Wide size dispersion and use of body composition and maturation improves the reliability of allometric exponent estimates

González-Sales, Mario; Holford, Nick; Bonnefois, Guillaume; Desrochers, Julie

doi:10.1007/s10928-021-09788-3

Cited by 9 publications

(14 citation statements)

References 47 publications

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“…This does not change the relationship between size and parameters; it simply changes the scale of the parameter. 15 The visual predictive check (VPC) is shown in Figure S1.…”

Section: Resultsmentioning

confidence: 99%

“…The pharmacokinetic parameter values were standardized for a body weight of 70 kg using allometric models 14 . This standardization allows comparison of rabbit parameter estimates with those reported for human adults 15 :

P_{i} goodbreak= P_{STD} goodbreak\times {(\frac{true}{{normalW}_{normali}})}^{EXP}

where P i is the parameter of the i th subject, W i is the weight of the i th subject, and P STD is the parameter of standard weight W STD of 70 kg. The EXP exponent was 0.75 for clearance and 1 for distribution volumes 16 …”

Section: Methodsmentioning

confidence: 99%

“…Population parameter estimates were estimated using an effect compartment model, a model valid for situations where there is an apparent temporal displacement between plasma concentration (Cp) and response, for example, neuromuscular blocking drugs 9 . A rate constant (keo, T 1/2 keo = Ln(2)/keo) links plasma concentration with effect site concentration (Ce).

Effect goodbreak= normalE 0 goodbreak+ E_{MAX} normalx \frac{true}{{Ce}^{normalN}}

The parameter E0 is the baseline measure (e.g., ACT 100, R 0.4 min), E MAX is the maximum drug effect, Ce 50 is the effect site concentration eliciting half of E MAX and N is the Hill coefficient describing the steepness of the concentration–response curve 15 …”

Section: Methodsmentioning

confidence: 99%

“…The parameter E0 is the baseline measure (e.g., ACT 100, R 0.4 min), E MAX is the maximum drug effect, Ce 50 is the effect site concentration eliciting half of E MAX and N is the Hill coefficient describing the steepness of the concentration-response curve. 15 An indirect effect model was also investigated. Indirect effect (turnover) models assume that pharmacodynamic effect (i.e., PD, inhibition, or stimulation) are due to factors (Cofact) that control the production or dissipation of drug response, for example, warfarin effect mediated through prothrombin complex.…”

Section: Pharmacodynamicsmentioning

confidence: 99%

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“…This does not change the relationship between size and parameters; it simply changes the scale of the parameter. 15 The visual predictive check (VPC) is shown in Figure S1.…”

Section: Resultsmentioning

confidence: 99%

P_{i} goodbreak= P_{STD} goodbreak\times {(\frac{true}{{normalW}_{normali}})}^{EXP}

Section: Methodsmentioning

confidence: 99%

Effect goodbreak= normalE 0 goodbreak+ E_{MAX} normalx \frac{true}{{Ce}^{normalN}}

Section: Methodsmentioning

confidence: 99%

Section: Pharmacodynamicsmentioning

confidence: 99%

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Delayed concentration effect models for dabigatran anticoagulation

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“…In agreement with previous findings, systemic absorption of levobupivacaine after caudal administration was relatively fast with an estimated absorption half-time (Tabs 1/2 ) of 6 min. The incorporation of PMA to describe metabolic clearance maturation provides a more physiologically appropriate description of clearance changes than previous models using PNA, since maturation of clearance begins in utero 13,14. Levobupivacaine used for caudal anesthesia in children <18 years of age has a reported elimination half-life of 14 h in neonates and 3.6 h in infants 6-18 months 15.…”

mentioning

confidence: 99%

Levobupivacaine plasma concentrations following repeat caudal anesthetics

Frawley

Cortínez

Anderson

et al. 2022

Pediatric Anesthesia

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Aim: A single caudal anesthetic at the start of lower abdominal surgery is unlikely to provide prolonged analgesia. A second caudal at the end of the procedure extends the analgesia duration but total plasma concentrations may be associated with toxicity. Our aim was to measure total plasma levobupivacaine concentrations after repeat caudal anesthesia in infants and to generate a pharmacokinetic model for prediction of plasma concentrations after repeat caudal anesthesia in neonates, infants and children. Methods: Infants undergoing definitive repair of anorectal malformations orHirschsprung's disease received a second caudal anesthesia at the end of the procedure. Total levobupivacaine concentrations were assayed 3-4 times in the first 6 h after the initial caudal. These data were pooled with data from four studies describing plasma concentrations after levobupivacaine caudal or spinal anesthesia. Population pharmacokinetic parameters were estimated using nonlinear mixed-effects models.Covariates included postmenstrual age and body weight. Parameter estimates were used to simulate concentrations after a repeat levobupivacaine 2.5 mg kg −1 caudal at 3 or 4 h following an initial levobupivacaine 2.5 mg kg −1 caudal.Results: Twenty-one infants (postnatal age 11-32 weeks, gestation 37-39 weeks, weight 5.2-8.6 kg) were included. The measured peak plasma concentration after repeat caudal levobupivacaine 2.5 mg kg −1 4 h after initial caudal was 1.38 mg L −1 (95% prediction interval 0.60-2.6 mg L −1 ) and 3 h after initial caudal was 1.46 mg L −1 (0.60-2.80) mg L −1 . Simulation of total plasma concentrations in neonates (7 kg, 57 weeks postmenstrual age) given caudal levobupivacaine 4 h after the initial caudal were 1.76 mg L −1 (0.68-3.50) mg L −1 if 2.5 mg kg −1 levobupivacaine was used and 0.88 mg L −1 (0.34-1.73) mg L −1 if 1.25 mg kg −1 of 0.125% levobupivacaine was used. In simulated older children (20 kg, 6 years), the mean maximum concentration was 1.43 mg L −1 (0.60-2.70) mg L −1 if 2.5 mg kg −1 levobupivacaine was repeated at 3 h. Conclusion:Repeat caudal levobupivacaine 2.5 mg kg −1 at 3 h after an initial 2.5 mg kg −1 dose does not exceed the concentration associated with systemic local anesthetic toxicity. In 2.5% of simulated neonates (weight 3.8 kg, PMA 40 weeks), repeat caudal

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Covariate modeling in pharmacometrics: General points for consideration

Sanghavi,

Ribbing,

Rogers

et al. 2024

CPT Pharmacom & Syst Pharma

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Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

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Wide size dispersion and use of body composition and maturation improves the reliability of allometric exponent estimates

Cited by 9 publications

References 47 publications

Delayed concentration effect models for dabigatran anticoagulation

Delayed concentration effect models for dabigatran anticoagulation

Levobupivacaine plasma concentrations following repeat caudal anesthetics

Covariate modeling in pharmacometrics: General points for consideration

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