Refine
Year of publication
Document Type
- Article (17)
- Contribution to a Periodical (2)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19) (remove)
Keywords
- Absorption (2)
- 4-fluoroamphetamine (1)
- ABCB1 (1)
- APP processing (1)
- Alzheimer’s disease (1)
- Amyloid-beta (1)
- Bioavailability (1)
- Boswellia serrata (1)
- Boswellic acids (1)
- Caco-2 cells (1)
Institute
YS-121 [2-(4-chloro-6-(2,3-dimethylphenylamino)pyrimidin-2-ylthio)octanoic acid] is the result of target-oriented structural derivatization of pirinixic acid. It is a potent dual PPARα/γ-agonist, as well as a potent dual 5-LO/mPGES-1-inhibitor. Additionally, recent studies showed an anti-inflammatory efficacy in vivo. Because of its interference with many targets, YS-121 is a promising drug candidate for the treatment of inflammatory diseases. Ongoing preclinical studies will thus necessitate huge amounts of YS-121. To cope with those requirements, we have optimized the synthesis of YS-121. Surprisingly, we isolated and characterized byproducts during the resulting from nucleophilic aromatic substitution reactions by different tertiary alkylamines at a heteroaromatic halide. These amines should actually serve as assisting bases, because of their low nucleophilicity. This astonishing fact was not described in former publications concerning that type of reaction and, therefore, might be useful for further reaction improvement in general. Furthermore, we could develop a proposal for the mechanism of that byproduct formation.
Gout is the most common arthritic disease in human but was long neglected and therapeutic options are not satisfying. However, with the recent approval of the urate transporter inhibitor lesinurad, gout treatment has experienced a major innovation. Here we show that lesinurad possesses considerable modulatory potency on peroxisome proliferator-activated receptor γ (PPARγ). Since gout has a strong association with metabolic diseases such as type 2 diabetes, this side-activity appears as very valuable contributing factor to the clinical efficacy profile of lesinurad. Importantly, despite robustly activating PPARγ in vitro, lesinurad lacked adipogenic activity, which seems due to differential coactivator recruitment and is characterized as selective PPARγ modulator (sPPARγM).
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.
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.
Shape complementarity is a compulsory condition for molecular recognition. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment with fuzzy pharmacophore scoring. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified...
Background: Current approved drugs for Alzheimer’s disease (AD) only attenuate symptoms, but do not cure the disease. The pirinixic acid derivate MH84 has been characterized as a dual gamma-secretase/proliferator activated receptor gamma (PPARγ) modulator in vitro. Pharmacokinetic studies in mice showed that MH84 is bioavailable after oral administration and reaches the brain. We recently demonstrated that MH84 improved mitochondrial dysfunction in a cellular model of AD. In the present study, we extended the pharmacological characterization of MH84 to 3-month-old Thy-1 AβPPSL mice (harboring the Swedish and London mutation in human amyloid precursor protein (APP)) which are characterized by enhanced AβPP processing and cerebral mitochondrial dysfunction, representing a mouse model of early AD.
Methods: Three-month-old Thy-1 AβPPSL mice received 12 mg/kg b.w. MH84 by oral gavage once a day for 21 days. Mitochondrial respiration was analyzed in isolated brain mitochondria, and mitochondrial membrane potential and ATP levels were determined in dissociated brain cells. Citrate synthase (CS) activity was determined in brain tissues and MitoTracker Green fluorescence was measured in HEK293-AβPPwt and HEK293-AβPPsw cells. Soluble Aβ1–40 and Aβ1–42 levels were determined using ELISA. Western blot analysis and qRT-PCR were used to measure protein and mRNA levels, respectively.
Results: MH84 reduced cerebral levels of the β-secretase-related C99 peptide and of Aβ40 levels. Mitochondrial dysfunction was ameliorated by restoring complex IV (cytochrome-c oxidase) respiration, mitochondrial membrane potential, and levels of ATP. Induction of PPARγ coactivator-1α (PGC-1α) mRNA and protein expression was identified as a possible mode of action that leads to increased mitochondrial mass as indicated by enhanced CS activity, OXPHOS levels, and MitoTracker Green fluorescence.
Conclusions: MH84 modulates β-secretase processing of APP and improves mitochondrial dysfunction by a PGC-1α-dependent mechanism. Thus, MH84 seems to be a new promising therapeutic agent with approved in-vivo activity for the treatment of AD.
Pirinixic acid derivatives, a new class of drug candidates for a range of diseases, interfere with targets including PPARα, PPARγ, 5-lipoxygenase (5-LO), and microsomal prostaglandin and E2 synthase-1 (mPGES1). Since 5-LO, mPGES1, PPARα, and PPARγ represent potential anti-cancer drug targets, we here investigated the effects of 39 pirinixic acid derivatives on prostate cancer (PC-3) and neuroblastoma (UKF-NB-3) cell viability and, subsequently, the effects of selected compounds on drug-resistant neuroblastoma cells. Few compounds affected cancer cell viability in low micromolar concentrations but there was no correlation between the anti-cancer effects and the effects on 5-LO, mPGES1, PPARα, or PPARγ. Most strikingly, pirinixic acid derivatives interfered with drug transport by the ATP-binding cassette (ABC) transporter ABCB1 in a drug-specific fashion. LP117, the compound that exerted the strongest effect on ABCB1, interfered in the investigated concentrations of up to 2μM with the ABCB1-mediated transport of vincristine, vinorelbine, actinomycin D, paclitaxel, and calcein-AM but not of doxorubicin, rhodamine 123, or JC-1. In silico docking studies identified differences in the interaction profiles of the investigated ABCB1 substrates with the known ABCB1 binding sites that may explain the substrate-specific effects of LP117. Thus, pirinixic acid derivatives may offer potential as drug-specific modulators of ABCB1-mediated drug transport.