Estimation of Pharmacokinetic Parameters Based on the Patient-Adjusted Population Data

Mehvar, Reza

doi:10.5688/aj700596

Cited by 9 publications

(13 citation statements)

References 19 publications

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“… 5 However, in subsequent research, it was found that only 26% of interindividual variability in digoxin CL can be explained by changes in Ccr. 13 Although a study reported by Muzzarelli et al 14 also supported the clinical validity of the Konishi equation for calculation of the individual digoxin dosage for Caucasians, the exclusion criteria in their study included severe renal insufficiency (Ccr <30 mL per minute) and their study population was small, which may cause deviation of the results.…”

Section: Discussionmentioning

confidence: 90%

“…Assuming no clinically significant interindividual difference in nonrenal digoxin CL owing to the lack of a compensatory increase in metabolic clearance with a decrease in the renal clearance, Konishi developed a predictive model in order to apply the equation in clinical practice for accurate and rapid determination of digoxin concentration 5. However, in subsequent research, it was found that only 26% of interindividual variability in digoxin CL can be explained by changes in Ccr 13. Although a study reported by Muzzarelli et al14 also supported the clinical validity of the Konishi equation for calculation of the individual digoxin dosage for Caucasians, the exclusion criteria in their study included severe renal insufficiency (Ccr <30 mL per minute) and their study population was small, which may cause deviation of the results.…”

Section: Discussionmentioning

confidence: 99%

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Efficiency of individual dosage of digoxin with calculated concentration

Zhao

Yang

et al. 2014

CIA

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BackgroundDigoxin is a frequently prescribed drug, particularly in the elderly population, in which there is an increased prevalence of atrial fibrillation and cardiac failure. With its complex pharmacokinetic profile and narrow therapeutic index, use of digoxin requires regular monitoring of blood levels. Recent evidence suggests that a lower concentration range (0.4–1.0 ng/mL) is preferable in patients with congestive heart failure and a higher range (0.8–2.0 ng/mL) is needed in patients with atrial tachyarrhythmia. The Konishi equation is widely used to predict the serum digoxin concentration (SDC) in Japan. This study assessed the correlation between SDC predicted by the Konishi equation and that actually measured in Chinese patients and investigated the impact of renal function on SDC.MethodsThe study subjects comprised 72 patients with cardiac failure or/and atrial tachyarrhythmia seen at our hospital from January 2012 to December 2013. The patients were divided into five groups according to Kidney Diseases Outcome Quality Initiative guidelines. SDCs were measured using the Abbott Architect i1000 immunology analyzer. The correlations between measured SDCs and calculated SDCs and between clearance of digoxin and creatinine clearance rate were assessed retrospectively.ResultsThe correlation between measured and predicted SDC calculated by the Konishi equation was significant (r=0.655, P<0.001) for the 72 patients overall; however, correlations within the different stages of renal function were nonsignificant, with a correlation found only in patients with stage 3 (30 mL per minute < creatinine clearance <60 mL per minute). With regard to the correlation between clearance of digoxin and creatinine clearance, our results show that although there was a significant correlation between clearance of digoxin and creatinine clearance in the group overall, correlations were not evident within the different stages of renal function.ConclusionThe results of this study indicate that clearance of digoxin and the creatinine clearance rate cannot be explained by renal function alone and that the validity of the Konishi equation for individualizing the digoxin dosage in Chinese patients is limited, being applicable only in stage 3 renal disease. Further research in larger numbers of patients across all stages of renal function will be required in the future to verify the original Konishi model.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Efficiency of individual dosage of digoxin with calculated concentration

