Ionic liquids as the effective technology for enhancing transdermal drug delivery: Design principles, roles, mechanisms, and future challenges

Ionic liquids (ILs) have been proven to be an effective technology for enhancing drug transdermal absorption. However, due to the unique structural components of ILs, the design of efficient ILs and elucidation of action mechanisms remain to be explored. In this review, basic design principles of ideal ILs for transdermal drug delivery system (TDDS) are discussed considering melting point, skin permeability, and toxicity, which depend on the molar ratios, types, functional groups of ions and inter-ionic interactions. Secondly, the contributions of ILs to the development of TDDS through different roles are described: as novel skin penetration enhancers for enhancing transdermal absorption of drugs;as novel solvents for improving the solubility of drugs in carriers;as novel active pharmaceutical ingredients (API-ILs) for regulating skin permeability, solubility, release, and pharmacokinetic behaviors of drugs;and as novel polymers for the development of smart medical materials. Moreover, diverse action mechanisms, mainly including the interactions among ILs, drugs, polymers, and skin components, are summarized. Finally, future challenges related to ILs are discussed, including underlying quantitative structure-activity relationships, complex interaction forces between anions, drugs, polymers and skin microenvironment, long-term stability, and in vivo safety issues. In summary, this article will promote the development of TDDS based on ILs.

Structure-Property Relationships and Models of Controlled Drug Delivery of Biodegradable Poly (D, L-lactic acid) Microspheres

An oil-in-water (O/W) solvent evaporation method was used to prepare biodegradable microspheresbased on poly(D,L-lactic acid) (PLA). Nifedipine, a hydrophobic drug, was chosen as a model molecule in the studyof drug entrapment and release. Effect of preparation conditions on the size, morphology, drug loading, and releaseprofiles of micropheres was investigated. Based on in vitro release experimental findings, a diffusion/dissolutionmodel was presented for quantitative description of the resulting release behaviors and drug release kinetics fromPLA microspheres analyzed. The mathematical models were used to predict the effect of microstructure on theresulting drug release. It provided an approach to determine the suitable structure parameters for microspheres toachieve desired drug release behaviors.

Quantitative Structure Activity Relationship Studies of Benzoxazinone Derivative Antithrombotic Drug Using New Three-dimensional Structure Descriptors

A novel three-dimensional holographic vector of atomic interaction field（3D-HoVAIF） was used to describe the chemical structures of 23 benzoxazinone derivatives as antithrombotic drugs.Here a quantitative structure activity relationship（QSAR） model was built by partial least-squares（PLS） regression.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The correlation coefficients of established PLS model,leave-one-out（LOO） cross-validation,and predicted values versus experimental ones of external samples were R2=0.899,RCV2=0.854 and Qext2=0.868,respectively.These values indicated that the built PLS model had both favorable estimation stability and good prediction capabilities.Furthermore,the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of benzoxazinone derivatives.

Analysis on Epidemiology Causality Relationship Theory under Food and Drug Safety Context

Violation of food and drug safety and other hazard crimes have the features of long latency and multiple factors. Traditional criminal law causality theory is no controversy to determine causality of criminal responsibility, thus it is necessary to introduce the epidemiology causality theory-it is a kind of causality theory based on epidemic diseases, and it is the high degree of probability in the determination of causality in criminal laws so as to solve the traditional attribution problem, but the theory also exists applicable restriction conditions in judicial practice.

Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach

The purpose of this study was to develop a quantitative structure–property relationship(QSPR) model based on the enhanced replacement method(ERM) and support vector machine(SVM) to predict the blood-to-brain barrier partitioning behavior(log BB) of various drugs and organic compounds. Different molecular descriptors were calculated using a dragon package to represent the molecular structures of the compounds studied. The enhanced replacement method(ERM) was used to select the variables and construct the SVM model. The correlation coefficient, R^2, between experimental results and predicted log BB was 0.878 and 0.986, respectively. The results obtained demonstrated that, for all compounds, the log BB values estimated by SVM agreed with the experimental data, demonstrating that SVM is an effective method for model development, and can be used as a powerful chemometric tool in QSPR studies.

