Identities from weighted 2-Motzkin paths
基于DNA条形码技术的连云港基岩岸常见帽贝物种识别

XW1
XW6
ห้องสมุดไป่ตู้
XW18
XW7
XW20
11(29.7%)
0(0.0%)
7(18.9%)
0(0.0%)
19(51.4%)
总数量 Totalnumber
37(100%)
夏季 Summer 秋季 Autumn
17(25.4%) 18(30.5%)
2(3.0%) 1(1.7%)
13(19.4%) 11(18.6%)
基于 Blast结果,利用这 5个种的代表序列 (样品编号为 XW1、XW6、XW7、XW18,X W20),结合 NCBI中 25条 COⅠ 基因序列(每个 种各 5条序列),构成一个系统发生分析数据集 (图 4),以此进一步佐证物种识别的结果。从系 统发生关系来看,本次识别的所有“真帽贝”均来 自笠贝科,“假帽贝”来自松螺科(Siphonariidae)。 Bootstrap计算结果显示,齿舌新笠贝(XW6)、笠 贝(XW1)、日本菊花螺(XW20)的识别有着较 高的可信度(图 4)。同时,本研究基于世界海洋 物种目录 数 据 库 还 进 行 了 图 片 检 索,结 果 发 现, XW20的外部形态与该数据库中的日本菊花螺 有着较高的相似性(图 1,图 5)。此外 ,XW1的
此外,随 着 社 会 经 济 的 发 展,沿 海 地 区 环 境 污染问题日益严重,世界各国对此采取了一系列 应对措施。其中,海洋环境污染的生物评价法是 了解污 染 水 平 的 有 效 手 段 之 一[8]。 而 帽 贝 因 其 自身特 点 已 成 为 重 要 的 污 染 评 价 指 示 生 物[9]。 据记载,中国沿海分布的真帽贝有 26种,其中, 中国大陆沿海的真帽贝物种主要分布于山东、浙 江、福建沿海[10]。连云港地处中纬度,东临黄海, 具有江苏省唯一的基岩海岸(长约 22km)[11],然 而,目 前 连 云 港 地 区 帽 贝 的 分 布 情 况 尚 不 清 楚 [1,12],因此有必 要 对 连 云 港 地 区 沿 岸 分 布 的 帽 贝种类进行研究。由于同一种帽贝在形态上可
机器学习_Census-IncomeDatabase(人口调查收入数据集)

机器学习_Census-IncomeDatabase(⼈⼝调查收⼊数据集)Census-Income Database(⼈⼝调查收⼊数据集)数据摘要:This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the years 1970, 1980, and 1990. The coding schemes have been standardized (by the IPUMS project) to be consistent across years.中⽂关键词:⼈⼝普查收⼊,洛杉矶,分类,IPUMS project,UCI KDD,英⽂关键词:Census income,Los Angeles,Classification,IPUMS project,UCI KDD,数据格式:TEXT数据⽤途:Classification,multivariate数据详细介绍:Census-Income DatabaseData TypemultivariateAbstractThis data set contains weighted census data extracted from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. The data contains demographic and employment related variables.SourcesOriginal OwnerU.S. Census BureauUnited States Department of CommerceDonorTerran Lane and Ronny KohaviData Mining and VisualizationSilicon Graphics.terran@/doc/b6fcebef4afe04a1b071deb6.html ,ronnyk@/doc/b6fcebef4afe04a1b071deb6.htmlDate Donated: March 7, 2000Data CharacteristicsThis data set contains weighted census data extracted from the 1994 and 1995 Current Population Surveys conducted by the U.S. Census Bureau. The data contains 41 demographic and employment related variables.The instance weight indicates the number of people in the population that each record represents due to stratified sampling. To do real analysis and derive conclusions, this field must be used. This attribute should *not* be used in the classifiers.More information detailing the meaning of the attributes can be found in the Census Bureau's documentation To make use of the data descriptions at this site, the following mappings to the Census Bureau's internal database column names will be needed:age AAGEclass of worker ACLSWKR industry code ADTIND occupation code ADTOCC adjusted gross income AGI education AHGAwage per hour AHRSPAY enrolled in edu inst last wk AHSCOL marital status AMARITL major industry code AMJIND major occupation code AMJOCC mace ARACE hispanic Origin AREORGN sex ASEX member of a labor union AUNMEM reason for unemployment AUNTYPE full or part time employment stat AWKSTATcapital gains CAPGAIN capital losses CAPLOSS divdends from stocks DIVVAL federal income