scholarly journals Bayesian forecasting for intravenous tobramycin dosing in adults with Cystic Fibrosis using one versus two serum concentrations in a dosing interval

2021 ◽  
Author(s):  
Philip G. Drennan ◽  
Yann Thoma ◽  
Lucinda Barry ◽  
Johan Matthey ◽  
Sheila Sivam ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Sampling Strategy ◽  
Prolonged Treatment ◽  
Accurate Estimation ◽  
Population Pharmacokinetic ◽  
List Type ◽  
Bayesian Forecasting ◽  
Concentration Data ◽  
Dosing Interval ◽  
Post Infusion

AbstractBackgroundIntravenous tobramycin requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of Cystic Fibrosis (CF). The 24 hour area under the concentration time curve (AUC24) is widely used to guide dosing, however there remains variability in practice around methods for its estimation.ObjectivesTo determine the potential for a sparse sampling strategy using a single post-infusion tobramycin concentration and Bayesian forecasting, to assess the AUC24 in routine practice.MethodsAdults with CF receiving once daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) following end of infusion as routine monitoring. We estimated AUC24 exposures using Tucuxi, a Bayesian forecasting application incorporating a validated population pharmacokinetic model. We performed simulations to estimate AUC24 using the full dataset using c1 and c2, compared to estimates using depleted datasets (c1 or c2 only), with and without concentration data from earlier in the course. We assessed agreement between each simulation condition and the reference graphically, and numerically using median difference (Δ) AUC24, and (relative) root mean square error (rRMSE) as measures of bias and accuracy respectively.Results55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ΔAUC24 <2 mg.h.l-1 and rRMSE of <15% for sequential c1 and c2 conditions.ConclusionsBayesian forecasting, using single post-infusion concentrations taken 2-6 hours following tobramycin administration can adequately estimate true exposure in this patient group and are suitable for routine TDM practice.Key Points-In stable adult patients with Cystic fibrosis without significant renal impairment, Bayesian forecasting allows accurate estimation of tobramycin AUC24 using a single blood sample taken 2-6 hours post-infusion with acceptable accuracy, especially when including prior measured concentrations.-A single sample approach with Bayesian forecasting is logistically less complicated than a two-sample approach, and could facilitate best-practice TDM in the outpatient setting.-A more intensive sampling strategy with Bayesian forecasting using two tobramycin concentrations in a dosing interval should be considered in unstable patients, or where observed concentrations deviate significantly from model predictions.

scholarly journals Population Pharmacokinetics of Amikacin in Adult Patients with Cystic Fibrosis

2018 ◽  
Vol 62 (10) ◽  
Author(s):  
Sílvia M. Illamola ◽  
Hoa Q. Huynh ◽  
Xiaoxi Liu ◽  
Zubin N. Bhakta ◽  
Catherine M. Sherwin ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Population Pharmacokinetics ◽  
Drug Disposition ◽  
Adult Patients ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Concentration Data ◽  
First Order ◽  
Dosing Regimens ◽  
Pulmonary Exacerbations

ABSTRACTPractitioners commonly use amikacin in patients with cystic fibrosis. Establishment of the pharmacokinetics of amikacin in adults with cystic fibrosis may increase the efficacy and safety of therapy. This study was aimed to establish the population pharmacokinetics of amikacin in adults with cystic fibrosis. We used serum concentration data obtained during routine therapeutic drug monitoring and explored the influence of patient covariates on drug disposition. We performed a retrospective chart review to collect the amikacin dosing regimens, serum amikacin concentrations, blood sampling times, and patient characteristics for adults with cystic fibrosis admitted for treatment of acute pulmonary exacerbations. Amikacin concentrations were retrospectively collected for 49 adults with cystic fibrosis, and 192 serum concentrations were available for analysis. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling with the first-order conditional estimation method. A two-compartment model with first-order elimination best described amikacin pharmacokinetics. Creatinine clearance and weight were identified as significant covariates for clearance and the volume of distribution, respectively, in the final model. Residual variability was modeled using a proportional error model. Typical estimates for clearance, central and peripheral volumes of distribution, and intercompartmental clearance were 3.06 liters/h, 14.4 liters, 17.1 liters, and 0.925 liters/h, respectively. The pharmacokinetics of amikacin in individuals with cystic fibrosis seems to differ from those in individuals without cystic fibrosis. However, further investigations are needed to confirm these results and, thus, the need for variations in amikacin dosing. Future pharmacodynamic studies will potentially establish the optimal amikacin dosing regimens for the treatment of acute pulmonary exacerbations in adult patients with CF.

