MODELS FOR BIOLOGICAL DATA RELEVANT TO HEALTH CARE
Davide Verotta, Associate Professor
University Of California San Francisco 3333 California St., Ste 315 San Francisco, Ca 941430962
Grant 5R29GM051197-05 from National Institute Of General Medical Sciences IRG: ZRG7
Abstract: This grant proposal is focused on developing new mathematical and statistical models to describe biological systems. Models to represent, help to understand, predict future behavior, and control biological systems are becoming more and more important and of widespread use in different fields related to biology and health care. Complex mathematical models are needed to model the complicated interactions between the physiological functions of biological systems, and to model the effect of interventions (e.g. therapy) on these functions. The specific aims of this grant focus on three areas of research. 1. Develop and investigate statistical models for biological population data. Biological data are always collected from some population of different individuals, and are often highly variable. This is mostly due to variability of physiological functions between individuals, and to measurement error. Statistical models are needed to deal with the complex structure of population data. I will (I) introduce a general methodology based on the use of sophisticated heteroscedastic statistical models, which does not explicitly formulate a model for interindividual variability but promises to be fast, efficient and unbiased; and (ii) investigate the performance of existing population models using realistic simulations including model misspecification. 2. Develop semi-mechanistic compartmental models. I focus on three main problems (i) the development and investigation a new general class of compartmental pharmacokinetics" pharmacodynamic (PK/PD) models, (ii) the development of semi-mechanistic black-box compartmental models to deal with non-linear PK systems, (iii) the development of the technology to apply well established semi-mechanistic linear black-box models to the purpose of PK control. 3. Develop new multivariate dynamic models. The main problem addressed is how to represent a system where multiple inputs (drugs) and multiple interrelated responses are measured. I propose different classes of models to do so based on spline networks and eventually neural networks. The proposed models can incorporate a compartmental sub-structure to easily deal with kinetics. Continuous and discrete time versions of the models are considered. The statistical and mathematical models introduced in the grant have widespread application to a variety of biological fields. However specific areas, directly linked to health care issues, are selected for active research and application of the proposed models. These areas correspond to experimental situations where the models proposed in the grant are particularly needed (nonlinear and multivariate dynamic), and represent continuations of already established collaborations with leading scientists. They include computer control of ultra-short acting anaesthetic drugs administration, pharmacokinetics/pharmacodynamic of short-acting anesthetics, pharmacodynamic of nicotine and nicotine tolerance development, adenosine kinetics and metabolism and their relationship to adenosine pharmacodynamic effects, modeling of cardiovascular drugs effects on pharmacy dynamic responses (heart rate, blood pressure, and breathing variability) sampled at high rates.
Keywords: adenosine, anesthetic, cardiovascular agent, computer simulation, model design /development, nicotine, pharmacokinetics, artificial intelligence, blood pressure, drug tolerance, heart rate, neurotransmitter metabolism, pulmonary respiration
Project start date: 1995-09-01
Project end date: 2001-08-31
5R29GM051197-05 (1999): $114592
Sponsored Links Excellgen http://Excellgen.com
MODELS FOR BIOLOGICAL DATA RELEVANT TO HEALTH CARE
Davide Verotta, Associate Professor
University Of California San Francisco 3333 California St., Ste 315 San Francisco, Ca 941430962
Grant 5R29GM051197-04 from National Institute Of General Medical Sciences IRG: ZRG7
Abstract: This grant proposal is focused on developing new mathematical and statistical models to describe biological systems. Models to represent, help to understand, predict future behavior, and control biological systems are becoming more and more important and of widespread use in different fields related to biology and health care. Complex mathematical models are needed to model the complicated interactions between the physiological functions of biological systems, and to model the effect of interventions (e.g. therapy) on these functions. The specific aims of this grant focus on three areas of research. 1. Develop and investigate statistical models for biological population data. Biological data are always collected from some population of different individuals, and are often highly variable. This is mostly due to variability of physiological functions between individuals, and to measurement error. Statistical models are needed to deal with the complex structure of population data. I will (I) introduce a general methodology based on the use of sophisticated heteroscedastic statistical models, which does not explicitly formulate a model for interindividual variability but promises to be fast, efficient and unbiased; and (ii) investigate the performance of existing population models using realistic simulations including model misspecification. 2. Develop semi-mechanistic compartmental models. I focus on three main problems (i) the development and investigation a new general class of compartmental pharmacokinetics" pharmacodynamic (PK/PD) models, (ii) the development of semi-mechanistic black-box compartmental models to deal with non-linear PK systems, (iii) the development of the technology to apply well established semi-mechanistic linear black-box models to the purpose of PK control. 3. Develop new multivariate dynamic models. The main problem addressed is how to represent a system where multiple inputs (drugs) and multiple interrelated responses are measured. I propose different classes of models to do so based on spline networks and eventually neural networks. The proposed models can incorporate a compartmental sub-structure to easily deal with kinetics. Continuous and discrete time versions of the models are considered. The statistical and mathematical models introduced in the grant have widespread application to a variety of biological fields. However specific areas, directly linked to health care issues, are selected for active research and application of the proposed models. These areas correspond to experimental situations where the models proposed in the grant are particularly needed (nonlinear and multivariate dynamic), and represent continuations of already established collaborations with leading scientists. They include computer control of ultra-short acting anaesthetic drugs administration, pharmacokinetics/pharmacodynamic of short-acting anesthetics, pharmacodynamic of nicotine and nicotine tolerance development, adenosine kinetics and metabolism and their relationship to adenosine pharmacodynamic effects, modeling of cardiovascular drugs effects on pharmacy dynamic responses (heart rate, blood pressure, and breathing variability) sampled at high rates.
