POPULATION PHARMACOKINETICS MODELING ON A SUPERCOMPUTER
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01RR011526-06 from National Center For Research Resources IRG: BLR
Abstract: Precise therapy with potentially toxic drugs demands optimal methods to model population with pharmacokinetic/dynamic (PK/PD) behavior in patients. In the past 2 years of our current 3 year project, we have implemented useful population modeling software on the San Diego Supercomputer Center s large parallel Cray T3E machine, creating an NCRR-USC-SDSC Research Resource for Population Modeling. which brings to this computationally intensive area the best and fastest hardware and software resources available. A job taking a week on a PC takes only a few minutes on this resource. In this competing renewal application, our aims now are 1) to implement a new non-parametric modeling algorithm for analysis of still larger models; 2) to implement methods for determining confidence limits for this non-parametric models, where none exist at present. This will, for the first time with non-parametric models, permit direction of statistically significant differences between populations of patients; 3) to develop methods to detect process noise (noise in the differential equations themselves) rather than the current incorrect method of dealing with environmental noise as if it were noise in the measurements, especially in non-linear models, and to detect changing parameter values over time (something totally new); 4) to implement still more optimal methods to sample subject or patient responses at optimal times, avoiding the current "Catch 2" that one is supposed to know the parameter values in advance in order to compute the optimal times to obtain samples for observing that system; and 5) to upgrade the user interface for the resource, making it more user- friendly and easy to use, and also to implement "maximum entropy" joint densities to convert old population models (whose original raw data is gone) to the discrete joint densities needed for optimal therapy with the new "multiple model" methods of dosage design. This resource has now been used to overcome the bottleneck in population PK/PD modeling on smaller machines, and to quantify the shared effects of combinatorial antiviral therapy for AIDS. It can do the same for anti-cancer, anti-bacterial, and other drug therapy. It will be even more friendly and useful with a new windows-like user interface for the PC user, easily and rapidly accessible over the internet and the world wide web.
Keywords: computer simulation, drug screening /evaluation, human population study, model design /development, pharmacokinetics, supercomputer, Internet, computer system design /evaluation, mathematical model, human data, statistics /biometry
Project start date: 1996-08-01
Project end date: 2003-07-31
5R01RR011526-06 (2001): $307490
Sponsored Links Excellgen http://Excellgen.com
POPULATION PHARMACOKINETIC MODELING ON A SUPERCOMPUTER
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01RR011526-03 from National Center For Research Resources IRG: BLR
Abstract: Taken from project ) In this resource-related research project, we propose to 1) Implement on a supercomputer our current nonparametric EM and iterative Bayesian software for population pharmacokinetic modeling, currently running on PC s; 2) Implement extensions of this software to include large nonlinear pharmacokinetic and pharmacodynamic (PK/PD) models of drug effect and toxicity; 3) Develop and implement new software to compute optimally informative schedules and protocols for monitoring serum drug levels and measuring drug effects in multicenter clinical drug trials; and 4) Develop and implement a friendly graphical user interface (GUI) and communication package for access to the supercomputer over the World Wide Web and the Internet, as a fast and efficient research resource. Population PK/PD modeling is the key to understanding how drugs behave in people. It provides the basic structure for understanding what happens in clinical drug trials, and for the intelligent design of clinical drug trials to evaluate treatment of a disease process. However, there are currently two significant bottlenecks in such modeling efforts 1) current software has been unfriendly for researchers to use, and 2) it takes a long time to analyze large data sets. As the programs are usually run on PC s, investigators have been significantly limited in the number of data sets from large clinical trials which they can analyze. A national research resource with a friendly GUI on the researcher s PC that can easily be connected to a supercomputer, available over the Web and the Internet, will be highly useful to many workers in the NIH, in academic centers, and in the pharmaceutical industry. With this resource they will be able easily and rapidly to analyze their data and estimate population parameter distributions and individual parameter values. Further, the software proposed to obtain the most informative and cost-effective strategies for measuring serum levels and drug effects, both therapeutic and toxic, will provide an easy and rapid method for optimal and most cost-effective design of protocols for multicenter drug trials and concentration controlled clinical trials. The population models and optimal monitoring schedules will also readily link to existing software for new "multiple model" design of drug regimens for optimally precise achievement of desired clinical goals, minimizing patient variability, both in "concentration controlled" and in "effect controlled" clinical drug trials.
