1
|
Oishi M, Sayama H, Toshimoto K, Nakayama T, Nagasaka Y. Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metab Pharmacokinet 2024; 56:101020. [PMID: 38797089 DOI: 10.1016/j.dmpk.2024.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/02/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.
Collapse
Affiliation(s)
- Masayo Oishi
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan.
| | - Hiroyuki Sayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takeshi Nakayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Yasuhisa Nagasaka
- Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| |
Collapse
|
2
|
Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
Collapse
Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| |
Collapse
|
3
|
Nayarisseri A. Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery. Curr Top Med Chem 2021; 20:1651-1660. [PMID: 32614747 DOI: 10.2174/156802662019200701164759] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.
Collapse
Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| |
Collapse
|
4
|
Yalcin GD, Danisik N, Baygin RC, Acar A. Systems Biology and Experimental Model Systems of Cancer. J Pers Med 2020; 10:E180. [PMID: 33086677 PMCID: PMC7712848 DOI: 10.3390/jpm10040180] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/13/2020] [Accepted: 10/16/2020] [Indexed: 12/29/2022] Open
Abstract
Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.
Collapse
Affiliation(s)
| | | | | | - Ahmet Acar
- Department of Biological Sciences, Middle East Technical University, Universiteler Mah. Dumlupınar Bulvarı 1, Çankaya, Ankara 06800, Turkey; (G.D.Y.); (N.D.); (R.C.B.)
| |
Collapse
|
5
|
Yuan D, Zhou H, Sun H, Tian R, Xia M, Sun L, Liu Y. Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics. Oncol Lett 2020; 20:1345-1359. [PMID: 32724377 PMCID: PMC7377160 DOI: 10.3892/ol.2020.11672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 04/01/2020] [Indexed: 12/13/2022] Open
Abstract
The emergence of resistance to chemotherapy drugs in patients with ovarian cancer is still the main cause of low survival rates. The present study aimed to identify key genes that may provide treatment guidance to reduce the incidence of drug resistance in patients with ovarian cancer. Original data of chemotherapy sensitivity and chemoresistance of ovarian cancer were obtained from the Gene Expression Omnibus dataset GSE73935. Differentially expressed genes (DEGs) between sensitive and resistant ovarian cancer cell lines were screened by Empirical Bayes methods. Overlapping DEGs between four chemoresistant groups were identified by Venn map analysis. Protein-protein interaction networks were also constructed, and hub genes were identified. The hub genes were verified by in vitro experiments as well as The Cancer Genome Atlas data. Results from the present study identified eight important genes that may guide treatment decisions regarding chemotherapy regimens for ovarian cancer, including epidermal growth factor-like repeats and discoidin I-like domains 3, NRAS proto-oncogene, hyaluronan and proteoglycan link protein 1, activated protein C receptor, CD53, cyclin-dependent kinase inhibitor 2A, insulin-like growth factor 1 receptor and roundabout guidance receptor 2 genes. Their expressions were found to have an impact on the prognosis of different treatment groups (cisplatin, paclitaxel, cisplatin + paclitaxel, cisplatin + doxorubicin and cisplatin + topotecan). The results indicated that these genes may minimise the occurrence of ovarian cancer drug resistance and may provide effective treatment options for patients with ovarian cancer.
Collapse
Affiliation(s)
- Danni Yuan
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Haohan Zhou
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Hongyu Sun
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Rui Tian
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Meihui Xia
- Department of Obstetrics, First Hospital, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Liankun Sun
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| | - Yanan Liu
- Key Laboratory of Pathobiology, Department of Pathophysiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, Jilin 130021, P.R. China
| |
Collapse
|
6
|
Leopold JA, Maron BA, Loscalzo J. The application of big data to cardiovascular disease: paths to precision medicine. J Clin Invest 2020; 130:29-38. [PMID: 31895052 DOI: 10.1172/jci129203] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.
