Population Pharmacokinetic Model of ADVATE in Pediatric and Adult Patients with Hemophilia A.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3492-3492
Author(s):  
Myungshin Oh ◽  
Sven Björkman ◽  
Phillip Schroth ◽  
Sandor Fritsch ◽  
Peter Collins ◽  
...  
Keyword(s):  
Body Weight ◽  
Hemophilia A ◽  
Individual Variability ◽  
Patient Specific ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Data Sets ◽  
Residual Variability ◽  
Population Pk ◽  
Variability Model

Abstract Abstract 3492 Poster Board III-429 Introduction The objective of this analysis was to characterize the population pharmacokinetic (PK) model of ADVATE® (Antihemophilic Factor (Recombinant), Plasma/Albumin-Free Method) in hemophilia A patients. This included estimation of typical population pharmacokinetic parameters and inter-individual and residual variability and identification of covariates that are significant predictors of variability in a pooled population of children and adults. Patients and Methods Plasma FVIII activity PK data were collected for 3 ADVATE® clinical trials in previously treated patients: 184 full PK data sets for 100 adults/adolescents, aged 10 to 65 years, and from 52 reduced sample PK data sets for 52 children, aged 1 to 6 years. Population PK analysis was conducted using non-linear mixed effects modeling with the first-order integral approximation method in SAS® software (NLMIXED procedure). A two-compartment model was used as the base model and the influence of age and weight were explored. Results Two-compartment PK models with additive plus proportional residual variability model and exponential inter-individual variability model adequately described the data. Clearance (CL) is significantly correlated with age and body weight and central volume of distribution (V1) is also related with body weight. The estimated population PK parameters were (mean parameter, (inter-individual variability %)): CL (2.92 mL/kg·h, 22%), V1 (0.46 dL/kg, 5.2%), peripheral volume V2 (0.09 dL/kg) and inter-compartmental clearance Q (2.07 mL/ kg·h). Conclusions A population PK model that describes the combined PK data from adults and pediatric studies has been constructed. A significant portion of inter-individual variability in both volume and clearance can be explained by subject weight. An additional smaller effect of age on clearance but not volume was observed. A population PK model for Factor VIII could provide the clinician with advantages in designing a patient specific treatment regimen. It could provide more relevant guidance in individual patient pharmacokinetics than just incremental recovery without the burden of a full PK assessment of the patient. Disclosures: Oh: Baxter: Employment. Off Label Use: Prophylaxis is not indicated in the US. Björkman:Baxter: Consultancy; Octapharma: Consultancy. Schroth:Baxter: Employment. Fritsch:Baxter: Employment. Collins:Bayer: Consultancy; Novo Nordisk: Consultancy; Baxter: Consultancy. Fischer:Bayer: Consultancy; Wyeth: Consultancy; Baxter: Consultancy. Blanchette:Bayer: Consultancy; Baxter: Consultancy. Casey:Baxter: Employment. Spotts:Baxter: Employment. Ewenstein:Baxter: Employment.

Use of a Population Pharmacokinetic Model of ADVATE In Pediatric and Adult Patients with Hemophilia A Permits Limited Blood Sampling for Individual Dose Tailoring.

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1416-1416
Author(s):  
MyungShin Oh ◽  
Sven Björkman ◽  
Phillip Schroth ◽  
Sandor Fritsch ◽  
Peter W Collins ◽  
...  
Keyword(s):  
Body Weight ◽  
Hemophilia A ◽  
Volume Of Distribution ◽  
Sampling Time ◽  
Adult Patients ◽  
Population Pharmacokinetic ◽  
Data Sets ◽  
Time Points ◽  
Population Pk ◽  
Post Infusion

