Pharmacokinetic Modeling

Byers, James P.; Sarver, Jeffrey

doi:10.1016/b978-0-12-369521-5.00010-5

Cited by 34 publications

(32 citation statements)

References 2 publications

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“…Concentrations after multiple doses were determined according to Byers and Sarver [22], assuming linear PK. Each subject-specific concentration-time curve following multiple doses of rAHF-PFM and rFVIIIFc was subsequently examined as to their yearly time below 3 IU dL À1 and yearly time above 10 IU dL…”

Section: Methodsmentioning

confidence: 99%

Recombinant full‐length factor VIII (FVIII) and extended half‐life FVIII products in prophylaxis – new insight provided by pharmacokinetic modelling

et al. 2015

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Summary. The pharmacokinetics (PK) of extended half-life factor VIII (FVIII) products might allow longer dosing intervals in prophylaxis, potentially affecting its efficacy. We used published population PK models of a recombinant full-length FVIII (rAHF-PFM) and a recombinant B-domain-deleted FVIII Fc fusion product (rFVIIIFc) to assess the time spent weekly with FVIII levels below 3 IU dL À1 or above 10 IU dL À1. These FVIII levels were chosen based on the observation that trough levels of 1 IU dL À1 may not be sufficient in all patients. This approach was applied to a simulated population of 1000 severe haemophilia A subjects with dosing regimens included in the prescribing information or evaluated in clinical trials. FVIII every 96 h or 120 h, and 65 IU kg À1 every 168 h. In conclusion, PK modelling indicates that choice and dosing intervals of standard and extended half-life FVIII products require careful evaluation of individual PK to allow more time at protective levels, especially in patients with active lifestyles.

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Section: Methodsmentioning

confidence: 99%

Recombinant full‐length factor VIII (FVIII) and extended half‐life FVIII products in prophylaxis – new insight provided by pharmacokinetic modelling

et al. 2015

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“…To methodically detect these effects, for every Bt gene, we computed two measures: (i) time‐averaged relative abundance (TA‐RA) and (ii) time‐averaged normalized effective coverage (TA‐NEC). The TA‐RA value is conceptually similar to a time‐integrated pharmacological dose value (Byers & Sarver, ); in our analysis, it represents the average “dose” of a particular donor gene, relative to all other donor genes present in vivo over a period of time. The TA‐NEC value quantifies the fraction of the gene that is effectively covered by reads over a period of time.…”

Section: Resultsmentioning

confidence: 99%

Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics

Yaung

Deng

et al. 2015

Molecular Systems Biology

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Elucidating functions of commensal microbial genes in the mammalian gut is challenging because many commensals are recalcitrant to laboratory cultivation and genetic manipulation. We present Temporal FUnctional Metagenomics sequencing (TFUMseq), a platform to functionally mine bacterial genomes for genes that contribute to fitness of commensal bacteria in vivo. Our approach uses metagenomic DNA to construct large‐scale heterologous expression libraries that are tracked over time in vivo by deep sequencing and computational methods. To demonstrate our approach, we built a TFUMseq plasmid library using the gut commensal Bacteroides thetaiotaomicron (Bt) and introduced Escherichia coli carrying this library into germfree mice. Population dynamics of library clones revealed Bt genes conferring significant fitness advantages in E. coli over time, including carbohydrate utilization genes, with a Bt galactokinase central to early colonization, and subsequent dominance by a Bt glycoside hydrolase enabling sucrose metabolism coupled with co‐evolution of the plasmid library and E. coli genome driving increased galactose utilization. Our findings highlight the utility of functional metagenomics for engineering commensal bacteria with improved properties, including expanded colonization capabilities in vivo.

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“…Briefly, a single-compartment model with first-order absorption and first-order elimination rates for ACE inhibitor drugs after oral administration is used. The analytical solution for the concentration of the biologically active diacid form of the drug for repeated doses over time t with uniform dose size d at constant time intervals τ is [15,36] [Drug] n (t ) = k a dF (k a − k e )V 1 − exp(−nk e τ) 1 − exp(−k e τ) exp(−k e t ) − 1 − exp(−nk a τ)…”

Section: Pharmacokinetic Modelmentioning

confidence: 99%

A Glucose-Dependent Pharmacokinetic/ Pharmacodynamic Model of ACE Inhibition in Kidney Cells

2019

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Diabetic kidney disease (DKD) is a major cause of renal failure. Podocytes are terminally differentiated renal epithelial cells that are key targets of damage due to DKD. Podocytes express a glucose-stimulated local renin-angiotensin system (RAS) that produces angiotensin II (ANG II). Local RAS differs from systemic RAS, which has been studied widely. Hyperglycemia increases the production of ANG II by podocyte cells, leading to podocyte injury. Angiotensin-converting enzyme (ACE) is involved in the production of ANG II, and ACE inhibitors are drugs used to suppress elevated ANG II concentration. As systemic RAS differs from the local RAS in podocytes, ACE inhibitor drugs should act differently in local versus systemic contexts. Experimental and computational studies have considered the pharmacokinetics (PK) and pharmacodynamics (PD) of ACE inhibition of the systemic RAS. Here, a PK/PD model for ACE inhibition is developed for the local RAS in podocytes. The model takes constant or dynamic subject-specific glucose concentration input to predict the ANG II concentration and the corresponding effects of drug doses locally and systemically. The model is developed for normal and impaired renal function in combination with different glucose conditions, thus enabling the study of various pathophysiological conditions. Parameter uncertainty is also analyzed. Such a model can improve the study of the effects of drugs at the cellular level and can aid in development of therapeutic approaches to slow the progression of DKD.

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Pharmacokinetic Modeling

Cited by 34 publications

References 2 publications

Recombinant full‐length factor VIII (FVIII) and extended half‐life FVIII products in prophylaxis – new insight provided by pharmacokinetic modelling

Recombinant full‐length factor VIII (FVIII) and extended half‐life FVIII products in prophylaxis – new insight provided by pharmacokinetic modelling

Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics

A Glucose-Dependent Pharmacokinetic/ Pharmacodynamic Model of ACE Inhibition in Kidney Cells

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