scholarly journals Population pharmacokinetic evaluation and optimization of amikacin dosage regimens for the management of mycobacterial infections

2020 ◽  
Vol 75 (10) ◽  
pp. 2933-2940
Author(s):  
Hinke Siebinga ◽  
Fiona Robb ◽  
Alison H Thomson
Keyword(s):  
Dose Adjustment ◽  
Population Pharmacokinetic ◽  
Limited Information ◽  
Once Daily ◽  
Mycobacterial Infections ◽  
Dosing Interval ◽  
Dosage Regimens ◽  
Population Pk ◽  
Post Infusion ◽  
Dosage Guidelines

Abstract Background There is limited information on amikacin pharmacokinetics (PK) and dose requirements in patients with mycobacterial infections. Objectives To conduct a population PK analysis of amikacin data from patients with mycobacterial infections and compare predicted concentrations from standard and modified dosage guidelines with recommended target ranges. Methods A population PK model was developed using NONMEM. Cmax, Cmin, concentration 1 h post-infusion (C1h) and AUC0–24 using 15 mg/kg daily (once daily), the WHO table, 25 mg/kg three times weekly (TTW) and modified guidelines were compared using Monte Carlo simulations of 1000 patients. Results Data were available from 124 patients (684 concentrations) aged 16–92 years. CL was 4.64 L/h per 100 mL/min CLCR; V was 0.344 L/kg. With once-daily regimens, Cmax was 35–45 mg/L in 30%–35% of patients and 35–50 mg/L in 46%–48%; C1h was 25–40 mg/L in 53%–59%. The WHO table produced high Cmax values in patients &lt;60 kg and low in patients &gt;75 kg. With TTW dosing, around 30% of Cmax values were 65–80 mg/L, 40% were 60–80 mg/L, and 48% of C1h were 45–65 mg/L. Increasing the dosage interval for patients with CLCR &lt;50 mL/min reduced Cmin values &gt;2 mg/L from 34% to 25% for once-daily dosing and from 18% to 13% for TTW. In patients whose Cmin was &lt;2 mg/L, 82% of AUC0–24 values were 100–300 mg.h/L. Conclusions Standard amikacin dosing guidelines achieve low percentages of target concentrations for mycobacterial infections. Extending the dosing interval in renal impairment and widening target ranges would reduce the need for dose adjustment.

scholarly journals O33 Towards personalised weaning of sedatives and analgesics using mechanism-based modelling of iatrogenic withdrawal in critically ill children

2019 ◽  
Vol 104 (6) ◽  
pp. e14.3-e15
Author(s):  
S Goulooze ◽  
E Krekels ◽  
M van Dijk ◽  
T Hankemeier ◽  
D Tibboel ◽  
...  
Keyword(s):  
Critically Ill ◽  
Rating Scale ◽  
Numerical Rating Scale ◽  
Statistical Significance ◽  
Prolonged Treatment ◽  
Morphine Dependence ◽  
Critically Ill Children ◽  
Population Pharmacokinetic ◽  
List Type ◽  
Over Time

BackgroundProlonged treatment with analgesics and sedatives can result in iatrogenic withdrawal syndrome (IWS) in children being weaned from these drugs.1Personalized weaning strategies might lower the incidence of IWS, but this requires a quantitative understanding of withdrawal over time in individual patients.MethodsData from 81 children (aged 1 month to 17 years) collected during an observational clinical study on IWS2 were used, including a total of 1782 withdrawal assessments performed by PICU nurses, on a numerical rating scale (NRSwithdrawal) from 0 (no withdrawal) to 10 (worst withdrawal possible). Population pharmacokinetic models from literature were used to generate concentration-time profiles in each patient of all key analgesics and sedatives: morphine, fentanyl, methadone, midazolam, lorazepam, propofol, esketamine and clonidine. A mechanism-based withdrawal model was developed using NONMEM 7.3 to quantify IWS over time. The final model was used to perform simulations in which different weaning strategies were compared.ResultsA novel mechanism-based withdrawal model structure was developed with a hypothetical compartment, which equilibrates with the central pharmacokinetic compartment, and which characterizes the development and disappearance of drug dependence over time. With this model and available data, withdrawal dynamics could be established with statistical significance for fentanyl (p< 10-6), morphine (p=0.043) and esketamine (p=0.002), and not for any of the other drugs. Compared with fentanyl, development and disappearance of esketamine and morphine dependence is slower.ConclusionsGiven the patient‘s use of fentanyl, morphine and esketamine, the developed model can dynamically predict IWS from these substances under different weaning strategies. The results show that the optimal strategy for weaning of drug dependent children depends on both the type of drug and the drug levels prior to weaning. In this study, there was insufficient information to characterise midazolam withdrawal dynamics, potentially because of slow midazolam weaning with insufficiently high NRSwithdrawal scores.ReferencesBest KM, Boullata JI, Curley MAQ. Risk factors associated with iatrogenic opioid and benzodiazepine withdrawal in critically ill pediatric patients: A Systematic Review and Conceptual Model. Pediatr Crit Care Med ( 2015) 16(2): 175–183.Ista E, de Hoog M, Tibboel D, Duivenvoorden HJ, van Dijk M. Psychometric evaluation of the sophia observation withdrawal symptoms scale in critically ill children. Pediatr Crit Care Med ( 2013).14(8): 761–769.Disclosure(s)Nothing to disclose

