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Haem, E., Karlsson, M. O. & Ueckert, S. (2025). Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials. Journal of Pharmacokinetics and Pharmacodynamics, 52(1), Article ID 4.
Open this publication in new window or tab >>Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials
2025 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 52, no 1, article id 4Article in journal (Refereed) Published
Abstract [en]

Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Total score data, Bounded integer model, Coarsened grid model, IRT-informed total score analysis
National Category
Probability Theory and Statistics Pharmaceutical Sciences Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-545737 (URN)10.1007/s10928-024-09949-0 (DOI)001374122800002 ()39656313 (PubMedID)2-s2.0-85212043719 (Scopus ID)
Funder
Uppsala University
Available from: 2025-01-07 Created: 2025-01-07 Last updated: 2025-01-07Bibliographically approved
Vongjarudech, T., Dosne, A.-G., Remmerie, B., Dooley, K. E., Brust, J. C. M., Maartens, G., . . . Svensson, E. (2025). Development and validation of a time-varying correction factor for QT interval assessment in drug-resistant tuberculosis patients. International Journal of Antimicrobial Agents, 65(4), Article ID 107460.
Open this publication in new window or tab >>Development and validation of a time-varying correction factor for QT interval assessment in drug-resistant tuberculosis patients
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2025 (English)In: International Journal of Antimicrobial Agents, ISSN 0924-8579, E-ISSN 1872-7913, Vol. 65, no 4, article id 107460Article in journal (Refereed) Published
Abstract [en]

Background: Tachycardia associated with active tuberculosis (TB) often diminishes when patients recover from TB. Elevated heart rate (HR) may lead to suboptimal correction, complicating the assessment of QT prolongation when using standard correction factors (CFs), such as Fridericia's formula (QTcF). Olliaro has proposed a CF for QT interval correction in pretreatment TB patients. However, the QT-HR correlation changes as HR decreases during treatment, indicating the need for time-varying correction.

Methods: We developed an HR model to capture the HR normalisation during successful treatment. Subsequently, a time-varying CF was constructed using the estimated HR change rate. The performance of CFs to make corrected QT (QTc) independent from HR was evaluated by linear regression analyses of QTc versus HR within defined time bins.

Results: The final HR model included asymptotic change in HR attributed to time on treatment, circadian rhythm cycles, M2 (bedaquiline-metabolite) concentration, and patient covariates. The time-varying CF decreased from 0.4081 to 0.33, with a half-life of 7.74 weeks. The slope (QTc/HR vs. Time) derived from the time-varying correction was not significantly different from 0 (95% CI -0.003 to 0.002), and the intercept was not significantly different from 0 (95% CI -0.089 to 0.006), demonstrating successful QT correction from pretreatment to the end of treatment.

Conclusion: The time-varying CF effectively captures the dynamic QT-HR relationship during TB treatment, reducing the risk of misdiagnosing QT prolongation or unnecessary discontinuation of treatment. By addressing underestimation and overestimation issues in QT interval assessment, this method enhances drug evaluation in clinical trials and supports improved treatment decisions for TB patients. (c) 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Tuberculosis, Tachycardia, Heart rate, QT interval, QT prolongation, Correction factors
National Category
Cardiology and Cardiovascular Disease
Identifiers
urn:nbn:se:uu:diva-552427 (URN)10.1016/j.ijantimicag.2025.107460 (DOI)001435151700001 ()39922239 (PubMedID)2-s2.0-85218850497 (Scopus ID)
Available from: 2025-03-14 Created: 2025-03-14 Last updated: 2025-03-14Bibliographically approved
Karlsson, M. O. (2025). In the Cradle of Pharmacometric Methodology: Introducing Population PKPD Modeling, Simultaneous Analysis, and the Effect-Compartment Model-Commentary on Sheiner et al.. Clinical Pharmacology and Therapeutics, 117(6), 1516-1532
Open this publication in new window or tab >>In the Cradle of Pharmacometric Methodology: Introducing Population PKPD Modeling, Simultaneous Analysis, and the Effect-Compartment Model-Commentary on Sheiner et al.
2025 (English)In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 117, no 6, p. 1516-1532Article in journal (Refereed) Published
Abstract [en]

In the decades preceding 1970s, there were considerable advances in theoretical descriptions of pharmacokinetics, drug action, and the time-course of dose-concentration-response relations. However, delayed drug effects and how to analyze data across individuals still offered a considerable challenge. Sheiner et al., through the formulation of the effect-compartment model, the utilization of nonlinear mixed effects models, and simultaneous PKPD analysis, offered solutions to these issues that still today are considered state-of-the-art.

