1
|
Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
Collapse
Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
| |
Collapse
|
2
|
Imredy JP, Roussignol G, Clouse H, Salvagiotto G, Mazelin-Winum L. Comparative assessment of Ca 2+ oscillations in 2- and 3-dimensional hiPSC derived and isolated cortical neuronal networks. J Pharmacol Toxicol Methods 2023; 123:107281. [PMID: 37390871 DOI: 10.1016/j.vascn.2023.107281] [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: 03/16/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 07/02/2023]
Abstract
Human induced Pluripotent Stem Cell (hiPSC) derived neural cells offer great potential for modelling neurological diseases and toxicities and have found application in drug discovery and toxicology. As part of the European Innovative Medicines Initiative (IMI2) NeuroDeRisk (Neurotoxicity De-Risking in Preclinical Drug Discovery), we here explore the Ca2+ oscillation responses of 2D and 3D hiPSC derived neuronal networks of mixed Glutamatergic/GABAergic activity with a compound set encompassing both clinically as well as experimentally determined seizurogenic compounds. Both types of networks are scored against Ca2+ responses of a primary mouse cortical neuronal 2D network model serving as an established comparator assay. Parameters of frequency and amplitude of spontaneous global network Ca2+ oscillations and the drug-dependent directional changes to these were assessed, and predictivity of seizurogenicity scored using contingency table analysis. In addition, responses between models were compared between both 2D models as well as between 2D and 3D models. Concordance of parameter responses was best between the hiPSC neurospheroid and the mouse primary cortical neuron model (77% for frequency and 65% for amplitude). Decreases in spontaneous Ca2+ oscillation frequency and amplitude were found to be the most basic shared determinants of risk of seizurogenicity between the mouse and the neurospheroid model based on testing of clinical compounds with documented seizurogenic activity. Increases in spontaneous Ca2+ oscillation frequency were primarily observed with the 2D hIPSC model, though the specificity of this effect to seizurogenic clinical compounds was low (33%), while decreases to spike amplitude in this model were more predictive of seizurogenicity. Overall predictivities of the models were similar, with sensitivity of the assays typically exceeding specificity due to high false positive rates. Higher concordance of the hiPSC 3D model over the 2D model when compared to mouse cortical 2D responses may be the result of both a longer maturation time of the neurospheroid (84-87 days for 3D vs. 22-24 days for 2D maturation) as well as the 3-dimensional nature of network connections established. The simplicity and reproducibility of spontaneous Ca2+ oscillation readouts support further investigation of hiPSC derived neuronal sources and their 2- and 3-dimensional networks for neuropharmacological safety screening.
Collapse
Affiliation(s)
- John P Imredy
- In Vitro Safety Pharmacology, Merck & Co., Inc., Rahway, NJ, USA.
| | | | - Holly Clouse
- In Vitro Safety Pharmacology, Merck & Co., Inc., Rahway, NJ, USA
| | | | | |
Collapse
|
3
|
Zhai J, Traebert M, Zimmermann K, Delaunois A, Royer L, Salvagiotto G, Carlson C, Lagrutta A. Comparative study for the IMI2-NeuroDeRisk project on microelectrode arrays to derisk drug-induced seizure liability. J Pharmacol Toxicol Methods 2023; 123:107297. [PMID: 37499956 DOI: 10.1016/j.vascn.2023.107297] [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: 03/03/2023] [Revised: 06/01/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023]
Abstract
INTRODUCTION In the framework of the IMI2-NeuroDeRisk consortium, three in vitro electrophysiology assays were compared to improve preclinical prediction of seizure-inducing liabilities. METHODS Two cell models, primary rat cortical neurons and human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons co-cultured with hiPSC-derived astrocytes were tested on two different microelectrode array (MEA) platforms, Maestro Pro (Axion Biosystems) and Multiwell-MEA-System (Multi Channel Systems), in three separate laboratories. Pentylenetetrazole (PTZ) and/or picrotoxin (PTX) were included in each plate as positive (n = 3-6 wells) and ≤0.2% DMSO was used as negative controls (n = 3-12 wells). In general, concentrations in a range of 0.1-30 μM were tested, anchored, when possible, on clinically relevant exposures (unbound Cmax) were tested. Activity thresholds for drug-induced changes were set at 20%. To evaluate