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Cysteinyl leukotriene receptor 1 antagonists (CysLT1RA) are frequently used as add-on medication for the treatment of asthma. Recently, these compounds have shown protective effects in cardiovascular diseases. This prompted us to investigate their influence on soluble epoxide hydrolase (sEH) and peroxisome proliferator activated receptor (PPAR) activities, two targets known to play an important role in CVD and the metabolic syndrome. Montelukast, pranlukast and zafirlukast inhibited human sEH with IC50 values of 1.9, 14.1, and 0.8 μM, respectively. In contrast, only montelukast and zafirlukast activated PPARγ in the reporter gene assay with EC50 values of 1.17 μM (21.9% max. activation) and 2.49 μM (148% max. activation), respectively. PPARα and δ were not affected by any of the compounds. The activation of PPARγ was further investigated in 3T3-L1 adipocytes. Analysis of lipid accumulation, mRNA and protein expression of target genes as well as PPARγ phosphorylation revealed that montelukast was not able to induce adipocyte differentiation. In contrast, zafirlukast triggered moderate lipid accumulation compared to rosiglitazone and upregulated PPARγ target genes. In addition, we found that montelukast and zafirlukast display antagonistic activities concerning recruitment of the PPARγ cofactor CBP upon ligand binding suggesting that both compounds act as PPARγ modulators. In addition, zafirlukast impaired the TNFα triggered phosphorylation of PPARγ2 on serine 273. Thus, zafirlukast is a novel dual sEH/PPARγ modulator representing an excellent starting point for the further development of this compound class.
Poster presentation In pharmaceutical research and drug development, machine learning methods play an important role in virtual screening and ADME/Tox prediction. For the application of such methods, a formal measure of similarity between molecules is essential. Such a measure, in turn, depends on the underlying molecular representation. Input samples have traditionally been modeled as vectors. Consequently, molecules are represented to machine learning algorithms in a vectorized form using molecular descriptors. While this approach is straightforward, it has its shortcomings. Amongst others, the interpretation of the learned model can be difficult, e.g. when using fingerprints or hashing. Structured representations of the input constitute an alternative to vector based representations, a trend in machine learning over the last years. For molecules, there is a rich choice of such representations. Popular examples include the molecular graph, molecular shape and the electrostatic field. We have developed a molecular similarity measure defined directly on the (annotated) molecular graph, a long-standing established topological model for molecules. It is based on the concepts of optimal atom assignments and iterative graph similarity. In the latter, two atoms are considered similar if their neighbors are similar. This recursive definition leads to a non-linear system of equations. We show how to iteratively solve these equations and give bounds on the computational complexity of the procedure. Advantages of our similarity measure include interpretability (atoms of two molecules are assigned to each other, each pair with a score expressing local similarity; this can be visualized to show similar regions of two molecules and the degree of their similarity) and the possibility to introduce knowledge about the target where available. We retrospectively tested our similarity measure using support vector machines for virtual screening on several pharmaceutical and toxicological datasets, with encouraging results. Prospective studies are under way.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Background: Threonine Aspartase 1 (Taspase1) mediates cleavage of the mixed lineage leukemia (MLL) protein and leukemia provoking MLL-fusions. In contrast to other proteases, the understanding of Taspase1's (patho)biological relevance and function is limited, since neither small molecule inhibitors nor cell based functional assays for Taspase1 are currently available. Methodology/Findings: Efficient cell-based assays to probe Taspase1 function in vivo are presented here. These are composed of glutathione S-transferase, autofluorescent protein variants, Taspase1 cleavage sites and rational combinations of nuclear import and export signals. The biosensors localize predominantly to the cytoplasm, whereas expression of biologically active Taspase1 but not of inactive Taspase1 mutants or of the protease Caspase3 triggers their proteolytic cleavage and nuclear accumulation. Compared to in vitro assays using recombinant components the in vivo assay was highly efficient. Employing an optimized nuclear translocation algorithm, the triple-color assay could be adapted to a high-throughput microscopy platform (Z'factor = 0.63). Automated high-content data analysis was used to screen a focused compound library, selected by an in silico pharmacophor