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Wanner IB, McCabe JT, Huie JR, Harris NG, Paydar A, McMann-Chapman C, Tobar A, Korotcov A, Burns MP, Koehler RC, Wan J, Allende Labastida J, Tong J, Zhou J, Davis LM, Radabaugh HL, Ferguson AR, Van Meter TE, Febo M, Bose P, Wang KK, Kobeissy F, Apiliogullari S, Zhu J, Rubenstein R, Awwad HO. Prospective Harmonization, Common Data Elements, and Sharing Strategies for Multicenter Pre-Clinical Traumatic Brain Injury Research in the Translational Outcomes Project in Neurotrauma Consortium. J Neurotrauma 2025; 42:877-897. [PMID: 39831841 DOI: 10.1089/neu.2023.0653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2025] Open
Abstract
Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science. Here, we present practical approaches for harmonization of TBI research across five centers providing needed vocabulary and structure to achieve centralized data organization and use. This includes data sharing as an essential step that enables validating data between domains, evaluating reproducibility between sites, and performing multimodal analyses. As part of this process, TOP-NT (1) produced a library of TBI-relevant standard operating procedures to coordinate workflow, (2) aligned 481 pre-clinical and clinical common data elements (CDEs), and (3) generated 272 new pre-clinical TBI CDEs. This consortium then (4) connected diverse data types to validate assessments across domains and to allow multivariable TBI phenotyping. Lastly, TOP-NT (5) specified technical quality controls for pre-clinical studies. These harmonization tools can facilitate reproducibility in team science, help distinguish a wide injury spectrum from technical variability, apply quality-controls, and ease higher level data analyses. TOP-NT uses three rat TBI models across four sites. Each site collects primary outcome measures, including magnetic resonance imaging (MRI) protocols and blood biomarkers of neuronal and glial injury, validated by histopathology and behavioral outcomes. Collected data are organized using the 481 TOP-NT pre-clinical CDEs, covering surgical, behavioral, biomarker, MRI, and quantitative histopathological methods. We report data curation steps suited for data storage using the Open Data Commons for TBI as a centralized data repository, allowing unbiased cross-site analysis. This approach leads to introducing a higher level, syndromic understanding of TBI signatures. TOP-NT authors outline a semantic and structural framework suggesting strategies for robust pre-clinical research in multicenter trials to improve translatability for TBI assessments. [Figure: see text].
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Affiliation(s)
- Ina-Beate Wanner
- Intellectual and Developmental Disability Center (IDDRC), David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, USA
| | - Joseph T McCabe
- Department of Anatomy, Physiology & Genetics, School of Medicine, Uniformed Services University, Bethesda, Maryland, USA
| | - J Russell Huie
- Brain and Spinal Injury Center (BASIC), Weill Institute for Neurosciences, University of California, San Francisco (UCSF), San Francisco, California, USA
- Principal Investigator, Veterans Affairs Healthcare System, Zuckerberg San Francisco General Hospital, San Francisco, California, USA
| | - Neil G Harris
- Department of Neurosurgery, Brain Research Injury Center (BIRC), Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Afshin Paydar
- Department of Neurosurgery, Brain Research Injury Center (BIRC), Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Chloe McMann-Chapman
- Intellectual and Developmental Disability Center (IDDRC), David Geffen School of Medicine, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, USA
| | - Anthony Tobar
- Semel Institute for Neuroscience and Human Behavior, IDDRC, Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Alexandru Korotcov
- Department of Radiology & Radiological Sciences, Uniformed Services University, Bethesda, Maryland, USA
| | - Mark P Burns
- Georgetown University Medical Center, Center for Neural Injury and Repair, Washington, District of Columbia, USA
| | - Raymond C Koehler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jieru Wan
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Javier Allende Labastida
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jonathan Tong
- Semel Institute for Neuroscience and Human Behavior, IDDRC, Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Jinyuan Zhou
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lex Maliga Davis
- Brain and Spinal Injury Center, University of California, San Francisco, California, USA
| | - Hannah L Radabaugh
- Brain and Spinal Injury Center, University of California, San Francisco, California, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center (BASIC), Weill Institute for Neurosciences, University of California, San Francisco (UCSF), San Francisco, California, USA
