The Experts below are selected from a list of 117078 Experts worldwide ranked by ideXlab platform
Fabrizio Bezzo - One of the best experts on this subject based on the ideXlab platform.
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On the Identifiability of Physiological Models: Optimal Design of Clinical Tests
Computer Aided Chemical Engineering, 2018Co-Authors: Fabrizio Bezzo, Federico GalvaninAbstract:Abstract Physiological Models are mathematical Models characterized by a Physiologically consistent mathematical structure (defined by the set of equations being used) and a set of model parameters to be estimated in the most precise and accurate way. However, systems in physiology and medicine are typically characterized by poor observability (i.e., possibility for the clinician to observe practically and quantify the relevant phenomena occurring in the body through clinical tests and investigations), high number of interacting and unmeasured variables (as an effect of the complexity of interactions), and poor controllability (i.e., limited capacity to drive the state of the system by acting on decision variables). All these factors may severely hinder the practical identifiability of these Models, i.e., the possibility to estimate the set of parameters in a statistically satisfactory way from clinical data. Identifiability is a structural property of a model, but it is also determined by the amount of useful information that can be generated by clinical data. Hence, the importance of designing clinical protocols that allow estimating the model parameters in the quickest and more reliable way. In this chapter, we discuss how the identifiability of a Physiological model can be characterized and analyzed, and how identifiability tests and model-based design of experiments (MBDoE) techniques can be exploited to tackle the identifiability issues arising from clinical tests. A case study related to the identification of Physiological Models of von Willebrand disease from clinical data will be presented where techniques and methods for testing the identifiability of PK Models have been used.
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Optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes in the presence of model mismatch
Medical & Biological Engineering & Computing, 2011Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:How to design a clinical test aimed at identifying in the safest, most precise and quickest way the subject-specific parameters of a detailed model of glucose homeostasis in type 1 diabetes is the topic of this article. Recently, standard techniques of model-based design of experiments (MBDoE) for parameter identification have been proposed to design clinical tests for the identification of the model parameters for a single type 1 diabetic individual. However, standard MBDoE is affected by some limitations. In particular, the existence of a structural mismatch between the responses of the subject and that of the model to be identified, together with initial uncertainty in the model parameters may lead to design clinical tests that are sub-optimal (scarcely informative) or even unsafe (the actual response of the subject might be hypoglycaemic or strongly hyperglycaemic). The integrated use of two advanced MBDoE techniques (online model-based redesign of experiments and backoff-based MBDoE) is proposed in this article as a way to effectively tackle the above issue. Online model-based experiment redesign is utilised to exploit the information embedded in the experimental data as soon as the data become available, and to adjust the clinical test accordingly whilst the test is running. Backoff-based MBDoE explicitly accounts for model parameter uncertainty, and allows one to plan a test that is both optimally informative and safe by design. The effectiveness and features of the proposed approach are assessed and critically discussed via a simulated case study based on state-of-the-art detailed Models of glucose homeostasis. It is shown that the proposed approach based on advanced MBDoE techniques allows defining safe, informative and subject-tailored clinical tests for model identification, with limited experimental effort.
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optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes mellitus
Industrial & Engineering Chemistry Research, 2009Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose-insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic Models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas.
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optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes mellitus
Computer-aided chemical engineering, 2009Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:Abstract A model-based experiment design techniques is used to design improved test protocols for the identification of the parameters of a detailed Physiological Models of diabetes specific to single subjects. This paper considers the problem of parameter identification focusing on the impact of some decision variables such as sampling frequency and test duration on both the design effectiveness and the ability to meet safety critical constraints on the subject response. The proposed methodology permits to establish the minimal experimental budget required to achieve a satisfactory parameter estimation from the planned test without upsetting the subject excessively.
Melvin E. Andersen - One of the best experts on this subject based on the ideXlab platform.
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Physiological pharmacokinetics and cancer risk assessment.
Cancer Letters, 1993Co-Authors: Melvin E. Andersen, Daniel Krewski, James R. WitheyAbstract:There has been considerable progress in recent years in developing Physiological Models for the pharmacokinetics of toxic chemicals and in the application of these Models in cancer risk assessment. Physiological pharmacokinetic Models consist of a number of individual compartments, based on the anatomy and physiology of the mammalian organism of interest, and include specific parameters for metabolism, tissue binding, and tissue reactivity. Because of the correspondence between these compartments and specific tissues or groups of tissues, these Models are particularly useful for predicting the doses of biologically active forms of toxic chemicals at target tissues under a wide variety of exposure conditions and in different animal species, including humans. Due to their explicit characterization of the biological processes governing pharmacokinetic behaviour, these Models permit more accurate predictions of the dose of active metabolites reaching target tissues in exposed humans and hence of potential cancer risk. In addition, Physiological Models also permit a more direct evaluation of the impact of parameter uncertainty and inter-individual variability in cancer risk assessment. In this article, we review recent developments in physiologic pharmacokinetic modeling for selected chemicals and the application of these Models in carcinogenic risk assessment. We examine the use of these Models in integrating diverse information on pharmacokinetics and pharmacodynamics and discuss challenges in extending these pharmacokinetic Models to reflect more accurately the biological events involved in the induction of cancer by different chemicals.