Keywords: computer program /software, drug screening /evaluation, mathematical model, pharmacokinetics, supercomputer, Internet, computer graphics /printing, computer human interaction, computer processing of clinical data, computer simulation, computer system design /evaluation
Project start date: 1996-08-01
Project end date: 1999-07-31
5R01RR011526-03 (1998): $267992
POPULATION PHARMACOKINETICS MODELING ON A SUPERCOMPUTER
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01RR011526-05 from National Center For Research Resources IRG: BLR
Abstract: Precise therapy with potentially toxic drugs demands optimal methods to model population with pharmacokinetic/dynamic (PK/PD) behavior in patients. In the past 2 years of our current 3 year project, we have implemented useful population modeling software on the San Diego Supercomputer Center s large parallel Cray T3E machine, creating an NCRR-USC-SDSC Research Resource for Population Modeling. which brings to this computationally intensive area the best and fastest hardware and software resources available. A job taking a week on a PC takes only a few minutes on this resource. In this competing renewal application, our aims now are 1) to implement a new non-parametric modeling algorithm for analysis of still larger models; 2) to implement methods for determining confidence limits for this non-parametric models, where none exist at present. This will, for the first time with non-parametric models, permit direction of statistically significant differences between populations of patients; 3) to develop methods to detect process noise (noise in the differential equations themselves) rather than the current incorrect method of dealing with environmental noise as if it were noise in the measurements, especially in non-linear models, and to detect changing parameter values over time (something totally new); 4) to implement still more optimal methods to sample subject or patient responses at optimal times, avoiding the current "Catch 2" that one is supposed to know the parameter values in advance in order to compute the optimal times to obtain samples for observing that system; and 5) to upgrade the user interface for the resource, making it more user- friendly and easy to use, and also to implement "maximum entropy" joint densities to convert old population models (whose original raw data is gone) to the discrete joint densities needed for optimal therapy with the new "multiple model" methods of dosage design. This resource has now been used to overcome the bottleneck in population PK/PD modeling on smaller machines, and to quantify the shared effects of combinatorial antiviral therapy for AIDS. It can do the same for anti-cancer, anti-bacterial, and other drug therapy. It will be even more friendly and useful with a new windows-like user interface for the PC user, easily and rapidly accessible over the internet and the world wide web.
Keywords: computer simulation, drug screening /evaluation, human population study, model design /development, pharmacokinetics, supercomputer, Internet, computer system design /evaluation, mathematical model, computer processing of clinical data, human data, statistics /biometry
Project start date: 1996-08-01
Project end date: 2002-07-31
5R01RR011526-05 (2000): $300037
POPULATION PHARMACOKINETIC MODELING ON A SUPERCOMPUTER
Roger W Jelliffe, Profesor Of Medicine
Medicineuniversity Of Southern California
department Of Contracts And Grants
los Angeles, Ca 90033
Grant 5R01RR011526-02 from National Center For Research Resources IRG: BLR
Keywords: computer program /software, drug screening /evaluation, mathematical model, pharmacokinetics, supercomputer Internet, computer graphics /printing, computer human interaction, computer processing of clinical data, computer simulation, computer system design /evaluation
Project start date: 1996-08-01
Project end date: 1999-07-31
5R01RR011526-02 (1997): $254758
Grants awarded to Roger W Jelliffe
NEW DECISION SUPPORTS AND DATABASES FOR DRUG DOSAGE
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01LM005401-03 from National Library Of Medicine IRG: BLR
Abstract: We propose to develop and implement new and improved software for better studies of the kinetic behavior of drugs in the body with new, more informative population pharmacokinetic and dynamic models of their behavior. These new models of individual drugs will also have new noise terms reflecting uncertainties in the therapeutic environment surrounding each patient s pharmacokinetic/dynamic model such as errors in preparation and administration of doses, errors in labelling times at which blood samples were obtained for serum level monitoring, and errors in measuring the serum concentrations of the drug and/or observed effects. The new models will thus have parameters which will serve as new databases for the quality of care (precision of therapy) each patient has received. When put together as population models, they will become indices of the quality of each center has given. The new software will be able to analyze the behavior of the great majority of drugs used in therapy today and in the future, free from corruption by the clinical environment. They will provide improved databases summarizing drug behavior and quality of therapy in patients. We also propose to develop and implement new controllers (dosage regimen designers) for decision support for drug dosage with these new models. They will now optimize the drug regimen with respect to rational performance criteria. We also propose to develop new larger population models, both linear and nonlinear, of drug behavior and effect, which can describe the additive, synergistic, or antagonistic effects of combined drug therapy on the hematopoetic system (the WBC, Hb, or platelet count), for example, and to develop new controllers for them as well. These resources are not available at present, though there is much activity in describing drug interactions and the combined effects of drugs. The proposed new models will permit, for the first time, truly quantitative, not just qualitative, descriptions of drug interactions. the new controllers for such models will permit adaptive control and decision support for drug therapy in the presence of these interactions, and optimal coordinated use of drug combinations to maximize benefits and minimize side effects such as those of drugs for AIDS and for cancer upon the hematopoetic system. Clinical studies will evaluate this work.
