Discovery of (2S)-N-(6-Cyano-5-(trifluoromethyl)pyridin-3-yl)-3-(6-(4-cyanophenyl)-3,6-diazabicyclo[3.1.1]heptan-3-yl)-2-hydroxy-2-methylpropanamide as a Highly Potent and Selective Topical Androgen Receptor Antagonist for Androgenetic Alopecia Treatment.

Androgenetic alopecia (AGA) is the most prevalent form of progressive hair loss disorder in both men and women, significantly impacting their appearance and overall quality of life. Overactivation of the AR signaling pathway in dermal papilla cells (DPCs) plays a crucial role in the development and progression of AGA. Considering the severe systemic side effects associated with oral AR antagonists, the idea of developing of topical AR antagonists with rapid metabolic deactivation properties emerged as a promising approach. Herein, through systematic structural optimization, we successfully identified compound 30a as a potent and selective AR antagonist with favorable pharmacokinetic properties, resulting in high skin exposure and low plasma exposure following topical administration. Importantly, in both hair-growth and AGA mouse models, compound 30a showed potent hair-growth-promoting effects without any noticeable toxicity. These findings suggest that compound 30a holds significant potential as a topical AR antagonist for treating AGA patients.

Gut microbiota profiling variated during colorectal cancer development in mouse.

BACKGROUND: The imbalance of intestinal flora may promote the occurrence and development of colorectal cancer, changes of the intestinal flora during the development of colorectal cancer and the mechanism that promotes the colorectal cancer were discovered in this study. Deep sequencing of the microbial 16 s ribosomal RNA gene was used to investigate alterations in feces samples of mice at the early inflammation stage and fully developed stage of colorectal cancer. RESULTS: According to PCoA analysis and ANOSIM test, we found the intestinal flora had significantly changed in mice with colorectal inflammation or colorectal cancer compared with healthy mice (p < 0.05). Using correlation analysis, we found that Muribaculaceae and Bacteroidaceae had strong excluding interactions. The functional changes of the gut microbiota include the up-regulation of the cancers pathway and the down-regulation of the replication and repair pathways. CONCLUSION: Our study found the intestinal flora of mice suffering from colorectal inflammation and colorectal cancer has changed significantly, especially the decrease of Muribaculaceae and the increase of Bacteroidaceae. We suppose that these two floras may play an important role in development of colorectal cancer.

Integrative analysis of the mouse fecal microbiome and metabolome reveal dynamic phenotypes in the development of colorectal cancer.

The gut microbiome and its interaction with host have been implicated as the causes and regulators of colorectal cancer (CRC) pathogenesis. However, few studies comprehensively investigate the compositions of gut bacteria and their interactions with host at the early inflammatory and cancerous stages of CRC. In this study, mouse fecal samples collected at inflammation and CRC were subjected to microbiome and metabolome analyses. The datasets were analyzed individually and integratedly using various bioinformatics approaches. Great variations in gut microbiota abundance and composition were observed in inflammation and CRC. The abundances of Bacteroides, S24-7_group_unidifineted, and Allobaculum were significantly changed in inflammation and CRC. The abundances of Bacteroides and Allobaculum were significantly different between inflammation and CRC. Furthermore, strong excluding and appealing microbial interactions were found in the gut microbiota. CRC and inflammation presented specific fecal metabolome profiling. Fecal metabolomic analysis led to the identification and quantification of 1,138 metabolites with 32 metabolites significantly changed in CRC and inflammation. 1,17-Heptadecanediol and 24,25,26,27-Tetranor-23-oxo-hydroxyvitamin D3 were potential biomarkers for CRC. 3α,7ß,12α-Trihydroxy-6-oxo-5α-cholan-24-oic Acid and NNAL-N-glucuronide were potential biomarkers for inflammation. The significantly changed bacterial species and metabolites contribute to inflammation and CRC diagnosis. Integrated microbiome and metabolomic analysis correlated microbes with host metabolites, and the variated microbe-metabolite association in inflammation and CRC suggest that microbes facilitate tumorigenesis of CRC through interfering host metabolism.

