Characterization of AUCs from sparsely sampled populations in toxicology studies

• Published in: Pharmaceutical research 1996-09, Vol.13 (9), p.1283-1290
• Main Authors: PAI, S. M, FETTNER, S. H, HAJIAN, G, CAYEN, M. N, BATRA, V. K
• Format: Article
• Language: English
• Subjects: Animals; Anticonvulsants - pharmacokinetics; Area Under Curve; Biological and medical sciences; Dose-Response Relationship, Drug; Felbamate; Female; General pharmacology; Histamine H1 Antagonists - pharmacokinetics; Loratadine - blood; Loratadine - pharmacokinetics; Loratadine - toxicity; Male; Medical sciences; Models, Statistical; Pharmacokinetics. Pharmacogenetics. Drug-receptor interactions; Pharmacology. Drug treatments; Phenylcarbamates; Propylene Glycols - blood; Propylene Glycols - pharmacokinetics; Propylene Glycols - toxicity; Rats; Reproducibility of Results; Sampling Studies
• ISSN: 0724-8741; 1573-904X
• DOI: 10.1023/a:1016097227603

The objective of this work was to develop and validate blood sampling schemes for accurate AUC determination from a few samples (sparse sampling). This will enable AUC determination directly in toxicology studies, without the need to utilize a large number of animals. Sparse sampling schemes were developed using plasma concentration-time (Cp-t) data in rats from toxicokinetic (TK) studies with the antiepileptic felbamate (F) and the antihistamine loratadine (L); Cp-t data at 13-16 time-points (N = 4 or 5 rats/time-point) were available for F, L and its active circulating metabolite descarboethoxyloratadine (DCL). AUCs were determined using the full profile and from 5 investigator designated time-points termed "critical" time-points. Using the bootstrap (re-sampling) technique, 1000 AUCs were computed by sampling (N = 2 rats/point, with replacement) from the 4 or 5 rats at each "critical" point. The data were subsequently modeled using PCNONLIN, and the parameters (ka, ke, and Vd) were perturbed by different degrees to simulate pharmacokinetic (PK) changes that may occur during a toxicology study due to enzyme induction/inhibition, etc. Finally Monte Carlo simulations were performed with random noise (10 to 40%) applied to Cp-t and/or PK parameters to examine its impact on AUCs from sparse sampling. The 5 time-points with 2 rats/point accurately and precisely estimated the AUC for F, L and DCL; the deviation from the full profile was approximately 10%, with a precision (%CV) of approximately 15%. Further, altered kinetics and random noise had minimal impact on AUCs from sparse sampling. Sparse sampling can accurately estimate AUCs and can be implemented in rodent toxicology studies to significantly reduce the number of animals for TK evaluations. The same principle is applicable to sparse sampling designs in other species used in safety assessments.