Design of dosage regimens: A multiple model stochastic control approach

Bayard, David S.; Milman, Mark H.; Schumitzky, Alan

doi:10.1016/0020-7101(94)90100-7

Cited by 33 publications

(19 citation statements)

References 3 publications

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“…To ensure greatest accuracy of dose adaptation based on drug concentration data, use of a stochastic control approach can be applied to define the timing and number of drug concentrations that should be taken from the patient and then used in the dose prediction 145 . This approach is particularly useful for drugs with high PK variability 72 .…”

Section: Solution: Individualised Antibiotic Dosingmentioning

confidence: 99%

Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions

Roberts

Abdul‐Aziz

Lipman

et al. 2014

The Lancet Infectious Diseases

822

681

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Summary Infections in critically ill patients are associated with persistently poor clinical outcomes. These patients have severely altered and variable antibiotic pharmacokinetics and are infected by less susceptible pathogens. Antibiotic dosing that does not account for these features is likely to result in sub-optimal outcomes. In this paper, we review the patient- and pathogen-related challenges that contribute to inadequate antibiotic dosing and discuss how a process for individualised antibiotic therapy, that increases the accuracy of dosing, can be implemented to further optimise care for the critically ill patient. The process for optimised antibiotic dosing firstly requires determination of the physiological derangements in the patient that can alter antibiotic concentrations including altered fluid status, microvascular failure, serum albumin concentrations as well as altered renal and hepatic function. Secondly, knowledge of the susceptibility of the infecting pathogen should be determined through liaison with the microbiology laboratory. The patient and pathogen challenges can then be solved by combining susceptibility data with measured antibiotic concentration data (where possible) into a clinical dosing software. Such software uses pharmacokinetic-pharmacodynamic (PK/PD) models from critically ill patients to accurately predict the dosing requirements for the individual patient with the aim of optimising antibiotic exposure and maximising effectiveness.

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Section: Solution: Individualised Antibiotic Dosingmentioning

confidence: 99%

Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions

Roberts

Abdul‐Aziz

Lipman

et al. 2014

The Lancet Infectious Diseases

822

681

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“…We incorporated the final model into a voriconazole multiplemodel Bayesian adaptive dosing controller (26)(27)(28), which we call a software "cartridge." The voriconazole cartridge included the structural model equations relating input (voriconazole dosing) to output (voriconazole plasma concentrations) and the discrete joint probability distribution of the values of the equation variables (pharmacokinetic parameters) in the population, consisting of support points.…”

Section: Subjects and Proceduresmentioning

confidence: 99%

Achieving Target Voriconazole Concentrations More Accurately in Children and Adolescents

Neely

Margol

et al. 2015

Antimicrob Agents Chemother

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f Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiplemodel Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, ؊17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was ؊0.7% (interquartile range, ؊7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, ؊1.4 to 14.7%; P ‫؍‬ 0.16 versus the full posterior parameter value) and the dose bias was ؊6.7% (interquartile range, ؊18.7 to 2.4%; P ‫؍‬ 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, ؊13.1 to 18%; P ‫؍‬ 0.32) and a dose bias of ؊3.5% (interquartile range, ؊18 to 14%; P ‫؍‬ 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.) V oriconazole is the approved first-line therapy for aspergillosis in patients who are at least 12 years of age in the United States and at least 2 years of age elsewhere. Numerous reports of studies in both adults (1-7), including a prospective randomized trial (8), and children (9-11) have documented improved outcomes when trough concentrations are maintained above 1 mg/liter, which is a readily measured clinical surrogate for the full area under the concentration-time curve (AUC) that drives efficacy (12-15).However, the pharmacokinetic behavior of voriconazole is complex and nonlinear, such that in many patients, small dose changes are associated with disproportionately large changes in the plasma concentrations of the drug. While it is more common in adults, nonlinear, saturated pharmacokinetic behavior is readily observed in children who receive doses higher than those that have been approved by regulatory agencies (16). This nonlinearity also makes the half-life and the time to steady state dependent on the dose and concentration, complicating the ability to compare steady-state trough concentrations to the accepted therapeu...

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“…5 More sophisticated studies of serumdrug concentration-effect/response relationships (simultaneous PK/PD modeling) are the logical next step in delineating the clinical pharmacology of SCI. 81 Relatively simple mathematical expressions that simultaneously relate changes in the concentration of drugs in biologic fluids and tissues to drug effects are accessible as tools to optimize clinical therapeutics in spinal humans. 84 When fully characterized and tested for predictive performance, descriptor/demographically sensitive approaches to population modeling incorporating experimentally derived population-specific PK/PD data can be used to characterize more completely the pathophysiology of SCI and to develop optimal strategies for dosing clinically important, commonly prescribed medications.…”

Section: The Relevance Of Population-specific Pk/pd Modelingmentioning

confidence: 99%

“…Both NONMEM and NPEM are sophisticated computational algorithms for carrying out the statistical analyses and calculations required to study, predict, and quantitatively characterize populationspecific pharmacokinetics. [79][80][81] Population statistics that are independent of subject-specific kinetic parameter estimates can be generated from sparse, fragmentary data. NPEM, however, employs nonparametric (relatively assumption-free) approaches to statistical analysis.…”

Section: The Relevance Of Population-specific Pk/pd Modelingmentioning

confidence: 99%

Optimal Drug Therapy and Therapeutic Drug Monitoring After Spinal Cord Injury: A Population-Specific Approach

Segal

Pathak²

2001

American Journal of Therapeutics

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Study of the clinical pharmacology of SCI has revealed population-specific patterns of drug metabolism and disposition. PD/PK profiles reflect the changed physiology associated with SCI and correlate well with the neurologic or anatomic level and the magnitude and completeness of the injury. The greatest value of SCI PK/PD profiles lies in their use in developing criteria and strategies for the optimal prescribing of drugs and in therapeutic drug monitoring. Patients with SCI, acute or long-standing, comprise a therapeutically unique and distinct population. Rational, efficacious, and cost-effective approaches to drug development and pharmacotherapy in spinal cord-injured patients can only come about when population-specific PK/PD behavior is incorporated early into the drug development process and used to develop safe, effective therapeutic guidelines.

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Design of dosage regimens: A multiple model stochastic control approach

Cited by 33 publications

References 3 publications

Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions

Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions

Achieving Target Voriconazole Concentrations More Accurately in Children and Adolescents

Optimal Drug Therapy and Therapeutic Drug Monitoring After Spinal Cord Injury: A Population-Specific Approach

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