Proteome-wide analysis of protein alterations in response to aristolochic acids in rat kidney and liver tissues.

Aristolochic acids (AAs), nephrotoxic components of herbs, have been previously demonstrated to cause DNA damage by forming DNA-AA adducts. However, the changes of tissue proteome profiles revealing AA toxicity need to be further studied. We conducted a proteomic study on the kidney and liver tissues of AA treated rats by a shotgun proteomics approach coupled with LC-MS/MS technology. A total of 1543 and 1641 proteins were identified and quantified in the kidneys and liver. Due to AA dosage, 10 and 4 proteins significantly changed in kidneys and the liver after multiple testing correction. Pathway enrichment analysis results were variant in kidneys and the liver. The enrichment analysis of metabolic pathways showed that gene expression and protein biosynthesis disorders were the common causes of AA toxicity to organs. Biological processes that positively responded to AAs in the liver probably have a detoxification function. SEC14-like protein 2 and synaptic vesicle membrane protein VAT-1 homolog were the mostly downregulated proteins in the liver and kidneys respectively.

The stochastic resonance algorithm with the direct current signal as external force and its application to the detection of weak chromatographic peaks.

As a potential tool for amplifying weak chromatographic peaks, the stochastic resonance algorithm was developed based upon a counterintuitive physical phenomenon. Therefore, the essential step, parameter optimization, was perplexing and difficult for analysts. In order to avoid optimizing the system parameters on a case-by-case basis, an improved algorithm was proposed by introducing a constant or direct current signal into the signal to be measured as the external force. The weak chromatographic peak can be amplified and detected by the new algorithm using the same set of parameters. Two sets of our previous experimental data were reanalyzed by using the developed algorithm and the results were satisfactory. A generalized solution was expected to come into being on account of the new algorithm.

The application of multiobjective genetic algorithm to the parameter optimization of single-well potential stochastic resonance algorithm aimed at simultaneous determination of multiple weak chromatographic peaks.

Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.

A rapid and sensitive LC-ESI-MS/MS method for the detection of YF-49-92.MLS in rat plasma.

BACKGROUND: YF-49-92.MLS is a novel candidate for TB treatment. An accurate, precise and specific LC-MS/MS method for the quantification of YF-49-92.MLS in rat plasma using verapamil as an IS is reported in this paper. METHODOLOGY: Proper retention time and excellent peak shape were acquired using an Agilent Zorbax(®) SB-C18 column with the mobile phase of 5 mmol/l ammonium acetate, 0.1% formic acid-methanol (30:70, v/v). The LLOQ was 1 ng/ml. The calibration curves encompassed concentrations from 20 to 5000 ng/ml. Intra- and inter-assay precision and accuracy were within 15% by determining low, medium and high concentration samples. Extraction recovery, stability, and matrix effects were also fully validated. CONCLUSION: This method has been validated to be rapid and sensitive, and successfully applied to the PK study of YF-49-92.MLS in rat plasma.

An adaptive single-well stochastic resonance algorithm applied to trace analysis of clenbuterol in human urine.

Based on the theory of stochastic resonance, an adaptive single-well stochastic resonance (ASSR) coupled with genetic algorithm was developed to enhance the signal-to-noise ratio of weak chromatographic signals. In conventional stochastic resonance algorithm, there are two or more parameters needed to be optimized and the proper parameters values were obtained by a universal searching within a given range. In the developed ASSR, the optimization of system parameter was simplified and automatic implemented. The ASSR was applied to the trace analysis of clenbuterol in human urine and it helped to significantly improve the limit of detection and limit of quantification of clenbuterol. Good linearity, precision and accuracy of the proposed method ensure that it could be an effective tool for trace analysis and the improvement of detective sensibility of current detectors.

Application of the stochastic resonance algorithm to the simultaneous quantitative determination of multiple weak peaks of ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry.