screening approach, as well as a collection of fungal extracts. Screening identified two compounds, N-[2-[(4-amino-6-oxo-3H-pyrimidin-2-yl)sulfanyl]ethyl]benzenesulfonamideand 2-benzyltriazole-4,5-dicarboxylic acid, which partially inhibited Taspase1 cleavage in living cells. Additionally, the assay was exploited to probe endogenous Taspase1 in solid tumor cell models and to identify an improved consensus sequence for efficient Taspase1 cleavage. This allowed the in silico identification of novel putative Taspase1 targets. Those include the FERM Domain-Containing Protein 4B, the Tyrosine-Protein Phosphatase Zeta, and DNA Polymerase Zeta. Cleavage site recognition and proteolytic processing of these substrates were verified in the context of the biosensor. Conclusions: The assay not only allows to genetically probe Taspase1 structure function in vivo, but is also applicable for high-content screening to identify Taspase1 inhibitors. Such tools will provide novel insights into Taspase1's function and its potential therapeutic relevance.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
Dimerization of Taspase1 activates an intrinsic serine protease function that leads to the catalytic Thr234 residue, which allows to catalyze the consensus sequence Q−3X−2D−1⋅G1X2D3D4, present in Trithorax family members and TFIIA. Noteworthy, Taspase1 performs only a single hydrolytic step on substrate proteins, which makes it impossible to screen for inhibitors in a classical screening approach. Here, we report the development of an HTRF reporter assay that allowed the identification of an inhibitor, Closantel sodium, that inhibits Taspase1 in a noncovalent fashion (IC50 = 1.6 μM). The novel inhibitor interferes with the dimerization step and/or the intrinsic serine protease function of the proenzyme. Of interest, Taspase1 is required to activate the oncogenic functions of the leukemogenic AF4-MLL fusion protein and was shown in several studies to be overexpressed in many solid tumors. Therefore, the inhibitor may be useful for further validation of Taspase1 as a target for cancer therapy.
Alkylglycerol monooxygenase (AGMO) is a tetrahydrobiopterin (BH4)-dependent enzyme with major expression in the liver and white adipose tissue that cleaves alkyl ether glycerolipids. The present study describes the disclosure and biological characterization of a candidate compound (Cp6), which inhibits AGMO with an IC50 of 30–100 µM and 5–20-fold preference of AGMO relative to other BH4-dependent enzymes, i.e., phenylalanine-hydroxylase and nitric oxide synthase. The viability and metabolic activity of mouse 3T3-L1 fibroblasts, HepG2 human hepatocytes and mouse RAW264.7 macrophages were not affected up to 10-fold of the IC50. However, Cp6 reversibly inhibited the differentiation of 3T3-L1 cells towards adipocytes, in which AGMO expression was upregulated upon differentiation. Cp6 reduced the accumulation of lipid droplets in adipocytes upon differentiation and in HepG2 cells exposed to free fatty acids. Cp6 also inhibited IL-4-driven differentiation of RAW264.7 macrophages towards M2-like macrophages, which serve as adipocyte progenitors in adipose tissue. Collectively, the data suggest that pharmacologic AGMO inhibition may affect lipid storage.
Designed multitarget ligands are a popular approach to generating efficient and safe drugs, and fragment-based strategies have been postulated as a versatile avenue to discover multitarget ligand leads. To systematically probe the potential of fragment-based multiple ligand discovery, we have employed a large fragment library for comprehensive screening on five targets chosen from proteins for which multitarget ligands have been successfully developed previously (soluble epoxide hydrolase, leukotriene A4 hydrolase, 5-lipoxygenase, retinoid X receptor, farnesoid X receptor). Differential scanning fluorimetry served as primary screening method before fragments hitting at least two targets were validated in orthogonal assays. Thereby, we obtained valuable fragment leads with dual-target engagement for six out of ten target combinations. Our results demonstrate the applicability of fragment-based approaches to identify starting points for polypharmacological compound development with certain limitations.
The bile acid activated transcription factor farnesoid X receptor (FXR) regulates numerous metabolic processes and is a rising target for the treatment of hepatic and metabolic disorders. FXR agonists have revealed efficacy in treating non-alcoholic steatohepatitis (NASH), diabetes and dyslipidemia. Here we characterize imatinib as first-in-class allosteric FXR modulator and report the development of an optimized descendant that markedly promotes agonist induced FXR activation in a reporter gene assay and FXR target gene expression in HepG2 cells. Differential effects of imatinib on agonist-induced bile salt export protein and small heterodimer partner expression suggest that allosteric FXR modulation could open a new avenue to gene-selective FXR modulators.