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Traditional Chinese herbal medications (TCHMs) are composed of a multitude of compounds and the identification of their active composition is an important area of research. Chromatography provides a visual representation of a TCHM sample’s composition by outputting a curve characterized by spikes corresponding to compounds in the sample. Across different experimental conditions, the location of the spikes can be shifted, preventing direct comparison of curves and forcing compound identification to be possible only within each experiment. In this article, we propose a sparse semiparametric nonlinear modeling framework for the establishment of a standardized chromatographic fingerprint. Data-driven basis expansion is used to model the common shape of the curves, while a parametric time warping function registers across individual curves. Penalized weighted least-squares with the adaptive lasso penalty provides a unified criterion for registration, model selection, and estimation. Furthermore, the adaptive lasso estimators possess attractive sampling properties. A back-fitting algorithm is proposed for estimation. Performance is assessed through simulation and we apply the model to chromatographic data of rhubarb collected from different experimental conditions and establish a standardized fingerprint as a first step in TCHM research. [ABSTRACT FROM AUTHOR]

The article discusses the need to refine and improve a proposed method for the study of traditional Chinese herbal medications (TCHMs) and extend it to other statistical and applied problems. The role of the mean chromatographic fingerprint technique in the simplification of the covariance structure is mentioned. The importance of combining the preprocessing and estimation procedures in biomedical applications is also noted.

Abstract: Ethnopharmacological relevance: The herbal formula Xiangdan injection and its modifications have been used in traditional Chinese medicine for about hundreds years to alleviate pain and promoting blood. Aim: To investigate the anti-hypoxia active fraction of Xiangdan injection. Materials and methods: Xiangdan injection was extracted by ligarine, chloroform, acetic ether, n-butanol and water, represented respectively by A, B, C, D and E. Five extractions were group on the L12(25) orthogonal designed table. NIH mices anti-hypoxia experiment under normal pressure with rats was adopted on the basis of orthogonal design, prediction pharmacodynamics model of TCM prescriptions was established, and the simulation bias was evaluated by combining two scatterplots. The anti-hypoxia active fraction of Xiangdan injection was determined by experiments. Results: The prediction model of TCM prescriptions established in this study can predict the drug actions for different formulas, and PE%≤25% was observed in 112 mice (93.3%). Conclusion: As indicated in model prediction and experimental confirmation, the pharmacodynamic actions of the 5 formulas are all superior to that of the original formula, and ADE has the best effect. [Copyright &y& Elsevier]