The stochastic resonance algorithm (SRA) has been developed as a potential tool for amplifying and determining weak chromatographic peaks in recent years. However, the conventional SRA cannot be applied directly to ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOFMS). The obstacle lies in the fact that the narrow peaks generated by UPLC contain high-frequency components which fall beyond the restrictions of the theory of stochastic resonance. Although there already exists an algorithm that allows a high-frequency weak signal to be detected, the sampling frequency of TOFMS is not fast enough to meet the requirement of the algorithm. Another problem is the depression of the weak peak of the compound with low concentration or weak detection response, which prevents the simultaneous determination of multi-component UPLC/TOFMS peaks. In order to lower the frequencies of the peaks, an interpolation and re-scaling frequency stochastic resonance (IRSR) is proposed, which re-scales the peak frequencies via linear interpolating sample points numerically. The re-scaled UPLC/TOFMS peaks could then be amplified significantly. By introducing an external energy field upon the UPLC/TOFMS signals, the method of energy gain was developed to simultaneously amplify and determine weak peaks from multi-components. Subsequently, a multi-component stochastic resonance algorithm was constructed for the simultaneous quantitative determination of multiple weak UPLC/TOFMS peaks based on the two methods. The optimization of parameters was discussed in detail with simulated data sets, and the applicability of the algorithm was evaluated by quantitative analysis of three alkaloids in human plasma using UPLC/TOFMS. The new algorithm behaved well in the improvement of signal-to-noise (S/N) compared to several normally used peak enhancement methods, including the Savitzky-Golay filter, Whittaker-Eilers smoother and matched filtration.

DDSolver: an add-in program for modeling and comparison of drug dissolution profiles.

In recent years, several mathematical models have been developed for analysis of drug dissolution data, and many different mathematical approaches have been proposed to assess the similarity between two drug dissolution profiles. However, until now, no computer program has been reported for simplifying the calculations involved in the modeling and comparison of dissolution profiles. The purposes of this article are: (1) to describe the development of a software program, called DDSolver, for facilitating the assessment of similarity between drug dissolution data; (2) to establish a model library for fitting dissolution data using a nonlinear optimization method; and (3) to provide a brief review of available approaches for comparing drug dissolution profiles. DDSolver is a freely available program which is capable of performing most existing techniques for comparing drug release data, including exploratory data analysis, univariate ANOVA, ratio test procedures, the difference factor f (1), the similarity factor f (2), the Rescigno indices, the 90% confidence interval (CI) of difference method, the multivariate statistical distance method, the model-dependent method, the bootstrap f (2) method, and Chow and Ki's time series method. Sample runs of the program demonstrated that the results were satisfactory, and DDSolver could be served as a useful tool for dissolution data analysis.

PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel.

This study presents PKSolver, a freely available menu-driven add-in program for Microsoft Excel written in Visual Basic for Applications (VBA), for solving basic problems in pharmacokinetic (PK) and pharmacodynamic (PD) data analysis. The program provides a range of modules for PK and PD analysis including noncompartmental analysis (NCA), compartmental analysis (CA), and pharmacodynamic modeling. Two special built-in modules, multiple absorption sites (MAS) and enterohepatic circulation (EHC), were developed for fitting the double-peak concentration-time profile based on the classical one-compartment model. In addition, twenty frequently used pharmacokinetic functions were encoded as a macro and can be directly accessed in an Excel spreadsheet. To evaluate the program, a detailed comparison of modeling PK data using PKSolver and professional PK/PD software package WinNonlin and Scientist was performed. The results showed that the parameters estimated with PKSolver were satisfactory. In conclusion, the PKSolver simplified the PK and PD data analysis process and its output could be generated in Microsoft Word in the form of an integrated report. The program provides pharmacokinetic researchers with a fast and easy-to-use tool for routine and basic PK and PD data analysis with a more user-friendly interface.

Coupled stochastic resonance to improve chromatography determinations.