The Impact of a Drug Safety Warning on Discussions between Doctors and Their Patients;the Case of Rosiglitazone

The goal of this study was to track the influence of a highly publicized report on discussions between doctors and their patients and prescribing decisions made in response to concerns about potential medication adverse side effects. This was a retrospective analysis of a primary care network’s electronic medical record database. From a diabetes registry of 12, 246 patients, 329 were identified as taking rosiglitazone prior to the June 14, 2007 release of an article in the New England Journal of Medicine;the article suggesting an increased risk of myocardial events. The entire content of all office visits, telephone messages, and medication lists for each patient were reviewed over a 2-year period subsequent to the article’s publication. Doctor/patient discussions regarding concerns for rosiglitazone were catalogued including the physician’s treatment recommendations. There were documented discussions on rosiglitazone’s potential adverse side effects for 64 patients;19.5 percent of this population. All of the discussions occurred between June 15 and October 30, 2007. Of the entire group, 59.3 percent (N = 195) remained on rosiglitazone. For those advised to continue rosiglitazone, the provider indicated that he/she wanted more data before determining if the drug was not safe or discounted the validity of the safety concerns. For those advised to discontinue rosiglitazone, 112 (83.6 percent) were placed on pioglitazone. An article suggesting potential adverse effects of rosiglitazone resulted in a documented discussion in 19.5 percent of patients on this medication. These findings suggest an awareness of this publication by patients, presumably derived from media reports. However, an awareness of this concern did not result in a substantial change in practice.The majority of patients remained on rosiglitazone. The content of these discussions suggest that most physicians’ recommended waiting for more published data before considering a change. While many factors influence physician’s prescribing behavior, this study demonstrates how a highly publicized report influences the doctor/ patient dialogue.

Chemoinformatic Resources for Organometallic Drug Discovery

Medicinal Organometallic Chemistry keeps contributing to drug discovery efforts including the development of diagnostic compounds. Despite the limiting issues of metal-based molecules, e.g., such as toxicity, there are drugs approved for clinical use and several others are under clinical and pre-clinical development. Indeed, several research groups continue working on organometallic compounds with potential therapeutic applications. For arguably historical reasons, chemoinformatic methods in drug discovery have been applied thus far mostly to organic compounds. Typically, metal-based molecules are excluded from compound data sets for analysis. Indeed, most software and algorithms for drug discovery applications are focused and parametrized for organic molecules. However, considering the emerging field of material informatics, the objective of this Commentary we emphasize the need to develop cheminformatic applications to further develop metallodrugs. For instance, one of the starting points would be developing a compound database of organometallic molecules annotated with biological activity. It is concluded that chemoinformatic methods can boost the research area of Medicinal Organometallic Chemistry.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

Why 90%of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies,which raised the question whether certain aspects in target validation and drug optimization are overlooked?Current drug optimization overly emphasizes potency/specificity using structure-activityrelationship(SAR)but overlooks tissue exposure/selectivity in disease/normal tissues using structure-tissue exposure/selectivity—relationship(STR),which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity.We propose structure-tissue exposure/selectivity—activity relationship(STAR)to improve drug optimization,which classifies drug candidates based on drug’s potency/selectivity,tissue exposure/selectivity,and required dose for balancing clinical efficacy/toxicity.ClassⅠdrugs have high specificity/potency and high tissue exposure/selectivity,which needs low dose to achieve superior clinical efficacy/safety with high success rate.ClassⅡdrugs have high specificity/potency and low tissue exposure/selectivity,which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated.ClassⅢdrugs have relatively low(adequate)specificity/potency but high tissue exposure/selectivity,which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked.ClassⅣdrugs have low specificity/potency and low tissue exposure/selectivity,which achieves inadequate efficacy/safety,and should be terminated early.STAR may improve drug optimization and clinical studies for the success of clinical drug development.

药物的定量结构色谱保留关系研究

采用人工神经网络(ANN)建立了110种药物的结构与色谱保留之间的定量关系(QSRR)模型,并将其与偏最小二乘回归(PLSR)模型进行比对.110种药物的ANN模型自相容能力的复相关系数(R^(2))为0.9191,泛化能力的复相关系数(R^(2))为0.9365;而PLSR模型的复相关系数(R^(2))为0.8275和0.8514.结果表明,ANN模型的自相容能力、泛化能力优于PLSR模型.采用集内集和集外集对模型进行检验时,ANN模型的复相关系数(R^(2))为0.9105,也优于PLSR模型,且ANN预测精密度和准确度更高.

