Teresa Kaserer

Bio: Teresa Kaserer is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Pharmacophore & Virtual screening. The author has an hindex of 12, co-authored 29 publications receiving 411 citations. Previous affiliations of Teresa Kaserer include Innsbruck Medical University & Institute of Cancer Research.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Abstract: Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

μ Opioid receptor: novel antagonists and structural modeling.

Abstract: The μ opioid receptor (MOR) is a prominent member of the G protein-coupled receptor family and the molecular target of morphine and other opioid drugs. Despite the long tradition of MOR-targeting drugs, still little is known about the ligand-receptor interactions and structure-function relationships underlying the distinct biological effects upon receptor activation or inhibition. With the resolved crystal structure of the β-funaltrexamine-MOR complex, we aimed at the discovery of novel agonists and antagonists using virtual screening tools, i.e. docking, pharmacophore- and shape-based modeling. We suggest important molecular interactions, which active molecules share and distinguish agonists and antagonists. These results allowed for the generation of theoretically validated in silico workflows that were employed for prospective virtual screening. Out of 18 virtual hits evaluated in in vitro pharmacological assays, three displayed antagonist activity and the most active compound significantly inhibited morphine-induced antinociception. The new identified chemotypes hold promise for further development into neurochemical tools for studying the MOR or as potential therapeutic lead candidates.

Interference of Paraben Compounds with Estrogen Metabolism by Inhibition of 17β-Hydroxysteroid Dehydrogenases.

Abstract: Parabens are effective preservatives widely used in cosmetic products and processed food, with high human exposure. Recent evidence suggests that parabens exert estrogenic effects. This work investigated the potential interference of parabens with the estrogen-activating enzyme 17β-hydroxysteroid dehydrogenase (17β-HSD) 1 and the estrogen-inactivating 17β-HSD2. A ligand-based 17β-HSD2 pharmacophore model was applied to screen a cosmetic chemicals database, followed by in vitro testing of selected paraben compounds for inhibition of 17β-HSD1 and 17β-HSD2 activities. All tested parabens and paraben-like compounds, except their common metabolite p-hydroxybenzoic acid, inhibited 17β-HSD2. Ethylparaben and ethyl vanillate inhibited 17β-HSD2 with IC50 values of 4.6 ± 0.8 and 1.3 ± 0.3 µM, respectively. Additionally, parabens size-dependently inhibited 17β-HSD1, whereby hexyl- and heptylparaben were most active with IC50 values of 2.6 ± 0.6 and 1.8 ± 0.3 µM. Low micromolar concentrations of hexyl- and heptylparaben decreased 17β-HSD1 activity, and ethylparaben and ethyl vanillate decreased 17β-HSD2 activity. However, regarding the very rapid metabolism of these compounds to the inactive p-hydroxybenzoic acid by esterases, it needs to be determined under which conditions low micromolar concentrations of these parabens or their mixtures can occur in target cells to effectively disturb estrogen effects in vivo.

Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio

Abstract: Background: Pharmacophore modeling has become an integrated tool in drug discovery. However, no prospective study compares the performance of the available software. Methods: The two widely used pharmacophore modeling and screening software programs Discovery Studio and LigandScout were used to generate, validate, and prospectively apply COX-1 and -2 models. Selected virtual hits were tested in cell-free enzymatic assays. The correct retrieval of active compounds was compared. Results: In the enzymatic testing, 10.5% of the tested hits for COX-2 and 6.6% of the predicted compounds for COX-1 were active. To directly compare the two models, both based on the same PDB entry, were selected for virtual screening. The two programs yielded vastly different hit lists, but both predicted active compounds. Conclusion: To obtain a comprehensive selection of active compounds, more than one program should be used for modeling.

Olanzapine: A potent agonist at the hM4D(Gi) DREADD amenable to clinical translation of chemogenetics

Abstract: Designer receptors exclusively activated by designer drugs (DREADDs) derived from muscarinic receptors not only are a powerful tool to test causality in basic neuroscience but also are potentially amenable to clinical translation. A major obstacle, however, is that the widely used agonist clozapine N-oxide undergoes conversion to clozapine, which penetrates the blood-brain barrier but has an unfavorable side effect profile. Perlapine has been reported to activate DREADDs at nanomolar concentrations but is not approved for use in humans by the Food and Drug Administration or the European Medicines Agency, limiting its translational potential. Here, we report that the atypical antipsychotic drug olanzapine, widely available in various formulations, is a potent agonist of the human M4 muscarinic receptor-based DREADD, facilitating clinical translation of chemogenetics to treat central nervous system diseases.

Discovery and resupply of pharmacologically active plant-derived natural products: A review.

Abstract: Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.

Neuroblastoma

Abstract: Neuroblastoma is a type of cancer that most often affects children. Neuroblastoma occurs when immature nerve cells called neuroblasts become abnormal and multiply uncontrollably to form a tumor. Most commonly, the tumor originates in the nerve tissue of the adrenal gland located above each kidney. Other common sites for tumors to form include the nerve tissue in the abdomen, chest, neck, or pelvis. Neuroblastoma can spread (metastasize) to other parts of the body such as the bones, liver, or skin.

Predicting drug metabolism: experiment and/or computation?

Abstract: Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

A Review on Applications of Computational Methods in Drug Screening and Design

Abstract: Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening.

Abstract: Autodock Vina is a very popular, and highly cited, open source docking program Here we present a scoring function which we call Vinardo (Vina RaDii Optimized) Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications Vinardo outperforms Vina in all tests performed, for all datasets analyzed The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v20 from http://sminasfnet Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address