tax liability FEDTAXtax filer status FILESTATregion of previous residence GRINREG state of previous residence GRINST detailed household and family stat HHDFMXdetailed household summary in household HHDREL instance weight MARSUPWT migration code-change in msa MIGMTR1 migration code-change in reg MIGMTR3 migration code-move within reg MIGMTR4 live in this house 1 year ago MIGSAME migration prev res in sunbelt MIGSUN num persons worked for employer NOEMP family members under 18 PARENT total person earnings PEARNVAL country of birth father PEFNTVTY country of birth mother PEMNTVTY country of birth self PENATVTY citizenship PRCITSHP total person income PTOTVAL own business or self employed SEOTR taxable income amount TAXINCfill inc questionnaire for veteran's admin VETQVAveterans benefits VETYNweeks worked in year WKSWORKNote that Incomes have been binned at the $50K level to present a binary classification problem, much like the originalUCI/ADULT database. The goal field of this data, however, was drawn from the "total person income" field rather than the "adjusted gross income" and may, therefore, behave differently than the orginal ADULT goal field.Basic statistics for this data setNumber of instances in data = 199523Duplicate or conflicting instances : 46716Number of instances in test = 99762Duplicate or conflicting instances : 20936Number of attributes = 40 (continuous : 7 nominal : 33)Data FormatOne instance per line with comma delimited fields. There are 199523 instances in the data file and 99762 in the test file.The data was split into train/test in approximately 2/3, 1/3 proportions using MineSet's MIndUtil mineset-to-mlc.数据预览:点此下载完整数据集。
D2-40

D2-40D2-40: An Overview of a Unique Immunohistochemical MarkerIntroductionImmunohistochemistry (IHC) is a widely used technique in the field of pathology that allows for the visualization of specific target antigens within tissues. One such marker is D2-40, which has gained significant attention due to its unique properties and potential diagnostic implications. In this article, we will discuss the characteristics of D2-40, its applications, and its relevance in various pathological conditions.D2-40: A Brief BackgroundD2-40, also known as Podoplanin, is a protein encoded by the PDPN gene. Initially identified as a podocyte membrane antigen, it was shown to play a crucial role in the development of lymphatic vessels. D2-40 is characterized by its expression in endothelial cells of lymphatic vessels, certain subsets of macrophages, and some neoplastic cells of mesothelial and epithelial origin.Expression Patterns and LocalizationD2-40 has shown a consistent pattern of expression in lymphatic endothelial cells throughout various organs and tissues. By utilizing D2-40 immunohistochemistry, these lymphatic vessels can be easily identified and distinguished from blood vessels, which are negative for D2-40 expression. The use of D2-40 has proven to be particularly helpful in studying lymphatic metastasis and lymphatic invasion in various cancers, such as breast, lung, and colorectal carcinomas.Applications in Tumor