Keywords: computer program /software, computer system design /evaluation, pharmacokinetics, chemotherapy, dosage, drug adverse effect, drug interaction, mathematical model
Project start date: 1992-01-01
Project end date: 1994-12-31
5R01LM005401-03 (1994): $508806
5R01LM005401-02 (1993): $310387
Population Pharmacokinetic Modeling And Optimal Control
Roger W Jelliffe, Professor
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01GM068968-04 from National Institute Of General Medical Sciences IRG: ZGM1
Abstract: This proposal is to develop new tools to optimize population drug modeling and individualized therapy. Aim 1 will develop new methods for consistent and efficient parametric population modeling using low -discrepancy Faure integration methods. Aim 2 will develop new methods for determining confidence intervals for nonparametric population analysis using profile likelihood techniques. Aim 3 develops a new approach to stochastic dosage optimization using the latest sampling-based methods in nonlinear filtering and a novel method for solving the Bellman stochastic dynamic programming equations. This work will have a broad impact on population modeling and patient care. Most researchers and clinicians now use parametric (P) methods which are not statistically consistent. Much effort is spent on clinical ananlyses using those flawed methods. The new consistent and efficient P method in Aim 1 will be a major advance that will put maximum likelihood P population modeling on a sound statistical footing. Nonparametric (NP) methods of population analysis are also consistent. They can in addition handle mixture distributions from genetically polymorphic populations, and are suitable for "multiple model" (MM) optimal dosage design. However, computationally intensive bootstrap methods are the only current way to obtain NP confidence intervals. Aim 2 will provide a new, more efficient, route to these intervals. Then both P and NP consistent methods will be available to the health care community. NP population modeling is a tool for optimal MM design of coordinated combination dosage regimens of toxic drugs for treatment of AIDS, cancer, infectious diseases, and transplant patients. This, plus feedback from serum, effect, and toxicity measurements can now be combined into a new and unique paradigm for planning and optimizing the entire process of learning about drug behavior in patients while treating them at the same time. This is Aim 3. Such a tool does not exist at present, to our kowledge. For any planned duration of therapy, one will be able to coordinate both the amounts and timing of the doses, and the number and timing of the measurements. The result will be an active therapeutic strategy which achieves desired target goals with optimal precision, rehearsing many future scenarios in advance (this is new). In all three Aims, the end product will be widely disseminated software with user interfaces for research and clinical use.
