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Research ArticleSpecial Section on Pediatric Drug Disposition and Pharmacokinetics

Examining Small Intestinal Transit Time as a Function of Age: Is There Evidence to Support Age-Dependent Differences among Children?

Anil R. Maharaj and Andrea N. Edginton
Drug Metabolism and Disposition July 2016, 44 (7) 1080-1089; DOI: https://doi.org/10.1124/dmd.115.068700
Anil R. Maharaj
School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada
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Andrea N. Edginton
School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada
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  • Fig. 1.
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    Fig. 1.

    Mean intestinal transit time (SITT or OCTT) estimates pertaining to each study group included within the analysis. Data are reflective of transit values from normal subjects, free of GI disease.

  • Fig. 2.
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    Fig. 2.

    SITT or OCTT segmented according to measurement method for all investigations (i.e., all age groups) documenting intestinal transit in normal subjects free of GI disease (open circles). The diameter of each circle is proportional to the 1/(Variancei)1/2, where Variancei represents the within-study variance. Mean values, as estimated according to a meta-regression model employing measurement method as the sole modulator, are displayed for reference (−).

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    Fig. 3.

    OCTT as a function of age for investigations employing lactulose H2 breath testing in normal subjects free of GI disease (open circles). The diameter of each circle is proportional to the 1/(Variancei)1/2, where Variancei represents the within-study variance. Estimates of OCTT based on a meta-regression model with age as a linear regressor have been superimposed for reference (mean, solid line; 95% confidence interval, dotted lines).

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    Fig. 4.

    SITT or OCTT as a function of age for investigations employing scintigraphy (black circles) and other measurement techniques (open circles) in normal subjects free of GI disease. The diameter of each circle is proportional to the 1/(Variancei)1/2, where Variancei represents the within-study variance. Estimates of mean intestinal transit time based on a meta-regression model with age as a linear regressor have been separately superimposed for studies utilizing scintigraphy (solid line) and other measurement techniques (dotted line).

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    Fig. 5.

    SITT as a function of age for investigations employing capsule endoscopy (open circles). The diameter of each circle is proportional to the 1/(Variancei)1/2, where Variancei represents the within-study variance. Estimates of SITT based on a meta-regression model with age as a linear regressor have been superimposed for reference (mean, solid line; 95% confidence interval, dotted lines).

Tables

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    Table 1

    Lactulose H2 Breath Tests—Linear Meta-Regression Model

    Summary Statisticska = 14QM (df=1)b = 0.5296 (p = 0.4668)I2c = 92.53%R2d = 0.00%
    EstimateStandard Errorp-Value95% CI (lower)95% CI (upper)
    Fixed Effects
     Intercept (B0)73.81424.5321*64.931582.6969
     Age (B1)−0.36630.50340.4668−1.35290.6203
    Random Effects
     Between study variance (τ2)239.7203116.7795———
     Tau (τ)15.4829————
    • ↵a k = number of subject groups;

    • ↵b QM = heterogeneity statistic (Cochran's Q) – tests whether any coefficient (not including the intercept) is significantly different than 0;

    • ↵c I2 = % of total variability due to heterogeneity;

    • ↵d R2 = % of total heterogeneity explained by the covariate(s);

    • ↵* p-Value<0.0001; CI, confidence interval; —, value does not need to be determined.

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    Table 2

    Scintigraphy and Other Techniques—Linear Meta-Regression Model

    Summary Statisticska = 38QM (df=2)b = 19.1664 (p < 0.0001)I2c = 84.57%R2d = 39.63%
    EstimateStandard Errorp-Value95% CI (lower)95% CI (upper)
    Fixed Effects
     Intercept (B0)206.96757.8990*191.4857222.4493
     Measurement Method (B1)61.590714.0951*33.964889.2166
     Age (B2)0.41830.46890.3723−0.50061.3373
    Random Effects
     Between study variance (τ2)1165.4337370.2652———
     Tau (τ)34.1384————
    • ↵a k = number of subject groups;

    • ↵b QM = heterogeneity statistic (Cochran's Q) – tests whether any coefficient (not including the intercept) is significantly different than 0;

    • ↵c I2 = % of total variability due to heterogeneity;

    • ↵d R2 = % of total heterogeneity explained by the covariate(s);

    • ↵* p-Value<0.0001; CI, confidence interval; —, value does not need to be determined.