scholarly journals Bayesian forecasting for intravenous tobramycin dosing in adults with cystic fibrosis using one versus two serum concentrations in a dosing interval

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Philip G. Drennan ◽  
Yann Thoma ◽  
Lucinda Barry ◽  
Johan Matthey ◽  
Sheila Sivam ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Serum Concentrations ◽  
Bayesian Forecasting ◽  
Dosing Interval

scholarly journals Creating a Population Model for PK-Tailored Dosing Using Real-World Data from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) Platform

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1191-1191
Author(s):  
Pierre Chelle ◽  
Cindy Yeung ◽  
Santiago Bonanad Boix ◽  
Juan Cristobal Morales ◽  
Margareth C Ozelo ◽  
...  
Keyword(s):  
Real World ◽  
Research Funding ◽  
Model Development ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Clotting Factor ◽  
Population Pharmacokinetic ◽  
Activity Levels ◽  
Bayesian Forecasting ◽  
Post Infusion

Abstract Objective: The Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) platform allows hemophilia treaters to estimate individual PK parameters for clotting factor concentrates using only a few blood samples drawn from their patients. Population pharmacokinetic (PopPK) models used with WAPPS-Hemo are usually built using clinical trial data provided by drug manufacturers. These trials can be restrictive with respect to patient covariates such as age and body weight. On the other hand, real-world pharmacokinetic data submitted to WAPPS captures the breadth of hemophilia patients. We tested the use of post-infusion clotting factor concentrate measurements collected through routine use of WAPPS-Hemo to develop a plasma-derived FVIII (Fanhdi/Alphanate)-specific model for which no trial data was available. Methods: Plasma factor activity measurements and information on hemophilia A patients were extracted from the WAPPS database. PopPK modeling was completed in NONMEM (ver 7.3, Icon, PLC). Evaluation of model-based Bayesian forecasting to derive the individual PK parameters included 10-fold internal cross validation, limited sampling analysis, and external validation using WAPPS data collected subsequently. The ability of the model to capture age related changes in PK was assessed through separate examination of predictive accuracy for children ≤ 12 yrs and for children and adults >12 yrs of age. Results: Post-infusion FVIII activity levels from 92 patients were used to derive and internally validate the model. Two-thirds of the patients originated from three centres (Campinas, Brazil; Valencia, Spain; and Santiago, Chile) with the remaining patients from 9 other WAPPS centers. Patients were 1-71 yrs (~33.7% ≤12 yrs) with a body weight range between 9 and 119 kg. Each patient provided between 1-8 activity levels up to 72 hours post infusion (386 data points in total). The final PopPK model followed 2-compartment kinetics with fat-free mass and age as covariates. Clearance was consistent across age until 25 years where clearance declined. The ability of the model to capture the PK in children ≤ 12 yrs was similar in accuracy to patients >12 yrs (Figure 1). Limited sampling analysis demonstrated that sampling strategies using two to three samples with one sample being a 72 hour post-infusion sample produced time-above-2% activity estimates, on average, <5% different than a sampling strategy using 16 post-infusion samples. Bayesian forecasting with additional WAPPS data collected following the model development process (n=10 patients) demonstrated that half-life, clearance, central volume and time-above-2% estimates were within those of the original patient population. Conclusions: Use of routine clinical care data for model development was feasible and expanded the covariate space (e.g. age) from what is traditionally included in trials. A promising approach would be to supplement clinical trial data with routine clinical data in order to build future PopPK models. On one hand, dense data from clinical trials brings stability to the model and provides a good description of the PK curve and may reduce random error if measured in a central lab; on the other hand, sparse data from routine practice widens the possible observations, inputs and covariates of the model, and could better perform the scope of individual Bayesian forecasting. Our analysis has demonstrated that collecting a few real world samples per patient not only allows accurately determining individual PK parameters, but was also effective for developing a model for a specific brand. This research was supported by Grifols, a manufacturer of plasma-derived FVIII/VWF concentrates. The WAPPS-Hemo team independently performed the derivation and validation of the model. All authors reviewed and approved the abstract as submitted. Disclosures Ozelo: BioMarin: Honoraria, Speakers Bureau; Grifols: Honoraria; Novo Nordisk: Honoraria, Research Funding, Speakers Bureau; Pfizer: Honoraria, Research Funding, Speakers Bureau; Shire: Honoraria, Research Funding, Speakers Bureau; Bioverativ: Honoraria, Research Funding. Iorio:Shire: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Shire; Pfizer: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Pfizer; NovoNordisk: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Novo Nordisk; Grifols: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Grifols; Octapharma: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Octapharma; CSL: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with CSL; Bayer: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Bayer; Roche: Other: Alfonso Iorio's Institution has received project based funding via research or service agreements with Roche. Spears:Grifols: Employment. Mir:Grifols: Employment. Edginton:Bayer: Honoraria.