Traditional bistable stochastic resonance has been demonstrated as an effective tool to detect the weak signal in a strong noise background. To achieve a better signal-to-noise ratio for the output signal, a coupled stochastic resonance was developed by nonlinearly coupling two double-well potential systems. The response characteristics of coupled stochastic resonance subjected to analytical signals have been investigated and compared with those of bistable stochastic resonance. The improvement of chromatographic determination with the proposed coupled stochastic resonance was validated by both simulated signals and chromatographic signals. The weak signals from both simulated data and plasma samples with different concentrations were all amplified significantly and the quantitative relationship between different concentrations and responses was kept well. It is reasonable to believe that coupled stochastic resonance could play an important role in applications where quantitative determination of low-concentration samples is crucial.

Tailoring noise frequency spectrum to improve NIR determinations.

Near infrared spectroscopy (NIR) contains excessive background noise and weak analytical signals caused by near infrared overtones and combinations. That makes it difficult to achieve quantitative determinations of low concentration samples by NIR. A simple chemometric approach has been established to modify the noise frequency spectrum to improve NIR determinations. The proposed method is to multiply one Savitzky-Golay filtered NIR spectrum with another reference spectrum added with thermal noises before the other Savitzky-Golay filter. Since Savitzky-Golay filter is a kind of low-pass filter and cannot eliminate low frequency components of NIR spectrum, using one step or two consecutive Savitzky-Golay filter procedures cannot improve the determination of NIR greatly. Meanwhile, significant improvement is achieved via the Savitzky-Golay filtered NIR spectrum processed with the multiplication alteration before the other Savitzky-Golay filter. The frequency range of the modified noise spectrum shifts toward higher frequency regime via multiplication operation. So the second Savitzky-Golay filter is able to provide better filtering efficiency to obtain satisfied result. The improvement of NIR determination with tailoring noise frequency spectrum technique was demonstrated by both simulated dataset and two measured NIR spectral datasets. It is expected that noise frequency spectrum technique will be adopted mostly in applications where quantitative determination of low concentration sample is crucial.

Rapid identification of anterior and posterior root of cauda equina nerves by near-infrared diffuse reflectance spectroscopy.

A new rapid chemometric method has been developed to identify the anterior and posterior roots of cauda equina nerves by near-infrared (NIR) diffuse reflectance spectroscopy. NIR spectra of nerves were measured using a Fourier transform NIR spectrometer equipped with a fiber-optic probe. The result revealed no observable difference in the spectra between the anterior and posterior root samples, but the two roots could be identified by cluster analysis based on the differences of their spectral features. The overall accuracy of the cluster analysis model was 87.5%, and the accuracy for the actual anterior root and actual posterior root were 95% and 80%, respectively. The result suggested that NIR spectroscopy in combination with the chemometrics method (cluster analysis) could be used to classify the anterior and posterior roots of cauda equina nerves. The proposed method required only a few minutes, while classical methods commonly required at least one hour. It was demonstrated that the new method could provide a rapid, correct, nondestructive and low-cost potential means to quickly differentiate anterior and posterior roots in mixed cauda equina nerves, which would be helpful for surgeons to align nerve stumps correctly.

Detection of trace triclocarban in water sample using solid-phase extraction-liquid chromatography with stochastic resonance algorithm.

Based on the theory of stochastic resonance, a new algorithm was presented to enhance the signal-to-noise ratio (SNR) of weak HPLC/UV signals of triclocarban in water samples with a new method for optimizing the system parameters. The method was applied to experimental weak signals of HPLC/ UV, which was embedded in the noise background, and the SNR was enhanced greatly. The algorithm was employed to detect triclocarban residue in water with solid-phase extraction--high-performance liquid chromatography. The limit of detection and the limit of quantification were improved from 10 ng L(-1) and 50 ng L(-1) to 1 ng L(-1) and 5 ng L(9-1), respectively. The results showed an excellent quantitative relationship between concentrations and chromatographic responses.

Application of two-dimensional near-infrared correlation spectroscopy to the discrimination of Chinese herbal medicine of different geographic regions.

