{"entity": "journal", "iuid": "69785d725301466baeb0c46bea744efd", "timestamp": "2026-05-12T22:14:11.611Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Curr.%20Pharm.%20Des..json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Curr.%20Pharm.%20Des."}}, "title": "Curr. Pharm. Des.", "issn": "1873-4286", "issn-l": "1381-6128", "publications_count": 4, "publications": [{"entity": "publication", "iuid": "67538a4e4ce54aeaab9abbd0e204294c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/67538a4e4ce54aeaab9abbd0e204294c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/67538a4e4ce54aeaab9abbd0e204294c"}}, "title": "A Network-Based Cancer Drug Discovery: From Integrated Multi-Omics Approaches to Precision Medicine.", "authors": [{"family": "Turanli", "given": "Beste", "initials": "B"}, {"family": "Karagoz", "given": "Kubra", "initials": "K"}, {"family": "Gulfidan", "given": "Gizem", "initials": "G"}, {"family": "Sinha", "given": "Raghu", "initials": "R"}, {"family": "Mardinoglu", "given": "Adil", "initials": "A"}, {"family": "Arga", "given": "Kazim Yalcin", "initials": "KY"}], "type": "journal article", "published": "2018-11-07", "journal": {"title": "Curr. Pharm. Des.", "issn": "1873-4286", "issn-l": "1381-6128", "volume": "24", "issue": "32", "pages": "3778-3790"}, "abstract": "A complex framework of interacting partners including genetic, proteomic, and metabolic networks that cooperate to mediate specific functional phenotypes drives human biological processes. Recent technological and analytical advances in \"omic\" sciences allow the identification and elucidation of reprogramming biological functions in response to perturbations in cells and tissues. To understand such a complex system, biological networks are generated to reduce the complexity into relatively simple models, and the integration of these molecular networks from different perspectives is implemented for a holistic interpretation of the entire system. Ultimately, network-based methods will effectively facilitate the development and improvement of precision medicine by directing therapies based on the underlying biology of a given patient's disease. The goal of precision medicine is to identify novel therapeutic strategies that can be optimized for each disease type or each patient based on the underlying genetic, environmental, and lifestyle factors. Pharmaco-omics analyses based on an integration of pharmacology and various \"omics\" data types can be employed to develop effective treatment strategies using particular drugs and doses that are tailored to each individual. In the current review, we first present the core elements of network-based systems biology in the context of pharmaco-omics followed by integration of multi-omics data using various biological networks. Next, we provide an opening into precise medicine and drug targeting based on network approaches. Lastly, we review the current significant efforts as well as the accomplishments and limitations in precise drug targeting with the utility of network-based guided drug discovery methods for effective treatment of breast cancer.", "doi": "10.2174/1381612824666181106095959", "pmid": "30398107", "labels": {"Adil Mardinoglu": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pii", "key": "CPD-EPUB-94299"}], "notes": [], "created": "2020-11-30T03:16:44.011Z", "modified": "2022-11-04T11:32:17.138Z"}, {"entity": "publication", "iuid": "abb4597779224e4292d04964997a70bd", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/abb4597779224e4292d04964997a70bd.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/abb4597779224e4292d04964997a70bd"}}, "title": "Systems Toxicology: Systematic Approach to Predict Toxicity.", "authors": [{"family": "Kiani", "given": "Narsis A", "initials": "NA"}, {"family": "Shang", "given": "Ming-Mei", "initials": "MM"}, {"family": "Tegner", "given": "Jesper", "initials": "J"}], "type": "journal article", "published": "2016-10-05", "journal": {"title": "Curr. Pharm. Des.", "issn": "1873-4286", "volume": "22", "issue": "46", "pages": "6911-6917", "issn-l": "1381-6128"}, "abstract": "Drug discovery is complex and expensive. Numerous drug candidates fail late in clinical trials or even after being released to the market. These failures are not only due to commercial considerations and less optimal drug efficacies but, adverse reactions originating from toxic effects also constitute a major challenge. During the last two decades, significant advances have been made enabling the early prediction of toxic effects using in silico techniques. However, by design, these essentially statistical techniques have not taken the disease driving pathophysiological mechanisms into account. The complexity of such mechanisms in combination with their interactions with drugspecific properties and environmental and life-style related factors renders the task of predicting toxicity on a purely statistical basis which is an insurmountable challenge. In response to this situation, an interdisciplinary field has developed, referred to as systems toxicology, where the notion of a network is used to integrate and model different types of information to better predict drug toxicity. In this study, we briefly review the merits and limitations of such recent promising predictive approaches integrating molecular networks, chemical compound networks, and protein drug association networks.", "doi": "10.2174/1381612822666161003115629", "pmid": "27697024", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "CPD-EPUB-78714"}], "notes": [], "created": "2018-12-06T12:37:18.966Z", "modified": "2018-12-06T12:37:18.980Z"}, {"entity": "publication", "iuid": "411254680d9a4526a8956eb57a8c9dee", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/411254680d9a4526a8956eb57a8c9dee.