- Principal Investigator, Veterans Affairs Healthcare System, Zuckerberg San Francisco General Hospital, San Francisco, California, USA
| | | | - Marcelo Febo
- Departmet Psychiatry, University of Florida, Gainesville, Florida, USA
- Department of Psychiatry, Advanced Magnetic Resonance Imaging and Spectroscopy Facility, University of Florida, Gainesville, Florida, USA
- Department of Psychiatry (Room L4-100F), McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Prodip Bose
- Department of Anesthesiology, and Department of Neurology at the College of Medicine, University of Florida, Gainesville, Florida, USA
- Brain Rehabilitation Research (Center), Malcom Randall VAMC, Gainesville, Florida, USA
| | - Kevin K Wang
- Department of Neurobiology, Center for Neurotrauma, Multiomics & Biomarkers, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - Firas Kobeissy
- Department of Neurobiology, Center for Neurotrauma, Multiomics & Biomarkers, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - Seza Apiliogullari
- Department of Neurobiology, Center for Neurotrauma, Multiomics & Biomarkers, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - Jiepei Zhu
- Department of Neurobiology, Center for Neurotrauma, Multiomics & Biomarkers, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - Richard Rubenstein
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York, USA
| | - Hibah O Awwad
- Division of Neuroscience, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA
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Reyes SJ, Lemire L, Molina RS, Roy M, L'Ecuyer-Coelho H, Martynova Y, Cass B, Voyer R, Durocher Y, Henry O, Pham PL. Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development. Biotechnol Prog 2024; 40:e3467. [PMID: 38660973 DOI: 10.1002/btpr.3467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024]
Abstract
The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.
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Affiliation(s)
- Sebastian-Juan Reyes
- Department of Chemical Engineering, Polytechnique Montreal, Montreal, Canada
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | - Lucas Lemire
- Department of Chemical Engineering, Polytechnique Montreal, Montreal, Canada
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | | | - Marjolaine Roy
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | | | - Yuliya Martynova
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | - Brian Cass
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | - Robert Voyer
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | - Yves Durocher
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
| | - Olivier Henry
- Department of Chemical Engineering, Polytechnique Montreal, Montreal, Canada
| | - Phuong Lan Pham
- Human Health Therapeutics Research Centre, National Research Council Canada, Canada
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3
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Sarygina E, Kozlova A, Deinichenko K, Radko S, Ptitsyn K, Khmeleva S, Kurbatov LK, Spirin P, Prassolov VS, Ilgisonis E, Lisitsa A, Ponomarenko E. Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines. Int J Mol Sci 2023; 24:15502. [PMID: 37958484 PMCID: PMC10648607 DOI: 10.3390/ijms242115502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/12/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
The long-read RNA sequencing developed by Oxford Nanopore Technology provides a direct quantification of transcript isoforms. That makes the number of transcript isoforms per gene an intrinsically suitable metric for alternative splicing (AS) profiling in the application to this particular type of RNA sequencing. By using this simple metric and recruiting principal component analysis (PCA) as a tool to visualize the high-dimensional transcriptomic data, we were able to group biospecimens of normal human liver tissue and hepatocyte-derived malignant HepG2 and Huh7 cells into clear clusters in a 2D space. For the transcriptome-wide analysis, the clustering was observed regardless whether all genes were included in analysis or only those expressed in all biospecimens tested. However, in the application to a particular set of genes known as pharmacogenes, which are involved in drug metabolism, the clustering worsened dramatically in the latter case. Based on PCA data, the subsets of genes most contributing to biospecimens' grouping into clusters were selected and subjected to gene ontology analysis that allowed us to determine the top 20 biological processes among which translation and processes related to its regulation dominate. The suggested metrics can be a useful addition to the existing metrics for describing AS profiles, especially in application to transcriptome studies with long-read sequencing.
