Follow that botanical: Challenges and recommendations for assessing absorption, distribution, metabolism and excretion of botanical dietary supplements.

Botanical dietary supplements are complex mixtures containing one or more botanical ingredient(s), each containing numerous constituents potentially responsible for its purported biological activity. Absorption, distribution, metabolism, and excretion (ADME) data are critical to understand the safety of botanical dietary supplements, including their potential for pharmacokinetic botanical-drug or botanical-botanical interactions. However, ADME data for botanical dietary supplements are rarely available and frequently inadequate to characterize their fate in vivo. Based on an assessment of the current status of botanical dietary supplements ADME research, the following key areas are identified that require robust data for human safety assessment: 1) phytochemical characterization including contaminant analysis and botanical authentication; 2) in vitro and/or in vivo data for identifying potential botanical-botanical or botanical-drug interactions and active/marker constituents; 3) robust ADME study design to include systemic exposure data on active/marker constituents using traditional or novel analytical chemistry and statistical approaches such as poly-pharmacokinetics; and 4) investigation of human relevance. A case study with Ginkgo biloba extract is used to highlight the challenges and proposed approaches in using ADME data for human safety assessment of botanical dietary supplements.

Application of TQSM polypharmacokinetics and its similarity approach to ascertain Q-marker by analyses of transitivity in vivo of five candidates in Buyanghuanwu injection.

BACKGROUND: It is well-known that the public still have been facing on a severe issue about the inconsistency of quality and therapeutic efficacy of traditional medicines. Recently, Professor Chang-Xiao Liu has created a new promising concept for identifying relevant quality-markers (Q-marker) from herbs, their formulas and manufacturing products. Therefore, building up a new approach is necessary for us to bridge over quality to efficacy of pharmaceutical products. STUDY DESIGN: In this paper, five candidate Q-markers, astragaloside IV, paeonflorin, amygdalin, tetramethylpyrazine, ferulic acid in Buyanghuanwu injection (BYHWI) had been designed to carry out in rat by using single and polypharmacokinetic models for total quanta to ascertain adequate Q-marker. METHODS: The Q-marker transitivity in vivo was studied with polypharmacokinetic model and its similarity approach, which were modeled with TQSM principle. The Q-marker was ascertained with transitive similarity and bioavailability in polypharmacokinetics. Their concentrations in plasma sample of white rat were determined by RP-HPLC. Data analyses were used by the DAS software for singles and myself-written-program with EXCEL for multiples. RESULTS: In BYHWI, five candidate Q-marker pharmacokinetic profiles were singly fixed to two compartmental models in rat using classical compartmental analysis, but there were tremendous differences among which the candidate parameters were fluctuated from nearly 3552 folds to equivalency. The theoretical value of TQSM polypharmacokinetic parameters such as AUCT, MRTT, VRTT, CLT, VT over the mixure of five drugs were 110.8 ±â€¯51.91 mg min ml-1, 176.0 ±â€¯36.5 min, 39,921 ±â€¯4311 min2, 0.3116 ±â€¯0.02347 ml min-1 kg-1, 54.83 ±â€¯7.683 ml kg-1 respectively. The TQSM polypharmacokinetic parameters in astragaloside Ⅳ ordered by AUCT, MRTT, VRTT, CLT, VT were 110.8 ±â€¯51.91 mg min ml-1, 176.0 ±â€¯36.5 min, 39,921 ±â€¯4311 min2, 0.3116 ±â€¯0.02347 ml min-1 kg-1, 54.83 ±â€¯7.683 ml kg-1, respectively, which were closed to the theoretical values. TQSM similarity versus astragaloside Ⅳ was 0.9661. CONCLUSION: The results represented that the optimum Q-marker in BYHWI is astragaloside Ⅳ, whose transitivity in vivo similarity was close to the behavior of polypharmacokinetics with maximum bioavailability to the total quanta. It is feasible for Q-marker in CMMs to screen on the comparison of single pharmacokinetic behavior and bioavailability to the total quanta.