Fast Determination of Phenanthrene in Soil by Gas Chromatography-Mass Spectrometry Using Chemometric Resolution and Standard Addition Method

A method for fast determination of the component in complex samples by using gas chromatography‐mass spectrometry (GC‐MS) was developed and used for quantitative analysis of phenanthrene in soils. In the method, window independent component analysis (WICA) was used for resolving the mass spectrum and non‐negative immune algorithm (NNIA) was employed for obtaining the chromatographic profile. Therefore, spectral and chromatographic information of a specific component can be obtained from the measured GC‐MS data of overlapping and high background. Six soil samples collected from different places were analyzed. The tedious pretreatments in preparing the samples and the elution in the separation were simplified for speeding up the analysis. Due to the complexity of the matrix, standard addition method was adopted for the final quantification. The applicability of the method was validated with a spiked sample and the results of the six samples are reasonable.     

化学基元组学与药物创新

化学基元组学（chemomics）是与化学信息学、生物信息学、合成化学等学科相关的交叉学科．生物系统从内源性小分子（天然砌块）出发,通过酶催化的化学反应序列制造天然产物．生物系统通过化学反应和天然砌块向目标天然产物“砌入”一组原子,这样的一组原子称为化学基元（chemoyl）．化学基元组（chemome）是生物组织中所含有的化学基元的全体．化学基元组学研究各种化学基元的结构、组装与演化的基本规律．在生存压力和繁衍需求的驱动下,生物系统已经进化出有效手段来合成天然产物以应付环境的变化,并产生了丰富多彩的生物和化学多样性．近年来,人们意识到药物创新的瓶颈之一是药物筛选资源的日益枯竭．化学基元组学可以解决这个瓶颈问题,它通过揭示生物系统制备化学多样性的规律,发展仿生合成方法制备类天然化合物库（quasi natural product libraries）以供药物筛选．本文综述了化学基元组学的主要研究内容及其在药物创新各领域中的潜在应用．     

基于新型分子距边矢量ν与多元统计方法对于烷烃13C NMR化学位移和(CSS)估计与预测的深入研究

基于本实验室提出一种新型以势能形式表达的分子距边矢量, 深入地系统研究了核磁共振碳-13谱化学位移和(CSS)规律以及分子拓扑指数矢量在定量结构波谱关系(QSSR)中的应用. 借助多种计量化学方法包括多元线性回归、逐步多元回归、主成分回归、主筛选回归等进行分子拟模和定量相关研究, 发现烷烃¹³C NMR 化学位移和(CSS)与其分子距边矢量及路径长度指数有良好线性相关性, 回归方程及其统计参数为:CSS=bν+cp₃=∑_^mj=0b_jν_j+b₁₁p₃=b₀ν+b₁ν₁+b₂ν₂+b₃ν₃+b₄ν₄+b₅ν₅+b₆ν₆+b₇ν₇+b₈ν₈+b₉ν₉+b₁₀ν₁₀+b₁₁p₃=-13.576+22.179ν₁+28.407ν₂+25 .950ν₃+26.690ν₄+14.498ν₅+5.726ν₆-5.379ν₇-3.214ν₈-15.021ν₉ -25.710ν₁₀+12.278p₃ n=63, R=0.997, EV=99.68%, RMS=3.7348, SD=4.1 18, F= 773.116, U=144228.844, Q=864.938; CV: R²_CV=0.980, EV=98.83%, RMS=7.126 1, SD_CV=7.634, F_CV=221.720, U_CV=142121.891, Q_CV=2971 .896.结果良好.     

Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity

Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis.     

化学信息学发展现状

本文叙述了目前化学领域的发展热点之一——化学信息学（Chemoinformatics)的发展概况,从信息学角度对其产生、成长及未来发展趋势进行了探讨。     

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.     

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material.     

A multivariate insight into the in vitro antitumour screen database of the National Cancer Institute: classification of compounds, similarities among cell lines and the influence of molecular targets

A multivariate insight into the in vitro antitumour screen database of the NCI by means of the SIMCA package allows to propose hypotheses on the mechanism of action of novel anticancer compounds. As an example, the application of multivariate analysis to the NCI standard database provided clues to the classification of drugs whose mechanism is either unknown or controversial. Moreover, the influence of intrinsic biochemical cell line properties (molecular targets) on the sensitivity to drug treatment could be evaluated simultaneously for classes of compounds which act by the same mechanism. Interestingly, the present approach can also provide a correlation between the molecular targets and the therapeutical fingerprint of novel active compounds thus suggesting specific biochemical studies for the investigation of new mechanisms of drug action and resistance. The statistical approach reported here represents a valuable tool for handling theenormous data sets deriving from recent genome-wide investigations of gene expression in the NCI cell lines.     

Impact of Genomics and in Silico Related Technologies in the Drug Discovery Process

In order to evaluate to what extent will genomics and in silico related technologies improve overall drug discovery process, we analyzed three studies comping cost, time and attrition rate at each step of the drug discovery process, between standard pharmaceutical and genomics based approaches.