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Affiliation(s)
- Elizaveta Sarygina
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Anna Kozlova
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Kseniia Deinichenko
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Sergey Radko
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Konstantin Ptitsyn
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Svetlana Khmeleva
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Leonid K. Kurbatov
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Pavel Spirin
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, 119991 Moscow, Russia
| | - Vladimir S. Prassolov
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, 119991 Moscow, Russia
| | - Ekaterina Ilgisonis
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Andrey Lisitsa
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
| | - Elena Ponomarenko
- Institute of Biomedical Chemistry, Pogodinskaya Street 10, 119121 Moscow, Russia; (E.S.); (A.K.); (S.R.)
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4
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Soliman ME, Adewumi AT, Akawa OB, Subair TI, Okunlola FO, Akinsuku OE, Khan S. Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation. AAPS PharmSciTech 2022; 23:86. [PMID: 35292867 DOI: 10.1208/s12249-022-02229-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/03/2022] [Indexed: 12/17/2022] Open
Abstract
The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlusTM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.
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5
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Jarusintanakorn S, Phechkrajang C, Khongkaew P, Mastrobattista E, Yamabhai M. Determination of Chinese hamster ovary (CHO) cell densities and antibody titers from small volumes of cell culture supernatants using multivariate analysis and partial least squares regression of UV-Vis spectra. Anal Bioanal Chem 2021; 413:5743-5753. [PMID: 34476523 PMCID: PMC8437849 DOI: 10.1007/s00216-021-03549-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/25/2021] [Accepted: 07/13/2021] [Indexed: 12/22/2022]
Abstract
Antibody titer and viable cell density (VCD) are two important parameters that need to be closely monitored during the process of cell line development and manufacturing of therapeutic antibodies. Typically, determination of each parameter requires 10–100 μL of supernatant sample, which is not suitable for small scale cultivation. In this study, we demonstrated that as low as 2 μL of culture supernatants were sufficient for the analysis using UV-Vis spectrum assisted with partial least squares (PLS) model. The results indicated that the optimal PLS models could be used to predict antibody titer and VCD with the linear relationship between reference values and predicted values at R2 values ranging from 0.8 to > 0.9 in supernatant samples obtained from four different single clones and in polyclones that were cultured in various selection stringencies. Then, the percentage of cell viability and productivity were predicted from a set of samples of polyclones. The results indicated that while all predicted % cell viability were very similar to the actual value at RSEP value of 6.7 and R2 of 0.8908, the predicted productivity from 14 of 18 samples were closed to the reference measurements at RSEP value of 22.4 and R2 of 0.8522. These results indicated that UV-Vis combined with PLS has potential to be used for monitoring antibody titer, VCD, and % cell viability for both online and off-line therapeutic production process.
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Affiliation(s)
- Salinthip Jarusintanakorn
- Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutics, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, Netherlands.,Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mahidol University, 447, Sri-Ayuthaya Road, Rajathevi, Bangkok, 10400, Thailand.,Molecular Biotechnology Laboratory, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Chutima Phechkrajang
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mahidol University, 447, Sri-Ayuthaya Road, Rajathevi, Bangkok, 10400, Thailand
| | - Putthiporn Khongkaew
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mahidol University, 447, Sri-Ayuthaya Road, Rajathevi, Bangkok, 10400, Thailand.,Faculty of Pharmaceutical Science, Burapha University, 169 Longhaad Bangsaen Road, Saensook, Muang, Chonburi, 20131, Thailand
| | - Enrico Mastrobattista
- Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutics, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, Netherlands.
| | - Montarop Yamabhai
- Molecular Biotechnology Laboratory, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
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6
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Digital Twin in biomanufacturing: challenges and opportunities towards its implementation. ACTA ACUST UNITED AC 2021. [DOI: 10.1007/s43393-021-00024-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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7
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Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes. J Ind Microbiol Biotechnol 2020; 47:947-964. [PMID: 32895764 PMCID: PMC7695667 DOI: 10.1007/s10295-020-02308-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/31/2020] [Indexed: 12/22/2022]
Abstract
The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.
