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  • 1.
    Abourraja, Mohamed Nezar
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Marzano, Luca
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Boodaghian Asl, Arsineh
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Lethvall, Sven
    Uppsala University Hospital,Uppsala,Sweden.
    Falk, Nina
    Uppsala University Hospital,Uppsala,Sweden.
    A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics2022In: Proceedings of the 2022 Winter Simulation Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    Demands for health care are becoming overwhelming for healthcare systems around the world regarding theavailability of resources, particularly, in emergency departments (EDs) that are continuously open and mustserve immediately any patient who comes in. Efficient management of EDs and their resources is requiredmore than ever. This could be achieved either by optimizing resource utilization or by the improvement ofhospital layout. This paper investigates, through data-driven simulation alternative designs of workflowsand layouts to operate the ED of the Uppsala University Hospital in Sweden. Results are analyzed tounderstand the requirements across the hospital for reduced waiting times in the ED. The main observationrevealed that introducing a new ward dedicated to patients having complex diagnoses with a capacity ofless than 20 beds leads to lower waiting times. Furthermore, the use of data-mining was of great help inreducing the efforts of building the simulation model.

  • 2. Ahmad, Amais
    et al.
    Pepin, Xavier
    Aarons, Leon
    Wang, Yuya
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Wood, J. Matthew
    Tannergren, Christer
    Karlsson, Eva
    Patterson, Claire
    Thörn, Helena
    Ruston, Linette
    Mattinson, Alex
    Carlert, Sara
    Berg, Staffan
    Murphy, Donal
    Engman, Helena
    Laru, Johanna
    Barker, Ric
    Flanagan, Talia
    Abrahamsson, Bertil
    Budhdeo, Shanoo
    Franek, Frans
    Moir, Andrea
    Hanisch, Gunilla
    Pathak, Shriram M.
    Turner, David
    Jamei, Masoud
    Brown, Jonathan
    Good, David
    Vaidhyanathan, Shruthi
    Jackson, Claire
    Nicolas, Olivier
    Beilles, Stephane
    Nguefack, Jean-Flaubert
    Louit, Guillaume
    Henrion, Louis
    Ollier, Celine
    Boulu, Laurent
    Xu, Christine
    Heimbach, Tycho
    Xiojun, Ren
    Lin, Wen
    Nguyen-Trung, Anh-Thu
    Zhang, Jin
    He, Handan
    Wu, Fan
    Bolger, Michael B.
    Mullin, James M.
    van Osdol, Bill
    Szeto, Ke
    Korjamo, Timo
    Pappinen, Sari
    Tuunainen, Johanna
    Zhu, Wei
    Xia, Binfeng
    Daublain, Pierre
    Wong, Suet
    Manthena, Varma
    Modi, Sweta
    Schäfer, Kerstin Julia
    Schmid, Katrin
    Lloyd, Richard
    Patel, Aarti
    Tistaert, Christophe
    Bevernage, Jan
    Nguyen, Mai Anh
    Lindley, David
    Carr, Robert
    Rostami-Hodjegan, Amin
    IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies2020In: European journal of pharmaceutics and biopharmaceutics, ISSN 0939-6411, E-ISSN 1873-3441, Vol. 156, p. 50-63Article in journal (Refereed)
    Abstract [en]

    Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlusTM (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project.

    Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters.

    On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.

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  • 3.
    Boodaghian Asl, Arsineh
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A hybrid modeling approach to simulate complex systems and classify behaviors2024In: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, ISSN 2192-6662, Vol. 13, no 1, article id 9Article in journal (Refereed)
    Abstract [en]

    Many important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to measure the vertices' behaviors. The network is enhanced by adding edge strengths in the form of correlations between vertices (established through literature). Such an approach allows us to exploit the network structure to qualify and quantify the vertices of the network, to overlay different processes over the network topology, to add and remove new vertices, and therefore interact dynamically. This in turn allows for the qualification of vertices' importance and network resilience. System dynamics simulation allows for policy analysis, where different scenarios are analyzed by stimulating a set of vertices and the effect over the network is observed. This approach allows for an abstract, flexible, yet comprehensive way of analyzing a complex social network at any scale.