scholarly journals Inflammation in children with cystic fibrosis: contribution of bacterial production of long-chain fatty acids

2021 ◽  
Author(s):  
Erin Felton ◽  
Aszia Burrell ◽  
Hollis Chaney ◽  
Iman Sami ◽  
Anastassios C. Koumbourlis ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Fatty Acid ◽  
Antibiotic Treatment ◽  
Bacterial Production ◽  
Clinical Status ◽  
Achromobacter Xylosoxidans ◽  
List Type ◽  
Future Studies ◽  
Long Chain

Abstract Background Cystic fibrosis (CF) affects >70,000 people worldwide, yet the microbiologic trigger for pulmonary exacerbations (PExs) remains unknown. The objective of this study was to identify changes in bacterial metabolic pathways associated with clinical status. Methods Respiratory samples were collected at hospital admission for PEx, end of intravenous (IV) antibiotic treatment, and follow-up from 27 hospitalized children with CF. Bacterial DNA was extracted and shotgun DNA sequencing was performed. MetaPhlAn2 and HUMAnN2 were used to evaluate bacterial taxonomic and pathway relative abundance, while DESeq2 was used to evaluate differential abundance based on clinical status. Results The mean age of study participants was 10 years; 85% received combination IV antibiotic therapy (beta-lactam plus a second agent). Long-chain fatty acid (LCFA) biosynthesis pathways were upregulated in follow-up samples compared to end of treatment: gondoate (p = 0.012), oleate (p = 0.048), palmitoleate (p = 0.043), and pathways of fatty acid elongation (p = 0.012). Achromobacter xylosoxidans and Escherichia sp. were also more prevalent in follow-up compared to PEx (p < 0.001). Conclusions LCFAs may be associated with persistent infection of opportunistic pathogens. Future studies should more closely investigate the role of LCFA production by lung bacteria in the transition from baseline wellness to PEx in persons with CF. Impact Increased levels of LCFAs are found after IV antibiotic treatment in persons with CF. LCFAs have previously been associated with increased lung inflammation in asthma. This is the first report of LCFAs in the airway of persons with CF. This research provides support that bacterial production of LCFAs may be a contributor to inflammation in persons with CF. Future studies should evaluate LCFAs as predictors of future PExs.

Population pharmacokinetics of the rilpivirine long-acting formulation after intramuscular dosing in healthy subjects and people living with HIV

2021 ◽  
Author(s):  
M Neyens ◽  
H M Crauwels ◽  
J J Perez-Ruixo ◽  
S Rossenu
Keyword(s):  
Population Pharmacokinetics ◽  
Healthy Subjects ◽  
Phase Iii ◽  
Intramuscular Administration ◽  
Population Pharmacokinetic ◽  
People Living With Hiv ◽  
Concentration Data ◽  
Flip Flop ◽  
Long Acting ◽  
Living With Hiv

Abstract Objectives To characterize the population pharmacokinetics of the rilpivirine long-acting (LA) formulation after intramuscular administration. Methods Rich and sparse rilpivirine plasma concentration data were obtained from seven clinical studies. In total, 18 261 rilpivirine samples were collected from 986 subjects (131 healthy subjects from Phase I studies and 855 people living with HIV from Phase IIb/III studies). Doses ranged from 300 to 1200 mg, as single-dose or multiple-dose regimens (every 4 or 8 weeks). In Phase III studies, an initiation injection of 900 mg followed by continuation injections of 600 mg every 4 weeks was used. Non-linear mixed-effects modelling was performed using NONMEM® software. Results A one-compartment model with linear elimination and two parallel absorption pathways (fast and slow) with sequential zero-first-order processes adequately captured rilpivirine flip-flop pharmacokinetics after intramuscular administration of the LA formulation. The estimated apparent elimination half-life of rilpivirine LA was 200 days. None of the evaluated covariates (age, body weight, BMI, sex, race, health status and needle length) had a clinically relevant impact on rilpivirine pharmacokinetics. Conclusions The population pharmacokinetic model suitably describes the time course and associated variability of rilpivirine plasma concentrations after rilpivirine LA intramuscular administration. The monthly regimen consists of an oral lead-in period (rilpivirine 25 mg tablets once daily for 4 weeks), followed by an initiation injection of 900 mg rilpivirine LA, then 600 mg rilpivirine LA continuation injections monthly. The absence of a clinically relevant effect of covariates on rilpivirine pharmacokinetics suggests that rilpivirine LA dose adjustments for specific subgroups are not warranted.