Place, publisher, year, edition, pages
John Wiley & Sons, 2025
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-558741 (URN)10.1002/cpt.3663 (DOI)001494702300028 ()40388069 (PubMedID)
Available from: 2025-06-12 Created: 2025-06-12 Last updated: 2025-06-12Bibliographically approved
Arrington, L., van Dijkman, S. C., Plan, E. L. & Karlsson, M. O. (2025). Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.. CPT: Pharmacometrics and Systems Pharmacology (PSP)
Open this publication in new window or tab >>Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.
2025 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306Article in journal (Refereed) Epub ahead of print
Abstract [en]

Approximately 15% of patients suspected of having Parkinson's disease (PD) present dopamine active transporter (DaT) scans without evidence of dopaminergic deficits (SWEDD), most of which will never develop PD. Leveraging Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores from the Parkinson's Progression Markers Initiative, three different models of varying complexity, (total score, item response theory (IRT) and artificial neural network (ANN)) were evaluated to determine their ability to differentiate between PD and SWEDDs. Each of the models provided as output a predicted probability of having PD (PDeNoPD). Both the IRT and ANN methods performed well as classifiers; ROC AUC > 80%, sensitivity > 93%, and precision ~90% when assuming a probability cutoff of PDeNoPD ≥ 50%. Specificity was 43% and 38% for IRT and ANN respectively. Matthews correlation coefficient (MCC) was also evaluated as a metric to address potential bias of majority positive class. At all cutoffs at or above 50%, the IRT and ANN model performed similarly and achieved a MCC of at least 0.3, indicating at least a moderate positive relationship for classifier performance. In contrast, the total score model was a poor classifier, for all metrics and cutoffs. Using item-level data the proposed methodologies differentiated PD patients from SWEDDs with a degree of sensitivity and specificity that may compete with clinical examination and could aid in selecting DaTscan candidates. The choice of cutoff criteria, quality metric, and classifier model are contingent upon specific clinical needs.

Place, publisher, year, edition, pages
John Wiley & Sons, 2025
Keywords
Parkinson's disease, classification, discrete data models, disease progression, item response theory, machine learning, mixed effect models, neuroscience, pharmacometrics
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-552594 (URN)10.1002/psp4.70000 (DOI)40045658 (PubMedID)
Available from: 2025-03-17 Created: 2025-03-17 Last updated: 2025-03-21
Chen, X., Nordgren, R., Belin, S., Hamdan, A., Wang, S., Yang, T., . . . Karlsson, M. O. (2024). A fully automatic tool for development of population pharmacokinetic models. CPT: Pharmacometrics and Systems Pharmacology (PSP), 13(10), 1784-1797
Open this publication in new window or tab >>A fully automatic tool for development of population pharmacokinetic models
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2024 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 13, no 10, p. 1784-1797Article in journal (Refereed) Published
Abstract [en]

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-545231 (URN)10.1002/psp4.13222 (DOI)001298315900001 ()39190006 (PubMedID)2-s2.0-85201392211 (Scopus ID)
Funder
Swedish Institute
Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-19Bibliographically approved
Lin, Y.-J., Zou, Y., Karlsson, M. O. & Svensson, E. (2024). A pharmacometric multistate model for predicting long-term treatment outcomes of patients with pulmonary TB. Journal of Antimicrobial Chemotherapy, 79(10), 2561-2569
Open this publication in new window or tab >>A pharmacometric multistate model for predicting long-term treatment outcomes of patients with pulmonary TB
2024 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 79, no 10, p. 2561-2569Article in journal (Refereed) Published
Abstract [en]