Diagnosis and PrognosisOne of the most significant clinical applications of D2-40 is in tumor diagnosis and prognosis. Research has shown that D2-40 expression is associated with lymphatic vessel density and lymph node metastasis in several types of cancers. In breast cancer, for example, the presence of D2-40-positive tumor cells in the lymphatic vessels has been correlated with an increased risk of nodal metastasis. Similarly, in lung cancer, D2-40 expression has been associated with an unfavorable prognosis.D2-40 as a Diagnostic MarkerThe distinctive expression pattern of D2-40 has made it a valuable diagnostic marker, especially in tumors where lymphatic invasion is an important factor. In malignant mesothelioma, D2-40 has been shown to be highly sensitive and specific for differentiating it from reactive mesothelial hyperplasia. The absence of D2-40 staining in reactive mesothelial cells helps in ruling out the possibility of malignancy, making it a useful tool for accurate diagnosis.Distinctive Features of D2-40Apart from its diagnostic significance, D2-40 also possesses some unique features that make it an interesting marker to study. Research has shown that D2-40 is involved in the regulation of cell migration, invasion, and metastasis. It has been demonstrated that D2-40 interacts with the C-type lectin-like receptor 2 (CLEC-2), leading to platelet aggregation and lymphatic vessel remodeling. These findings suggest potential therapeutic implications for targeting D2-40 in metastatic disease.ConclusionD2-40, or Podoplanin, is a unique immunohistochemical marker with specific expression in lymphatic endothelial cells and some neoplastic cells. It has become an important diagnostic tool in various cancers, aiding in the identification of lymphatic invasion and predicting prognosis. Additionally, its distinct features and involvement in cellular processes make D2-40 an intriguing marker with potential therapeutic applications. Further research and studies are needed to fully explore the role of D2-40 in various pathological conditions and its potential as a therapeutic target.。
TIE2配体寡肽的设计、筛选及其在基因治疗中的靶向导入作用

TlE2配体寡肽的设计、筛选及其在基因治疗中的靶向导入作用博十研究生:导师:复旦人学医学院吴向华顾健人教授上海市肿痛研究所中文摘要肿瘤基因治疗将成为除手术、放疗和化疗等方法外又‘新的抗癌策略。
尤其在抗肿瘤转移和复发方面将起重要作用。
目前还缺乏将基因导入人体细胞的高效靶向性载体系统,这是基冈治疗至今尚未成为临床常规治疗措施的关键因素之。
因此研发~种高效靶盼陛基冈导入系统成为当务之急。
本研究旨在建立一种靶向Tie2受体的非病毒摹因导入系统并检验其何效性,为今后肿瘤的基因治疗提供可靠的理沦依据与实践指导。
第一部分Tie2配体寡肽的设计与筛选【目的】掌握特定受体的配体寡肽的设计与筛选方法;获得Tie2受体的候选配体寡敝;【方法】(1)应用基于Tie2受体天然配体Angiopoietin一2的同源序列比较/二级结构分析及疏水性分析方法没计Tie2受体的配体寡肽并化学合成;(2)RT-PCR和WestemBloting筛选Tie2阴性表达细胞株;构建pCDNA3.0一ExTie2质粒并转染Tie2表达阴性细胞,G418筛选mTie2稳定表达细胞系;分别以重组人Tie2融合蛋白(rh—Tie2/Fc)与稳定表达Tie2受体的细胞为筛选靶,用噬菌体展示随机12肽库避行筛选。
经过5轮筛选的噬菌体,经测序、ELISA、免疫组化及噬菌体回收试验鉴定出高度富集的阳性噬葡体克隆;然后化学合成高亲和力阳性噬菌体克隆展示肽。
【结果】基于Tie2受体的配体AnDopoietin一2设计的22肽命名为GA3(WTIIQRREDGSVDFQRTWKEYK);筛选到Tie2阴性表达细胞株SMMC7721;建立了Tie2{急定表达细胞系SMMC7721一ExTie2;以rh—Tie2/Fc与SMMC7721-ExTie2为筛选靶获得的高亲和力富集噬菌体展示肽序列羧基端各加一个酪氨酸后分别命名为GA4(HATGTHGLsLsHY),}FIGA5(NsLsNAsEFRAPY)。
近年版高考英语一轮复习精选提分专练第三周星期五新闻媒体(2021学年)(1)