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Physiological modelling of organic compounds.
The Annals of occupational hygiene, 1991Co-Authors: Melvin E. AndersenAbstract:In pharmacokinetic modelling the body is represented as a set of compartments. The characteristics of these compartments are defined either by fitting predetermined mathematical equations to the data ('data-based compartments') or by defining compartments based on the actual biological structure of the animal ('Physiologically based compartments'). Physiological Models of chemical disposition are developed using these Physiologically based compartments. These Models then consist of sets of organs or types of tissue compartments whose characteristics are based as far as possible on the anatomy and physiology of the test species. Individual organs or types of tissue are defined with respect to their blood flow, volume, kinetic constants for metabolism, storage capacity for the compound involved, protein binding and other relevant characteristics. Linking these compartments together in a proper anatomical arrangement yields the Physiological model for compound disposition. This paper provides an overview of the basics for constructing Physiological Models for organic compounds, focusing on the structure of individual compartments in these Models and the data required for model development. Some past applications of Physiological Models are reviewed and speculation offered on future developments in this field.
Kevin Peikert - One of the best experts on this subject based on the ideXlab platform.
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muscle spindles in the human bulbospongiosus and ischiocavernosus muscles
Muscle & Nerve, 2015Co-Authors: Kevin Peikert, Christian Albrecht MayAbstract:Introduction: Muscle spindles are crucial for neuronal regulation of striated muscles, but their presence and involvement in the superficial perineal muscles is not known. Methods: Bulbospongiosus and ischiocavernosus muscle specimens were obtained from 31 human cadavers. Serial sections were stained with hematoxylin and eosin, Sirius red, antibodies against Podocalyxin, myosin heavy chain isoforms (MyHC-slow tonic, S46; MyHC-2a/2x, A4.74), and neurofilament for the purpose of muscle spindle screening, counting, and characterization. Results: A low but consistent number of spindles were detected in both muscles. The muscles contained few intrafusal fibers, but otherwise showed normal spindle morphology. The extrafusal fibers of both muscles were small in diameter. Conclusions: The presence of muscle spindles in bulbospongiosus and ischiocavernosus muscles supports Physiological Models of pelvic floor regulation and may provide a basis for further clinical observations regarding sexual function and micturition. The small number of muscle spindles points to a minor level of proprioceptive regulation. Muscle Nerve 52: 55–62, 2015
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Muscle spindles in the human bulbospongiosus and ischiocavernosus muscles.
Muscle & Nerve, 2015Co-Authors: Kevin PeikertAbstract:INTRODUCTION: Muscle spindles are crucial for neuronal regulation of striated muscles, but their presence and involvement in the superficial perineal muscles is not known. METHODS: Bulbospongiosus and ischiocavernosus muscle specimens were obtained from 31 human cadavers. Serial sections were stained with hematoxylin and eosin, Sirius red, antibodies against Podocalyxin, myosin heavy chain isoforms (MyHC-slow tonic, S46; MyHC-2a/2x, A4.74), and neurofilament for the purpose of muscle spindle screening, counting, and characterization. RESULTS: A low but consistent number of spindles were detected in both muscles. The muscles contained few intrafusal fibers, but otherwise showed normal spindle morphology. The extrafusal fibers of both muscles were small in diameter. CONCLUSIONS: The presence of muscle spindles in bulbospongiosus and ischiocavernosus muscles supports Physiological Models of pelvic floor regulation and may provide a basis for further clinical observations regarding sexual function and micturition. The small number of muscle spindles points to a minor level of proprioceptive regulation.
S. Andreassen - One of the best experts on this subject based on the ideXlab platform.
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Using Physiological Models and decision theory for selecting appropriate ventilator settings
Journal of Clinical Monitoring and Computing, 2006Co-Authors: S. E. Rees, C. Allerød, D. Murley, Y. Zhao, B. W. Smith, S. Kjærgaard, P. Thorgaard, S. AndreassenAbstract:Objective To present a decision support system for optimising mechanical ventilation in patients residing in the intensive care unit. Methods Mathematical Models of oxygen transport, carbon dioxide transport and lung mechanics are combined with penalty functions describing clinical preference toward the goals and side-effects of mechanical ventilation in a decision theoretic approach. Penalties are quantified for risk of lung barotrauma, acidosis or alkalosis, oxygen toxicity or absorption atelectasis, and hypoxaemia. Results The system is presented with an example of its use in a post-surgical patient. The mathematical Models describe the patient’s data, and the system suggests an optimal ventilator strategy in line with clinical practice. Conclusions The system illustrates how mathematical Models combined with decision theory can aid in the difficult compromises necessary when deciding on ventilator settings.