Keywords: adenosine, anesthetic, cardiovascular agent, computer simulation, model design /development, nicotine, pharmacokinetics, artificial intelligence, blood pressure, drug tolerance, heart rate, neurotransmitter metabolism, pulmonary respiration
Project start date: 1995-09-01
Project end date: 2000-08-31
5R29GM051197-04 (1998): $104964
5R29GM051197-03 (1997): $98970
5R29GM051197-02 (1996): $93340
Grants awarded to Davide Verotta
Modeling Complex Pharmacokinetics And Pharmacodynamics
Davide Verotta, Associate Professor
University Of California San Francisco 3333 California St., Ste 315 San Francisco, Ca 941430962
Grant 5R01AI050587-05 from National Institute Of Allergy And Infectious Diseases IRG: ZRG1
Abstract: Today s knowledge of the parts of a pharmacokinetics and pharmacodynamic (PK/PD) system is increasingly detailed, and correspondingly complex models need to be devised for its description. The investigation of optimal experimental designs and the development of models which allow the incorporation of prior scientific knowledge and the integration of diverse pharmacological effects into a coherent picture of drug action is the main purpose of this grant proposal. Aim 1 investigates experimental design problems associated with PK/PD models. We will develop new methods for sample size calculations in population PK/PD studies. We will also investigate optimal sampling and optimal dosage regimen designs for PK/PD models when complete prior knowledge on the parts of a model is not available. We will develop algorithms to produce optimal designs for standard widely used PK/PD models, and investigate the performance of optimized designs in respect to empirical, frequently used ones. Aim 2 investigates specific applications of Bayesian analysis to complex physiologically based PK/PD models, concentrating on their performance in respect to semi empirical models, and their sensitivity to assumptions. We plan to develop and investigate the performance of novel Bayesian models for (i) HIV-1 modeling in the presence of multi drug (HAART) therapy (incorporating virus phenotype, drug compliance and PK information), (ii) drug absorption using multi-site models and in-vivo/in vitro correlation, and (iii) in vitro/in vivo correlation of liposome distribution in anti-cancer therapy. Computer implementations of the models successfully developed will be provided. Aim 3 will provide a Bayesian framework for model-independent representations that do not require the sophistication of physiological models but retain the capability to incorporate prior knowledge. We will first investigate estimation methods incorporating prior knowledge for deconvolution in linear systems and develop Bayesian Marcov Chain Monte Carlo (MCMC) methods to obtain the desired estimates (an unknown absorption or input rate function) and their posterior distribution. Second, we will extend these MCMC methodologies to non-linear systems, providing improved algorithms for the estimation of Volterra representations, and developing a general method ("high order deconvolution") to estimate the input to a non linear system given its Volterra representation. Finally, we will develop a computer program for Bayesian linear and non-linear system analysis, which allows investigators to use the (model independent representations and MCMC-based methods we will investigate.