Keywords: mathematical model, method development, pharmacokinetics, statistics /biometry, chemotherapy, computer program /software, computer system design /evaluation, human population study, Internet
Project start date: 2003-06-15
Project end date: 2007-07-31
5R01GM068968-04 (2006): $219020
5R01GM068968-03 (2005): $223750
5R01GM068968-02 (2004): $223700
1R01GM068968-01 (2003): $238719
POPULATON PHARMACOKINETIC MODELING AND OPTIMAL CONTROL
Roger W Jelliffe
University Of Southern California, Department Of Contracts And Grants, Los Angeles, Ca 90033
Grant 5R01GM068968-08 from National Institute Of General Medical Sciences
Abstract: The original project was GM 068968, responding to Joint DMS/NIGMS Initiative to Support Research in Mathematical Biology, PA NSF 02-125. This competing renewal application is continues to propose new mathematical innovations in biomedical computational science and technology. Modeling the pharmacokinetic and pharmacodynamic (PK/PD) behavior of drugs has serious statistical flaws. The PK/PD community still uses mainly parametric methods of modeling based on approximate likelihoods, with no guarantee that studying more subjects will obtain parameter estimates closer to the true values (they often get worse). In contrast, our laboratory has developed methods, both parametric (P) and nonparametric (NP), which are statistically consistent. However, there is still no way to obtain rigorous confidence intervals on P or NP parameter estimates. This is a great weakness. Also, current dosing policies are based only on information available now, though we know we will monitor the patient and adjust dosage in the future. These known future actions are ignored. Our aims are (1) TO DEVELOP A NEW SEQUENTIAL BAYESIAN METHOD FOR MAKING PK/PD POPULATION MODELS. We propose an exciting new method to obtain rigorous confidence intervals for parameter estimates for both P and NP population PK/PD models. It is an outgrowth of our previous work in GM 068968. It should also provide rigorous confidence intervals on a clinician´s ability to hit a desired therapeutic target serum concentration. This will provide a firm mathematical foundation for all population modeling, and for our current work to optimize coordinated combination drug therapy for which we have recently been funded under grant EB 005803. It is also sequential, and thus permits new subjects to be added to a model rather than having to remake it from scratch. This will greatly aid community hospitals to add their own patients to the original model as desired. (2) TO CONTINUE WORK ON OUR ACTIVE CONTROL STRATEGY TO OPTIMIZE LEARNING ABOUT THE PATIENT WHILE TREATING HIM/HER AT THE SAME TIME. Current dosage regimens use only information available up to now. We know we will monitor the patient and adjust dosage in the future. This is ignored. The dosage regimen is not designed to aid in learning about the patient. We now propose to use the dosage regimen as an active partner in the learning process, by calculating how far (and safely) one can deviate a bit from the target goal to probe the patient´s system thoughtfully to learn more about it, and thus to maximize therapeutic precision over the projected duration of therapy. We propose to explore future clinical scenarios in advance, now. Our approach is to approximate the Stochastic Dynamic Programming (SDP) equations of Bellman using the IPS (Iteration in Policy Space) algorithm, and a Particle Filter to solve the underlying nonlinear estimation problem. This should make patient care still more intelligent and optimal
Keywords: Active Learning; Algorithms; Area; Articulation; Bayesian Method; Behavior; Biological Models; Biomedical Computing; Bite; Blood Serum; Clinical; Combination Drug Therapy; Communities; Community Hospitals; Computational Science; Computer Programs; Computer software; Confidence Intervals; Data; Dose; Drug Industry; Drug Kinetics; Drug Therapy; Drugs; Equation; Foundations; Funding; Future; Goals; Grant; Industry, Pharmaceutic; Investigators; Joints; Laboratories; Learning; Learning, Experiential; Mathematical Biology; Medical; Medication; Methods; Model System; Modeling; Models, Biologic; Monitor; Patient Care; Patient Care Delivery; Patient Monitoring; Patients; Pharmaceutic Preparations; Pharmaceutical Industry; Pharmaceutical Preparations; Pharmacodynamics; Pharmacokinetics; Pharmacotherapy; Policies; Polychemotherapy; Population; Process; Programs (PT); Programs [Publication Type]; Regimen; Research; Research Personnel; Research Support; Researchers; Safety; Sampling; Scientist; Serum; Software; Stimulus; System; System, LOINC Axis 4; Technology; Therapeutic; Time; Work; active control; base; biomedical computation; clinical care; combination pharmacotherapy; computer program/software; design; designing; dosage; drug/agent; expectation; innovate; innovation; innovative; novel; particle; pharmacodynamic model; pharmacokinetic model; programs; response; therapeutic target; therapy duration
Project start date: 2003-06-15
Project end date: 2011-07-31
Budget start date: 1-AUG-2010
Budget end date: 31-JUL-2011
5R01GM068968-08 (2010): $306603
5R01GM068968-07 (2009): $309700
5R01GM068968-06 (2008): $309700
2R01GM068968-05 (2007): $309700
Sponsored Links Excellgen http://Excellgen.com
NEW DECISION SUPPORTS AND DATABASES FOR DRUG DOSAGE
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5R01LM005401-09 from National Library Of Medicine IRG: BLR