sensitivity, specificity and predictivity of the cell models, seizurogenic responses were defined as changes in 4 or more endpoints. Concentration dependence trends were also considered. RESULTS Neuronal activity of 33 compounds categorized as positive tool drugs, seizure-positive or seizure-negative compounds was evaluated. Acute drug effects (<60 min) were compared to baseline recordings. Time points < 15 min exhibited stronger, less variable responses to many of the test agents. For many compounds a reduction and cessation of neuronal activity was detected at higher test concentrations. There was not a single pattern of seizurogenic activity detected, even among tool compounds, likely due to different mechanisms of actions and/or off-target profiles. A post-hoc analysis focusing on changes indicative of neuronal excitation is presented. CONCLUSION All cell models showed good sensitivity, ranging from 70 to 86%. Specificity ranged from 40 to 70%. Compared to more conventional measurements of evoked activity in hippocampal slices, these plate-based models provide higher throughput and the potential to study subacute responses. Yet, they may be limited by the random, spontaneous nature of their network activity.
Collapse
Affiliation(s)
- Jin Zhai
- Merck & Co., Inc., Rahway, NJ, USA
| | | | | | | | | | | | - Coby Carlson
- Fujifilm Cellular Dynamics, Inc., Madison, WI, USA
| | | |
Collapse
|
4
|
Utsumi Y, Taketoshi M, Miwa M, Tominaga Y, Tominaga T. Assessing seizure liability in vitro with voltage-sensitive dye imaging in mouse hippocampal slices. Front Cell Neurosci 2023; 17:1217368. [PMID: 37680865 PMCID: PMC10481167 DOI: 10.3389/fncel.2023.1217368] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Non-clinical toxicology is a major cause of drug candidate attrition during development. In particular, drug-induced seizures are the most common finding in central nervous system (CNS) toxicity. Current safety pharmacology tests for assessing CNS functions are often inadequate in detecting seizure-inducing compounds early in drug development, leading to significant delays. This paper presents an in vitro seizure liability assay using voltage-sensitive dye (VSD) imaging techniques in hippocampal brain slices, offering a powerful alternative to traditional electrophysiological methods. Hippocampal slices were isolated from mice, and VSD optical responses evoked by stimulating the Schaffer collateral pathway were recorded and analyzed in the stratum radiatum (SR) and stratum pyramidale (SP). VSDs allow for the comprehensive visualization of neuronal action potentials and postsynaptic potentials on a millisecond timescale. By employing this approach, we investigated the in vitro drug-induced seizure liability of representative pro-convulsant compounds. Picrotoxin (PiTX; 1-100 μM), gabazine (GZ; 0.1-10 μM), and 4-aminopyridine (4AP; 10-100 μM) exhibited seizure-like responses in the hippocampus, but pilocarpine hydrochloride (Pilo; 10-100 μM) did not. Our findings demonstrate the potential of VSD-based assays in identifying seizurogenic compounds during early drug discovery, thereby reducing delays in drug development and providing insights into the mechanisms underlying seizure induction and the associated risks of pro-convulsant compounds.
Collapse
Affiliation(s)
- Yuichi Utsumi
- Graduate School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki, Japan
- Institute of Neuroscience, Tokushima Bunri University, Sanuki, Japan
| | - Makiko Taketoshi
- Institute of Neuroscience, Tokushima Bunri University, Sanuki, Japan
| | - Michiko Miwa
- Kagawa School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki, Japan
| | - Yoko Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Sanuki, Japan
| | - Takashi Tominaga
- Graduate School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki, Japan
- Institute of Neuroscience, Tokushima Bunri University, Sanuki, Japan
- Kagawa School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki, Japan
| |
Collapse
|
5
|
Mehrpour O, Saeedi F, Abdollahi J, Amirabadizadeh A, Goss F. The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2023; 28:49. [PMID: 37496638 PMCID: PMC10366979 DOI: 10.4103/jrms.jrms_602_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 02/14/2023] [Accepted: 03/27/2023] [Indexed: 07/28/2023]
Abstract
Background Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning. Materials and Methods We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated. Results Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine. Conclusion Our study demonstrates the application of ML in the prediction of DPH poisoning.