Zhao

Yang

et al. 2014

CIA

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“…Population pharmacokinetic parameter values are often used to estimate drug dosing regimen designs for individual patients in whom patient-specific parameter values are not available ( 16 , 17 ); however, opioid pharmacokinetic parameter values are scattered about in the vast pharmacology literature. Therefore, we culled a large number of studies using references ( 18 – 21 ), the reference lists therein, the reference lists of the references therein, and literature searches using PubMed.gov and Scholar.Google.com with search terms entered: opioids pharmacokinetics, opioids pharmacodynamics, and the latter terms, substituting the word opioids for each of the 12 individual opioids studied, so as to obtain pharmacokinetic parameter values for the following 12 opioids: ( 1 ) morphine, ( 2 ) tramadol, ( 3 ) codeine, ( 4 ) meperidine, ( 5 ), hydrocodone, ( 6 ) oxycodone immediate-release (IR), ( 7 ) oxycodone controlled-release (CR), ( 8 ) hydromorphone, ( 9 ) oxymorphone, ( 10 ) methadone, ( 11 ) fentanyl, and ( 12 ) buprenorphine.…”

Section: Methodsmentioning

confidence: 99%

“…The required data to design an opioid dosage regimen using COP is information about the pharmacokinetics of the opioid, the values reported in Table 1 ), and the opioid's therapeutic range ( 31 ). Pharmacokinetic parameter values calculated using COP can be expected to have an inter-individual variation of about 25%, which may be clinically acceptable.…”

Section: Methodsmentioning

confidence: 99%

Computational Opioid Prescribing: A Novel Application of Clinical Pharmacokinetics

Linares

2011

Journal of Pain & Palliative Care Pharmacotherapy

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We implemented a pharmacokinetics-based mathematical modeling technique using algebra to assist pre-scribers with point-of-care opioid dosing. We call this technique computational opioid prescribing (COP). Because population pharmacokinetic parameter values are needed to estimate drug dosing regimen designs for individual patients using COP, and those values are not readily available to prescribers because they exist scattered in the vast pharmacology literature, we estimated the population pharmacokinetic parameter values for 12 commonly prescribed opioids from various sources using the bootstrap resampling technique. Our results show that opioid dosing regimen design, evaluation, and modification is feasible using COP. We conclude that COP is a new technique for the quantitative assessment of opioid dosing regimen design evaluation and adjustment, which may help prescribers to manage acute and chronic pain at the point-of-care. Potential benefits include opioid dose optimization and minimization of adverse opioid drug events, leading to potential improvement in patient treatment outcomes and safety.

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“…Rational individualization of dosing regimens in the clinic is well achieved using a two-stage process ( 9 ). Before a drug is given to a patient, one could estimate an initial dosage requirement using average population measures of PKP, with modification as required (Stage 1).…”

Section: Background: Why Use Bayesian Pharmacokinetics?mentioning

confidence: 99%

Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring

Brocks¹,

Hamdy²

2020

Res Pharma Sci

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Bayesian estimation of pharmacokinetic parameters (PKP), as discussed in this review, provides a powerful approach towards the individualization of dosing regimens. The method was first described by Lewis Sheiner and colleagues and it is well suited in clinical environs where few blood fluid measures of drugs are available in the clinic. This makes it a valuable tool in the effective implementation of therapeutic drug monitoring. The principle behind the method is Bayes theorem, which incorporates elements of variability in a priori -known population estimates and variability in the pharmacokinetic parameters, and known errors intrinsic to the assay method used to estimate the blood fluid drug concentrations. This manuscript reviews the Bayesian method. The literature was scanned using Pubmed to provide background into the Bayesian method. An Add-in for Excel program was used to show the ability of the method to estimate PKP using sparse blood fluid concentration vs time data. Using a computer program, the method was able to find reasonable estimates of individual pharmacokinetic parameters, assessed by comparing the estimated data to the true PKP. Education of students in clinical pharmacokinetics is incomplete without some mention and instruction of the Bayesian forecasting method. For a complete understanding, a computer program is needed to demonstrate its utility.

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Estimation of Pharmacokinetic Parameters Based on the Patient-Adjusted Population Data

Cited by 9 publications

References 19 publications

Efficiency of individual dosage of digoxin with calculated concentration

Efficiency of individual dosage of digoxin with calculated concentration

Computational Opioid Prescribing: A Novel Application of Clinical Pharmacokinetics

Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring

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