Abstract Abstract 1416 Introduction: A population pharmacokinetic (PK) model of a recombinant FVIII (rFVIII) was established on ADVATE® (Antihemophilic Factor (Recombinant), Plasma/Albumin-Free Method) studies in pediatric and adult patients with hemophilia A. The objective of this analysis was to evaluate the effect of reduced PK sampling time points on the estimated PK parameters in the population PK model. Patients and Methods: Plasma FVIII activity PK data were collected for 3 ADVATE® clinical trials in previously treated patients: 184 full PK data sets (11 time points) for 100 adults/adolescents, aged 10 to 65 years, and from 52 reduced sample PK data sets (5 time points) for 52 children, aged 1 to 6 years. A population PK analysis was conducted on a two-compartment structure model and the covariate effect of age and weight was explored. Four reduced sampling scenarios from the full 10 post-infusion sampling time points, were investigated: 1) Reduced to 4 (1 hr, 9 hr, 24 hr, and 48 hr), 2) Reduced to 3 (6 hr, 24 hr, and 48 hr), 3) Reduced to 2 (6 hr and 24 hr), and 4) Reduced to 1 sampling time points (24 hr post-infusion). After applying the reduced sampling on a random 10% of sampling set at a time in the population PK model, the differences in model estimates and individual PK estimates between full and reduced sampling, were evaluated. Results: The two-compartment population PK model adequately described the data. Clearance (CL) was significantly correlated with age and body weight and central volume of distribution was also related with body weight. Absolute deviations (%) from the estimates using full PK sampling in the Individual PK estimates (CL, Vss, and Half-life) using each of the reduced sampling time points were showed in the below table. Conclusions: It appears that PK parameters estimated using population PK model are robust to reduced sampling time points. Accurate measurement of PK on reduced samples gives patients and clinicians the opportunity to design treatment regimens that are better tailored to individuals. Disclosures: Oh: Baxter: Employment. Björkman:Baxter: Consultancy; Octapharma: Consultancy. Schroth:Baxter: Employment. Fritsch:Baxter: Employment. Collins:NovoNordisk: Consultancy, Honoraria, The EACH2 registry was funded by Novonordisk; Baxter Healthcare: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Fischer:Baxter: Consultancy; NovoNordisk: Consultancy. Blanchette:Bayer: Consultancy; Baxter: Research Support. Casey:Baxter: Employment. Spotts:Baxter: Employment. Ewenstein:Baxter Bioscience: Employment.

scholarly journals A Population Pharmacokinetic Model for Concizumab Based on Phase 1 and Phase 2 Trial Data

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 4-4
Author(s):  
Trine Høyer Rose ◽  
Christian Hollensen ◽  
Henrik Agersø ◽  
Rune Viig Overgaard
Keyword(s):  
Rate Constant ◽  
Drug Disposition ◽  
Hemophilia A ◽  
Phase 1 ◽  
Population Pharmacokinetic ◽  
Phase 2 ◽  
Publicly Traded ◽  
Phase 3 ◽  
Target Mediated Drug Disposition ◽  
Population Pk

Introduction Concizumab is a high-affinity anti-tissue factor pathway inhibitor (TFPI) monoclonal antibody in clinical investigation for the subcutaneous (SC) treatment of patients with hemophilia. The data generated from phase 1 and 2 concizumab trials have been used to develop a population pharmacokinetic (PK) model with the aim of supporting dose selection for phase 3 trials. WMethods The objective of this study was to develop a model to describe the PK of concizumab across administration routes in various groups of patients with hemophilia to generate a generally applicable population PK model of concizumab. The model was developed based on available PK data from four phase 1 trials (for both intravenous [IV] and SC concizumab administration) and two phase 2 trials (for SC concizumab administration). Trial populations in the phase 1 trials included both healthy subjects and patients with hemophilia, whilst the phase 2 trials enrolled patients with hemophilia A or B with inhibitors and patients with hemophilia A without inhibitors. A structural population PK model was first developed based on phase 1 data and the final population PK model was then estimated using data from both phase 1 and phase 2 trials. Simulations were performed for phase 3 concizumab exposure using a full parametric simulation (n=10,000), including both inter-individual and intra-individual variability for the selected population. Randomly sampled body weights from a normal distribution with mean and variance corresponding to body weight distribution from phase 2 trials were used to simulate patient profiles. WResults The population PK dataset used for the model comprised 1,504 observations from 119 subjects (89 patients and 30 healthy individuals), with a mean age of 35 years (range: 18-65 years) and mean body weight of 74.4 kg (range: 47.1-130 kg). The PK model parameters were first estimated based on phase 1 data alone, and after fixing the majority in order to ensure robustness of the model only a few parameters were re-estimated based on phase 1 and 2 data combined. The PK model (Figure 1) was evaluated by standard goodness-of-fit plots and qualification assessments. Using visual predictive checks, it was shown that the model was able to reproduce the median and the 5th and 95th percentiles of the observed concizumab concentrations from phase 1 and 2 trials, and so it was deemed suitable for simulation purposes. The PK model suggested a target-mediated drug disposition following concizumab binding to TFPI at the endothelium, and subsequent elimination of the complex to account for the non-linear elimination. WConclusions The developed model accurately described the PK of concizumab delivered at a wide dose range by either SC or IV administration to both healthy subjects and patients with hemophilia A or B with and without inhibitors. The model was used for simulations to select the dosing regimen for subsequent phase 3 studies. Figure 1. Concizumab pharmacokinetic model. Structure of the final concizumab PK model for SC and IV dosing with target-mediated drug disposition via the endothelial TFPI. CL, clearance; doseiv, intravenous dose; dosesc, subcutaneous dose; IV, intravenous; ka, absorption rate constant; kcom, elimination rate constant of the concizumab-TFPI complex; kon and koff, rate constants for binding of concizumab to the endothelial TFPI; ktr, rate constant from the transit compartment; Q, inter-compartmental clearance; Rtot, amount of endothelial TFPI available for concizumab binding; SC, subcutaneous; TFPI, tissue factor pathway inhibitor; V, volume. Figure Disclosures Høyer Rose: Novo Nordisk A/S: Current Employment, Divested equity in a private or publicly-traded company in the past 24 months. Hollensen:Novo Nordisk: Current Employment, Current equity holder in private company, Current equity holder in publicly-traded company. Agersø:Novo Nordisk A/S: Current Employment. Viig Overgaard:Novo Nordisk A/S: Current Employment, Current equity holder in publicly-traded company.