Collapse
|
7
|
A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol 2019; 17:183-194. [DOI: 10.1038/s41571-019-0273-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2019] [Indexed: 02/06/2023]
|
8
|
Popa ML, Albulescu R, Neagu M, Hinescu ME, Tanase C. Multiplex assay for multiomics advances in personalized-precision medicine. J Immunoassay Immunochem 2019; 40:3-25. [PMID: 30632882 DOI: 10.1080/15321819.2018.1562940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Building the future of precision medicine is the main focus in cancer domain. Clinical trials are moving toward an array of studies that are more adapted to precision medicine. In this domain, there is an enhanced need for biomarkers, monitoring devices, and data-analysis methods. Omics profiling using whole genome, epigenome, transcriptome, proteome, and metabolome can offer detailed information of the human body in an integrative manner. Omes profiles reflect more accurately real-time physiological status. Personalized omics analyses both disease as a whole and the main disease processes, for a better understanding of the individualized health. Through this, multi-omic approaches for health monitoring, preventative medicine, and personalized treatment can be targeted simultaneously and can lead clinicians to have a comprehensive view on the diseasome.
Collapse
Affiliation(s)
- Maria-Linda Popa
- a Biochemistry-Proteomics Department , Victor Babes National Institute of Pathology , Bucharest , Romania
- b Cellular and Molecular Biology and Histology Department , "Carol Davila" University of Medicine and Pharmacy , Bucharest , Romania
| | - Radu Albulescu
- a Biochemistry-Proteomics Department , Victor Babes National Institute of Pathology , Bucharest , Romania
- c Pharmaceutical Biotechnology Department , National Institute for Chemical-Pharmaceutical R&D , Bucharest , Romania
| | - Monica Neagu
- a Biochemistry-Proteomics Department , Victor Babes National Institute of Pathology , Bucharest , Romania
- d Faculty of Biology , University of Bucharest , Bucharest , Romania
| | - Mihail Eugen Hinescu
- a Biochemistry-Proteomics Department , Victor Babes National Institute of Pathology , Bucharest , Romania
- b Cellular and Molecular Biology and Histology Department , "Carol Davila" University of Medicine and Pharmacy , Bucharest , Romania
| | - Cristiana Tanase
- a Biochemistry-Proteomics Department , Victor Babes National Institute of Pathology , Bucharest , Romania
- e Cajal Institute , Titu Maiorescu University , Bucharest , Romania
| |
Collapse
|
9
|
Abstract
Biomarkers are critical to the rational development of medical therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice, particularly in the fields of chronic disease and nutrition. Clarification of the definitions of different biomarkers and a better understanding of their appropriate application could result in substantial benefits. This review examines biomarker definitions recently established by the U.S. Food and Drug Administration and the National Institutes of Health as part of their joint Biomarkers, EndpointS, and other Tools (BEST) resource. These definitions are placed in context of their respective uses in patient care, clinical research, or therapeutic development. We explore the distinctions between biomarkers and clinical outcome assessments and discuss the specific definitions and applications of diagnostic, monitoring, pharmacodynamic/response, predictive, prognostic, safety, and susceptibility/risk biomarkers. We also explore the implications of current biomarker development trends, including complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies. Finally, we discuss the challenges and potential benefits of biomarker-driven predictive toxicology and systems pharmacology, the need to ensure quality and reproducibility of the science underlying biomarker development, and the importance of fostering collaboration across the entire ecosystem of medical product development. Impact statement Biomarkers are critical to the rational development of medical diagnostics and therapeutics, but significant confusion persists regarding fundamental definitions and concepts involved in their use in research and clinical practice. Clarification of the definitions of different biomarker classes and a better understanding of their appropriate application could yield substantial benefits. Biomarker definitions recently established in a joint FDA-NIH resource place different classes of biomarkers in the context of their respective uses in patient care, clinical research, or therapeutic development. Complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies, together with biomarker-driven predictive toxicology and systems pharmacology, are reshaping development of diagnostic and therapeutic technologies. An approach to biomarker development that prioritizes the quality and reproducibility of the science underlying biomarker development and incorporates collaborative regulatory science involving multiple disciplines will lead to rational, evidence-based biomarker development that keeps pace with scientific and clinical need.
Collapse
Affiliation(s)
- Robert M Califf
- 1 12277 School of Medicine, Duke University , Durham, NC 27710, USA.,2 Verily Life Sciences (Alphabet), South San Francisco, CA 94043, USA.,3 Department of Medicine, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
10
|
Personalized medicine-a modern approach for the diagnosis and management of hypertension. Clin Sci (Lond) 2017; 131:2671-2685. [PMID: 29109301 PMCID: PMC5736921 DOI: 10.1042/cs20160407] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 09/22/2017] [Accepted: 09/25/2017] [Indexed: 12/15/2022]
Abstract
The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.