A New Strategy in Drug Design of Chinese Medicine:Theory,Method and Techniques

The research and development （R＆D） process of Chinese medicine, with one notable feature, clinical application based, is significantly different from which of chemical and biological medicine, from laboratory research to clinics. Besides, compound prescription is another character. Therefore, according to different R＆D theories between Chinese and Western medicine, we put forward a new strategy in drug design of Chinese medicine, which focuses on ＂combination- activity relationship （CAR）＂, taking prescription discovery, component identification and formula optimization as three key points to identify the drugs of high efficacy and low toxicity. The method of drug design of Chinese medicine includes： new prescription discovery based on clinical data and literature information, component identification based on computing and experimental research, as well as formula optimization based on system modeling. This paper puts forward the concept,research framework and techniques of drug design of Chinese medicine, which embodies the R＆D model of Chinese medicine, hoping to support the drug design of Chinese medicine theoretically and technologically.

他汀类药物与骨密度:一项药物靶向孟德尔随机化分析

背景:观察性研究表明他汀类药物可能对骨密度具有保护作用,这使其成为潜在的骨质疏松症治疗药物之一。目的:通过孟德尔随机化方法来评估药物靶点介导的脂质表型与骨密度之间的因果关系。方法:从IEU Open GWAS数据库获取了与他汀类药物相关的单核苷酸多态性以及骨密度相关数据。主要分析方法是逆方差加权法,同时也使用了加权中位数法、简单中位数法、加权中值方法和MR-Egger回归法。使用β值和95%CI来评估他汀类药物与骨密度之间的因果关系;另外,进行敏感性分析以验证结果的可靠性,使用Cochran’s Q检验来评估异质性,使用MR-Egger截距检验是否存在水平多效性。使用留一法分析确定是否有单个或多个单核苷酸多态性影响了结果。结果与结论:他汀类药物作用靶点——3-羟基-3-甲基戊二酰辅酶A还原酶介导的低密度脂蛋白胆固醇与足跟定量超声骨密度(β=-0.086,95%CI:-0.117至-0.055,P=5.42×10^(-8))和全身骨密度(β=-0.193,95%CI:-0.288至-0.098,P=7.35×10^(-5))呈显著相关。该研究结果支持了他汀类药物对骨密度的保护作用。这些发现不仅加深了对胆固醇相关基因和骨骼健康关系的理解,还揭示了改善骨密度的潜在治疗靶点。

β-咔啉杂合咪唑类化合物的合成及体外抗肿瘤活性

以L-色氨酸为原料,经Pictet-Spengler环化反应、氧化脱羧、N^(9)-烷基化等反应步骤,合成了一系列β-咔啉杂合咪唑类化合物。目标化合物经1HNMR、13CNMR和HRMS确证结构。采用MTT(噻唑蓝)法检测了目标化合物对肺癌细胞(A-549)、胃癌细胞(BGC-823)、结肠癌细胞(CT-26)、肝癌细胞(Bel-7402)和乳腺癌细胞(MCF-7)的体外抗肿瘤活性。结果表明,大部分化合物对这5种肿瘤细胞株表现出了中等及优良的抑制活性。特别是3-苄基-11-(3-苯基丙基)-11H-咪唑并[1',5':1,2]吡啶并[3,4-b]吲哚(Ⅴr)对CT-26、Bel-7402和MCF-7细胞株的体外抗肿瘤活性较高,对应的半数抑制浓度(IC_(50))分别为(9.5±0.4)、(7.4±0.3)和(8.8±0.6)µmol/L。分子对接结果表明,3-苄基-11-甲基-11H-咪唑并[1',5':1,2]吡啶并[3,4-b]吲哚(Ⅴa)、3-苄基-11-丁基-11H-咪唑并[1',5':1,2]吡啶并[3,4-b]吲哚(Ⅴf)和Ⅴr与VEGFR^(2)激酶的多个氨基酸残基具有良好的结合作用。