Vancomycin volume of distribution estimation in adults with class III obesity

2019 ◽  
Author(s):  
Ryan D Dunn ◽  
Ryan L Crass ◽  
Joseph Hong ◽  
Manjunath P Pai ◽  
Lynne C Krop
Keyword(s):  
Body Weight ◽  
Total Body Weight ◽  
Volume Of Distribution ◽  
Patient Specific ◽  
Pharmacokinetic Analysis ◽  
Pharmacokinetic Parameters ◽  
Parameter Estimates ◽  
Class Iii ◽  
Class Iii Obesity ◽  
Total Body

Abstract Purpose To compare methods of estimating vancomycin volume of distribution (V) in adults with class III obesity. Methods A retrospective, multicenter pharmacokinetic analysis of adults treated with vancomycin and monitored through measurement of peak and trough concentrations was performed. Individual pharmacokinetic parameter estimates were obtained via maximum a posteriori Bayesian analysis. The relationship between V and body weight was assessed using linear regression. Mean bias and root-mean-square error (RMSE) were calculated to assess the precision of multiple methods of estimating V. Results Of 241 patients included in the study sample, 159 (66.0%) had a BMI of 40.0–49.9 kg/m2, and 82 (34.0%) had a BMI of ≥50.0 kg/m2. The median (5th, 95th percentile) weight of patients was 136 (103, 204) kg, and baseline characteristics were similar between BMI groups. The mean ± S.D. V was lower in patients with a BMI of 40.0–49.9 kg/m2 than in those with a BMI of ≥50.0 kg/m2 (72.4 ± 19.6 L versus 79.3 ± 20.6 L, p = 0.009); however, body size poorly predicted V in regression analyses (R2 < 0.20). A fixed estimate of V (75 L) or use of 0.52 L/kg by total body weight yielded similar bias and error in this population. Conclusion Results of the largest analysis of vancomycin V in class III obesity to date indicated that use of a fixed V value (75 L) and use of a TBW-based estimate (0.52 L/kg) for estimation of vancomycin V in patients with a BMI of ≥40.0 kg/m2 have similar bias. Two postdistribution vancomycin concentrations are needed to accurately determine patient-specific pharmacokinetic parameters, estimate AUC, and improve the precision of vancomycin dosing in this patient population.