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/411254680d9a4526a8956eb57a8c9dee"}}, "title": "Characterization of the effect of a novel \u03b3-secretase modulator on A\u03b2: a clinically translatable model.", "authors": [{"family": "Portelius", "given": "Erik", "initials": "E"}, {"family": "Appelkvist", "given": "Paulina", "initials": "P"}, {"family": "Stromberg", "given": "Kia", "initials": "K"}, {"family": "Hoglund", "given": "Kina", "initials": "K"}], "type": "journal article", "published": "2013-07-19", "journal": {"title": "Curr. Pharm. Des.", "issn": "1873-4286", "volume": "20", "issue": "15", "pages": "2484-2490", "issn-l": "1381-6128"}, "abstract": "Alzheimer's disease (AD) is a slowly progressing disease and the evaluation of clinical effects of candidate drugs requires large clinical cohorts as well as long treatment trials. There is a great need for central biomarkers and translatable pre-clinical models to provide early indication of treatment effects. We set out to evaluate the guinea pig as a clinically translatable model looking at A\u03b2 peptides. Our data demonstrate homology between \u03b2-amyloid (A\u03b2) peptide pattern in cerebrospinal fluid (CSF) from human and guinea pig. To further evaluate the model a novel \u03b3-secretase modulator was used. Dose and time response studies confirm the modulatory properties with a statistically significant decrease in relative levels of A\u03b21-40, A\u03b21-42 and increase in A\u03b21-37 already one hour after administration. We suggest that the guinea pig is a compelling pre-clinical model for evaluating and translating central effects on A\u03b2 peptides in CSF after treatment. Further quantitative data are needed to confirm our data together with data from clinical trials in order to back translate and validate our findings.", "doi": "10.2174/13816128113199990499", "pmid": "23859556", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "CPD-EPUB-54005"}], "notes": [], "created": "2018-12-05T15:12:59.942Z", "modified": "2022-01-03T10:14:05.350Z"}, {"entity": "publication", "iuid": "df494e41ee8e40439e1772b79ae52be2", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/df494e41ee8e40439e1772b79ae52be2.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/df494e41ee8e40439e1772b79ae52be2"}}, "title": "Context-dependent action of transforming growth factor \u03b2 family members on normal and cancer stem cells.", "authors": [{"family": "Caja", "given": "Laia", "initials": "L"}, {"family": "Kahata", "given": "Kaoru", "initials": "K"}, {"family": "Moustakas", "given": "Aristidis", "initials": "A"}], "type": "journal article", "published": "2012-05-29", "journal": {"volume": "18", "issn": "1873-4286", "issue": "27", "pages": "4072-4086", "title": "Curr. Pharm. Des.", "issn-l": "1381-6128"}, "abstract": "The transforming growth factor \u03b2 (TGF\u03b2) family embraces many growth factors including the Activins and bone morphogenetic proteins (BMPs). The pathways mediated by these growth factors are implicated in many fundamental biological processes such as early embryonic development, organ morphogenesis and adult tissue homeostasis and in a large number of pathologies including cancer. The action of these pathways is often contextual, which means that different cell types present different physiological responses to these ligands or that the response of one cell type to a certain ligand differs depending on the presence of other signaling proteins that stimulate the target cell together with TGF\u03b2/BMP. The latter usually reflects developmental stage or progression to a specific pathological stage. Not only diverse growth factors and cytokines can influence the response of tissues to TGF\u03b2/BMP, but a single cell type may also show drastically different physiological outcomes to TGF\u03b2 or Activin signaling as compared to BMP signaling. This review describes differential physiological outcomes of TGF\u03b2 and BMP signaling in normal embryonic or adult stem cells and eventually in cancer stem cells and the process of epithelial-mesenchymal transition. We also summarize evidence on the mechanistic antagonism between TGF\u03b2 and BMP signaling as established in vascular differentiation and the progression of tissue fibrosis and cancer. The article ends by discussing possible advantages that the acquired knowledge of these signaling mechanisms offers to new regimes of cancer therapy and the ever-lasting problem of drug resistance elicited by tumor initiating cells.", "doi": "10.2174/138161212802430459", "pmid": "22630079", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "CPD-EPUB-20120522-22"}], "notes": [], "created": "2018-12-05T14:57:50.873Z", "modified": "2022-01-03T10:14:36.893Z"}], "created": "2018-12-05T14:57:50.887Z", "modified": "2020-11-27T13:12:57.634Z"}