Collapse
|
11
|
Roberts CA, Miller JH, Atkinson PH. The genetic architecture in Saccharomyces cerevisiae that contributes to variation in drug response to the antifungals benomyl and ketoconazole. FEMS Yeast Res 2017; 17:3787663. [DOI: 10.1093/femsyr/fox027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Accepted: 04/29/2017] [Indexed: 12/14/2022] Open
|
12
|
Grixti JM, O'Hagan S, Day PJ, Kell DB. Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine. Front Pharmacol 2017; 8:155. [PMID: 28396636 PMCID: PMC5366350 DOI: 10.3389/fphar.2017.00155] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/10/2017] [Indexed: 12/23/2022] Open
Abstract
The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.
Collapse
Affiliation(s)
- Justine M Grixti
- Faculty of Biology, Medicine and Health, University of ManchesterManchester, UK; Manchester Institute of Biotechnology, University of ManchesterManchester, UK
| | - Steve O'Hagan
- Manchester Institute of Biotechnology, University of ManchesterManchester, UK; School of Chemistry, University of ManchesterManchester, UK; Centre for Synthetic Biology of Fine and Speciality Chemicals, University of ManchesterManchester, UK
| | - Philip J Day
- Faculty of Biology, Medicine and Health, University of ManchesterManchester, UK; Manchester Institute of Biotechnology, University of ManchesterManchester, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, University of ManchesterManchester, UK; School of Chemistry, University of ManchesterManchester, UK; Centre for Synthetic Biology of Fine and Speciality Chemicals, University of ManchesterManchester, UK
| |
Collapse
|
13
|
Affiliation(s)
- Calum A MacRae
- From Cardiovascular Medicine Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA (C.A.M., J.L.); Harvard Medical School, Boston, MA (C.A.M., J.L.); and Cardiology Division, Department of Medicine, Vanderbilt University Medical School, Nashville, TN (D.M.R.).
| | - Dan M Roden
- From Cardiovascular Medicine Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA (C.A.M., J.L.); Harvard Medical School, Boston, MA (C.A.M., J.L.); and Cardiology Division, Department of Medicine, Vanderbilt University Medical School, Nashville, TN (D.M.R.)
| | - Joseph Loscalzo
- From Cardiovascular Medicine Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA (C.A.M., J.L.); Harvard Medical School, Boston, MA (C.A.M., J.L.); and Cardiology Division, Department of Medicine, Vanderbilt University Medical School, Nashville, TN (D.M.R.)
| |
Collapse
|
14
|
Abstract
The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.
Collapse
Affiliation(s)
- Elliott M Antman
- Brigham and Women's Hospital, TIMI Study Group, 350 Longwood Avenue, Office Level One, Boston, Massachusetts 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts 02115, USA
| |
Collapse
|
15
|
Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives. Hum Genet 2016; 135:259-72. [PMID: 26839113 PMCID: PMC4759222 DOI: 10.1007/s00439-016-1636-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 01/15/2016] [Indexed: 02/07/2023]
Abstract
Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases.
Collapse
|
16
|
Antman EM, Benjamin EJ, Harrington RA, Houser SR, Peterson ED, Bauman MA, Brown N, Bufalino V, Califf RM, Creager MA, Daugherty A, Demets DL, Dennis BP, Ebadollahi S, Jessup M, Lauer MS, Lo B, MacRae CA, McConnell MV, McCray AT, Mello MM, Mueller E, Newburger JW, Okun S, Packer M, Philippakis A, Ping P, Prasoon P, Roger VL, Singer S, Temple R, Turner MB, Vigilante K, Warner J, Wayte P. Acquisition, Analysis, and Sharing of Data in 2015 and Beyond: A Survey of the Landscape: A Conference Report From the American Heart Association Data Summit 2015. J Am Heart Assoc 2015; 4:e002810. [PMID: 26541391 PMCID: PMC4845234 DOI: 10.1161/jaha.115.002810] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 10/14/2015] [Indexed: 01/11/2023]
Abstract
BACKGROUND A 1.5-day interactive forum was convened to discuss critical issues in the acquisition, analysis, and sharing of data in the field of cardiovascular and stroke science. The discussion will serve as the foundation for the American Heart Association's (AHA's) near-term and future strategies in the Big Data area. The concepts evolving from this forum may also inform other fields of medicine and science. METHODS AND RESULTS A total of 47 participants representing stakeholders from 7 domains (patients, basic scientists, clinical investigators, population researchers, clinicians and healthcare system administrators, industry, and regulatory authorities) participated in the conference. Presentation topics included updates on data as viewed from conventional medical and nonmedical sources, building and using Big Data repositories, articulation of the goals of data sharing, and principles of responsible data sharing. Facilitated breakout sessions were conducted to