Population Pharmacokinetics of Cyclophosphamide in Patients with Thalassaemia Major Undergoing HSCT Shows Body Weight, CYP450, GST and ALDH Polymorphisms as Covariates Explaining Inter-Individual Variation.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1182-1182 ◽  
Author(s):  
Poonkuzhali Balasubramanian ◽  
John Carl Panetta ◽  
Salamun Desire ◽  
Shaji R Velayudhan ◽  
Vikram Mathews ◽  
...  
Keyword(s):  
Body Weight ◽  
Individual Variation ◽  
Conflicts Of Interest ◽  
Conditioning Regimen ◽  
Elimination Rate ◽  
Pharmacokinetic Parameters ◽  
Thalassaemia Major ◽  
Hematopoietic Stem ◽  
Beta Thalassaemia ◽  
Population Pk

Abstract Abstract 1182 Poster Board I-204 Cyclophosphamide (Cy) in combination with busulfan is an important component of myeloablative conditioning regimen used prior to hematopoietic stem cell transplantation (HSCT) for both malignant and non-malignant conditions. We have previously reported up to 20 fold inter-individual variation in the pharmacokinetics (PK) of Cy in patients with beta thalassaemia undergoing HSCT [Blood (ASH Annual Meeting Abstracts), Nov 2004; 104: 99]. PK parameters of Cy have been shown to be associated with regimen related toxicity and outcome of transplant. To explain the basis of the inter-individual variation in Cy PK, we have developed a population PK model. We analyzed the PK of Cy in consecutive children with beta thalassaemia major who received HSCT from HLA identical matched sibling donor at the Christian Medical College, Vellore from 2001 till 2004. A total of 900 cyclophosphamide concentration measurements from 55 patients were included and correlated with age, sex, body weight and 10 polymorphisms in enzymes involved in the metabolism or biotransformation of Cy namely GST A1, M1, T1, P1, CYP2B6, CYP2C9, CYP2C19 and ALDH genes. Non-linear mixed effects modeling analysis was performed with Monolix (version 2.4, www.monolix.org) to investigate the effect of patient covariates on PK, and to estimate the relative magnitude of inter-individual and inter-occasion variability. A two-compartment pharmacokinetic model was used to describe the data. The pharmacokinetic parameters estimated included elimination rate constant and volume (ke (1/hr), V (L or L/kg)), and the inter-compartmental parameters (k12 and k21 (1/hr)). The distribution of the parameters was assumed log-normal. Body weight was the main covariate which explained the largest portion of the IIV (28% and 20% of V and ke IIV, respectively). In addition, the following genotypes showed differences in the pharmacokinetics: GSTP1*B (1.7X higher ke in MUT versus WT or HET; p<0.05), CYP3A4*1B (2X higher ke in HET versus WT; p<0.05), and ALDH1A1*2 (2X higher ke in HET versus WT; p<0.05). We have developed a population PK model for Cy in thalassaemic children by considering morphological and biological covariates, which explains more than 45% and 22% (V and ke IIV, respectively) of the variation in Cy PK in these patients. This model-based algorithm may be used to design and plan targeted dose therapy in this group of pediatric patients and to predict the risk of toxicity and outcome of HSCT. Disclosures: No relevant conflicts of interest to declare.

Population Pharmacokinetics of Siplizumab (MEDI-507), a Humanized Anti-CD2 Monoclonal Antibody, in Cancer Patients.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2670-2670
Author(s):  
Chunlin Chen ◽  
John E Janik ◽  
Karen Kaucic ◽  
Lorin Roskos ◽  
Bahija Jallal ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Body Weight ◽  
Serum Concentration ◽  
Dose Escalation ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Serum Concentrations ◽  
Concentration Data ◽  
Non Linear ◽  
Volumes Of Distribution