  • 4.
    Boodaghian Asl, Arsineh
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Simulation and Model Validation for Mental Health Factors Using a Multi-Methodology Hybrid Approach2021In: Proceedings - Winter Simulation Conference, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper (Refereed)
    Abstract [en]

    To promote policy analysis and decision-making in mental health and well-being, simulations are used to scrutinize causal maps and provide policymakers reasonable evidence. This paper proposes and illustrates a multi-methodology hybrid approach by building a hierarchy of models, moving from a systems dynamics model to a simulation based on PageRank to quantify and assess a complex mental health map. The motives are: (1) to aid scenario analysis and comparison for possible policy interventions, (2) to quantify and validate mental health factors, and (3) to gain new insights into the core and confounding factors that affect mental health. The results indicate that the approach identifies factors that cause significant and frequent variation on mental health. Furthermore, validation confirms PageRank accuracy and detects minor fluctuations and variation in model's output behavior.

  • 5.
    Boodaghian Asl, Arsineh
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Using pageRank and social network analysis to specify mental health factors2021In: Proceedings of the Design Society: 23rd International Conference on Engineering Design, ICED 2021,, Cambridge University Press (CUP) , 2021, Vol. 1, p. 3379-3388Conference paper (Refereed)
    Abstract [en]

    Various factors influence mental well-being, and span individual, social and familial levels. These factors are connected in many ways, forming a complex web of factors and providing pathways for developing programs to improve well-being and for further research. These factors can be studied individually using traditional methods and mapped together to be analyzed holistically from a complex system perspective. This study provides a novel approach using PageRank and social network analysis to understand such maps. The motives are: (1) to realize the most influential factors in such complex networks, (2) to understand factors that influence variations from different network aspects. A previously developed map for children's mental well-being was adopted to evaluate the approach. To achieve our motives, we have developed an approach using PageRank and Social Network Analysis. The results indicate that regardless of the network scale, two key factors called "Quantity and Quality of Relationships" and "Advocacy" can influence children's mental well-being significantly. Moreover, the divergence analysis reveals that one factor, "Recognition/Value Placed on well-being at School" causes a wide range of diffusion throughout the system.

  • 6.
    Danell Lindström, Emma
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Towards a systems model to inform policy and interventions for medication adherence in chronic disease2021In: International Journal of Clinical Pharmacy, ISSN 2210-7703, E-ISSN 2210-7711, Vol. 43, no 1, p. 293-293Article in journal (Other academic)
  • 7.
    Darwich, Adam S.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Bostroem, Anne-Marie
    Karolinska Inst, Div Nursing, Dept Neurobiol Care Sci & Soc, Huddinge, Sweden.;Karolinska Univ Hosp, Theme Inflammat & Aging, Huddinge, Sweden.;Stockholms Sjukhem, Res & Dev Unit, Stockholm, Sweden..
    Guidetti, Susanne
    Karolinska Inst, Div Occupat Hlth, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden.;Karolinska Univ Hosp, Med Unit Occupat Therapy & Physiotherapy, Theme Womens Hlth & Allied Profess, Stockholm, Sweden..
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study2023In: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, article id e42283Article in journal (Refereed)
    Abstract [en]

    Background: Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use.Objective: The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems.Methods: The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods.Results: The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements "workload" and "distress" were important determinants of home care staff health, provision, and quality of care.Conclusions: The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.

  • 8.
    Darwich, Adam S.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Hatley, Oliver J.
    Simcyp Division, Certara UK Ltd, Sheffield, United Kingdom.
    Olivares-Morales, Andrés
    Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
    Salem, Farzaneh
    Simcyp Division, Certara UK Ltd, Sheffield, United Kingdom.
    Margolskee, Alison
    Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, United Kingdom.
    Rostami-Hodjegan, Amin
    Simcyp Division, Certara UK Ltd, Sheffield, United Kingdom;Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, United Kingdom.
    The Interplay Between Drug Release and Intestinal Gut-Wall Metabolism2022In: Oral Drug Delivery for Modified Release Formulations / [ed] Edmund S. Kostewicz, Maria Vertzoni, Heather A. E. Benson, Michael S. Roberts, John Wiley & Sons, 2022, 1, p. 65-86Chapter in book (Other academic)
  • 9.
    Darwich, Adam S.
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Polasek, Thomas M.
    Aronson, Jeffrey K.
    Ogungbenro, Kayode
    Wright, Daniel F.B.
    Achour, Brahim
    Reny, Jean-Luc
    Daali, Youssef
    Eiermann, Birgit
    Cook, Jack
    Lesko, Lawrence
    McLachlan, Andrew J.
    Rostami-Hodjegan, Amin
    Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy2021In: Annual Review of Pharmacology and Toxicology, ISSN 0362-1642, E-ISSN 1545-4304, Vol. 61, no 36, p. 1-21Article in journal (Refereed)
    Abstract [en]