Abstract: Taken from application ) Precise therapy with potentially toxic drugs demands precise dosage regimens to achieve and maintain selected therapeutic goals precisely, minimizing variability in patient response. We developed methods to model population drug behavior, determining the entire parameter joint probability density, capable of discovering subgroups of fast and slow metabolizers, for example, when not anticipated. This NPEM program provides useful databases of drug behavior. We coupled it with our Multiple Model (MM) dosage designer for drug dosage decision support. It specifically minimizes variability in patient response about the selected goal(s). We have now developed similar software for implementing on supercomputers large linear and nonlinear pharmacokinetic and pharmacodynamic (PK/PD) population models of multiple drugs and their effects. We will build on these successes by implementing the MM control method for these large models, and an actively adaptive MM controller for smaller linear models. The active controller rehearses future scenarios in advance. It uses the dose as well as serum levels to learn the patient s model to best achieve desired goal(s). It optimizes learning about the patient while treating him at the same time. We will also develop stochastic strategies for optimal experimental design and therapeutic drug monitoring (TDM). One then can optimize coordinated PK/PD therapy with combination drug regimens for cancer, AIDS, bacterial, and viral infections. We will develop software for simulation of clinical drug trials to optimize their design, including concentration-controlled and effect-controlled trials. The MM control and optimal experimental design features will further optimize clinical trial design, exposing fewer patients to risk and reducing drug development costs. We will implement discrete MM parameter distributions from literature data of means, standard deviations, and ranges, for use with the MM controllers, and enhance our clinical MM program with linked general models of effect, diffusion into endocardial vegetations, and bacterial (and viral) growth and kill. Lastly, we will use telemedicine techniques to make both the clinical MM PC programs and the larger clinical PK/PD supercomputer programs available for collaborative telecomputing and videoconferencing. Both caller and consultant will see the same computer output and also each other. This will enhance and disseminate consulting, training, and optimal drug therapy in areas remote from the PC or supercomputer.
Keywords: chemotherapy, computer assisted medical decision making, computer system design /evaluation, health care facility information system, pharmacokinetics, clinical trial, combination chemotherapy, dosage, experimental design, health care model, mathematical model, model design /development, parallel processing, statistics /biometry, telemedicine, computer program /software, human data, supercomputer
Project start date: 1992-01-01
Project end date: 2001-09-29
5R01LM005401-09 (2000): $523561
5R01LM005401-08 (1999): $513220
2R01LM005401-07 (1998): $434323
5R01LM005401-06 (1997): $443755
2R01LM005401-04 (1995): $412029
Optimizing Coordinated Combination Drug Therapy
Roger W Jelliffe, Professor
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 1R01EB005803-01A1 from National Institute Of Biomedical Imaging And Bioengineering IRG: ZRG1
Abstract: A major problem in optimally coordinating combination drug therapy is the inability to quantify and minimize the highly variable relationships between dosage of the various drugs, patient adherence, serum concentrations, drug -- drug interactions, shared therapeutic and toxic effects of the combination regimens given, and patient outcomes. Combination therapy is now the norm in many clinical settings. Our cross-disciplinary laboratory has developed parametric and especially nonparametric (NP) population modeling software to capture these relationships with statistical consistency and precision. We have also developed the new "multiple model" (MM) method of dosage design to hit desired therapeutic target goals with maximum precision (minimum weighted squared error), for models of single drugs having analytic solutions to their differential equations. We have now begun clinical testing in pilot collaborative projects, to make NP population models, and to achieve target goals with maximum precision. We also have NP software to make models of the larger, nonlinear and complex interacting systems of combination therapy with multiple drugs, and their shared combination therapeutic and toxic effects. Aim 1 We will implement all the above in a new Windows interface. Aim 2 We are developing MM dosage design for the combination drug regimens. Preliminary results are most encouraging. We will develop integrated software to ensure maximally precise coordinated combination drug therapy for patients with HIV, cancer, transplants, heart failure, TB, epilepsy, those requiring combination antibiotic and antifungal therapy, and even, perhaps, diabetes mellitus. Failure to consider drugs in combination means that while each drug can be individualized, the interactions are never considered, each drug appears variable and capricious, as the changing doses of the other drugs, and their effects, are not considered, and dosage adjustment is always behind the events. Our exciting new tool should now optimize the individualization and coordination of combination drug therapy for patients, with essentially optimal Bayesian MM feedback and dosage adjustment. Subsequent feedback should tend to be more confirmatory, and dosage adjustments should be fewer and smaller. 1 can also monitor effects such as Hb, WBC, platelets, viral load, CD-4, other responses, and then make adjustments of dosage to hit all selected therapeutic targets most precisely, including tolerable degrees of toxicity (Hb=10, WBC=1200, plts=100,000 for example). All this is highly feasible and most urgently needed clinically. Aim 3 This work will be studied and evaluated in several collaborating pilot clinical projects, 1 on-site, others off-site. This work should greatly improve our understanding and control of combination and interacting drug relationships, and the quality and precision of combination drug therapy for patients who must receive potentially toxic drugs.