白芥子穴位给药配伍对延胡索乙素药效动力学影响的研究

目的:探讨“冬病夏治”全方配伍和无白芥子配伍延胡索乙素在模型家兔“肺俞”穴皮下药代动力学特征及药代动力学-药效动力学(PK-PD)模型的相关性。方法:支气管哮喘模型家兔随机分成延胡索单方组、缺白芥子组、全方组,微透析技术收集14 h穴位皮下透析液,液相色谱-质谱法(Liquid Chromatography Mass Spectrometry,LCMS)法检测方中君药延胡索主要成分延胡索乙素浓度,获得药代动力学参数;酶联免疫吸附试验(ELISA)法检测对应时间点模型动物血清中IgE水平,获得药效学参数;对药动学、药效学参数进行PK-PD模型拟合。结果:白芥子配伍后的药峰浓度(C_(max))、药时曲线下面积(AUC_(0-t))、平均滞留时间(MRT_(0-t))均显著增加(P<0.01,P<0.01,P<0.05),达峰时间(T_(max))提前(P<0.01);“浓度-时间-效应”三维曲线表明,方中有白芥子配伍时,药效出现更快、消退更慢,起效时间晚于峰浓度,具有一定滞后性。结论:动力学参数、PK-PD模型结果表明,白芥子配伍能够改变“方中君药”——延胡索的主要成分延胡索乙素穴位局部的皮下分布,促进方中君药有效成分快速吸收,延长滞留时间,在方剂中起到主药、改善其他药物分布的“双重”作用。

菊科橐吾属植物中艾里莫芬烷型倍半萜化合物细胞毒性研究进展

艾里莫芬烷型倍半萜是由15个碳原子组成的双环倍半萜,主要从菊科植物橐吾属植物中提取,具有抗肿瘤、抗菌、抗炎等生物活性。艾里莫芬烷型倍半萜的基本结构类型主要包括内酯环、二聚体、呋喃环、降碳型和C19等。本文综述了艾里莫芬烷型倍半萜的相关结构及其潜在的细胞毒性,阐述其特定化学结构特点对抗肿瘤活性的影响,以期为研究其抗肿瘤的临床治疗效果提供参考。

不同体位对膝关节镜手术患者蛛网膜下腔阻滞罗哌卡因ED50的影响

目的研究不同体位对膝关节镜手术患者蛛网膜下腔阻滞罗哌卡因半数有效剂量(ED50)的影响。方法选取90例需要进行择期单侧膝关节镜手术的患者,年龄18~64岁,ASA分级为I或Ⅱ级,体质量指数(BMI)为18~24 kg/m^(2),根据不同的体位,这些患者被分为三组,分别是头高脚低0°组、10°组和20°组。试验采用序贯法,每组第1例患者均获得蛛网膜下腔注射0.75%罗哌卡因11.25 mg,接下来根据前1例患者蛛网膜下腔阻滞是否有效,决定下一位患者的剂量是否减少或增加0.75 mg。使用概率单位回归分析法,计算罗哌卡因在不同体位下的蛛网膜下腔阻滞ED50。结果三组的ED50分别为:0°组为11.625 mg(95%CI:11.259~11.991 mg);10°组为11.325 mg(95%CI:10.968~11.669 mg);20°组为9.373 mg(95%CI:8.963~9.765 mg)。与0°组比较,10°组和20°组运动阻滞恢复时间缩短,20°组ED50降低、最高感觉阻滞平面下降、运动阻滞起效时间延长(P<0.05);与10°组比较,20°组ED50降低、运动阻滞恢复时间缩短(P<0.05)。三组术中低血压和恶心呕吐发生率比较差异无统计学意义(P>0.05)。结论膝关节镜手术患者在保持头高脚低0°、10°、20°体位时,蛛网膜下腔阻滞罗哌卡因的ED50分别为11.625 mg、11.325 mg和9.373 mg。通过头高脚低20°的姿势可以降低蛛网膜下腔阻滞的罗哌卡因ED50。

The relationship between antimicrobial consumption and the rates of resistance of Klebsiela pneumoniae in respiratory unit

