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Hoch M, Smita S, Cesnulevicius K, Schultz M, Lescheid D, Wolkenhauer O, Gupta S. Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model. J Inflamm (Lond) 2023; 20:12. [PMID: 36973809 PMCID: PMC10044762 DOI: 10.1186/s12950-023-00335-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/21/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution. RESULTS We advance previous studies by mapping the data onto the "Atlas of Inflammation Resolution", followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury. CONCLUSIONS Our results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.
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Affiliation(s)
- Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | - Suchi Smita
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | | | | | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany.
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Factors Determining Plasticity of Responses to Drugs. Int J Mol Sci 2022; 23:ijms23042068. [PMID: 35216184 PMCID: PMC8877660 DOI: 10.3390/ijms23042068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 12/16/2022] Open
Abstract
The plasticity of responses to drugs is an ever-present confounding factor for all aspects of pharmacology, influencing drug discovery and development, clinical use and the expectations of the patient. As an introduction to this Special Issue of the journal IJMS on pharmacological plasticity, we address the various levels at which plasticity appears and how such variability can be controlled, describing the ways in which drug responses can be affected with examples. The various levels include the molecular structures of drugs and their receptors, expression of genes for drug receptors and enzymes involved in metabolism, plasticity of cells targeted by drugs, tissues and clinical variables affected by whole body processes, changes in geography and the environment, and the influence of time and duration of changes. The article provides a rarely considered bird’s eye view of the problem and is intended to emphasize the need for increased awareness of pharmacological plasticity and to encourage further debate.
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The Impact of Diet and Exercise on Drug Responses. Int J Mol Sci 2021; 22:ijms22147692. [PMID: 34299312 PMCID: PMC8304791 DOI: 10.3390/ijms22147692] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/15/2021] [Indexed: 12/14/2022] Open
Abstract
It is well known that lifestyle changes can alter several physiological functions in the human body. For exercise and diet, these effects are used sensibly in basic therapies, as in cardiovascular diseases. However, the physiological changes induced by exercise and a modified diet also have the capacity to influence the efficacy and toxicity of several drugs, mainly by affecting different pharmacokinetic mechanisms. This pharmacological plasticity is not clinically relevant in all cases but might play an important role in altering the effects of very common drugs, particularly drugs with a narrow therapeutic window. Therefore, with this review, we provide insights into possible food–drug and exercise–drug interactions to sharpen awareness of the potential occurrence of such effects.
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Kringel D, Malkusch S, Lötsch J. Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis. Int J Mol Sci 2021; 22:7250. [PMID: 34298869 PMCID: PMC8311652 DOI: 10.3390/ijms22147250] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 12/14/2022] Open
Abstract
Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.
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Affiliation(s)
- Dario Kringel
- Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany; (D.K.); (S.M.)
| | - Sebastian Malkusch
- Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany; (D.K.); (S.M.)
| | - Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany; (D.K.); (S.M.)
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
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Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
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Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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Emmerich CH, Gamboa LM, Hofmann MCJ, Bonin-Andresen M, Arbach O, Schendel P, Gerlach B, Hempel K, Bespalov A, Dirnagl U, Parnham MJ. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat Rev Drug Discov 2021; 20:64-81. [PMID: 33199880 PMCID: PMC7667479 DOI: 10.1038/s41573-020-0087-3] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 02/06/2023]
Abstract
Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.
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Affiliation(s)
| | - Lorena Martinez Gamboa
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Berlin, Germany
| | - Martine C J Hofmann
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine & Pharmacology TMP, Frankfurt am Main, Germany
| | - Marc Bonin-Andresen
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Olga Arbach
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- SPARK-Validation Fund, Berlin Institute of Health, Berlin, Germany
| | - Pascal Schendel
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Katja Hempel
- Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Anton Bespalov
- PAASP GmbH, Heidelberg, Germany
- Valdman Institute of Pharmacology, Pavlov Medical University, St. Petersburg, Russia
| | - Ulrich Dirnagl
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Berlin, Germany
| | - Michael J Parnham
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine & Pharmacology TMP, Frankfurt am Main, Germany
- Faculty of Biochemistry, Chemistry & Pharmacy, J.W. Goethe University Frankfurt, Frankfurt am Main, Germany
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