Abstract Abstract 2670 Poster Board II-646 Introduction: Siplizumab, a humanized IgG1k class monoclonal antibody that targets CD2 expressing T-and NK-cells, was evaluated in phase I dose-escalation trials in patients with CD2-positive lymphoproliferative disorder. The objective of this study was to develop a population pharmacokinetic (PK) model for siplizumab and identify covariates that could explain the variability in siplizumab pharmacokinetic parameters. Methods: A Phase 1, open label, dose-escalation study was conducted in 29 patients (14 males/15 females, age range 34–79 years) who received 0.2–4.8 mg/kg of siplizumab as 1-3 consecutive daily doses every 14 days for a total of 1-8 cycles. Siplizumab serum concentration data was analyzed using a nonlinear mixed effects modeling approach with software (NONMEM). Based on exploratory analysis, 1-and 2-compartment non-linear models were evaluated. Demographic covariates including body weight, age, sex and race (Caucasian/Black/Asian) were screened using Generalized Additive Model (GAM) analysis. Covariates selected during the GAM analysis were further tested for significance in NONMEM using the forward inclusion and backward elimination approach. Results: Siplizumab concentrations were obtained from all 29 patients in the study yielding a total of 619 serum concentration observations. Pronounced non-linearity in siplizumab serum concentrations was observed after the initial and later dosing cycles, with serum concentrations declining faster at lower dose levels. The data was best described by a two-compartment pharmacokinetic model with zero-order input with parallel linear and non-linear elimination pathways. Goodness of fit plots and model diagnostics indicated good agreement between observed and model predicted serum concentration values. The population estimates for linear clearance was 0.168 L/day with inter-subject variability (ISV) of 50%, and inter-compartmental clearance was 2.83 L/day. Nonlinear elimination parameters, Vmax and Km were 10.32 mcg/day (56% ISV) and 51.8 mcg/L, respectively. Sex of the patients was identified as a significant covariate impacting volumes of distribution. Male patients had higher central and peripheral volumes of distribution of 2.8 L and 3.0 L, respectively, compared to1.38 L and 2.4 L in females [32% vs 50% ISV]. Conclusion: The serum concentration-time profile of siplizumab was adequately described by a two-compartment non-linear PK model. Population parameters were precisely estimated and correspond well to reported PK behaviour of monocolonal antibodies with significant target mediated elimination. The lower volume distribution in females is most likely due to lower body weight compared to males in this study. The population PK model combined with pharmacodynamic data could serve as a tool to guide selection of optimal dose regimens for siplizumab. Disclosures: No relevant conflicts of interest to declare.

Population pharmacokinetics of pegylated liposomal CKD-602 (S-CKD602) in patients with advanced solid tumors

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 2545-2545
Author(s):  
H. Wu ◽  
R. K. Ramanathan ◽  
S. Srychor ◽  
B. A. Zamboni ◽  
S. Ramalingam ◽  
...  
Keyword(s):  
Body Weight ◽  
Solid Tumors ◽  
Topoisomerase I ◽  
Plasma Concentrations ◽  
Ideal Body Weight ◽  
Compartment Model ◽  
Individual Variability ◽  
Synthetic Analogue ◽  
Population Pk ◽  
Linear And Nonlinear

2545 Background: CKD-602, a semi-synthetic analogue of campothecin, is a potent topoisomerase I inhibitor. S-CKD602, a PEGylated long-circulating liposomal formulation of CKD-602, was developed to achieve a longer intra-tumoral exposure of CKD602 and a higher therapeutic index. Age and body composition were reported to affect the pharmacokinetics (PK) of S- CKD602 (Zamboni, ASCO'07). A population PK model for encapsulated and released CKD-602 following administration of S- CKD602 was developed to assess factors that may influence S-CKD602 PK. Methods: Plasma samples from 45 patients (pts) with solid tumors were collected in a phase I study. S-CKD602 was administered as a 1 h IV infusion with doses ranging from 0.1 to 2.5 mg/m2. Plasma concentrations of encapsulated (n=292) and released (n=268) CKD-602 were measured by LC-MS/MS, and population PK modeling was performed using NONMEM. Results: Pts were classified as linear and nonlinear pts according to the clearance (CL) of encapsulated CKD-602 using a classic two stage PK modeling approach. Mean ± SD ratio of total body weight to ideal body weight of pts with linear and nonlinear CL of encapsulated CKD-602 was 1.13 ± 0.16 and 1.53 ± 0.29, respectively (P = 0.003). PK of encapsulated CKD-602 was described by 1-compartment model with nonlinear CL (Michaelis-Menten kinetics). PK of released CKD-602 was described by a 2- compartment model with linear CL for all pts. The presence of primary or metastatic tumor(s) located in the liver decreased the inter- individual variability (IIV) in the CL of encapsulated CKD-602 by 13%. Typical values of Vmax of encapsulated CKD-602 in pts with and without hepatic tumor(s) were 156 and 103 μg/h, respectively (P < 0.001). The inclusion of age decreased IIV in the release of CKD-602 from S-CKD602 by 22%. Typical values of release of CKD-602 from S-CKD602 in pts < 60 years old (yo) and pts ≥ 60 yo were 0.21 and 0.10 L/h, respectively (P < 0.001). Conclusions: These data suggest that older patients (pts ≥ 60 yo) have a reduced release of CKD-602 from S-CKD602. In addition, pts with tumors in the liver may have an increased clearance of S-CKD602. These observations have potential implications in the optimal dosing of liposomal agents. [Table: see text]