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using Physiological Models and decision theory for selecting appropriate ventilator settings
Journal of Clinical Monitoring and Computing, 2006Co-Authors: S. E. Rees, C. Allerød, D. Murley, Y. Zhao, B. W. Smith, S. Kjærgaard, P. Thorgaard, S. AndreassenAbstract:Objective To present a decision support system for optimising mechanical ventilation in patients residing in the intensive care unit.
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Selecting ventilator settings using INVENT: a system including Physiological Models and penalty functions
1999Co-Authors: S. E. Rees, S. Andreassen, Morten Freundlich, C. J. Morgan, Ewart R. Carson, P. ThorgaardAbstract:This paper describes INVENT, a system for selecting ventilator setting based on Physiological Models and penalty functions. The Models and penalty functions included in INVENT are first described followed by an example of the use of the system in selecting ventilator settings for a patient residing in the intensive care unit. For this patient, the advice given by the system is shown to be reasonable and consistent with clinical opinion.
Federico Galvanin - One of the best experts on this subject based on the ideXlab platform.
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On the Identifiability of Physiological Models: Optimal Design of Clinical Tests
Computer Aided Chemical Engineering, 2018Co-Authors: Fabrizio Bezzo, Federico GalvaninAbstract:Abstract Physiological Models are mathematical Models characterized by a Physiologically consistent mathematical structure (defined by the set of equations being used) and a set of model parameters to be estimated in the most precise and accurate way. However, systems in physiology and medicine are typically characterized by poor observability (i.e., possibility for the clinician to observe practically and quantify the relevant phenomena occurring in the body through clinical tests and investigations), high number of interacting and unmeasured variables (as an effect of the complexity of interactions), and poor controllability (i.e., limited capacity to drive the state of the system by acting on decision variables). All these factors may severely hinder the practical identifiability of these Models, i.e., the possibility to estimate the set of parameters in a statistically satisfactory way from clinical data. Identifiability is a structural property of a model, but it is also determined by the amount of useful information that can be generated by clinical data. Hence, the importance of designing clinical protocols that allow estimating the model parameters in the quickest and more reliable way. In this chapter, we discuss how the identifiability of a Physiological model can be characterized and analyzed, and how identifiability tests and model-based design of experiments (MBDoE) techniques can be exploited to tackle the identifiability issues arising from clinical tests. A case study related to the identification of Physiological Models of von Willebrand disease from clinical data will be presented where techniques and methods for testing the identifiability of PK Models have been used.
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Optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes in the presence of model mismatch
Medical & Biological Engineering & Computing, 2011Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:How to design a clinical test aimed at identifying in the safest, most precise and quickest way the subject-specific parameters of a detailed model of glucose homeostasis in type 1 diabetes is the topic of this article. Recently, standard techniques of model-based design of experiments (MBDoE) for parameter identification have been proposed to design clinical tests for the identification of the model parameters for a single type 1 diabetic individual. However, standard MBDoE is affected by some limitations. In particular, the existence of a structural mismatch between the responses of the subject and that of the model to be identified, together with initial uncertainty in the model parameters may lead to design clinical tests that are sub-optimal (scarcely informative) or even unsafe (the actual response of the subject might be hypoglycaemic or strongly hyperglycaemic). The integrated use of two advanced MBDoE techniques (online model-based redesign of experiments and backoff-based MBDoE) is proposed in this article as a way to effectively tackle the above issue. Online model-based experiment redesign is utilised to exploit the information embedded in the experimental data as soon as the data become available, and to adjust the clinical test accordingly whilst the test is running. Backoff-based MBDoE explicitly accounts for model parameter uncertainty, and allows one to plan a test that is both optimally informative and safe by design. The effectiveness and features of the proposed approach are assessed and critically discussed via a simulated case study based on state-of-the-art detailed Models of glucose homeostasis. It is shown that the proposed approach based on advanced MBDoE techniques allows defining safe, informative and subject-tailored clinical tests for model identification, with limited experimental effort.
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optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes mellitus
Industrial & Engineering Chemistry Research, 2009Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose-insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic Models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas.
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optimal design of clinical tests for the identification of Physiological Models of type 1 diabetes mellitus
Computer-aided chemical engineering, 2009Co-Authors: Federico Galvanin, Massimiliano Barolo, Sandro Macchietto, Fabrizio BezzoAbstract:Abstract A model-based experiment design techniques is used to design improved test protocols for the identification of the parameters of a detailed Physiological Models of diabetes specific to single subjects. This paper considers the problem of parameter identification focusing on the impact of some decision variables such as sampling frequency and test duration on both the design effectiveness and the ability to meet safety critical constraints on the subject response. The proposed methodology permits to establish the minimal experimental budget required to achieve a satisfactory parameter estimation from the planned test without upsetting the subject excessively.