Fructus Lycii is a traditional Chinese medicinal herb. The objective of this paper was to apply two-dimensional (2D) near-infrared (NIR) correlation spectroscopy to the discrimination of Fructus Lycii of four different geographic regions. Generalized 2D-NIR correlation spectroscopy was able to enhance spectral resolution, simplify the spectrum with overlapped bands, and provide information about temperature-induced spectral intensity variations that was hard to obtain from one-dimensional NIR spectroscopy. The 2D synchronous and asynchronous spectra showed remarkable differences within the range of 4950-5700cm(-1) between samples of different geographic regions. Using NIR instead of IR made the 2D approach more convenient and fast, and it can be applied to more area like process control. This approach can also be applied analogously to the discrimination of other Chinese herbal medicine of different geographic regions.

Improved stochastic resonance algorithm for enhancement of signal-to-noise ratio of high-performance liquid chromatographic signal.

Based on the theory of stochastic resonance, an improved stochastic resonance algorithm with a new criterion for optimizing system parameters to enhance signal-to-noise ratio (SNR) of HPLC/UV chromatographic signal for trace analysis was presented in this study. Compared with the conventional criterion in stochastic resonance, the proposed one can ensure satisfactory SNR as well as good peak shape of chromatographic peak in output signal. Application of the criterion to experimental weak signals of HPLC/UV was investigated and the results showed an excellent quantitative relationship between different concentrations and responses.

Trace determination of carbendazim and thiabendazole in drinking water by liquid chromatography and using linear modulated stochastic resonance algorithm.

The following paper addresses an attempt to determine the trace levels of two benzimidazole fungicides (carbendazim, CAS 10605-21-7 and thiabendazole, CAS 148-79-8) in drinking water samples using the newly proposed linear modulated stochastic resonance algorithm. In order to implement an adaptive and intelligent algorithm, a two-step optimization procedure was developed for the parameter selection to give attention to both the signal-to-noise ratio and the peak shape of output signal. How to limit the ranges of the parameters to be searched was discussed in detail. The limits of detection for carbendazim and thiabendazole were improved to 0.012 microg x L(-1) and 0.015 microg x L(-1), respectively. The successful application demonstrated the ability of the algorithm for detecting two or more weak chromatographic peaks simultaneously.

A linear modulation-based stochastic resonance algorithm applied to the detection of weak chromatographic peaks.

A simple stochastic resonance algorithm based on linear modulation was developed to amplify and detect weak chromatographic peaks. The output chromatographic peak is often distorted when using the traditional stochastic resonance algorithm due to the presence of high levels of noise. In the new algorithm, a linear modulated double-well potential is introduced to correct for the distortion of the output peak. Method parameter selection is convenient and intuitive for linear modulation. In order to achieve a better signal-to-noise ratio for the output signal, the performance of two-layer stochastic resonance was evaluated by comparing it with wavelet-based stochastic resonance. The proposed algorithm was applied to the quantitative analysis of dimethyl sulfide and the determination of chloramphenicol residues in milk, and the good linearity of the method demonstrated that it is an effective tool for detecting weak chromatographic peaks.

Determination of phenazopyridine in human plasma via LC-MS and subsequent development of a pharmacokinetic model.

This paper describes a new LC-MS method for the determination of phenazopyridine and the subsequent development of a pharmacokinetic model for phenazopyridine in vivo. Phenazopyridine hydrochloride is a strong analgesic used in the treatment of urinary tract infections. Although it has been used as a clinical treatment for a very long time, pharmacokinetic data and suitable methods for its determination in plasma are currently lacking. The study described in this paper used high performance liquid chromatography-mass spectrometry, HPLC-MS, to determine the plasma concentrations of phenazopyridine in human subjects after oral administration. After liquid-liquid extraction, the phenazopyridine in the plasma was analyzed on a C18 column under SIM mode. A double-peak phenomenon was observed in most of the concentration-time profiles of the subjects. Although some drugs are known to cause this phenomenon, phenazopyridine has not been reported to do so. Several possible causes were analyzed in order to obtain an explanation. We proposed a two-site absorption compartment model to fit the concentration data in vivo, which has one more absorption site than the classical one-compartment model. The model describes the concentration profiles in different dose groups well and could provide an explanation for the double-peak phenomenon. The three dose groups exhibited similar model parameters and a linear pharmacokinetic process over the dose range used.