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Screening methods using sulfamethazine for determining acetylator phenotype.

P du Souich, A J McLean, K Stoeckel

    Clinical Pharmacology and Therapeutics
    |December 1, 1979
    PubMed
    Summary
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    This study investigated sulfamethazine (SMZ) acetylation phenotyping. Computer simulations suggest the plasma N-acetyl SMZ (PI6) method is least sensitive to absorption and elimination variations, potentially improving phenotyping accuracy.

    Area of Science:

    • Pharmacokinetics
    • Drug Metabolism
    • Clinical Chemistry

    Background:

    • Sulfamethazine (SMZ) pharmacokinetics exhibit significant intersubject variability.
    • Clinical phenotyping of acetylation capacity relies on SMZ metabolism markers.
    • Nonmetabolic factors may influence the accuracy of phenotyping procedures.

    Purpose of the Study:

    • To assess the impact of absorption and urinary elimination rates on SMZ phenotyping.
    • To evaluate the reliability of different SMZ phenotyping methods under varying kinetic conditions.
    • To identify phenotyping procedures least susceptible to nonmetabolic parameter variability.

    Main Methods:

    • Analysis of SMZ kinetics in seven normal subjects (slow and fast acetylators).
    • Computer simulations modeling variations in absorption and urinary elimination rate constants.

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  • Assessment of SMZ phenotyping markers: plasma SMZ half-life, Km, and N-acetyl SMZ percentages (PI6, UI5-6, UI6).

    • Main Results:

    • Simulations demonstrated that all common phenotyping procedures are sensitive to changes in absorption and urinary elimination.
    • The plasma N-acetyl SMZ (PI6) method showed the least sensitivity to variations in nonmetabolic parameters.
    • These findings suggest PI6 may offer a more robust measure for phenotyping screening.

    Conclusions:

    • Nonmetabolic parameters significantly affect the accuracy of standard SMZ phenotyping methods.
    • The PI6 method appears most reliable for clinical screening due to its lower sensitivity to kinetic variability.
    • Further experimental validation is recommended to confirm the predictive power of these simulation results.