Collapse
Affiliation(s)
- Omid Mehrpour
- Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, Michigan, United States
- Rocky Mountain Poison and Drug Safety, Denver Health and Hospital Authority, Denver, CO, United States
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center, Birjand University of Medical Sciences, Birjand, Iran
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Jafar Abdollahi
- Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
| | - Alireza Amirabadizadeh
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Foster Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| |
Collapse
|
6
|
Lipponen A, Kajevu N, Natunen T, Ciszek R, Puhakka N, Hiltunen M, Pitkänen A. Gene Expression Profile as a Predictor of Seizure Liability. Int J Mol Sci 2023; 24:ijms24044116. [PMID: 36835526 PMCID: PMC9963992 DOI: 10.3390/ijms24044116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.
Collapse
Affiliation(s)
- Anssi Lipponen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
- Expert Microbiology Unit, Finnish Institute for Health and Welfare, P.O. Box 95, FIN-70701 Kuopio, Finland
| | - Natallie Kajevu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Teemu Natunen
- Institute of Biomedicine, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Robert Ciszek
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Noora Puhakka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Mikko Hiltunen
- Institute of Biomedicine, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
| | - Asla Pitkänen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland
- Correspondence: ; Tel.: +358-50-517-2091; Fax: +358-17-16-3030
| |
Collapse
|
7
|
Eresen A, Sun C, Zhou K, Shangguan J, Wang B, Pan L, Hu S, Ma Q, Yang J, Zhang Z, Yaghmai V. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI. Acad Radiol 2022; 29:1378-1386. [PMID: 34933803 PMCID: PMC10029937 DOI: 10.1016/j.acra.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.
Collapse
Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Quanhong Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Jia Yang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California.
| |
Collapse
|
8
|
Mallikarjuna B, Shrivastava G, Sharma M. Blockchain technology: A DNN token-based approach in healthcare and COVID-19 to generate extracted data. EXPERT SYSTEMS 2022; 39:e12778. [PMID: 34511692 PMCID: PMC8420355 DOI: 10.1111/exsy.12778] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/28/2021] [Accepted: 07/07/2021] [Indexed: 05/23/2023]
Abstract
The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.
Collapse
Affiliation(s)
- Basetty Mallikarjuna
- School of Computing Science and EngineeringGalgotias UniversityGreater NoidaIndia
| | - Gulshan Shrivastava
- Department of Computer Science and EngineeringSharda UniversityGreater NoidaIndia
| | - Meenakshi Sharma
- School of Computing Science and EngineeringGalgotias UniversityGreater NoidaIndia
| |
Collapse
|
9
|
Matsuda N, Odawara A, Kinoshita K, Okamura A, Shirakawa T, Suzuki I. Raster plots machine learning to predict the seizure liability of drugs and to identify drugs. Sci Rep 2022; 12:2281. [PMID: 35145132 PMCID: PMC8831568 DOI: 10.1038/s41598-022-05697-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 01/03/2022] [Indexed: 11/17/2022] Open
Abstract
In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.