scholarly journals External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

2020 ◽  
Author(s):  
Sunae Ryu ◽  
Woo Jin Jung ◽  
Zheng Jiao ◽  
Jung Woo Chae ◽  
Hwi-yeol Yun
Keyword(s):  
External Validation ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Bayesian Forecasting ◽  
Predictive Capability ◽  
Young Infants ◽  
Simulation Based

Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

scholarly journals Incorporating Nonlinear Kinetics to Improve the Predictive Performance of Population Pharmacokinetic Models for Ciclosporin in Adult Renal Transplant Recipients: A Comparison of Modelling Strategies

2020 ◽  
Author(s):  
Jun-Jun Mao ◽  
Zheng Jiao ◽  
Xiao-Yan Qiu ◽  
Ming Zhang ◽  
Ming-Kang Zhong
Keyword(s):  
Renal Transplant ◽  
Predictive Performance ◽  
Population Pharmacokinetic ◽  
List Type ◽  
Renal Transplant Recipients ◽  
Transplant Recipients ◽  
Inhibitory Effects ◽  
Nonlinear Kinetics ◽  
Nonlinear Properties ◽  
Modelling Strategies

AbstractAimCiclosporin (CsA) has been shown to follow nonlinear pharmacokinetics in renal transplant recipients who received Neoral-based triple immunosuppressive therapy. Some of these nonlinear properties have not been fully considered in population pharmacokinetic (popPK) analysis. Therefore, the aim of this study was to determine the potential influence of nonlinearity and the functional forms of covariates on model predictability.MethodsA total of 2969 CsA whole-blood measurements, including 1328 pre-dose and 1641 2-h post-dose concentrations, were collected from 173 patients who underwent their first renal transplantation. Four popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based, linear and nonlinear compartmental kinetic models and empirical formulae on model predictability. Prediction-based and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine the stability and predictive performance of these four models.ResultsModel predictability improved when nonlinearity was considered. The theory-based nonlinear model which incorporated nonlinear property based on known theoretical relationships performed better than the other two compartmental models. The nonlinear Michaelis-Menten model showed a remarkable improvement in predictive performance over that of the other three compartmental models. The saturated binding of CsA to erythrocytes, and auto-inhibition that arose from the inhibitory effects of CsA on CYP3A4/P-gp and CsA-prednisolone drug interaction may have contributed to the nonlinearity.ConclusionsIncorporating nonlinear properties are likely to be a promising approach for improving CsA model predictability. However, CsA nonlinear kinetics resources need further investigation. Until then, Michaelis-Menten empirical model can be used for CsA dose adjustments.What is already known about this subjectCsA in renal transplant recipients receiving Neoral-based triple immunosuppressive therapy followed nonlinear pharmacokinetics.Nonlinearity is rarely incorporated into CsA population pharmacokinetic (popPK) modelling processes.What this study addsFour popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based compartmental kinetic models and empirical formulae, as well as the effect of nonlinearity on CsA model predictability.Based on the four models, incorporating nonlinear properties is likely to be a promising approach for improving CsA model predictability.Saturated distribution into red blood cells, and auto-inhibition that arose from the inhibitory effects of CsA on CYP3A4/P-gp and CsA-prednisolone drug interaction may be the main sources of CsA PK nonlinearity.Principal Investigator statementThe authors confirm that the Principal Investigator for this paper is Zheng Jiao and that he had direct clinical responsibility for patients.

scholarly journals Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

2014 ◽  
Vol XL-8 ◽  
pp. 1353-1363 ◽  
Author(s):  
G. S. Tagore ◽  
G. D. Bairagi ◽  
R. Sharma ◽  
P. K. Verma
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Sampling Strategy ◽  
Spatial Prediction ◽  
Accurate Estimation ◽  
Suitable Alternative ◽  
Available N ◽  
Physico Chemical

A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.

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