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Article ; Online: Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.

Rudik, Anastassia / Dmitriev, Alexander / Lagunin, Alexey / Filimonov, Dmitry / Poroikov, Vladimir

2022 Volume 27, Issue 18

Abstract: Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug-drug interactions and ... ...

Abstract	Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug-drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.
MeSH term(s)	Cytochrome P-450 Enzyme System/metabolism ; Drug Interactions ; Heme ; Humans ; Microsomes, Liver/metabolism ; Protein Isoforms ; Xenobiotics
Chemical Substances	Protein Isoforms ; Xenobiotics ; Heme (42VZT0U6YR) ; Cytochrome P-450 Enzyme System (9035-51-2)
Language	English
Publishing date	2022-09-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules27185875
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.

Paremskaia, Anastasiia Iu / Rudik, Anastassia V / Filimonov, Dmitry A / Lagunin, Alexey A / Poroikov, Vladimir V / Tarasova, Olga A

Viruses

2023 Volume 15, Issue 11

Abstract: Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase ... ...

Abstract	Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.
MeSH term(s)	Humans ; Anti-HIV Agents/pharmacology ; Anti-HIV Agents/therapeutic use ; Bayes Theorem ; Amino Acid Substitution ; Reproducibility of Results ; HIV Reverse Transcriptase/genetics ; Reverse Transcriptase Inhibitors/pharmacology ; HIV Infections/drug therapy ; Drug Resistance, Viral/genetics ; HIV Protease/genetics
Chemical Substances	Anti-HIV Agents ; HIV Reverse Transcriptase (EC 2.7.7.49) ; Reverse Transcriptase Inhibitors ; HIV Protease (EC 3.4.23.-)
Language	English
Publishing date	2023-11-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v15112245
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body.

Filimonov, Dmitry A / Rudik, Anastassia V / Dmitriev, Alexander V / Poroikov, Vladimir V

International journal of molecular sciences

2020 Volume 21, Issue 20

Abstract: Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites ...

Abstract	Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds' interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients' interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.
MeSH term(s)	Computer Simulation ; Computer-Aided Design ; Drug Design ; Drug Discovery/methods ; Humans ; Reproducibility of Results ; Software ; Structure-Activity Relationship
Language	English
Publishing date	2020-10-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms21207492
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Article: In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms.

Dmitriev, Alexander V / Rudik, Anastassia V / Karasev, Dmitry A / Pogodin, Pavel V / Lagunin, Alexey A / Filimonov, Dmitry A / Poroikov, Vladimir V

Pharmaceutics

2021 Volume 13, Issue 4

Abstract: Drug-drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and ... ...

Abstract	Drug-drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure-activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.
Language	English
Publishing date	2021-04-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527217-2
ISSN	1999-4923
ISSN	1999-4923
DOI	10.3390/pharmaceutics13040538
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts.

Tarasova, Olga A / Biziukova, Nadezhda Yu / Rudik, Anastassia V / Dmitriev, Alexander V / Filimonov, Dmitry A / Poroikov, Vladimir V

Journal of chemical information and modeling

2021 Volume 61, Issue 4, Page(s) 1683–1690

Abstract: The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used ...

Abstract	The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).
Language	English
Publishing date	2021-03-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.0c01054
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: QNA-Based Prediction of Sites of Metabolism.

Tarasova, Olga / Rudik, Anastassia / Dmitriev, Alexander / Lagunin, Alexey / Filimonov, Dmitry / Poroikov, Vladimir

Molecules (Basel, Switzerland)

2017 Volume 22, Issue 12

Abstract: Metabolism of xenobiotics ( ... ...