examine what each of the 7 stakeholder domains wants from Big Data under ideal circumstances and the possible roles that the AHA might play in meeting their needs. Important areas that are high priorities for further study regarding Big Data include a description of the methodology of how to acquire and analyze findings, validation of the veracity of discoveries from such research, and integration into investigative and clinical care aspects of future cardiovascular and stroke medicine. Potential roles that the AHA might consider include facilitating a standards discussion (eg, tools, methodology, and appropriate data use), providing education (eg, healthcare providers, patients, investigators), and helping build an interoperable digital ecosystem in cardiovascular and stroke science. CONCLUSION There was a consensus across stakeholder domains that Big Data holds great promise for revolutionizing the way cardiovascular and stroke research is conducted and clinical care is delivered; however, there is a clear need for the creation of a vision of how to use it to achieve the desired goals. Potential roles for the AHA center around facilitating a discussion of standards, providing education, and helping establish a cardiovascular digital ecosystem. This ecosystem should be interoperable and needs to interface with the rapidly growing digital object environment of the modern-day healthcare system.
Collapse
|
17
|
Antman EM. Saving and Improving Lives in the Information Age: Presidential Address at the American Heart Association 2014 Scientific Sessions. Circulation 2015; 131:2238-42. [PMID: 26099959 DOI: 10.1161/cir.0000000000000224] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
18
|
Wang RS, Maron BA, Loscalzo J. Systems medicine: evolution of systems biology from bench to bedside. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:141-61. [PMID: 25891169 DOI: 10.1002/wsbm.1297] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 03/04/2015] [Accepted: 03/06/2015] [Indexed: 12/11/2022]
Abstract
High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.
Collapse
Affiliation(s)
- Rui-Sheng Wang
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Cardiology, Veterans Affairs Boston Healthcare System, West Roxbury, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| |
Collapse
|
19
|
Fox CS, Hall JL, Arnett DK, Ashley EA, Delles C, Engler MB, Freeman MW, Johnson JA, Lanfear DE, Liggett SB, Lusis AJ, Loscalzo J, MacRae CA, Musunuru K, Newby LK, O'Donnell CJ, Rich SS, Terzic A. Future translational applications from the contemporary genomics era: a scientific statement from the American Heart Association. Circulation 2015; 131:1715-36. [PMID: 25882488 DOI: 10.1161/cir.0000000000000211] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The field of genetics and genomics has advanced considerably with the achievement of recent milestones encompassing the identification of many loci for cardiovascular disease and variable drug responses. Despite this achievement, a gap exists in the understanding and advancement to meaningful translation that directly affects disease prevention and clinical care. The purpose of this scientific statement is to address the gap between genetic discoveries and their practical application to cardiovascular clinical care. In brief, this scientific statement assesses the current timeline for effective translation of basic discoveries to clinical advances, highlighting past successes. Current discoveries in the area of genetics and genomics are covered next, followed by future expectations, tools, and competencies for achieving the goal of improving clinical care.
Collapse
|
20
|
Hall KT, Loscalzo J, Kaptchuk TJ. Genetics and the placebo effect: the placebome. Trends Mol Med 2015; 21:285-94. [PMID: 25883069 DOI: 10.1016/j.molmed.2015.02.009] [Citation(s) in RCA: 152] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 02/19/2015] [Accepted: 02/24/2015] [Indexed: 12/19/2022]
Abstract
Placebos are indispensable controls in randomized clinical trials (RCTs), and placebo responses significantly contribute to routine clinical outcomes. Recent neurophysiological studies reveal neurotransmitter pathways that mediate placebo effects. Evidence that genetic variations in these pathways can modify placebo effects raises the possibility of using genetic screening to identify placebo responders and thereby increase RCT efficacy and improve therapeutic care. Furthermore, the possibility of interaction between placebo and drug molecular pathways warrants consideration in RCT design. The study of genomic effects on placebo response, 'the placebome', is in its infancy. Here, we review evidence from placebo studies and RCTs to identify putative genes in the placebome, examine evidence for placebo-drug interactions, and discuss implications for RCTs and clinical care.