Objective To investigate the relationship between the consumption of antibacterial agents and resistance rate of Klebsiela pneumoniae(KP)in the hospital respiratory unit for 3 consecutive years in 2005-2007.Methods The total antibacterial consumption expressed as defined DDDs/100BD,as well as resistance rate of total KP and producing ESBLs KP were collected,and their correlation was analyzed.Results The rate of resistance of KP to cefoperazone/sulbactam,Cefepime,Imipenem,Moxifloxacin was significantly positively associated with the consumption of Cefotaxime,Ceftazidime,Moxifloxacin,Amikacin respectively;A significant positive association was observed between the rate of resistance of KP to Piperacillin/Tazobactam,Ceftriaxone and the consumption of Imipenem;The rate of resistance of KP to Piperacillin,Cefotaxime,Ciprofloxacin was significantly positively associated with the consumption of Levofloxacin.ESBLs producing bacilli of KP were detected in 44 of 75 isolates(58.7%),The rate of resistance of producing ESBLs KP to Piperacillin/Tazobactam,Ceftriaxone was significantly positively associated with the consumption of Imipenem,Ceftazidime;A significant positive association was observed between the rate of resistance of producing ESBLs KP to Piperacillin,Imipenem and the consumption of Moxifloxacin.There was no significant correlation in other drugs.Conclusions A relationship existed between antimicrobial consumption and rates of resistance of KP in the hospital respiratory unit.We must use antibiotics carefully and with reason to control and lessen the drug resistance of bacterial.

阿司匹林对认知功能的影响

阿司匹林是预防心脑血管事件的常用药物,有多个专家共识推荐使用低剂量阿司匹林(75~150 mg/d)对心脑血管疾病进行二级预防。该文旨在探讨阿司匹林发挥对心脑血管事件预防作用以及对认知功能的影响。

A Strategy to Find Novel Candidate DKAs Inhibitors Using Modified QSAR Model with Favorable Druggability Properties

The study dealed with quantitative structure-activity relationship(QSAR)to explore the important features of diketo acid(DKA)derivatives for exerting potent HIV-1 integrase inhibitors activity.A three-step screening method was proposed to choose descriptors.Then,additional descriptors were used in the CoMFA and CoMSIA.Lastly,a modified CoMSIA m7 model,constructed by adding Csp^2_03_F descriptor,showed better predictive ability.Validation parameters(Q^2 and R^2)for the models were 0.722 and 0.925,respectively.In addition,external validation for the models using a test group revealed R^2pred=0.892.Contour maps analysis defined favored and disfavored regions of the compounds,and two new compounds with the descriptor structure were designed with better activities than Raltegravir(RAL),well drug-likeness and low toxicity.The research provides a base for further DKA development.

通过语义三元组和网络药理学发现抑郁症药物

基于抑郁症相关医学文献信息挖掘潜在抗抑郁药并揭示其抗抑郁相关机制和通路,为抑郁症药物的研发提供方向。通过SemRep提取与抑郁症相关的语义三元组,限定语义关系和语义类型确定潜在药物。从PubChem、GeneCards等数据库获取潜在药物和抑郁症的靶点并取二者的交集后,构建交集靶点的蛋白相互作用(protein-protein interaction,PPI)网络。通过Cytoscape分析PPI网络,确定核心靶点。通过R软件对核心靶点进行GO(gene ontology)和KEGG(kyoto encyclopedia of genes and genomes)分析。最后,通过AutodockTool软件对核心靶点与潜在药物进行分子对接分析。结果表明Hydrocortisone、Benzodiazepine、Curcumin、Metformin、Nicotine、Risperidone等6种药物为潜在抗抑郁药,这些药物通过炎症、神经递质的调节等生物过程以及MAPK(mitogen activated protein kinase)、TNF(tumor necrosis factor)等信号通路发挥抗抑郁作用,并且与抑郁症核心靶点间结合性能良好。此外,Benzodiazepine和Nicotine在临床实践中存在成瘾和滥用的风险,在抑郁症治疗中的作用可能有限。可见基于语义三元组和网络药理学发现抑郁症药物新知识,可以节约时间和经济成本,也能为临床药物使用提供新的方向。