Abstract	Metabolism of xenobiotics (Greek
MeSH term(s)	Bayes Theorem ; Cytochrome P-450 Enzyme System/chemistry ; Cytochrome P-450 Enzyme System/metabolism ; Datasets as Topic ; Humans ; Ligands ; Machine Learning ; Models, Chemical ; Molecular Structure ; Neural Networks (Computer) ; Xenobiotics/chemistry ; Xenobiotics/metabolism
Chemical Substances	Ligands ; Xenobiotics ; Cytochrome P-450 Enzyme System (9035-51-2)
Language	English
Publishing date	2017-12-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules22122123
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Molecular property diagnostic suite for diabetes mellitus (MPDS

Gaur, Anamika Singh / Nagamani, Selvaraman / Tanneeru, Karunakar / Druzhilovskiy, Dmitry / Rudik, Anastassia / Poroikov, Vladimir / Narahari Sastry, G

Journal of biomedical informatics

2018 Volume 85, Page(s) 114–125

Abstract: Molecular Property Diagnostic Suite - Diabetes Mellitus ( ... ...

Abstract	Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDS
MeSH term(s)	Computational Biology ; Diabetes Mellitus/diagnosis ; Diabetes Mellitus/drug therapy ; Diabetes Mellitus/genetics ; Drug Discovery/statistics & numerical data ; Drug Evaluation, Preclinical ; Drug Repositioning/statistics & numerical data ; Humans ; Hypoglycemic Agents/chemistry ; Hypoglycemic Agents/pharmacology ; Internet ; Molecular Diagnostic Techniques/statistics & numerical data ; Molecular Docking Simulation ; User-Computer Interface
Chemical Substances	Hypoglycemic Agents
Language	English
Publishing date	2018-08-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2018.08.003
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review.

Lagunin, Alexey A / Goel, Rajesh K / Gawande, Dinesh Y / Pahwa, Priynka / Gloriozova, Tatyana A / Dmitriev, Alexander V / Ivanov, Sergey M / Rudik, Anastassia V / Konova, Varvara I / Pogodin, Pavel V / Druzhilovsky, Dmitry S / Poroikov, Vladimir V

Natural product reports

2014 Volume 31, Issue 11, Page(s) 1585–1611

Abstract: In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of ...

Abstract	In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.
MeSH term(s)	Computational Biology ; Databases, Factual ; Drug Discovery ; Medicine, Traditional ; Molecular Structure ; Plants, Medicinal/chemistry
Language	English
Publishing date	2014-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2002546-4
ISSN	1460-4752 ; 0265-0568
ISSN (online)	1460-4752
ISSN	0265-0568
DOI	10.1039/c4np00068d
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review

Lagunin, Alexey A. / Goel, Rajesh K. / Gawande, Dinesh Y. / Pahwa, Priynka / Gloriozova, Tatyana A. / Dmitriev, Alexander V. / Ivanov, Sergey M. / Rudik, Anastassia V. / Konova, Varvara I. / Pogodin, Pavel V. / Druzhilovsky, Dmitry S. / Poroikov, Vladimir V.

Natural product reports. 2014 Oct. 8, v. 31, no. 11

2014

Abstract: Covering: up to 2014 In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond ... ...

Abstract	Covering: up to 2014 In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.
Keywords	bioinformatics ; computer simulation ; drugs ; therapeutics
Language	English
Dates of publication	2014-1008
Size	p. 1585-1611.
Publishing place	The Royal Society of Chemistry
Document type	Article
ZDB-ID	2002546-4
ISSN	1460-4752 ; 0265-0568
ISSN (online)	1460-4752
ISSN	0265-0568
DOI	10.1039/c4np00068d
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A community effort in SARS-CoV-2 drug discovery.

Schimunek, Johannes / Seidl, Philipp / Elez, Katarina / Hempel, Tim / Le, Tuan / Noé, Frank / Olsson, Simon / Raich, Lluís / Winter, Robin / Gokcan, Hatice / Gusev, Filipp / Gutkin, Evgeny M / Isayev, Olexandr / Kurnikova, Maria G / Narangoda, Chamali H / Zubatyuk, Roman / Bosko, Ivan P / Furs, Konstantin V / Karpenko, Anna D /