Keywords: computer program /software, drug screening /evaluation, experimental design, mathematical model, method development, pharmacokinetics, AIDS therapy, antineoplastic, antiviral agent, combination chemotherapy, dosage, drug interaction, human immunodeficiency virus 1, neoplasm /cancer chemotherapy, clinical research, computer simulation
Project start date: 2002-09-30
Project end date: 2009-03-31
5R01AI050587-05 (2006): $295880
5R01AI050587-04 (2005): $303000
5R01AI050587-03 (2004): $265125
5R01AI050587-02 (2003): $264688
1R01AI050587-01A1 (2002): $131688
PHARMACOKINETIC / PHARMACODYNAMIC DATA ANALYSIS
Davide Verotta, Professor
University Of California San Francisco, 3333 California St., Ste 315, San Francisco, Ca 94143-0962
Grant 2R01GM026676-28 from National Institute Of General Medical Sciences
Abstract: This modified grant proposal develops methods to address the complexity of modern Pharmacokinetics/Pharmacodynamics (PKPD) population studies focusing on computational PKPD and modeling of complex PKPD systems. AIM 1, Statistical Estimation Methods for Population PK/PD Data, develops improved estimation methods for population PKPD models, and general methods to incorporate patients compliance behavior and uncertain dosing history into PKPD models. AIM 2, Modeling System- PKPD, proposes two specific projects modeling system level PKPD across multiple organs, in particular for Cocaine/Nicotine PKPD and addiction dynamics modeling, and drug absorption physiological modeling. AIM 3, Population Stochastic Differential Equations (SDE), investigates novel approaches to population model building that are based on the use of stochastic differential equation, in particular investigating methodology to include covariates into population PKPD modelsm, and apply SDE to drug-absortion modeling
Keywords: 8-Azabicyclo(3.2.1)octane-2-carboxylic acid, 3-(benzoyloxy)-8-methyl-, methyl ester, (1R-(exo, exo))-; Absorption; Accounting; Address; Algorithms; Analysis, Data; Applications Grants; Arts; Behavior; Biological Models; Cocaine; Complex; Compliance behavior; Data; Data Analyses; Dose; Drug Kinetics; Drug effect disorder; Drugs; Equation; Grant Proposals; Grants, Applications; History; In Vitro; Individual; Intestinal Absorption; Knowledge; Medication; Method LOINC Axis 6; Methodology; Methods; Model System; Modeling; Models, Biologic; Models, Structural; Nicotine; Noise; Organ; Patient Compliance; Patient Cooperation; Pharmaceutic Preparations; Pharmaceutical Preparations; Pharmacodynamics; Pharmacokinetics; Physiologic; Physiological; Population; Population Study; Position; Positioning Attribute; Process of absorption; Pyridine, 3-(1-methyl-2-pyrrolidinyl)-, (S)-; Recording of previous events; Research; Source; Structural Models; System; System, LOINC Axis 4; TimeLine; Treatment Compliance; Work; absorption; addiction; base; compliance cooperation; drug action; drug development; drug/agent; gastrointestinal absorption; improved; in vivo; innovate; innovation; innovative; new approaches; novel; novel approaches; novel strategies; novel strategy; patient adherence; pharmacodynamic model; therapy compliance; therapy cooperation; tool
Relevance: The relevance of the research we propose consists in providing state of the art methodology to aid in the development of drugs, increasing the understanding of their pharmacokinetcs and pharmacodynamics, and the factors that explain their action in populations and individuals
Project start date: 1979-09-01
Project end date: 2011-08-31
Budget start date: 15-SEP-2009
Budget end date: 31-AUG-2010
PFA/PA: PA-07-070
2R01GM026676-28 (2009): $115232
MODELING COMPLEX PHARMACOKINETICS AND PHARMACODYNAMICS
Davide Verotta, Professor
University Of California San Francisco, 3333 California St., Ste 315, San Francisco, Ca 94143-0962
Grant 5R01AI050587-08 from National Institute Of Allergy And Infectious Diseases
Abstract: This grant proposal develops methods to address the complexity of modern PKPD population studies. AIM 1 Design will investigate population experimental design. We will develop novel methodology to increase the reliability of methods for sample size calculation, develop a novel semi-parametric approach to PK and PKPD sample size calculations, generalize currently available methods for sample size calculation and optimal experimental design to arbitrary parametric models, and develop and investigate design for multi-response multi-input studies. AIM 2 Model Selection will investigate the selection of competing structural models as well as factors such as covariates. The algorithms we propose are based on Bayesian model selection and on reversible jump Markov Chain Monte Carlo. They apply to parametric models, as well as to novel generalized models that use flexible representations (splines) for the relationship between parameters and covariates. AIM 3 Modeling will develop complex parametric models for Post-Operative Pain, HIV-1 Plasma/CNS T-cells trafficking, and drug induced Haematotoxicity. We will also develop a novel class of models generalizing indirect and direct action PKPD models and capable of representing a large variety of PKPD experiments. We will develop novel general mixed effect semi-parametric models that can incorporate fixed or time-varying covariates and can be used for final analysis or to aid in the development of parametric models. AIM 4 Software implements the methodologies we propose as self-standing computer programs as well as interfaces with the widely used computer program NONMEM. of the research we propose consists in providing state of the art methodology to aid in the development of drugs, increasing the understanding of their PKPD and the factors that explain their action in populations and individuals. This grant proposal develops methods to address the complexity of modern pharmacokinetics and pharmacodynamics (PKPD) population studies. We propose novel methodology for optimal experimental design, novel approaches to model selection, models applying to specific scientific problems ( Post-Operative Pain, HIV-1 Plasma/CNS T-cells trafficking, drug induced Haematotoxicity), and provide novel generalized PKPD representations which can be applied to a large variety of PKPD data. We also develop publicly available software implementing our design and model selection methodologies