Collapse
Affiliation(s)
- N Matsuda
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - A Odawara
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - K Kinoshita
- Drug Safety Research Labs, Astellas Pharma Inc., 21 Miyukigaoka, Tsukuba, Ibaraki, 305-8585, Japan
| | - A Okamura
- Drug Safety Research Labs, Astellas Pharma Inc., 21 Miyukigaoka, Tsukuba, Ibaraki, 305-8585, Japan
| | - T Shirakawa
- Drug Safety Research Labs, Astellas Pharma Inc., 21 Miyukigaoka, Tsukuba, Ibaraki, 305-8585, Japan
| | - I Suzuki
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan.
| |
Collapse
|
10
|
Matsuzaki T, Kato Y, Mizoguchi H, Yamada K. A machine learning model that emulates experts’ decision making in vancomycin initial dose planning. J Pharmacol Sci 2022; 148:358-363. [DOI: 10.1016/j.jphs.2022.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/26/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2022] Open
|
11
|
Zhai J, Zhou YY, Lagrutta A. Sensitivity, specificity and limitation of in vitro hippocampal slice and neuron-based assays for assessment of drug-induced seizure liability. Toxicol Appl Pharmacol 2021; 430:115725. [PMID: 34536444 DOI: 10.1016/j.taap.2021.115725] [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: 05/08/2021] [Revised: 09/01/2021] [Accepted: 09/13/2021] [Indexed: 10/20/2022]
Abstract
An effective in vitro screening assay to detect seizure liability in preclinical development can contribute to better lead molecule optimization prior to candidate selection, providing higher throughput and overcoming potential brain exposure limitations in animal studies. This study explored effects of 26 positive and 14 negative reference pharmacological agents acting through different mechanisms, including 18 reference agents acting on glutamate signaling pathways, in a brain slice assay (BSA) of adult rat to define the assay's sensitivity, specificity, and limitations. Evoked population spikes (PS) were recorded from CA1 pyramidal neurons of hippocampus (HPC) in the BSA. Endpoints for analysis were PS area and PS number. Most positive references (24/26) elicited a concentration-dependent increase in PS area and/or PS number. The negative references (14/14) had little effect on the PS. Moreover, we studied the effects of 15 reference agents testing positive in the BSA on spontaneous activity in E18 rat HPC neurons monitored with microelectrode arrays (MEA), and compared these effects to the BSA results. From these in vitro studies we conclude that the BSA provides 93% sensitivity and 100% specificity in prediction of drug-induced seizure liability, including detecting seizurogenicity by 3 groups of metabotropic glutamate receptor (mGluR) ligands. The MEA results seemed more variable, both quantitatively and directionally, particularly for endpoints capturing synchronized electrical activity. We discuss these results from the two models, comparing each with published results, and provide potential explanations for differences and future directions.
Collapse
Affiliation(s)
- Jin Zhai
- Department of Genetic Toxicology and In Vitro Cellular Toxicity, Safety Assessment & Laboratory Animal Resources (SALAR), Merck & Co., Inc., West Point, PA 19486, USA.
| | - Ying-Ying Zhou
- Program Discovery and Development, Safety Assessment & Laboratory Animal Resources (SALAR), Merck & Co., Inc., West Point, PA 19486, USA
| | - Armando Lagrutta
- Program Discovery and Development, Safety Assessment & Laboratory Animal Resources (SALAR), Merck & Co., Inc., West Point, PA 19486, USA
| |
Collapse
|
12
|
Melo MCR, Maasch JRMA, de la Fuente-Nunez C. Accelerating antibiotic discovery through artificial intelligence. Commun Biol 2021; 4:1050. [PMID: 34504303 PMCID: PMC8429579 DOI: 10.1038/s42003-021-02586-0] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
Collapse
Affiliation(s)
- Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline R M A Maasch
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
13
|
Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| |
Collapse
|
14
|
Piroozmand F, Mohammadipanah F, Sajedi H. Spectrum of deep learning algorithms in drug discovery. Chem Biol Drug Des 2021; 96:886-901. [PMID: 33058458 DOI: 10.1111/cbdd.13674] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/11/2020] [Accepted: 02/19/2020] [Indexed: 12/16/2022]
Abstract
Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.
