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Erabi H, Okada G, Shibasaki C, Setoyama D, Kang D, Takamura M, Yoshino A, Fuchikami M, Kurata A, Kato TA, Yamawaki S, Okamoto Y. Kynurenic acid is a potential overlapped biomarker between diagnosis and treatment response for depression from metabolome analysis. Sci Rep 2020; 10:16822. [PMID: 33033336 PMCID: PMC7545168 DOI: 10.1038/s41598-020-73918-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 09/24/2020] [Indexed: 02/06/2023] Open
Abstract
Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.
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Affiliation(s)
- Hisayuki Erabi
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Chiyo Shibasaki
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Daiki Setoyama
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Manabu Fuchikami
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Akiko Kurata
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Shigeto Yamawaki
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
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Brunn M, Diefenbacher A, Courtet P, Genieys W. The Future is Knocking: How Artificial Intelligence Will Fundamentally Change Psychiatry. ACADEMIC PSYCHIATRY : THE JOURNAL OF THE AMERICAN ASSOCIATION OF DIRECTORS OF PSYCHIATRIC RESIDENCY TRAINING AND THE ASSOCIATION FOR ACADEMIC PSYCHIATRY 2020; 44:461-466. [PMID: 32424706 DOI: 10.1007/s40596-020-01243-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 04/28/2020] [Indexed: 06/11/2023]
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Nemeroff CB. The State of Our Understanding of the Pathophysiology and Optimal Treatment of Depression: Glass Half Full or Half Empty? Am J Psychiatry 2020; 177:671-685. [PMID: 32741287 DOI: 10.1176/appi.ajp.2020.20060845] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Major depressive disorder is a remarkably common and often severe psychiatric disorder associated with high levels of morbidity and mortality. Patients with major depression are prone to several comorbid psychiatric conditions, including posttraumatic stress disorder, anxiety disorders, obsessive-compulsive disorder, and substance use disorders, and medical conditions, including cardiovascular disease, diabetes, stroke, cancer, which, coupled with the risk of suicide, result in a shortened life expectancy. The goal of this review is to provide an overview of our current understanding of major depression, from pathophysiology to treatment. In spite of decades of research, relatively little is known about its pathogenesis, other than that risk is largely defined by a combination of ill-defined genetic and environmental factors. Although we know that female sex, a history of childhood maltreatment, and family history as well as more recent stressors are risk factors, precisely how these environmental influences interact with genetic vulnerability remains obscure. In recent years, considerable advances have been made in beginning to understand the genetic substrates that underlie disease vulnerability, and the interaction of genes, early-life adversity, and the epigenome in influencing gene expression is now being intensively studied. The role of inflammation and other immune system dysfunction in the pathogenesis of major depression is also being intensively investigated. Brain imaging studies have provided a firmer understanding of the circuitry involved in major depression, providing potential new therapeutic targets. Despite a broad armamentarium for major depression, including antidepressants, evidence-based psychotherapies, nonpharmacological somatic treatments, and a host of augmentation strategies, a sizable percentage of patients remain nonresponsive or poorly responsive to available treatments. Investigational agents with novel mechanisms of action are under active study. Personalized medicine in psychiatry provides the hope of escape from the current standard trial-and-error approach to treatment, moving to a more refined method that augurs a new era for patients and clinicians alike.
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Affiliation(s)
- Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, University of Texas Dell Medical School in Austin, and Mulva Clinic for the Neurosciences, UT Health Austin
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54
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Predicting second-generation antidepressant effectiveness in treating sadness using demographic and clinical information: A machine learning approach. J Affect Disord 2020; 272:295-304. [PMID: 32553371 DOI: 10.1016/j.jad.2020.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/24/2020] [Accepted: 04/17/2020] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Current guidelines for choosing antidepressant medications involve a trial-and-error process. Most patients try multiple antidepressants before finding an effective antidepressant. This study uses demographic and clinical information to create models predicting effectiveness of different antidepressants in treating sadness in a nationally representative sample of US adults. METHODS A secondary analysis of the Collaborative Psychiatric Epidemiology Survey (CPES) was performed. Participants with or without a mental health diagnosis who reported sadness as a symptom, and were taking fluoxetine (n=156), sertraline (n=224), citalopram (n=91), paroxetine (n=156), venlafaxine (n=69), bupropion (n=92), or trazadone (n=26) within the past year were included. Two sets of principal component analyses (PCAs) and logistic regressions were performed: one determined associations between symptom clusters and antidepressant effectiveness for sadness, and the other created models to predict effectiveness. Both PCAs controlled for psychiatric and medical diagnoses, substance use, psychiatric medications, alternative treatments, and demographics. RESULTS Anxiety was associated with ineffectiveness of fluoxetine in treating sadness. Low mood scores were associated with ineffectiveness of paroxetine and venlafaxine, and fatigue was associated with ineffectiveness of sertraline. The models for predicting drug effectiveness had a mean accuracy of 83% and internal validity of 72%. LIMITATIONS CPES data were collected from 2001-2003, so newer drugs were not included. Effectiveness was for sadness, so results are not directly comparable to studies using overall depressive symptom reductions as outcomes. CONCLUSION Since fewer than 50% of patients currently respond to their first antidepressant, this model could provide modest improvement to choosing starting antidepressants in treating sadness.