Kornoushenko, Yury V / Shuldau, Mikita / Yushkevich, Artsemi / Benabderrahmane, Mohammed B / Bousquet-Melou, Patrick / Bureau, Ronan / Charton, Beatrice / Cirou, Bertrand C / Gil, Gérard / Allen, William J / Sirimulla, Suman / Watowich, Stanley / Antonopoulos, Nick / Epitropakis, Nikolaos / Krasoulis, Agamemnon / Itsikalis, Vassilis / Theodorakis, Stavros / Kozlovskii, Igor / Maliutin, Anton / Medvedev, Alexander / Popov, Petr / Zaretckii, Mark / Eghbal-Zadeh, Hamid / Halmich, Christina / Hochreiter, Sepp / Mayr, Andreas / Ruch, Peter / Widrich, Michael / Berenger, Francois / Kumar, Ashutosh / Yamanishi, Yoshihiro / Zhang, Kam Y J / Bengio, Emmanuel / Bengio, Yoshua / Jain, Moksh J / Korablyov, Maksym / Liu, Cheng-Hao / Marcou, Gilles / Glaab, Enrico / Barnsley, Kelly / Iyengar, Suhasini M / Ondrechen, Mary Jo / Haupt, V Joachim / Kaiser, Florian / Schroeder, Michael / Pugliese, Luisa / Albani, Simone / Athanasiou, Christina / Beccari, Andrea / Carloni, Paolo / D'Arrigo, Giulia / Gianquinto, Eleonora / Goßen, Jonas / Hanke, Anton / Joseph, Benjamin P / Kokh, Daria B / Kovachka, Sandra / Manelfi, Candida / Mukherjee, Goutam / Muñiz-Chicharro, Abraham / Musiani, Francesco / Nunes-Alves, Ariane / Paiardi, Giulia / Rossetti, Giulia / Sadiq, S Kashif / Spyrakis, Francesca / Talarico, Carmine / Tsengenes, Alexandros / Wade, Rebecca C / Copeland, Conner / Gaiser, Jeremiah / Olson, Daniel R / Roy, Amitava / Venkatraman, Vishwesh / Wheeler, Travis J / Arthanari, Haribabu / Blaschitz, Klara / Cespugli, Marco / Durmaz, Vedat / Fackeldey, Konstantin / Fischer, Patrick D / Gorgulla, Christoph / Gruber, Christian / Gruber, Karl / Hetmann, Michael / Kinney, Jamie E / Padmanabha Das, Krishna M / Pandita, Shreya / Singh, Amit / Steinkellner, Georg / Tesseyre, Guilhem / Wagner, Gerhard / Wang, Zi-Fu / Yust, Ryan J / Druzhilovskiy, Dmitry S / Filimonov, Dmitry A / Pogodin, Pavel V / Poroikov, Vladimir / Rudik, Anastassia V / Stolbov, Leonid A / Veselovsky, Alexander V / De Rosa, Maria / De Simone, Giada / Gulotta, Maria R / Lombino, Jessica / Mekni, Nedra / Perricone, Ugo / Casini, Arturo / Embree, Amanda / Gordon, D Benjamin / Lei, David / Pratt, Katelin / Voigt, Christopher A / Chen, Kuang-Yu / Jacob, Yves / Krischuns, Tim / Lafaye, Pierre / Zettor, Agnès / Rodríguez, M Luis / White, Kris M / Fearon, Daren / Von Delft, Frank / Walsh, Martin A / Horvath, Dragos / Brooks, Charles L / Falsafi, Babak / Ford, Bryan / García-Sastre, Adolfo / Yup Lee, Sang / Naffakh, Nadia / Varnek, Alexandre / Klambauer, Günter / Hermans, Thomas M

Molecular informatics

2023 Volume 43, Issue 1, Page(s) e202300262

Abstract: The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and ... ...

Abstract	The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Pandemics ; Biological Assay ; Drug Discovery
Language	English
Publishing date	2023-11-14
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2537668-8
ISSN	1868-1751 ; 1868-1743
ISSN (online)	1868-1751
ISSN	1868-1743
DOI	10.1002/minf.202300262
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