Collapse
Affiliation(s)
- Firoozeh Piroozmand
- Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Fatemeh Mohammadipanah
- Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Hedieh Sajedi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| |
Collapse
|
15
|
Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
|
16
|
Minias A, Żukowska L, Lechowicz E, Gąsior F, Knast A, Podlewska S, Zygała D, Dziadek J. Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era. Front Microbiol 2021; 11:618168. [PMID: 33603720 PMCID: PMC7884339 DOI: 10.3389/fmicb.2020.618168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/30/2020] [Indexed: 12/14/2022] Open
Abstract
Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.
Collapse
Affiliation(s)
- Alina Minias
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
| | - Lidia Żukowska
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
- BioMedChem Doctoral School of the University of Lodz and the Institutes of the Polish Academy of Sciences in Lodz, Lodz, Poland
| | - Ewelina Lechowicz
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
- Institute of Microbiology, Biotechnology and Immunology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
| | - Filip Gąsior
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
- BioMedChem Doctoral School of the University of Lodz and the Institutes of the Polish Academy of Sciences in Lodz, Lodz, Poland
| | - Agnieszka Knast
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
- Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Lodz, Poland
| | - Sabina Podlewska
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, Krakow, Poland
- Maj Institute of Pharmacology, Polish Academy of Sciences, Krakow, Poland
| | - Daria Zygała
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
- Institute of Microbiology, Biotechnology and Immunology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland
| | - Jarosław Dziadek
- Laboratory of Genetics and Physiology of Mycobacterium, Institute of Medical Biology, Polish Academy of Sciences, Lodz, Poland
| |
Collapse
|
17
|
Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2020; 34:217-239. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.
Collapse
Affiliation(s)
- Marcus W H Wang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.,MRC Toxicology Unit, University of Cambridge, Hodgkin Building, Lancaster Road, Leicester LE1 7HB, United Kingdom
| |
Collapse
|
18
|
Nixon SA, Welz C, Woods DJ, Costa-Junior L, Zamanian M, Martin RJ. Where are all the anthelmintics? Challenges and opportunities on the path to new anthelmintics. Int J Parasitol Drugs Drug Resist 2020; 14:8-16. [PMID: 32814269 PMCID: PMC7452592 DOI: 10.1016/j.ijpddr.2020.07.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/07/2020] [Accepted: 07/10/2020] [Indexed: 01/03/2023]
Abstract
Control of helminth parasites is a key challenge for human and veterinary medicine. In the absence of effective vaccines and adequate sanitation, prophylaxis and treatment commonly rely upon anthelmintics. There are concerns about the development of drug resistance, side-effects, lack of efficacy and cost-effectiveness that drive the need for new classes of anthelmintics. Despite this need, only three new drug classes have reached the animal market since 2000 and no new classes of anthelmintic have been approved for human use. So where are all the anthelmintics? What are the barriers to anthelmintic discovery, and what emerging opportunities can be used to address this? This was a discussion group focus at the 2019 8th Consortium for Anthelmintic Resistance and Susceptibility (CARS) in Wisconsin, USA. Here we report the findings of the group in the broader context of the human and veterinary anthelmintic discovery pipeline, highlighting challenges unique to antiparasitic drug discovery. We comment on why the development of novel anthelmintics has been so rare. Further, we discuss potential opportunities for drug development moving into the 21st Century.
Collapse
Affiliation(s)
- Samantha A Nixon
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Australia; CSIRO Agriculture and Food, Queensland Bioscience Precinct, St Lucia, Australia
| | | | - Debra J Woods
- Zoetis, Veterinary Medicine Research and Development, Kalamazoo, MI, USA
| | - Livio Costa-Junior
- Federal University of Maranhão, Pathology Department, São Luís, Maranhão, Brazil
| | - Mostafa Zamanian
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard J Martin
- Department of Biomedical Sciences, Iowa State University, Ames, IA, USA.