scholarly journals Paediatric population pharmacokinetic and pharmacodynamic modelling of ambrisentan in pulmonary arterial hypertension and comparison with adult data

2021 ◽  
Author(s):  
Malek Okour ◽  
Mita Thapar ◽  
Colm Farrell ◽  
Mary Ann Lukas ◽  
Maurice Beghetti ◽  
...  
Keyword(s):  
Body Weight ◽  
Steady State ◽  
Adult Population ◽  
Systemic Exposure ◽  
Paediatric Population ◽  
Walking Distance ◽  
Population Pharmacokinetic ◽  
Paediatric Patients ◽  
Population Pk ◽  
Pulmonary Arterial

Aims: To develop a population pharmacokinetic (PK) model of ambrisentan in paediatric patients aged 8 to <18 years with pulmonary arterial hypertension (PAH), compare paediatric ambrisentan systemic exposure to historical adult data, and assess PK–PD relationships. Methods: A previously developed adult population PK model provided an initial step for modelling the 211 PK observations from 39 paediatric patients with PAH in the randomised Phase IIb study AMB112529 (NCT01332331). Subsequently, a population PK model was developed using only paediatric PK data. Steady-state systemic exposure metrics were estimated for the paediatric population and compared with historical adult data (adult patients with PAH and healthy volunteers). Exploratory exposure–response analysis assessed ambrisentan systemic exposure versus change from baseline in 6-minute walking distance in paediatric patients; findings were compared with adult data. An exploratory analysis of ambrisentan exposure versus incidence of ambrisentan-related adverse events in paediatric patients was also performed. Results: The final paediatric population PK model was a two-compartment model which includes the effect of body weight (allometric scaling), first-order absorption and elimination, and absorption lag time. Similar steady-state ambrisentan exposure was confirmed in paediatric patients and historical adult data when differences in body weight were accounted for. There was no apparent correlation in the paediatric or adult population between ambrisentan exposure and change in 6-minute walking distance, or between ambrisentan exposure and incidence of ambrisentan-related adverse events in paediatric patients. Conclusions: Similar ambrisentan exposure and PK–PD profiles were observed in paediatric and adult populations with PAH.

scholarly journals Dose Tailoring of Vancomycin Through Population Pharmacokinetic Modeling Among Surgical Patients in Pakistan

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Muaaz Munir ◽  
Huma Rasheed ◽  
Muhammad Imran Khokhar ◽  
Rizwan Rasul Khan ◽  
Hafiz Asad Saeed ◽  
...  
Keyword(s):  
Body Weight ◽  
Plasma Concentration ◽  
Goodness Of Fit ◽  
Volume Of Distribution ◽  
Surgical Patients ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Time Data ◽  
Concentration Data ◽  
Population Pharmacokinetic Modeling

Background: Vancomycin is a narrow therapeutic agent, and it is necessary to optimize the dose to achieve safe therapeutic outcomes. The purpose of this study was to identify the significant covariates for vancomycin clearance and to optimize the dose among surgical patients in Pakistan.Methods: Plasma concentration data of 176 samples collected from 58 surgical patients treated with vancomycin were used in this study. A population pharmacokinetic model was developed on NONMEM® using plasma concentration–time data. The effect of all available covariates was evaluated on the pharmacokinetic parameters of vancomycin by stepwise covariate modeling. The final model was evaluated using bootstrap, goodness-of-fit plots, and visual predictive checks.Results: The pharmacokinetics of vancomycin followed a one-compartment model with first-order elimination. The vancomycin clearance (CL) and volume of distribution (Vd) were 2.45 L/h and 22.6 l, respectively. Vancomycin CL was influenced by creatinine clearance (CRCL) and body weight of the patients; however, no covariate was significant for its effect on the volume of distribution. Dose tailoring was performed by simulating dosage regimens at a steady state based on the CRCL of the patients. The tailored doses were 400, 600, 800, and 1,000 mg for patients with a CRCL of 20, 60, 100, and 140 ml/min, respectively.Conclusion: Vancomycin CL is influenced by CRCL and body weight of the patient. This model can be helpful for the dose tailoring of vancomycin based on renal status in Pakistani patients.