Collapse
Affiliation(s)
- Kathryn T Hall
- Program in Placebo Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ted J Kaptchuk
- Program in Placebo Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| |
Collapse
|
21
|
Nair S, Kong ANT. Architecture of Signature miRNA Regulatory Networks in Cancer Chemoprevention. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/s40495-014-0014-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
|
22
|
Androulakis IP. Systems engineering meets quantitative systems pharmacology: from low-level targets to engaging the host defenses. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:101-12. [DOI: 10.1002/wsbm.1294] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 02/03/2015] [Accepted: 02/04/2015] [Indexed: 11/11/2022]
|
23
|
Park HW, Tantisira KG, Weiss ST. Pharmacogenomics in asthma therapy: where are we and where do we go? Annu Rev Pharmacol Toxicol 2014; 55:129-47. [PMID: 25292431 DOI: 10.1146/annurev-pharmtox-010814-124543] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The response to drug treatment in asthma is a complex trait and is markedly variable even in patients with apparently similar clinical features. Pharmaco-genomics, which is the study of variations of human genome characteristics as related to drug response, can play a role in asthma therapy. Both a traditional candidate-gene approach to conducting genetic association studies and genome-wide association studies have provided an increasing list of genes and variants associated with the three major classes of asthma medications: β2-agonists, inhaled corticosteroids, and leukotriene modifiers. Moreover, a recent integrative, systems-level approach has offered a promising opportunity to identify important pharmacogenomics loci in asthma treatment. However, we are still a long way away from making this discipline directly relevant to patients. The combination of network modeling, functional validation, and integrative omics technologies will likely be needed to move asthma pharmacogenomics closer to clinical relevance.
Collapse
Affiliation(s)
- Heung-Woo Park
- The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115; , ,
| | | | | |
Collapse
|
24
|
Abstract
PURPOSE OF REVIEW The current guidelines for asthma diagnosis and management do not recognize that different phenotypes of asthma exist, with significant variations in the manifestation of airway inflammation, symptoms, severity, and response to treatment. This article will critically review new approaches to classify asthma together with the emerging endotype-driven therapeutic strategies. RECENT FINDINGS Several new approaches for classifying asthma are available, from precision and deep phenotyping to identification of novel causal pathways and translation of biomarkers into pathway-specific diagnostic tests. New phenotypes, such as epigenetic phenotypes, asthmatic granulomatosis, or neurophenotypes are described. Large clinical trials testing the endotype-driven approach are increasingly successful, but the dissociated effect and the drug efficacy at the target site remain unsolved issues. Profiling the Th2 low and the resident cell compartment of asthma are major unmet needs in asthma endotyping. SUMMARY Each of the hallmark characteristics of asthma (inflammation, remodeling, airway hyperreactivity) is the expression of a complex network of molecules, very diverse both within any given patient in time and between any two patients. Some of these networks are repetitive across individuals with asthma and specific for clinical expression, gene-environment interaction and inflammatory cell profiles represent novel endotype-specific diagnostic and therapeutic strategies.
Collapse
|
25
|
|
26
|
Dimitrakopoulou K, Dimitrakopoulos GN, Sgarbas KN, Bezerianos A. Tamoxifen integromics and personalized medicine: dynamic modular transformations underpinning response to tamoxifen in breast cancer treatment. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2013; 18:15-33. [PMID: 24299457 DOI: 10.1089/omi.2013.0055] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Recent advances in pharmacogenomics technologies allow bold steps to be taken towards personalized medicine, more accurate health planning, and personalized drug development. In this framework, systems pharmacology network-based approaches offer an appealing way for integrating multi-omics data and set the basis for defining systems-level drug response biomarkers. On the road to individualized tamoxifen treatment in estrogen receptor-positive breast cancer patients, we examine the dynamics of the attendant pharmacological response mechanisms. By means of an "integromics" network approach, we assessed the tamoxifen effect through the way the high-order organization of interactome (i.e., the modules) is perturbed. To accomplish that, first we integrated the time series transcriptome data with the human protein interaction data, and second, an efficient module-detecting algorithm was applied onto the composite graphs. Our findings show that tamoxifen induces severe modular transformations on specific areas of the interactome. Our modular biomarkers in response to tamoxifen attest to the immunomodulatory role of tamoxifen, and further reveal that it deregulates cell cycle and apoptosis pathways, while coordinating the proteasome and basal transcription factors. To the best of our knowledge, this is the first report that informs the fields of personalized medicine and clinical pharmacology about the actual dynamic interactome response to tamoxifen administration.