Keywords: chemotherapy, dosage, model, children, hospital, pharmacokinetics, clinical research
Project start date: 2006-07-01
Project end date: 2010-04-30
1R01EB005803-01A1 (2006): $509830
5R01EB005803-04 (2009): $580049
5R01EB005803-02 (2007): $538987
New Decision Supports And Databases For Drug Dosage
Roger W Jelliffe, Profesor Of Medicine
Medicineuniversity Of Southern California
department Of Contracts And Grants
los Angeles, Ca 90033
Grant 5R01GM065619-11 from National Institute Of General Medical Sciences IRG: BLR
Project start date: 1992-01-01
Project end date: 2004-08-31
5R01GM065619-11 (2002): $512047
9R01GM065619-10 (2001): $430625
Sponsored Links Excellgen http://Excellgen.com
5R01LM005401-05 (1996): $431187
POPULATION PHARMACOKINETIC MODELING ON A SUPERCOMPUTER
Roger W Jelliffe, Profesor Of Medicine
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 1R01RR011526-01A1 from National Center For Research Resources IRG: BLR
Project start date: 1996-08-01
Project end date: 1999-07-31
1R01RR011526-01A1 (1996): $267867
DOSAGE REGIMES OF CARDIAC DRUGS--PROCAINAMIDE STUDY
Roger W Jelliffe
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5M01RR000043-210187 from National Center For Research Resources
Keywords: CARDIOVASCULAR DISORDERS CHEMOTHERAPY, DOSAGE AND ROUTE, DOSAGE, PHENYLAMIDES, PROCAINE AMIDE, computer simulation, HUMAN, CLINICAL
MATHEMATIC MODEL OF HEPARIN KINETICS IN MAN
Roger W Jelliffe
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5M01RR000043-180145 from National Center For Research Resources
Keywords: DRUGS, PHARMACOLOGY, BIOAVAILABILITY, MODELS, MATHEMATICAL, POLYSACCHARIDES, GLYCOSAMINOGLYCANS, HEPARIN, BLOOD AND RE DISORDERS CHEMOTHERAPY, BLOOD COAGULATION, ANTICOAGULANTS, HUMAN, CLINICAL
DIGITOXIN BIOAVAILABILITY STUDY
Roger W Jelliffe
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5M01RR000043-170073 from National Center For Research Resources
Keywords: CARDIAC GLYCOSIDES, DIGITOXIN, DRUGS, PHARMACOLOGY, BIOAVAILABILITY, DOSAGE AND ROUTE, DOSAGE, DOSAGE AND ROUTE, ROUTE OF ADMINISTRATION, DRUGS, PHARMACOLOGY, BIOCHEMICAL, cardiovascular pharmacology, HUMAN, CLINICAL
Roger W Jelliffe
University Of Southern California Department Of Contracts And Grants Los Angeles, Ca 90033
Grant 5M01RR000043-120014 from National Center For Research Resources
Keywords: CARDIAC GLYCOSIDES, DIGITALIS, CARDIOVASCULAR AGENTS, CARDIOTONIC DRUGS (GENERAL), MODELS, MATHEMATICAL, CARDIAC GLYCOSIDES, DIGITOXIN, CARDIAC GLYCOSIDES, DIGOXIN, CARDIOVASCULAR DISORDERS CHEMOTHERAPY, COMPUTER ANALYSIS*, HUMAN, CLINICAL*