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8
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Pais DAM, Portela RMC, Carrondo MJT, Isidro IA, Alves PM. Enabling PAT in insect cell bioprocesses:
In situ
monitoring of recombinant adeno‐associated virus production by fluorescence spectroscopy. Biotechnol Bioeng 2019; 116:2803-2814. [DOI: 10.1002/bit.27117] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/22/2019] [Accepted: 07/09/2019] [Indexed: 12/25/2022]
Affiliation(s)
- Daniel A. M. Pais
- iBET, Instituto de Biologia Experimental e Tecnológica Oeiras Portugal
- Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de Lisboa Oeiras Portugal
| | - Rui M. C. Portela
- iBET, Instituto de Biologia Experimental e Tecnológica Oeiras Portugal
| | | | - Inês A. Isidro
- iBET, Instituto de Biologia Experimental e Tecnológica Oeiras Portugal
- Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de Lisboa Oeiras Portugal
| | - Paula M. Alves
- iBET, Instituto de Biologia Experimental e Tecnológica Oeiras Portugal
- Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de Lisboa Oeiras Portugal
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9
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Kizhedath A, Wilkinson S, Glassey J. Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope. Arch Toxicol 2016; 91:1595-1612. [PMID: 27766364 PMCID: PMC5364268 DOI: 10.1007/s00204-016-1876-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 10/12/2016] [Indexed: 12/31/2022]
Abstract
Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.
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Affiliation(s)
- Arathi Kizhedath
- Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE17RU, UK. .,Medical Toxicology Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4AA, UK.
| | - Simon Wilkinson
- Medical Toxicology Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4AA, UK
| | - Jarka Glassey
- Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE17RU, UK
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10
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Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder. ScientificWorldJournal 2015; 2015:987435. [PMID: 26380376 PMCID: PMC4562094 DOI: 10.1155/2015/987435] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 04/15/2015] [Accepted: 04/27/2015] [Indexed: 11/18/2022] Open
Abstract
Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer's and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients' age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.
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11
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Melcher M, Scharl T, Spangl B, Luchner M, Cserjan M, Bayer K, Leisch F, Striedner G. The potential of random forest and neural networks for biomass and recombinant protein modeling in
Escherichia coli
fed‐batch fermentations. Biotechnol J 2015; 10:1770-82. [DOI: 10.1002/biot.201400790] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 04/13/2015] [Accepted: 06/26/2015] [Indexed: 11/08/2022]
Affiliation(s)
- Michael Melcher
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Theresa Scharl
- Austrian Centre of Industrial Biotechnology, Graz, Austria
| | - Bernhard Spangl
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Markus Luchner
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Monika Cserjan
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Karl Bayer
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Friedrich Leisch
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Gerald Striedner
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
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12
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Tsang VL, Wang AX, Yusuf-Makagiansar H, Ryll T. Development of a scale down cell culture model using multivariate analysis as a qualification tool. Biotechnol Prog 2013; 30:152-60. [DOI: 10.1002/btpr.1819] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Revised: 09/29/2013] [Indexed: 01/25/2023]
Affiliation(s)
- Valerie Liu Tsang
- Cell Culture Development; Biogen Idec, Inc.; 5000 Davis Drive; Research Triangle Park; NC 27709
| | - Angela X. Wang
- Cell Culture Development; Biogen Idec, Inc.; 5000 Davis Drive; Research Triangle Park; NC 27709
| | | | - Thomas Ryll
- Cell Culture Development; Biogen Idec, Inc.; 5000 Davis Drive; Research Triangle Park; NC 27709
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