Population Pharmacokinetic Modeling of BeneFIX in Pediatric and Adult Patients with Hemophilia B Demonstrates Weight as An Important Factor Contributing to Inter-Patient PK Variability.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1218-1218 ◽  
Author(s):  
Chandrasekhar Udata ◽  
Sharon Sullivan ◽  
Patrick Kelly ◽  
David A. Roth ◽  
Xu Meng
Keyword(s):  
Clinical Trials ◽  
Body Weight ◽  
Individual Variability ◽  
Factor Ix ◽  
Adult Patients ◽  
Hemophilia B ◽  
Peripheral Compartment ◽  
Population Pharmacokinetic ◽  
Population Pharmacokinetic Modeling ◽  
First Time

Abstract Clinical trials in patients with hemophilia B have demonstrated considerable inter-patient variability in the pharmacokinetics (PK) of Factor IX (FIX) replacement therapy, including the recovery, an important PK parameter from which individualized clinical dosing decisions are calculated. In clinical trials of plasma-derived and recombinant factor IX replacement therapies, the age of the patient has been demonstrated to affect recovery (younger patients have lower recovery values than older patients), however the specific contribution of age, as well as additional covariates such as body weight and race to PK variability has not been systematically evaluated. We analyzed an extensive database of BeneFIX PK data collected from 8 separate clinical trials conducted over 13 years. A systematic approach involving population PK modeling and simulation was utilized for the first time to estimate the effects of individual-specific covariate factors on PK of BeneFIX in the pooled population that included pediatric and adult patients. A total of 4025 plasma FIX activity PK data sets collected from 191 patients, aged 0 to 69 years were used for the analysis. There were 111 children (£15 years) including 53 infants &lt;2 years, and 80 adults (&gt;15 years) in the pooled data. The majority (84%) of patients were Caucasian. The remaining patients were African American (7%), Hispanic (4%), Asian/Japanese (3%), and other ethnicity (3%). The data were analyzed using nonlinear mixed-effects modeling with the NONMEM software system. Age, weight, and race were examined as covariates for the ability to explain inter-individual variability in the BeneFIX PK. The PK in pediatric and adult patients was described by a two-compartment model with first-order elimination and a zero-order input using the following parameters: clearance (CL), volume of central compartment (V1), volume of peripheral compartment (V2) and inter-compartmental clearance (Q). Population predicted BeneFIX PK parameters, standardized to a 70 kg patient, were 7.46 (standard error; 0.20) mL/hr/kg, 131 (4.4) mL/kg, 71.5 (2.1) mL/kg and 12.1 (1.1) mL/h/kg, for CL, V1, V2 and Q, respectively. The final model was able to simulate data in close agreement with the actual study observations. Variability (%CV) in BeneFIX PK was explained most significantly by allometrically scaled body weight (Figure 1a), while age and race had no discernible effects on BeneFIX PK in the population studied. Observed recovery values were slightly lower in children (£15 years) compared with those in adults (&gt;15 years) since the initial volume of distribution (V1), normalized to body weight, was slightly higher in children than in adults, while the variability in the observed recovery values was comparable between children and adults (Figure 1b). In conclusion, the present analysis, for the first time, systematically describes and quantifies the sources of age-dependent variability of factor IX PK, using BeneFIX data, and provides a better understanding of the importance of body weight in the disposition of BeneFIX. This confirms existing weight-based dosing recommendations and further supports consideration of dosing adjustments that are individualized based on the patient’s body weight in the context of the achieving the desired clinical response, such as recovery. This also may be important in pediatric patients during growth periods associated with significant weight change. Figure 1a. Clearance versus Body weight Figure 1a. Clearance versus Body weight Figure 1b. Recovery versus Body weight Figure 1b. Recovery versus Body weight

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