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55
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Bica I, Alaa AM, Lambert C, van der Schaar M. From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges. Clin Pharmacol Ther 2020; 109:87-100. [PMID: 32449163 DOI: 10.1002/cpt.1907] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/14/2020] [Indexed: 12/21/2022]
Abstract
Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.
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Affiliation(s)
- Ioana Bica
- University of Oxford, Oxford, UK.,The Alan Turing Institute, London, UK
| | - Ahmed M Alaa
- University of California - Los Angeles, Los Angeles, California, USA
| | - Craig Lambert
- Clinical Pharmacology and Safety Sciences, Research and Development, AstraZeneca, Cambridge, UK
| | - Mihaela van der Schaar
- The Alan Turing Institute, London, UK.,University of California - Los Angeles, Los Angeles, California, USA.,University of Cambridge, Cambridge, UK
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56
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Athreya AP, Iyer R, Wang L, Weinshilboum RM, Bobo WV. Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computational intelligence be used to augment clinical assessments? Pharmacogenomics 2020; 20:983-988. [PMID: 31559920 DOI: 10.2217/pgs-2019-0119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Arjun P Athreya
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Ravishankar Iyer
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, IL 61820, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - William V Bobo
- Department of Psychiatry & Psychology, Mayo Clinic, Jacksonville, FL 32224, USA
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57
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Russell LE, Schwarz UI. Variant discovery using next-generation sequencing and its future role in pharmacogenetics. Pharmacogenomics 2020; 21:471-486. [DOI: 10.2217/pgs-2019-0190] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.
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Affiliation(s)
- Laura E Russell
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, London, ON, N6A 5C1, Canada
| | - Ute I Schwarz
- Department of Physiology & Pharmacology, Western University, Medical Sciences Building, London, ON, N6A 5C1, Canada
- Division of Clinical Pharmacology, Department of Medicine, Western University, London Health Sciences Centre – University Hospital, 339 Windermere Road, London, ON, N6A 5A5, Canada
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58
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Lin E, Lin CH, Lane HY. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Int J Mol Sci 2020; 21:ijms21030969. [PMID: 32024055 PMCID: PMC7037937 DOI: 10.3390/ijms21030969] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 01/25/2020] [Accepted: 01/30/2020] [Indexed: 12/22/2022] Open
Abstract
A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40402, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
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59
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Primorac D, Bach-Rojecky L, Vađunec D, Juginović A, Žunić K, Matišić V, Skelin A, Arsov B, Boban L, Erceg D, Ivkošić IE, Molnar V, Ćatić J, Mikula I, Boban L, Primorac L, Esquivel B, Donaldson M. Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data. Pharmacogenomics 2020; 21:141-156. [PMID: 31950879 DOI: 10.2217/pgs-2019-0134] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.
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Affiliation(s)
- Dragan Primorac
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Split School of Medicine, 21 000 Split, Croatia.,Eberly College of Science, 517 Thomas St, State College, Penn State University, PA 16803, USA.,The Henry C Lee College of Criminal Justice & Forensic Sciences, University of New Haven, West Haven, CT 06516, USA.,University of Osijek School of Medicine, 31000 Osijek, Croatia.,University of Rijeka School of Medicine, 51000 Rijeka, Croatia.,Srebrnjak Children's Hospital, 10000 Zagreb, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia
| | - Lidija Bach-Rojecky
- University of Zagreb Faculty of Pharmacy & Biochemistry, 10000 Zagreb, Croatia
| | - Dalia Vađunec
- University of Zagreb Faculty of Pharmacy & Biochemistry, 10000 Zagreb, Croatia
| | - Alen Juginović
- University of Split School of Medicine, 21 000 Split, Croatia
| | | | - Vid Matišić
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Andrea Skelin
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
| | - Borna Arsov
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Luka Boban
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Damir Erceg
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,Srebrnjak Children's Hospital, 10000 Zagreb, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia.,Croatian Catholic University, 10000 Zagreb, Croatia
| | - Ivana Erceg Ivkošić
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia
| | - Vilim Molnar
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Jasmina Ćatić
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Osijek School of Medicine, 31000 Osijek, Croatia.,Clinical Hospital Dubrava, Department of Cardiology, 10000 Zagreb, Croatia
| | - Ivan Mikula
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University North, Nursing Department, 42000 Varaždin, Croatia
| | | | - Lara Primorac
- Wharton Business School, University of Pennsylvania, Philadelphia, PA 19104, USA
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60
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Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med 2019; 2:115. [PMID: 31799423 PMCID: PMC6877584 DOI: 10.1038/s41746-019-0193-y] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/01/2019] [Indexed: 12/12/2022] Open
Abstract
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
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Affiliation(s)
- Mark Alber
- Department of Mathematics, University of California, Riverside, CA USA
| | | | - William R. Cannon
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, WA USA
| | - Suvranu De
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY USA
| | | | - Krishna Garikipati
- Departments of Mechanical Engineering and Mathematics, University of Michigan, Ann Arbor, MI USA
| | | | - William W. Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY USA
| | - Paris Perdikaris
- Department of Mechanical Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Linda Petzold
- Department of Computer Science and Mechanical Engineering, University of California, Santa Barbara, CA USA
| | - Ellen Kuhl
- Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA USA
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61
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Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ Digit Med 2019; 2:112. [PMID: 31799421 PMCID: PMC6872596 DOI: 10.1038/s41746-019-0191-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 10/29/2019] [Indexed: 12/23/2022] Open
Abstract
The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.