Background Studying long-term treatment outcomes of TB is time-consuming and impractical. Early and reliable biomarkers reflecting treatment response and capable of predicting long-term outcomes are urgently needed.Objectives To develop a pharmacometric multistate model to evaluate the link between potential predictors and long-term outcomes.Methods Data were obtained from two Phase II clinical trials (TMC207-C208 and TMC207-C209) with bedaquiline on top of a multidrug background regimen. Patients were typically followed throughout a 24 week investigational treatment period plus a 96 week follow-up period. A five-state multistate model (active TB, converted, recurrent TB, dropout, and death) was developed to describe observed transitions. Evaluated predictors included patient characteristics, baseline TB disease severity and on-treatment biomarkers.Results A fast bacterial clearance in the first 2 weeks and low TB bacterial burden at baseline increased probability to achieve conversion, whereas patients with XDR-TB were less likely to reach conversion. Higher estimated mycobacterial load at the end of 24 week treatment increased the probability of recurrence. At 120 weeks, the model predicted 55% (95% prediction interval, 50%-60%), 6.5% (4.2%-9.0%) and 7.5% (5.2%-10%) of patients in converted, recurrent TB and death states, respectively. Simulations predicted a substantial increase of recurrence after 24 weeks in patients with slow bacterial clearance regardless of baseline bacterial burden.Conclusions The developed multistate model successfully described TB treatment outcomes. The multistate modelling framework enables prediction of several outcomes simultaneously, and allows mechanistically sound investigation of novel promising predictors. This may help support future biomarker evaluation, clinical trial design and analysis.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS, 2024
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-541024 (URN)10.1093/jac/dkae256 (DOI)001281443200001 ()39087258 (PubMedID)
Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Brekkan, A., Lledo-Garcia, R., Lacroix, B., Jonsson, S., Karlsson, M. O. & Plan, E. L. (2024). Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach. Journal of Pharmacokinetics and Pharmacodynamics, 51(1), 65-75
Open this publication in new window or tab >>Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach
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2024 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 51, no 1, p. 65-75Article in journal (Refereed) Published
Abstract [en]

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Anti-drug antibody formation, Certolizumab pegol, anti-TNF, hidden-Markov model
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-528415 (URN)10.1007/s10928-023-09890-8 (DOI)001099164200001 ()37943398 (PubMedID)
Available from: 2024-05-28 Created: 2024-05-28 Last updated: 2024-05-28Bibliographically approved
Centanni, M., Nijhuis, J., Karlsson, M. O. & Friberg, L. E. (2024). Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy. PharmacoEconomics (Auckland)
Open this publication in new window or tab >>Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy
2024 (English)In: PharmacoEconomics (Auckland), ISSN 1170-7690, E-ISSN 1179-2027Article in journal (Refereed) Epub ahead of print
Abstract [en]

Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Place, publisher, year, edition, pages
Springer, 2024
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:uu:diva-545158 (URN)10.1007/s40273-024-01438-z (DOI)001321553500001 ()2-s2.0-85205035560 (Scopus ID)
Funder
Swedish Cancer Society, CAN 23 2921 PjSwedish Cancer Society, CAN 20 1226 PjFUppsala University
Available from: 2024-12-12 Created: 2024-12-12 Last updated: 2024-12-17
Arrington, L. & Karlsson, M. (2024). Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models. AAPS Journal, 26, Article ID 21.
Open this publication in new window or tab >>Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models
2024 (English)In: AAPS Journal, E-ISSN 1550-7416, Vol. 26, article id 21Article in journal (Refereed) Published
Abstract [en]

There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the "simultaneous" and "sequential" methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson's Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Estimation methods, Item characteristic function, Item response theory, Pharmacometrics
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-522883 (URN)10.1208/s12248-023-00883-6 (DOI)001148749300001 ()38273096 (PubMedID)
Funder
Swedish Research Council, 2018-03317Uppsala University
Available from: 2024-02-12 Created: 2024-02-12 Last updated: 2025-03-21Bibliographically approved
Sanghavi, K., Ribbing, J., Rogers, J. A., Ahmed, M. A., Karlsson, M. O., Holford, N., . . . Wilkins, J. J. (2024). Covariate modeling in pharmacometrics: General points for consideration. CPT: Pharmacometrics and Systems Pharmacology (PSP), 13(5), 710-728
Open this publication in new window or tab >>Covariate modeling in pharmacometrics: General points for consideration
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2024 (English)In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 13, no 5, p. 710-728Article, review/survey (Refereed) Published
Abstract [en]

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-545229 (URN)10.1002/psp4.13115 (DOI)001195572400001 ()38566433 (PubMedID)2-s2.0-85189962207 (Scopus ID)
Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-18Bibliographically approved
Projects
Model-based development of anti-tuberculosis drug combinations [2011-03442_VR]; Uppsala UniversityComposite score models for efficient use of patient data in decision making for development and usage of drugs [2018-03317_VR]; Uppsala University; Publications
Wellhagen, G., Yassen, A., Garmann, D., Broeker, A., Solms, A., Zhang, Y., . . . Karlsson, M. O. (2024). Evaluation of covariate effects in item response theory models. CPT: Pharmacometrics and Systems Pharmacology (PSP), 13(5), 812-822
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1258-8297

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