How to identify “Material basis–Quality markers” more accurately in Chinese herbal medicines from modern chromatography-mass spectrometry data-sets: Opportunities and challenges of chemometric tools / 中草药·英文版

Modern chromatography - mass spectrometer (MS) technology is an essential weapon in the exploration of traditional Chinese medicines (TCMs) which is based on the “effectiveness-material basis-quality markers (Q-markers)”. Nevertheless, the hardware bottleneck and irregular operation will limit the accuracy and comprehensiveness of test results. Chemometrics was thereby used to solve the existing problems: 1) The method of ‘design-modeling-optimization’ can be adopted to solve the multi-factor and multi-level problems in sample preparation/ parameter setting; 2) The approaches of signal processing can be used to calibrate the deviation from retention time (rt) dimension and mass-to-charge ratio (m/z) dimension in different types of instruments; 3) The methods of multivariate calibration and multivariate resolution can be utilized to analyze the co-eluting peaks in complex samples. When the researchers need to capture essential information on raw data sets extracting the higher level of information on essential features, 1) The significant components which affects the drug properties/efficacy can be find by the pattern recognition and variable selection; 2) Fingerprint-efficacy modeling is explored to clarify the material basis, or to screen out the Q-markers of biological significance; 3) Chemometric tools can apply to integrate chemical (metabolic) fingerprints with network pharmacology, bioinformatics, omics and others from a multi-level perspective. Under these programs, the qualitative/quantitative works will achieve in chemical (metabolic) fingerprint and metabolic trajectories, which leads to an accurate reflection of “material basis and Q-markers” in TCMs. Likewise, an in-depth hidden information can be disclosed, so that the components of drug properties/efficacy will be found. More importantly, multidimensional data can be integrated with fingerprints to acquire more hidden information.

Rapid identification of microbial contaminants in pharmaceutical products using a PCA/LDA-based FTIR-ATR method

Microbiological quality of pharmaceuticals is fundamental in ensuring efficacy and safety of medicines. Conventional methods for microbial identification in non-sterile drugs are widely used; however they can be time-consuming and laborious. The aim of this paper was to develop a chemometric-based rapid microbiological method (RMM) for identifying contaminants in pharmaceutical products using Fourier transform infrared with attenuated total reflectance spectrometry (FTIR-ATR). Principal components analysis (PCA) and linear discriminant analysis (LDA) were used to obtain a predictive model capable of distinguishing Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538), and Staphylococcus epidermidis (ATCC 12228) microbial growth. FTIR-ATR spectra provide data on proteins, DNA/RNA, lipids, and carbohydrates constitution of microbial growth. Microbial identification provided by PCA/LDA based on FTIR-ATR method were compatible with those obtained using traditional microbiological methods. The chemometric-based FTIR-ATR method for rapid identification of microbial contaminants in pharmaceutical products was validated by assessing the sensitivity (93.5%), specificity (83.3%), and limit of detection (17-23 CFU/mL of sample). Therefore, we propose that FTIR-ATR spectroscopy may be used for rapid identification of microbial contaminants in pharmaceutical products and taking into account the samples studied

Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs.

Carcinogenicity is one of the toxicological endpoints causing the highest concern. Also, the standard bioassays in rodents used to assess the carcinogenic potential of chemicals and drugs are extremely long, costly and require the sacrifice of large numbers of animals. For these reasons, we have attempted development of a global quantitative structure–activity relationship (QSAR) model using a data set of 1464 compounds (the Galvez data set available from http://www.uv.es/~galvez/tablevi.pdf), including many marketed drugs for their carcinogenesis potential. Though experimental toxicity testing using animal models is unavoidable for new drug candidates at an advanced stage of drug development, yet the developed global QSAR model can in silico predict the carcinogenicity of new drug compounds to provide a tool for initial screening of new drug candidate molecules with reduced number of animal testing, money and time. Considering large number of data points with diverse structural features used for model development (ntraining = 732) and model validation (ntest = 732), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.731, R2pred = 0.716). Our developed model suggests that higher lipophilicity values and conjugated ring systems, thioketo and nitro groups contribute positively towards drug carcinogenicity. On the contrary, tertiary and secondary nitrogens, phenolic, enolic and carboxylic OH fragments and presence of three-membered rings reduce the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-induced carcinogenicity. One may consider all these points to reduce carcinogenic potential of the molecules.