    Model-informed precision dosing (MIPD) has become synonymous with modern approaches forindividualizing drug therapy, in which the characteristics of each patient are considered as opposedto applying a one-size-fits-all alternative. This review provides a brief account of the currentknowledge, practices, and opinions on MIPD while defining an achievable vision for MIPDin clinical care based on available evidence.We begin with a historical perspective on variabilityin dose requirements and then discuss technical aspects of MIPD, including the need for clinicaldecision support tools, practical validation, and implementation of MIPD in health care.Wealso discuss novel ways to characterize patient variability beyond the common perceptions of geneticcontrol. Finally, we address current debates on MIPD from the perspectives of the new drugdevelopment, health-care economics, and drug regulations.

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  • 10. El-Katheeb, Eman
    et al.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Achour, Brahim
    Athwal, Varinder
    Rostami-Hodjegan, Amin
    Time to revisit Child-Pugh score as the basis for predicting drug clearance in hepatic impairment2021In: Alimentary Pharmacology and Therapeutics, ISSN 0269-2813, E-ISSN 1365-2036, Vol. 54, no 4, p. 388-401Article in journal (Refereed)
    Abstract [en]

    BackgroundPrescription information for many drugs entering the market lacks dosage guidance for hepatic impairment. Dedicated studies for assessing the fate of drugs in hepatic impairment commonly stratify patients using Child-Pugh score. Child-Pugh is a prognostic clinical score with limitations in reflecting the liver's metabolic capacity.

    AimsTo demonstrate the need for better drug dosing approaches in hepatic impairment, summarise the current status, identify knowledge gaps related to drug kinetic parameters in hepatic impairment, propose solutions for predicting the liver disease impact on drug exposure and discuss barriers to dosing guidance in those patients.

    MethodsRelevant reports on dosage adjustment in hepatic impairment were analysed concerning the prediction of the impairment impact on drug kinetics using physiologically-based pharmacokinetic (PBPK) modelling.

    ResultsPBPK models are suggested as a potential framework to understand drug clearance changes in hepatic impairment. Quantifying changes in abundance and activity of drug-metabolising enzymes and transporters, understanding the impact of shunting, and accounting for interindividual variations in drug absorption could help in extending the success of these models in hepatically-impaired populations. These variables might not correlate with Child-Pugh score as a whole. Therefore, new metabolic activity markers, imaging techniques and other scoring systems are proposed to either support or substitute Child-Pugh score.

    ConclusionsMany physiological changes in hepatic impairment determining the fate of drugs do not necessarily correlate with Child-Pugh score. Quantifying these changes in individual patients is essential in future hepatic impairment studies. Further studies assessing Child-Pugh alternatives are recommended to allow better prediction of drug exposure.

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  • 11. El-Khateeb, Eman
    et al.
    Chinnadurai, Rajkumar
    Al Qassabi, Jokha
    Scotcher, Daniel
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Kalra, Philip A.
    Rostami-Hodjegan, Amin
    Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease2023In: Therapeutic Drug Monitoring, ISSN 0163-4356, E-ISSN 1536-3694, Vol. 45, no 6, p. 743-753Article in journal (Refereed)
    Abstract [en]

    Background: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance (CLint) of tacrolimus as a case example.

    Methods: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate (eGFR) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method.

    Results: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m2. A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients.

    Conclusions: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

  • 12.
    Marzano, Luca
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Dan, A.
    Karolinska Inst, Stockholm, Sweden..
    Tendler, S.
    Karolinska Inst, Stockholm, Sweden..
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    De Petris, L.
    Karolinska Inst, Stockholm, Sweden..
    Lewensohn, R.
    Karolinska Inst, Stockholm, Sweden..
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients2023In: Journal of Thoracic Oncology, ISSN 1556-0864, E-ISSN 1556-1380, Vol. 18, no 11, p. S697-S697Article in journal (Other academic)
  • 13.
    Marzano, Luca
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Sven, Lethvall
    Uppsala University Hospital, Uppsala, Sweden.
    Falk, Nina
    Uppsala University Hospital, Uppsala, Sweden.
    Bodeby, Patrik
    Uppsala University Hospital, Uppsala, Sweden.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Diagnosing an overcrowded emergency department from its Electronic Health Records2024In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 9955Article in journal (Refereed)
    Abstract [en]

    Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

  • 14.
    Marzano, Luca
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Tendler, Salomon
    Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
    Dan, Asaf
    Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
    Lewensohn, Rolf
    Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
    De Petris, Luigi
    Department of Oncology‐Pathology Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital Stockholm Sweden.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study2022In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 15, no 10, p. 2437-2447Article in journal (Refereed)
    Abstract [en]

    In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

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  • 15.
    Marzano, Luca
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Dan, Asaf
    Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
    Tendler, Salomon
    Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
    De Petris, Luigi
    Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
    Lewensohn, Rolf
    Department of Oncology-Pathology, Karolinska Institutet and the Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden.
    Raghothama, Jayanth
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis2023In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, p. 18-22Article in journal (Refereed)
    Abstract [en]

    Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

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  • 16. McAllister, Mark
    et al.
    Flanagan, Talia
    Cole, Susan
    Abend, Andreas
    Kotzagiorgis, Evangelos
    Limberg, Jobst
    Mead, Heather
    Mangas-Sanjuan, Victor
    Dickinson, Paul A.
    Moir, Andrea
    Pepin, Xavier
    Zhou, Diansong
    Tistaert, Christophe
    Dokoumetzidis, Aristides
    Anand, Om
    Le Merdy, Maxime
    Turner, David B.
    Griffin, Brendan T.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Dressman, Jennifer
    Mackie, Claire
    Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series2022In: Pharmaceutics, ISSN 1999-4923, E-ISSN 1999-4923, Vol. 14, no 5, p. 1010-1010Article in journal (Refereed)
    Abstract [en]

    A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

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  • 17. Melillo, Nicola
    et al.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A latent variable approach to account for correlated inputs in global sensitivity analysis2021In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744Article in journal (Refereed)
    Abstract [en]

    In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

  • 18. Orfanidis, C.
    et al.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Cheong, R.
    Fafoutis, X.
    Monitoring neurological disorders with AI-enabled wearable systems2022In: DigiBiom 2022: Proceedings of the 2022 Emerging Devices for Digital Biomarkers, Association for Computing Machinery (ACM) , 2022, p. 24-28Conference paper (Refereed)
    Abstract [en]

    The age distribution has changed in Europe over the last decade. The group of 45 year-olds and above has increased and the median age in the EU is estimated to increase by 4.5 years during the next 3 decades reaching a median age of approximately 48.2 years according to Eurostat. A similar trend is noticeable in the United States, where the median age increased by 3.3 years from 2000 to 2020 according to Statista. Neurological diseases, such as Huntington disease, have a highly variable onset of 30 - 50 years but they are more prevalent to the older population. One of the first observable physical symptoms is chorea which includes random, uncontrollable and involuntary movements. Internet of Things and wearable systems can assist long-term monitoring of digital biomarkers such as plantar pressure and gait pattern which are associated with the aforementioned neurological disease. Emerging artificial intelligence models can be utilized to monitor the related digital biomarkers and check if these demonstrate a potential pattern denoting the presence or the development of a neurological disease. Enabling long-term monitoring by utilizing a unobtrusive wearable will increase the possibilities of early diagnosis, a longer life expectancy, and an improved quality of life for the patient.

  • 19.
    Scotcher, Daniel
    et al.
    Univ Manchester, Sch Hlth Sci, Ctr Appl Pharmacokinet Res, Manchester M13 9PL, Lancs, England..
    Melillo, Nicola
    Univ Manchester, Sch Hlth Sci, Ctr Appl Pharmacokinet Res, Manchester M13 9PL, Lancs, England..
    Tadimalla, Sirisha
    Univ Leeds, Div Med Phys, Leeds LS2 9JT, W Yorkshire, England.;Univ Sydney, Inst Med Phys, Sydney, NSW, Australia..
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics. Univ Manchester, Sch Hlth Sci, Ctr Appl Pharmacokinet Res, Manchester M13 9PL, Lancs, England.
    Ziemian, Sabina
    Bayer AG, MR & CT Contrast Media Res, D-13342 Berlin, Germany..
    Ogungbenro, Kayode
    Univ Manchester, Sch Hlth Sci, Ctr Appl Pharmacokinet Res, Manchester M13 9PL, Lancs, England..
    Schuez, Gunnar
    Bayer AG, MR & CT Contrast Media Res, D-13342 Berlin, Germany..
    Sourbron, Steven
    Univ Sheffield, Dept Infect Immun & Cardiovasc Dis, Sheffield S10 2TN, S Yorkshire, England..
    Galetin, Aleksandra
    Univ Manchester, Sch Hlth Sci, Ctr Appl Pharmacokinet Res, Manchester M13 9PL, Lancs, England..
    Physiologically Based Pharmacokinetic Modeling of TransporterMediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats2021In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 18, no 8, p. 2997-3009Article in journal (Refereed)
    Abstract [en]

    Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug-drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro-in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis-Menten constant (K-m,K-u) of gadoxetate was 106 mu M (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK-IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE-MRI profiles and elimination from the liver. Therefore, in vivo rat DCE-MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE-MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

  • 20. Takita, Hiroyuki
    et al.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics. The University of Manchester.
    Ahmad, Amais
    Rostami-Hodjegan, Amin
    Application of the nested enzyme-within-enterocyte (NEWE) turnover model for predicting the time course of pharmacodynamic effects2020In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 9, no 11, p. 617-627Article in journal (Refereed)
    Abstract [en]

    The gut wall consists of many biological elements including enterocytes. Rapid turnover, a prominent feature of the enterocytes, has generally been ignored in the development of enterocyte-targeting drugs, although it has a comparable rate to other kinetic rates. Here, we investigated the impact of enterocyte turnover on the pharmacodynamics of enterocyte-targeting drugs by applying a model accounting for turnover of enterocytes and target proteins. Simulations showed that the pharmacodynamics depend on enterocyte lifespan when drug-target affinity is strong and half-life of target protein is long. Inter-individual variability of enterocyte lifespan, which can be amplified by disease conditions, has a substantial impact on the variability of response. However, our comprehensive literature search showed that the enterocyte turnover causes a marginal impact on currently approved enterocyte-targeting drugs due to their relatively weak target affinities. This study proposes a model-informed drug development approach for selecting enterocyte-targeting drugs and their optimal dosage regimens.

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  • 21. Wilson, Clive G.
    et al.
    Aarons, Leon
    Augustijns, Patrick
    Brouwers, Joachim
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    De Waal, Tom
    Garbacz, Grzegorz
    Hansmann, Simone
    Hoc, Dagmara
    Ivanova, Anela
    Koziolek, Mirko
    Reppas, Christos
    Schick, Philipp
    Vertzoni, Maria
    García-Horsman, J. Arturo
    Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective.2022In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 172, article id 106100Article in journal (Refereed)
    Abstract [en]

    This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area.

    This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

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  • 22. Yau, Estelle
    et al.
    Olivares-Morales, Andrés
    Gertz, Michael
    Parrott, Neil
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Aarons, Leon
    Ogungbenro, Kayode
    Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution2020In: AAPS Journal, E-ISSN 1550-7416, Vol. 22, no 2, p. 1-13Article in journal (Refereed)
    Abstract [en]

    In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

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  • 23.
    Zhang, Chen
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Härenstam, Karin Pukk
    Karolinska Univ Hosp, Pediat Emergency Dept, Huddinge, Sweden..
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Darwich, Adam S.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Serious Gaming of Logistics Management in Pediatric Emergency Medicine2020In: INTERNATIONAL JOURNAL OF SERIOUS GAMES, E-ISSN 2384-8766, Vol. 7, no 1, p. 47-77Article in journal (Refereed)
    Abstract [en]

    Access blocks throughout the entire healthcare system and overcrowding issues are pervasive in many emergency departments where the coordination and strategic management of resources could be supported by serious games and simulations approaches. However, existing studies have not addressed the reciprocal relation between patient inflow and working systems in serious games design in order to reflect the logistical features of an emergency department and to facilitate the players improve the work performance of the system. To address the issue, this paper presents a serious game based on a multi-method simulation approach of complex healthcare processes as well as the game mechanics selected to promote understanding the logistical features of an ED, which points to the next level of conducting simulations or gaming aimed for training decision making skills in operative environments. Results of the experiment confirmed that the serious game encouraged participants to proactively manage the human resources of the emergency department. Certain managerial recommendations can be made: a patient flow multiplier of 120% could lead to a significant erosion of the system's defensive ability; however, proactive anticipation from management is the key for making an emergency organization more resilient.

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