Collapse
|
27
|
Vandamme D, Minke BA, Fitzmaurice W, Kholodenko BN, Kolch W. Systems biology-embedded target validation: improving efficacy in drug discovery. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 6:1-11. [PMID: 24214316 DOI: 10.1002/wsbm.1253] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 09/24/2013] [Accepted: 10/11/2013] [Indexed: 12/31/2022]
Abstract
The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment.
Collapse
Affiliation(s)
- Drieke Vandamme
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | | | | | | |
Collapse
|
28
|
Liu X, Wu WY, Jiang BH, Yang M, Guo DA. Pharmacological tools for the development of traditional Chinese medicine. Trends Pharmacol Sci 2013; 34:620-8. [PMID: 24139610 DOI: 10.1016/j.tips.2013.09.004] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 08/25/2013] [Accepted: 09/16/2013] [Indexed: 02/08/2023]
Abstract
Pharmacology as a modern science was introduced in China approximately 150 years ago, and has been used since then to study traditional Chinese medicine (TCM). Pharmacology has experienced its own development over this time and continues to provide new tools for the study of TCM. In the present review, three models for the pharmacological study of TCM are considered: (i) chemistry-focused study; (ii) target-directed study; and (iii) systems-biology-based study. These approaches correspond to recent developments in pharmacology, and in particular to new tools available to the field. Representative achievements and the pharmacological tools used to study TCM are reviewed. Pharmacology has played, and will continue to play, an indispensable role in elucidating the chemical basis, biological targets, and mechanisms of action of TCM medicines, and in developing a scientific basis for the theory of TCM.
Collapse
Affiliation(s)
- Xuan Liu
- Shanghai Research Center for TCM Modernization, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, P.R. China
| | | | | | | | | |
Collapse
|
29
|
Kell DB. Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it. FEBS J 2013; 280:5957-80. [PMID: 23552054 DOI: 10.1111/febs.12268] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2013] [Revised: 03/20/2013] [Accepted: 03/26/2013] [Indexed: 12/16/2022]
Abstract
Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.
Collapse
Affiliation(s)
- Douglas B Kell
- School of Chemistry, The University of Manchester, UK; Manchester Institute of Biotechnology, The University of Manchester, UK
| |
Collapse
|
30
|
Ioannidis JPA, Khoury MJ. Are randomized trials obsolete or more important than ever in the genomic era? Genome Med 2013; 5:32. [PMID: 23673134 PMCID: PMC3707036 DOI: 10.1186/gm436] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- John PA Ioannidis
- Stanford Prevention Research Center, Department of Medicine and Department of Health Research and Policy, Stanford University School of Medicine, 1265 Welch Road, MSOB X306, Stanford, CA 94305, USA
- Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA 94305, USA
| | - Muin J Khoury
- Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD 20892, USA
| |
Collapse
|
31
|
Medina MÁ. Systems biology for molecular life sciences and its impact in biomedicine. Cell Mol Life Sci 2013; 70:1035-53. [PMID: 22903296 PMCID: PMC11113420 DOI: 10.1007/s00018-012-1109-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 07/24/2012] [Accepted: 07/25/2012] [Indexed: 01/02/2023]
Abstract
Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.
Collapse
Affiliation(s)
- Miguel Ángel Medina
- Department of Molecular Biology and Biochemistry, University of Málaga, Malaga, Spain.
| |
Collapse
|
32
|
Chen R, Snyder M. Promise of personalized omics to precision medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012. [PMID: 23184638 DOI: 10.1002/wsbm.1198] [Citation(s) in RCA: 201] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. The ability to study biological phenomena at omics levels in turn is expected to lead to significant advances in personalized and precision medicine. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases toward disease prevention and early diagnostics. We discuss here the advances and challenges in systems biology-powered personalized medicine at its current stage, as well as a prospective view of future personalized health care at the end of this review.
Collapse
Affiliation(s)
- Rui Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | |
Collapse
|
33
|
Abstract
The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.
Collapse
Affiliation(s)
- Stephen Y Chan
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | | |
Collapse
|