Control-relevant modeling in drug delivery.

The development of control-relevant models for a variety of biomedical engineering drug delivery problems is reviewed in this paper. A summary of each control problem is followed by a review of relevant patient models from literature, an examination of the control approaches taken to solve the problem, and a discussion of the control-relevance of the models used in each case. The areas examined are regulating the depth of anesthesia, blood pressure control, optimal cancer chemotherapy, regulation of cardiac assist devices, and insulin delivery to diabetic patients.

[1]  E Salzsieder,et al.  Computer-aided systems in the management of type I diabetes: the application of a model-based strategy. , 1990, Computer methods and programs in biomedicine.

[2]  A M Albisser,et al.  A circulation and organs model for insulin dynamics. , 1979, The American journal of physiology.

[3]  James F. Antaki,et al.  Control system architecture for mechanical cardiac assist devices , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[4]  J. Jaremko,et al.  Advances Toward the Implantable Artificial Pancreas for Treatment of Diabetes , 1998, Diabetes Care.

[5]  H. Unbehauen,et al.  Application and comparison of different identification schemes under industrial conditions , 1988 .

[6]  G. F. Webb,et al.  A NONLINEAR CELL POPULATION MODEL OF PERIODIC CHEMOTHERAPY TREATMENT , 1992 .

[7]  C Cobelli,et al.  Estimation of beta-cell secretion and insulin hepatic extraction by the minimal modelling technique. , 1990, Computer methods and programs in biomedicine.

[8]  J S Schwaber,et al.  Analysis of heart rate-based control of arterial blood pressure. , 1996, The American journal of physiology.

[9]  James F. Martin,et al.  Multiple-Model Adaptive Control of Blood Pressure Using Sodium Nitroprusside , 1987, IEEE Transactions on Biomedical Engineering.

[10]  R J Roy,et al.  A circulatory model for combined nitroprusside-dopamine therapy in acute heart failure. , 1990, Medical progress through technology.

[11]  Guruprasad A. Giridharan,et al.  Controller Design for Ventricular Assist Devices , 1999 .

[12]  M. Morari,et al.  Understanding the Dynamic Behavior of Distillation Columns , 1988 .

[13]  S. Shah,et al.  A control-relevant identification strategy for GPC , 1992 .

[14]  Sirish L. Shah,et al.  Evaluation of a Long-Range Adaptive Predictive Controller for Computerized Drug Delivery Systems , 1992 .

[15]  H Ying,et al.  Expert-system-based fuzzy control of arterial pressure by drug infusion. , 1988, Medical progress through technology.

[16]  Claudio Cobelli,et al.  Evaluation of Portal/Peripheral Route and of Algorithms for Insulin Delivery in the Closed-Loop Control of Glucose in Diabetes - A Modeling Study , 1983, IEEE Transactions on Biomedical Engineering.

[17]  T. Yoneyama,et al.  A robust controller for insulin pumps based on H-infinity theory , 1993, IEEE Transactions on Biomedical Engineering.

[18]  B. Aufderheide,et al.  Automated regulation of hemodynamic variables , 2001, IEEE Engineering in Medicine and Biology Magazine.

[19]  Manfred Morari,et al.  Improving regulation of mean arterial blood pressure during anesthesia through estimates of surgery effects , 2000, IEEE Transactions on Biomedical Engineering.

[20]  E. Lightfoot,et al.  A model for multiple subcutaneous insulin injections developed from individual diabetic patient data. , 1995, The American journal of physiology.

[21]  P.M. Mäkilä,et al.  Worst-case control-relevant identification , 1995, Autom..

[22]  John A. Adam,et al.  A mathematical model of cycle-specific chemotherapy , 1995 .

[23]  P Wach,et al.  Simulation studies on neural predictive control of glucose using the subcutaneous route. , 1998, Computer methods and programs in biomedicine.

[24]  H. Kaufman,et al.  Multiple-model adaptive predictive control of mean arterial pressure and cardiac output , 1992, IEEE Transactions on Biomedical Engineering.

[25]  R. Larsson,et al.  A general model for time‐dissociated pharmacokinetic‐pharmacodynamic relationships exemplified by paclitaxel myelosuppression , 1998, Clinical pharmacology and therapeutics.