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Affiliation(s)
- Mohammed Uddin
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
- 2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON Canada
| | - Yujiang Wang
- 3Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
- 4School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Marc Woodbury-Smith
- 2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON Canada
- 3Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
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62
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Wang L, Weinshilboum R. Pharmacogenomics in Practice. Clin Pharmacol Ther 2019; 106:936-938. [PMID: 31498426 PMCID: PMC6857970 DOI: 10.1002/cpt.1600] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/06/2019] [Indexed: 12/22/2022]
Affiliation(s)
- Liewei Wang
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Richard Weinshilboum
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
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63
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Giri J, Moyer AM, Bielinski SJ, Caraballo PJ. Concepts Driving Pharmacogenomics Implementation Into Everyday Healthcare. Pharmgenomics Pers Med 2019; 12:305-318. [PMID: 31802928 PMCID: PMC6826176 DOI: 10.2147/pgpm.s193185] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/07/2019] [Indexed: 12/13/2022] Open
Abstract
Pharmacogenomics (PGx) is often promoted as the domain of precision medicine with the greatest potential to readily impact everyday healthcare. Rapid advances in PGx knowledge derived from extensive basic and clinical research along with decreasing costs of laboratory testing have led to an increased interest in PGx and expectations of imminent clinical translation with substantial clinical impact. However, the implementation of PGx into clinical workflows is neither simple nor straightforward, and comprehensive processes and multidisciplinary collaboration are required. Several national and international institutions have pioneered models for implementing clinical PGx, and these initial models have led to a better understanding of unresolved challenges. In this review, we have categorized and explored the most relevant of these challenges to highlight potential gaps and present possible solutions. We describe the ongoing need for basic and clinical research to drive further developments in evidence-based medicine. Integration into daily clinical workflows introduces new challenges requiring innovative solutions; specifically those related to the electronic health record and embedded clinical decision support. We describe advances in PGx testing and result reporting and describe the critical need for increased standardization in these areas across laboratories. We also explore the complexity of the PGx knowledge required for clinical practice and the need for educational strategies to ensure adequate understanding among members of current and future healthcare teams. Finally, we evaluate knowledge obtained from previous implementation efforts and discuss how to best apply these learnings to future projects. Despite these challenges, the future of precision medicine appears promising due to the rapidity of recent advances in the field and current multidisciplinary efforts to effectively translate PGx to everyday clinical practice.
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Affiliation(s)
- Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - Pedro J Caraballo
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
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64
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Leeder JS. Who Believes They Are "Just Average": Informing the Treatment of Individual Patients Using Population Data. Clin Pharmacol Ther 2019; 106:939-941. [PMID: 31509232 DOI: 10.1002/cpt.1612] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 07/26/2019] [Indexed: 12/14/2022]
Affiliation(s)
- J Steven Leeder
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Department of Pediatrics, Children's Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, USA
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Yamamoto PA, Conchon Costa AC, Lauretti GR, de Moraes NV. Pharmacogenomics in chronic pain therapy: from disease to treatment and challenges for clinical practice. Pharmacogenomics 2019; 20:971-982. [DOI: 10.2217/pgs-2019-0066] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Pharmacogenomics (PGx) has emerged as an encouraging tool in chronic pain therapy. Genetic variations associated with drug effectiveness or adverse reactions (amitriptyline/nortriptyline/codeine/oxycodone/tramadol-CYP2D6, amitriptyline-CYP2C19, carbamazepine-HLA-A, carbamazepine/oxcarbazepine-HLA-B) can be used to guide chronic pain management. Despite this evidence, many obstacles still need to be overcome for the effective clinical implementation of PGx. To translate the pharmacogenetic testing into actionable clinical decisions, the Clinical Pharmacogenetics Implementation Consortium has been developing guidelines for several drug–gene pairs. This review will show the applicability of PGx in chronic pain from disease to treatment; report the drug–gene pairs with strongest evidences in the clinic; and the challenges for the clinical implementation of PGx.
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Affiliation(s)
- Priscila Akemi Yamamoto
- São Paulo State University, UNESP - School of Pharmaceutical Sciences, Araraquara, SP, Brazil
| | - Ana Carolina Conchon Costa
- São Paulo University, USP – School of Pharmaceutical Sciences of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | - Gabriela Rocha Lauretti
- São Paulo University, USP – School of Medicine of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | - Natália Valadares de Moraes
- São Paulo State University, UNESP - School of Pharmaceutical Sciences, Araraquara, SP, Brazil
- Queen's University Belfast, Belfast, UK
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