Keywords: AIDS Virus; Absorption; Address; African American; Afro American; Afroamerican; Algorithms; Applications Grants; Arts; Black Populations; Black or African American; Blood Circulation; Blood Plasma; Bloodstream; Boxing; Circulation; Complex; Computer Programs; Computer Programs and Programming; Computer software; Data; Development; Drug Exposure; Drug Kinetics; Drugs; Experimental Designs; Grant Proposals; Grants, Applications; HIV-1; HIV-I; HIV1; Human immunodeficiency virus 1; Immunodeficiency Virus Type 1, Human; Individual; Investigation; Knowledge; Markov Chains; Markov Process; Medication; Method LOINC Axis 6; Methodology; Methods; Modeling; Pain, Postoperative; Performance; Pharmaceutic Preparations; Pharmaceutical Preparations; Pharmacodynamics; Pharmacokinetics; Plasma; Population; Population Study; Post-operative Pain; Postoperative Pain; Process of absorption; Programs (PT); Programs [Publication Type]; Research; Reticuloendothelial System, Serum, Plasma; Sample Size; Serum, Plasma; Site; Software; Structural Models; System; System, LOINC Axis 4; T-Cells; T-Lymphocyte; Thymus-Dependent Lymphocytes; Time; Venous; absorption; ing; base; black American; computer program; computer program/software; computer programming; design; designing; drug development; drug/agent; experiment; experimental research; experimental study; flexibility; human T cell leukemia virus III; human T lymphotropic virus III; model design; new approaches; novel; novel approaches; novel strategies; novel strategy; pharmacodynamic model; pharmacokinetic model; programs; public health relevance; research study; response; task analysis; thymus derived lymphocyte; tool; trafficking
Project start date: 2002-09-30
Project end date: 2012-05-31
Budget start date: 1-JUN-2010
Budget end date: 31-MAY-2011
PFA/PA: PA-07-070
5R01AI050587-08 (2010): $267671
5R01AI050587-07 (2009): $270375
PHARMACOKINETIC/PHARMACODYNAMIC DATA ANALYSIS
Davide Verotta, Associate Professor
University Of California San Francisco 3333 California St., Ste 315 San Francisco, Ca 941430962
Grant 5R01GM026676-27 from National Institute Of General Medical Sciences IRG: NSS
Abstract: As stated in the original grant, "a major goal of research in Clinical Pharmacology is to characterize and understand the Therapeutic Response Surface, the quantitative relationships between patient factors, drug dosage, drug exposure, and drug effects .... " Data analyses according to predictive (mechanistic) hierarchical dynamic pharmacokinetic-pharmacodynamic (PKPD) models (so-called "population" PKPD models), "are increasingly seen as a means to gain knowledge about the response surface." GM26676 was funded to develop and implement methods for such analyses, and progress reports since our last renewal document our considerable success in doing so. Indeed, that success justifies this supplementary application we have been encouraged increasingly to apply the mechanistic modeling to every larger and more complex problems, and now confront serious limitations imposed by the computational burden associated with optimizing large-scale dynamic models to large-scale data sets, a limitation not foreseen in the original application. We propose to implement and test a new methodology for model optimization founded on approximating the solution to the PKPD system dynamics conditional on the population parameters, which is the main computational cost of evaluating and maximizing the likelihood function through which the model is produced. The approximation is accomplished by mixing the techniques of solution mapping and computer experiments, as successfully applied to similar problems in engineering. The computational burden of optimizing the likelihood function is thereby reduced by over an order of magnitude. The specific aims of this supplementary application continue the three of the original application, and are Aim 4 To develop, apply, and evaluate solution-mapping methods to the solution to large systems of linked differential equations, by 4.A. Defining (1) subject-level PKPD differential equation models, and (2) population level hierarchical models of increasing complexity to be used in testing solution-mapping approximations; 4.B. Developing/applying algorithms and prototype software for solution-mapping approximations to models (A.1) incorporating designs for efficient computer experiments to define solution-mapping approximations. 4.C. Assessing performance of methods (4.B) on models (4.A.1) in terms of ability to precisely estimate models (4.A.2); and Aim 5 Implementing successful methods of Aim (4) in prototype exportable software that naturally interfaces with NONMEM.
Keywords: data collection methodology /evaluation, drug metabolism, drug screening /evaluation, pharmacokinetics, computer simulation, dosage, human population study, mathematical model, human data, statistics /biometry
Project start date: 1979-09-01
Project end date: 2008-08-31
5R01GM026676-27 (2005): $374527
Davide Verotta
University Of California San Francisco
Project start date: 1979-09-01
Project end date: 2011-08-31
Sponsored Links Excellgen http://Excellgen.com