[26]  Robert S. Parker,et al.  Advanced model predictive control (MPC) for type I diabetic patient blood glucose control , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[27]  R C Boston,et al.  Alternative method for minimal model analysis of intravenous glucose tolerance data. , 1989, The American journal of physiology.

[28]  R. B. Martin,et al.  Optimal control drug scheduling of cancer chemotherapy , 1992, Autom..

[29]  Model based optimal control scheme for high-dose methotrexate ch motherapy followed by leucovorin rescue , 1990 .

[30]  Clemens Ah,et al.  Feedback control dynamics for glucose controlled insulin infusion system. , 1979 .

[31]  Kok Lay Teo,et al.  Optimal insulin infusion control via a mathematical blood glucoregulatory model with fuzzy parameters , 1991 .

[32]  Dietmar P. F. Möller,et al.  Biomedical Modeling and Simulation on a PC , 1993, Advances in Simulation.

[33]  G. Bonadonna,et al.  Nonlinear pharmacokinetics and metabolism of paclitaxel and its pharmacokinetic/pharmacodynamic relationships in humans. , 1995, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  Antti J. Koivo,et al.  Automatic Continuous-Time Blood Pressure Control in Dogs by Means of Hypotensive Drug Injection , 1980, IEEE Transactions on Biomedical Engineering.

[35]  João Borges de Sousa,et al.  An Optimal Control Algorithm For Multidrug Cancer Chemotherapy Design , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  T. Kobayashi,et al.  The Pharmacokinetics of Insulin After Continuous Subcutaneous Infusion or Bolus Subcutaneous Injection in Diabetic Patients , 1983, Diabetes.

[37]  G Albrecht,et al.  A model-based system for the individual prediction of metabolic responses to improve therapy in type I diabetes. , 1990, Hormone and metabolic research. Supplement series.

[38]  Michael E. Fisher,et al.  Mathematical Models Of The Control Of Drug Resistant Tumour Growth Using One Or Two Chemotherapeutic Agents , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[39]  L. Norton A Gompertzian model of human breast cancer growth. , 1988, Cancer research.

[40]  G Albrecht,et al.  Estimation of individually adapted control parameters for an artificial beta cell. , 1984, Biomedica biochimica acta.

[41]  M O Karlsson,et al.  Pharmacokinetic models for the saturable distribution of paclitaxel. , 1999, Drug metabolism and disposition: the biological fate of chemicals.

[42]  W. Zingg,et al.  An Artificial Endocrine Pancreas , 1974, Diabetes.

[43]  A. V. Sebald,et al.  An adaptive fuzzy controller for blood pressure regulation , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[44]  Zvia Agur,et al.  Reduction of cytotoxicity to normal tissues by new regimens of cell-cycle phase-specific drugs , 1988 .

[45]  D. Gough,et al.  Is blood glucose predictable from previous values? A solicitation for data. , 1999, Diabetes.

[46]  David Clarke,et al.  Advances in model-based predictive control , 1994 .

[47]  Francis J. Doyle,et al.  Robust H∞ glucose control in diabetes using a physiological model , 2000 .

[48]  L. C. Sheppard,et al.  A Model for Design of a Blood Pressure Controller for Hypertensive Patients , 1979 .

[49]  M. Egorin,et al.  Paclitaxel pharmacokinetics and pharmacodynamics. , 1995, Seminars in oncology.

[50]  K. Behbehani,et al.  A controller for regulation of mean arterial blood pressure using optimum nitroprusside infusion rate , 1991, IEEE Transactions on Biomedical Engineering.

[51]  Manfred Morari,et al.  Automation in anesthesia , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[52]  V. Bolie,et al.  Coefficients of normal blood glucose regulation. , 1961, Journal of applied physiology.

[53]  J. White,et al.  Application of network thermodynamics to the computer modeling of the pharmacology of anticancer agents: a network model for methotrexate action as a comprehensive example. , 1981, Pharmacology & therapeutics.

[54]  Zlatko Trajanoski,et al.  Neural Predictive Controller for Closed-Loop Control of Glucose Using the Subcutaneous Route: A Simulation Study , 1997 .

