions of amino acids in the binding site. The new pharmacophore model showed a similar ability to discriminate as the previous model. A comparison of the 3D structures and pharmacophore models of D1 and D2 receptors revealed differences in shape and ligand‐interacting features that determine selectivity of D1 and D2 receptor agonists. A hydrogen bond pharmacophoric feature (Ser‐TM5) was shown to contribute most to the selectivity. Non‐conserved residues in the binding pocket that strongly contribute to D1/D2 receptor agonist selectivity were also identified; those were Ser/Cys3.36, Tyr/Phe5.38, Ser/Tyr5.41, and Asn/His6.55 in the transmembrane (TM) helix region, together with Ser/Ile and Leu/Asn in the second extracellular loop (EC2). This work provides useful information for the design of new selective D1 and D2 agonists. The combined receptor structure and pharmacophore modeling approach is considered to be general, and could therefore be applied to other ligand–protein interactions for which experimental information is limited.

Behavioral Analysis of Dopaminergic Activation in Zebrafish and Rats Reveals Similar Phenotypes

Zebrafish is emerging as a complement to mammals in behavioral studies; however, there is a lack of comparative studies with rodents and humans to establish the zebrafish as a predictive translational model. Here we present a detailed phenotype evaluation of zebrafish larvae, measuring 300-3000 variables and analyzing them using multivariate analysis to identify the most important ones for further evaluations. The dopamine agonist apomorphine has previously been shown to have a complex U-shaped dose-response relationship in the variable distance traveled. In this study, we focused on breaking down distance traveled into more detailed behavioral phenotypes for both zebrafish and rats and identified in the multivariate analysis low and high dose phenotypes with characteristic behavioral features. Further analysis of single parameters also identified an increased activity at the lowest concentration indicative of a U-shaped dose-response. Apomorphine increased the distance of each swim movement (bout) at both high and low doses, but the underlying behavior of this increase is different; at high dose, both bout duration and frequency increased whereas bout max speed was higher at low dose. Larvae also displayed differences in place preference. The low dose phenotype spent more time in the center, indicative of an anxiolytic effect, while the high-dose phenotype had a wall preference. These dose-dependent effects corroborated findings in a parallel rat study and previous observations in humans. The translational value of pharmacological zebrafish studies was further evaluated by comparing the amino acid sequence of the dopamine receptors (D1-D4), between zebrafish, rats and humans. Humans and zebrafish share 100% of the amino acids in the binding site for D1 and D3 whereas D2 and D4 receptors share 85-95%. Molecular modeling of dopamine D2 and D4 receptors indicated that nonconserved amino acids have limited influence on important ligand-receptor interactions.

Synthesis and Evaluation of a Set of Para-Substituted 4-Phenylpiperidines and 4-Phenylpiperazines as Monoamine Oxidase (MAO) Inhibitors

A series of para-substituted 4-phenylpiperidines/piperazines have been synthesized and their affinity to recombinant rat cerebral cortex monoamine oxidases A (MAO A) and B (MAO B) determined. Para-substituents with low dipole moment increased the affinity to MAO A, whereas groups with high dipole moment yielded compounds with no or weak affinity. In contrast, the properties affecting MAO B affinity were the polarity and bulk of the para-substituent, with large hydrophobic substituents producing compounds with high MAO B affinity. In addition, these compounds were tested in freely moving rats and the effect on the post-mortem neurochemistry was measured. A linear correlation was demonstrated between the affinity for MAO A, but not MAO B, and the levels of 3,4-dihydroxyphenylacetic acid (DOPAC) and 3-methoxytyramine (3-MT) in the striatum.

Investigation of D2 Receptor–Agonist Interactions Using a Combination of Pharmacophore and Receptor Homology Modeling

A combined modeling approach was used to identify structural factors that underlie the structure–activity relationships (SARs) of full dopamine D2 receptor agonists and structurally similar inactive compounds. A 3D structural model of the dopamine D2 receptor was constructed, with the agonist (−)‐(R)‐2‐OH‐NPA present in the binding site during the modeling procedure. The 3D model was evaluated and compared with our previously published D2 agonist pharmacophore model. The comparison revealed an inconsistency between the projected hydrogen bonding feature (Ser‐TM5) in the pharmacophore model and the TM5 region in the structure model. A new refined pharmacophore model was developed, guided by the shape of the binding site in the receptor model and with less emphasis on TM5 interactions. The combination of receptor and pharmacophore modeling also identified the importance of His3936.55 for agonist binding. This convergent 3D pharmacophore and protein structure modeling strategy is considered to be general and can be highly useful in less well‐characterized systems to explore ligand–receptor interactions. The strategy has the potential to identify weaknesses in the individual models and thereby provides an opportunity to improve the discriminating predictivity of both pharmacophore searches and structure‐based virtual screens.

Synthesis, pharmacological evaluation and QSAR modeling of mono-substituted 4-phenylpiperidines and 4-phenylpiperazines

A series of mono-substituted 4-phenylpiperidines and -piperazines have been synthesized and their effects on the dopaminergic system tested in vivo. The structure activity relationship (SAR) revealed that the position and physicochemical character of the aromatic substituent proved to be critical for the levels of 3,4-dihydroxyphenylacetic acid (DOPAC) in the brain of freely moving rats. In order to investigate how the structural properties of these compounds affect the response, a set of tabulated and calculated physicochemical descriptors were modeled against the in vivo effects using partial least square (PLS) regression. Furthermore, the binding affinities to the dopamine D2 (DA D2) receptor and monoamine oxidase A (MAO A) enzyme were determined for a chosen subset and QSAR models using the same descriptors as in the in vivo model were produced to investigate the mechanisms leading to the observed DOPAC response. These models, in combination with a strong correlation between the levels of striatal DOPAC and the affinities to DA D2 and MAO A, provides a comprehensive understanding of the biological response for compounds in this class.