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    Table 3

    Simulated vs. Observed Theophylline Absorption PK at 1 Week Following Daily Administration of a Sustained Release Formulation in Older Children (8-14 yrs)

    SourceMean Cmax [mcg/mL] (CV%a)Mean Cmin [mcg/mL] (CV%)Mean Percent Fluctuation (%)b (CV%)
    iciidiiiiii
    Observed
     Pedersen and Steffensen, 198712.8612.186.797.2995.7872.80
    (24.84%)(26.07%)(33.03%)(31.69%)(26.77%)(40.34%)
    Simulated-PBPK
     SITT (adult)12.53e8.4852.39
     LITT (adult)(27.15%)(37.68%)(25.60%)
     SITT (↓ 25%)f12.538.4852.38
     LITT (adult)(27.16%)(37.68%)(25.63%)
     SITT (↓ 50%)12.538.4852.38
     LITT (adult)(27.15%)(37.68%)(25.63%)
     SITT (↓ 25%)12.538.4852.40
     LITT (↓ 25%)g(27.14%)(37.68%)(25.65%)
     SITT (↓ 50%)11.157.8146.91
     LITT (↓ 50%)(29.01%)(38.66%)(24.49%)
    • ↵a coefficient of variation;

    • ↵b percent fluctuation between peak and trough plasma concentration values over a given dosing interval (i.e. [peak – trough]/trough);

    • ↵c Data reported by Pedersen and Steffensen on Day 6 following oral maintenance (q24h) therapy with a sustained release theophylline formulation (Noctelin – Riker Labs Inc, Loughborough, UK) [n = 14];

    • ↵d Data reported for 10 of 14 children investigated by Pedersen and Steffensen on Day 7 following oral maintenance (q24h) therapy with a sustained release theophylline formulation (Noctelin – Riker Labs) [n = 10 – same study group as depicted above; data for 4 children was unavailable];

    • ↵e PBPK models were not parameterized to include intradose variability. (i.e. once steady-state was achieved, concentration-time values were congruent between dosing intervals). As such, simulated data is only provided as a single value obtained on day 7 of theophylline maintenance dosing [n = 50];

    • ↵f 25% reduction in small intestinal transit time (SITT) from adult values. SITT for normal adults was parameterized as 2.1h - the default PK-Sim® v5.2 value. This represent the time span between gastric emptying of 63% of a nonabsorbable marker and localization of 90% of the marker within the caecum.

    • ↵g 25% reduction in large intestinal transit time (LITT) from adult values. LITT for normal adults was parameterized as 44.2h - the default PK-Sim® v5.2 value. This represent the time span between 90% of a nonabsorbable marker reaching the caecum and localization of 70% of the marker within the feces;

    • View popup
    Table 4

    Capsule Endoscopy Studies—Linear Meta-Regression Model

    Summary Statisticska = 16QM (df=1)b = 7.6931 (p = 0.0055)I2c = 93.76%R2d = 41.58%
    EstimateStandard Errorp-Value95% CI (lower)95% CI (upper)
    Fixed Effects
     Intercept (B0)270.34428.8046*253.0875287.6009
     Age (B1)−1.06950.38560.0055−1.8252−0.3137
    Random Effects
     Between study variance (τ2)908.1523432.9293———
     Tau (τ)30.1356————
    • ↵a k, number of subject groups;

    • ↵b QM, heterogeneity statistic (Cochran's Q) – tests whether any coefficient (not including the intercept) is significantly different than 0

    • ↵c I2, % of total variability due to heterogeneity;

    • ↵d R2, % of total heterogeneity explained by the covariate(s);

    • * p-Value<0.0001; CI, confidence interval; —, value does not need to be determined.

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Drug Metabolism and Disposition: 44 (7)
Drug Metabolism and Disposition
Vol. 44, Issue 7
1 Jul 2016
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Research ArticleSpecial Section on Pediatric Drug Disposition and Pharmacokinetics

Examining Small Intestinal Transit Time as a Function of Age

Anil R. Maharaj and Andrea N. Edginton
Drug Metabolism and Disposition July 1, 2016, 44 (7) 1080-1089; DOI: https://doi.org/10.1124/dmd.115.068700

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Research ArticleSpecial Section on Pediatric Drug Disposition and Pharmacokinetics

Examining Small Intestinal Transit Time as a Function of Age

Anil R. Maharaj and Andrea N. Edginton
Drug Metabolism and Disposition July 1, 2016, 44 (7) 1080-1089; DOI: https://doi.org/10.1124/dmd.115.068700
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