| |
Collapse
|
19
|
Tukker AM, Wijnolts FMJ, de Groot A, Westerink RHS. Applicability of hiPSC-Derived Neuronal Cocultures and Rodent Primary Cortical Cultures for In Vitro Seizure Liability Assessment. Toxicol Sci 2020; 178:71-87. [PMID: 32866265 PMCID: PMC7657345 DOI: 10.1093/toxsci/kfaa136] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Seizures are life-threatening adverse drug reactions which are investigated late in drug development using rodent models. Consequently, if seizures are detected, a lot of time, money and animals have been used. Thus, there is a need for in vitro screening models using human cells to circumvent interspecies translation. We assessed the suitability of cocultures of human-induced pluripotent stem cell (hiPSC)-derived neurons and astrocytes compared with rodent primary cortical cultures for in vitro seizure liability assessment using microelectrode arrays. hiPSC-derived and rodent primary cortical neuronal cocultures were exposed to 9 known (non)seizurogenic compounds (pentylenetetrazole, amoxapine, enoxacin, amoxicillin, linopirdine, pilocarpine, chlorpromazine, phenytoin, and acetaminophen) to assess effects on neuronal network activity using microelectrode array recordings. All compounds affect activity in hiPSC-derived cocultures. In rodent primary cultures all compounds, except amoxicillin changed activity. Changes in activity patterns for both cell models differ for different classes of compounds. Both models had a comparable sensitivity for exposure to amoxapine (lowest observed effect concentration [LOEC] 0.03 µM), linopirdine (LOEC 1 µM), and pilocarpine (LOEC 0.3 µM). However, hiPSC-derived cultures were about 3 times more sensitive for exposure to pentylenetetrazole (LOEC 30 µM) than rodent primary cortical cultures (LOEC 100 µM). Sensitivity of hiPSC-derived cultures for chlorpromazine, phenytoin, and enoxacin was 10-30 times higher (LOECs 0.1, 0.3, and 0.1 µM, respectively) than in rodent cultures (LOECs 10, 3, and 3 µM, respectively). Our data indicate that hiPSC-derived neuronal cocultures may outperform rodent primary cortical cultures with respect to detecting seizures, thereby paving the way towards animal-free seizure assessment.
Collapse
Affiliation(s)
- Anke M Tukker
- Neurotoxicology Research Group, Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, NL-3508 TD Utrecht, The Netherlands
| | - Fiona M J Wijnolts
- Neurotoxicology Research Group, Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, NL-3508 TD Utrecht, The Netherlands
| | - Aart de Groot
- Neurotoxicology Research Group, Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, NL-3508 TD Utrecht, The Netherlands
| | - Remco H S Westerink
- Neurotoxicology Research Group, Toxicology Division, Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, NL-3508 TD Utrecht, The Netherlands
| |
Collapse
|
20
|
Takahashi K, Sato K. [Strong demands for the new preclinical assessment system of the CNS adverse effects]. Nihon Yakurigaku Zasshi 2020; 155:295-298. [PMID: 32879167 DOI: 10.1254/fpj.20029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Human induced pluripotent stem cell-derived neurons (hiPSC-neurons) have potentials to improve the predictability of the adverse effects (AEs) in the human central nervous system (CNS). We have succeeded in reproducing the human neural networks stably on dish. This is important because most of the CNS AEs were caused not by the neuronal death but by the abnormal neural network activities. Our group have been attempting to establish the in vitro assay system to detect abnormal neural network activities by microelectrode array system (MEA). Furthermore, the algorithm that can determine the synchronized burst firing (SBF) objectively was developed in the collaboration of our iPS Non-clinical Experiments for Nervous System (iNCENS) project and Consortium for Safety Assessment using Human iPS Cells (CSAHi). iNCENS and CSAHi are also involved in the international Life Sciences Institute (ILSI): Health and Environmental Sciences Institute (HESI) HESI NeuTox MEA sub team, and are participating in the pilot study using 12 compounds. In the big flow to develop physiological CNS safety assessment system, we have to show Japan initiative based on our innovative iPS technology we have developed so far.