[55]  M. Quon,et al.  Non-Insulin-Mediated Glucose Disappearance in Subjects With IDDM: Discordance Between Experimental Results and Minimal Model Analysis , 1994, Diabetes.

[56]  Sørensen Jt,et al.  Use of a physiologic pharmacokinetic model of glucose homeostasis for assessment of performance requirements for improved insulin therapies. , 1982 .

[57]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[58]  George Stephanopoulos,et al.  Wavelet‐based modulation in control‐relevant process identification , 1998 .

[59]  R. Bonnecaze,et al.  Measurement and modeling of the transient difference between blood and subcutaneous glucose concentrations in the rat after injection of insulin. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[60]  J L Boldrini,et al.  Optimal chemotherapy: a case study with drug resistance, saturation effect, and toxicity. , 1994, IMA journal of mathematics applied in medicine and biology.

[61]  G. Pajunen,et al.  Model reference adaptive control with constraints for postoperative blood pressure management , 1990, IEEE Transactions on Biomedical Engineering.

[62]  W. G. He,et al.  Multiple Model Adaptive Control Procedure for Blood Pressure Control , 1986, IEEE Transactions on Biomedical Engineering.

[63]  R. Day,et al.  Treatment sequencing, asymmetry, and uncertainty: protocol strategies for combination chemotherapy. , 1986, Cancer research.

[64]  Daniel E. Rivera,et al.  Control-relevant prefiltering: a systematic design approach and case study , 1992 .

[65]  Z. Bajzer,et al.  Conceptual frameworks for mathematical modeling of tumor growth dynamics , 1996 .

[66]  D.S. Ward,et al.  Open loop control of multiple drug effects in anesthesia , 1995, IEEE Transactions on Biomedical Engineering.

[67]  R. Gleason,et al.  A Model of Glucose-insulin Homeostasis in Man that Incorporates the Heterogeneous Fast Pool Theory of Pancreatic Insulin Release , 1978, Diabetes.

[68]  Frank Allgöwer,et al.  Nonlinear structure identification of chemical processes , 1997 .

[69]  R. Bassanezi,et al.  Drug kinetics and drug resistance in optimal chemotherapy. , 1995, Mathematical biosciences.

[70]  R. Meier,et al.  Fuzzy logic control of human blood pressure during anesthesia , 1992, IEEE Control Systems.

[71]  John Thomas Sorensen,et al.  A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes , 1985 .

[72]  E Salzsieder,et al.  Mixed graphical models for simultaneous model identification and control applied to the glucose-insulin metabolism. , 1998, Computer methods and programs in biomedicine.

[73]  Vincent C. Rideout,et al.  Mathematical and Computer Modeling of Physiological Systems , 1991 .

[74]  D. Finegood,et al.  Reduced glucose effectiveness associated with reduced insulin release: an artifact of the minimal-model method. , 1996, The American journal of physiology.

[75]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[76]  James F. Antaki,et al.  Control of rotary heart assist devices , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[77]  G. W. Swan,et al.  Cancer chemotherapy: Optimal control using the Verhulst-Pearl equation , 1986, Bulletin of mathematical biology.

[78]  Cynthia L. Stokes,et al.  Biological systems modeling: Powerful discipline for biomedical e‐R&D , 2000 .

[79]  S. Saeger,et al.  An electrocatalytic glucose sensor for in-vivo application. , 1991, Biomedical instrumentation & technology.

[80]  Yibei Ling,et al.  Entropic analysis of biological growth models. , 1993, IEEE transactions on bio-medical engineering.

[81]  Claudio Cobelli,et al.  An integrated mathematical model of the dynamics of blood glucose and its hormonal control , 1982 .

[82]  B. Teicher,et al.  Influence of schedule on alkylating agent cytotoxicity in vitro and in vivo. , 1989, Cancer research.

[83]  Francis J. Doyle,et al.  Analysis and Neuronal Modeling of the Nonlinear Characteristics of a Local Cardiac Reflex in the Rat , 2001, Neural Computation.