Collapse
Affiliation(s)
- Kanako Takahashi
- Laboratory of Neuropharmacology, Division of Pharmacology, National Institute of Health Sciences
| | - Kaoru Sato
- Laboratory of Neuropharmacology, Division of Pharmacology, National Institute of Health Sciences
| |
Collapse
|
21
|
Pei Z, Shi M, Guo J, Shen B. Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives. Curr Top Med Chem 2020; 20:1640-1650. [PMID: 32493191 DOI: 10.2174/1568026620666200603105002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 02/28/2020] [Accepted: 03/02/2020] [Indexed: 02/08/2023]
Abstract
Heart rate variability (HRV) signals are reported to be associated with the personalized drug
response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc.
But the relationships between HRV signals and the personalized drug response in different diseases and
patients are complex and remain unclear. With the fast development of modern smart sensor technologies
and the popularization of big data paradigm, more and more data on the HRV and drug response
will be available, it then provides great opportunities to build models for predicting the association of
the HRV with personalized drug response precisely. We here review the present status of the HRV data
resources and models for predicting and evaluating of personalized drug responses in different diseases.
The future perspectives on the integration of knowledge and personalized data at different levels such as,
genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of
drug therapy and their response will be provided.
Collapse
Affiliation(s)
- Zejun Pei
- Nanjing Medical University Affiliated Wuxi Second Hospital, No. 68,Zhongshan road, Wuxi, Jiangsu, China
| | - Manhong Shi
- Centre for Systems Biology, Soochow University, Suzhou 215006, China
| | - Junping Guo
- The Affiliated Yixing Hospital of Jiangsu University, No. 75, Tongzhenguan Road, Yixing, Jiangsu, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
22
|
Egli A, Schrenzel J, Greub G. Digital microbiology. Clin Microbiol Infect 2020; 26:1324-1331. [PMID: 32603804 PMCID: PMC7320868 DOI: 10.1016/j.cmi.2020.06.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. OBJECTIVE This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. SOURCES We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology. CONTENT We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. IMPLICATIONS We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.
Collapse
Affiliation(s)
- A Egli
- Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland; Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland.
| | - J Schrenzel
- Laboratory of Bacteriology, University Hospitals of Geneva, Geneva, Switzerland
| | - G Greub
- Institute of Medical Microbiology, University Hospital Lausanne, Lausanne, Switzerland
| |
Collapse
|
23
|
Lai NH, Shen WC, Lee CN, Chang JC, Hsu MC, Kuo LN, Yu MC, Chen HY. Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 188:105307. [PMID: 31911332 DOI: 10.1016/j.cmpb.2019.105307] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 12/02/2019] [Accepted: 12/27/2019] [Indexed: 05/03/2023]
Abstract
BACKGROUND The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity. METHODS The clinical and genomic data of patients treated with anti-tuberculosis drugs at Taipei Medical University-Wanfang Hospital were used as training sets, and those at Taipei Medical University-Shuang Ho Hospital served as test sets. Features were selected through a univariate risk factor analysis and literature evaluation. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve were calculated to compare the traditional, genomic, and combined models of the three techniques. RESULTS Nine models were created with 7 clinical factors and 4 genotypes. Artificial neural network with clinical and genomic factors exhibited the best performance, with an accuracy of 88.67%, a sensitivity of 80%, and a specificity of 90.4% for the test set. The area under the receiver operating characteristic curve of this best model reached 0.894 for training set and 0.898 for test set, which was significantly better than 0.801 for training set and 0.728 for test set by support vector machine and 0.724 for training set and 0.718 for test set by random forest. CONCLUSIONS Artificial neural network with clinical and genomic data can become a clinical useful tool in predicting anti-tuberculosis drug-induced hepatotoxicity. The machine learning technique can be an innovation to predict and prevent adverse drug reaction.
Collapse
Affiliation(s)
- Nai-Hua Lai
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Wan-Chen Shen
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Chun-Nin Lee
- Department of Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Pulmonary Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Jui-Chia Chang
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Man-Ching Hsu
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Li-Na Kuo
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chih Yu
- Department of Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Pulmonary Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Hsiang-Yin Chen
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
| |
Collapse
|
24
|
Ting HW, Chung SL, Chen CF, Chiu HY, Hsieh YW. A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan. BMC Health Serv Res 2020; 20:312. [PMID: 32293426 PMCID: PMC7158008 DOI: 10.1186/s12913-020-05166-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 03/27/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them. METHODS We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. RESULTS Our results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%). CONCLUSIONS In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.
Collapse
Affiliation(s)
- Hsien-Wei Ting
- Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan.,Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan
| | - Sheng-Luen Chung
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Chih-Fang Chen
- Pharmaceutical Department, Mackay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Rd, Taipei City, 10449, Taiwan.
| | - Hsin-Yi Chiu
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Yow-Wen Hsieh
- Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan
| |
Collapse
|
25
|
Hemmerich J, Ecker GF. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020; 10:e1475. [PMID: 35866138 PMCID: PMC9286356 DOI: 10.1002/wcms.1475] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
Collapse
Affiliation(s)
- Jennifer Hemmerich
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| |
Collapse
|
26
|
Executive summary of the 2019 ASHP Commission on Goals: Impact of artificial intelligence on healthcare and pharmacy practice. Am J Health Syst Pharm 2019; 76:2087-2092. [DOI: 10.1093/ajhp/zxz205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
27
|
Idakwo G, Thangapandian S, Luttrell J, Zhou Z, Zhang C, Gong P. Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data. Front Physiol 2019; 10:1044. [PMID: 31456700 PMCID: PMC6700714 DOI: 10.3389/fphys.2019.01044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/30/2019] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
Collapse
Affiliation(s)
- Gabriel Idakwo
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
| | - Sundar Thangapandian
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, United States
| | - Joseph Luttrell
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
| | - Zhaoxian Zhou
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, United States
| |
Collapse
|
28
|
Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters. J Pharmacol Sci 2019; 140:20-25. [PMID: 31105026 DOI: 10.1016/j.jphs.2019.03.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/23/2019] [Accepted: 03/25/2019] [Indexed: 12/25/2022] Open
Abstract
Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient.
Collapse
|
29
|
Kalinin AA, Higgins GA, Reamaroon N, Soroushmehr S, Allyn-Feuer A, Dinov ID, Najarian K, Athey BD. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 2018; 19:629-650. [PMID: 29697304 PMCID: PMC6022084 DOI: 10.2217/pgs-2018-0008] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 03/09/2018] [Indexed: 01/02/2023] Open
Abstract
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.
Collapse
Affiliation(s)
- Alexandr A Kalinin
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource (SOCR), University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
| | - Gerald A Higgins
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Narathip Reamaroon
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Sayedmohammadreza Soroushmehr
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Ari Allyn-Feuer
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Ivo D Dinov
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource (SOCR), University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Brian D Athey
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
- Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI 48109, USA
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| |
Collapse
|
30
|
Can a collaborative healthcare network improve the care of people with epilepsy? Epilepsy Behav 2018; 82:189-193. [PMID: 29573986 DOI: 10.1016/j.yebeh.2018.02.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 01/31/2023]
Abstract
New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.
Collapse
|
31
|
Mamoshina P, Ojomoko L, Yanovich Y, Ostrovski A, Botezatu A, Prikhodko P, Izumchenko E, Aliper A, Romantsov K, Zhebrak A, Ogu IO, Zhavoronkov A. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 2017; 9:5665-5690. [PMID: 29464026 PMCID: PMC5814166 DOI: 10.18632/oncotarget.22345] [Citation(s) in RCA: 238] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 11/02/2017] [Indexed: 12/19/2022] Open
Abstract
The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.
Collapse
Affiliation(s)
- Polina Mamoshina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Lucy Ojomoko
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | | | | | | | | | - Eugene Izumchenko
- Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Konstantin Romantsov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Alexander Zhebrak
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Iraneus Obioma Ogu
- Africa Blockchain Artificial Intelligence for Healthcare Initiative, Insilico Medicine, Inc, Abuja, Nigeria
| | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,The Biogerontology Research Foundation, London, United Kingdom
| |
Collapse
|