[84]  Qingsheng Zheng,et al.  Control-relevant identification of Volterra series models , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[85]  T. Secomb,et al.  Theoretical models for drug delivery to solid tumors. , 1997, Critical reviews in biomedical engineering.

[86]  Daniel E. Rivera,et al.  Control Relevant Model Reduction of Volterra Series Models , 1998 .

[87]  D Rodbard,et al.  Computer Simulation of Plasma Insulin and Glucose Dynamics After Subcutaneous Insulin Injection , 1989, Diabetes Care.

[88]  D B Menzel,et al.  Planning and using PB-PK models: an integrated inhalation and distribution model for nickel. , 1988, Toxicology letters.

[89]  U. Fischer,et al.  Kinetic Modeling of the Glucoregulatory System to Improve Insulin Therapy , 1985, IEEE Transactions on Biomedical Engineering.

[90]  H. Skipper,et al.  EXPERIMENTAL EVALUATION OF POTENTIAL ANTICANCER AGENTS. XIII. ON THE CRITERIA AND KINETICS ASSOCIATED WITH "CURABILITY" OF EXPERIMENTAL LEUKEMIA. , 1964, Cancer chemotherapy reports.

[91]  S. Isaka,et al.  Control strategies for arterial blood pressure regulation , 1993, IEEE Transactions on Biomedical Engineering.

[92]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[93]  A S Glicksman,et al.  Growth in solid heterogeneous human colon adenocarcinomas: comparison of simple logistical models , 1987, Cell and tissue kinetics.

[94]  Claudio Cobelli,et al.  Models of subcutaneous insulin kinetics. A critical review , 2000, Comput. Methods Programs Biomed..

[95]  Yaman Arkun,et al.  Control of Nonlinear Systems Using Input Output Information , 1995 .

[96]  W. Schenk,et al.  Does Physiological Blood Glucose Control Require an Adaptive Control Strategy? , 1987, IEEE Transactions on Biomedical Engineering.

[97]  Kok Lay Teo,et al.  Optimal Control of Drug Administration in Cancer Chemotherapy , 1993 .

[98]  L C Gatewood,et al.  Model studies of blood-glucose regulation. , 1965, The Bulletin of mathematical biophysics.

[99]  E Salzsieder,et al.  Experimental validation of a glucose-insulin control model to stimulate patterns in glucose turnover. , 1990, Computer methods and programs in biomedicine.

[100]  R.S. Parker,et al.  A model-based algorithm for blood glucose control in Type I diabetic patients , 1999, IEEE Transactions on Biomedical Engineering.

[101]  Rein Luus,et al.  OPTIMAL DRUG SCHEDULING OF CANCER CHEMOTHERAPY , 1994 .

[102]  James F. Antaki,et al.  Control issues in rotary heart assist devices , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[103]  E. Ackerman,et al.  A Mathematical Model of the Glucose-tolerance test , 1964 .

[104]  N. Saijo,et al.  Indirect‐response model for the time course of leukopenia with anticancer drugs , 1998, Clinical pharmacology and therapeutics.

[105]  Riccardo Bellazzi,et al.  Qualitative models and fuzzy systems: an integrated approach for learning from data , 1998, Artif. Intell. Medicine.

[106]  Z Trajanoski,et al.  Pharmacokinetic Model for the Absorption of Subcutaneously Injected Soluble Insulin and Monomeric Insulin - Analogues - Pharmakokinetisches Modell für die Absorption von subkutan injiziertem löslichem Insulin und monomeren Insulinanaloga , 1993, Biomedizinische Technik. Biomedical engineering.

[107]  M. Shichiri,et al.  Closed-loop subcutaneous insulin infusion algorithm with a short-acting insulin analog for long-term clinical application of a wearable artificial endocrine pancreas. , 1997, Frontiers of medical and biological engineering : the international journal of the Japan Society of Medical Electronics and Biological Engineering.

[108]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[109]  M E Andersen,et al.  Biologically based pharmacodynamic models: tools for toxicological research and risk assessment. , 1991, Annual review of pharmacology and toxicology.

[110]  S. V. Gaikwad,et al.  Multivariable frequency-response curve fitting with application to control-relevant parameter estimation , 1997, Autom..