(江苏版)2019版高考英语一轮复习精选提分专练第三周星期五新闻媒体编辑整理:尊敬的读者朋友们:这里是精品文档编辑中心,本文档内容是由我和我的同事精心编辑整理后发布的,发布之前我们对文中内容进行仔细校对,但是难免会有疏漏的地方,但是任然希望((江苏版)2019版高考英语一轮复习精选提分专练第三周星期五新闻媒体)的内容能够给您的工作和学习带来便利。
同时也真诚的希望收到您的建议和反馈,这将是我们进步的源泉,前进的动力。
本文可编辑可修改,如果觉得对您有帮助请收藏以便随时查阅,最后祝您生活愉快业绩进步,以下为(江苏版)2019版高考英语一轮复习精选提分专练第三周星期五新闻媒体的全部内容。
新闻媒体单词识记:abuse accuse motivationmournattempt blockbrief casecampaign condemn conclude realityreporter discrimination dismiss confidentialconflictviolence welfare magazine短语扫描:make/giveareport 做报告advertise for登广告征求(寻找)put an advertisement in a newspaper在报纸上刊登广告the mass media大众传媒spread out 伸展;摊开;分散takea photo照相expose.。
to...将……暴露于……;使……接触……come out出版;发行;出来;结果是havea nose for对……敏感;善于发现be critical of对……不满[跟踪训练]Ⅰ.语境填词1。
The success of the documentary should be attributed to sincerity and (realize).2. (brief) speaking,English is so usefulthat allof us students should learn it well。
高三英语植物遗传修饰单选题50题

高三英语植物遗传修饰单选题50题1. The process of plant genetic modification often involves ____ genes from one organism to another.A. transferringB. transformingC. transmittingD. transplanting答案:A。
解析:本题考查与植物遗传修饰相关的动词辨析。
A选项transferring有转移、传递( 尤指将某物从一个地方、人或事物转移到另一个地方、人或事物)的意思,在植物遗传修饰中,经常涉及将基因从一个生物体转移到另一个生物体,符合概念。
B选项transforming主要表示改变、转变,强调的是形态、性质等方面的彻底改变,而不是基因的转移这个概念。
C选项transmitting侧重于传播、传送( 信号、信息等),不太适用于基因的操作。
D选项transplanting 主要指移植 器官、植物等),通常是比较宏观的物体,与基因的操作不符。
2. In plant genetic modification, a ____ is a small circular piece of DNA that can be used to carry new genes into a plant cell.A. plasmidB. plastidC. plasmodiumD. plasma答案:A。
解析:A选项plasmid(质粒)在植物遗传修饰中是一种小的环状DNA,可以用来携带新基因进入植物细胞,这是植物遗传修饰中重要的工具。
B选项plastid(质体)是植物细胞中的一种细胞器,与携带基因进入细胞的概念不同。
C选项plasmodium( 疟原虫)与植物遗传修饰毫无关系。
D选项plasma(血浆、等离子体)也与植物遗传修饰概念不相关。
3. Which of the following is a common method for plant genetic modification?A. Cross - breedingB. Mutation breedingC. Gene editingD. All of the above答案:D。
PreservCyt Solution套件使用指南说明书
Obtain……an adequate sampling from the ectocervix using a plastic spatula.Rinse……the spatula as quickly as possible into the PreservCyt®Solutionvial by swirling the spatula vigorously in the vial 10 times.Discard the spatula.Obtain……an adequate sampling from the endocervix using an endocervicalbrush device. Insert the brush into the cervix until only the bottom-most fibers are exposed. Slowly rotate 1/4 or 1/2turn in onedirection. DO NOT OVER-ROTATE.Rinse……the brush as quickly as possible in the PreservCyt Solution byrotating the device in the solution 10 times while pushing againstthe PreservCyt vial wall. Swirl the brush vigorously to furtherrelease material. Discard the brush.Tighten……the cap so that the torque line on the cap passes the torque lineon the vial.Record……the patient’s name and ID number on the vial.…the patient information and medical history on the cytologyrequisition form.Place……the vial and requisition in a specimen bag for transport tothe laboratory.Endocervical Brush/Spatula ProtocolPart No.85217-002 Rev. HThe Test You TrustObtain……an adequate sampling from the cervix using a broom-like device. Insert the central bristles of the broom into the endo-cervical canal deep enough to allow the shorter bristles to fully contact the ectocervix. Push gently , and rotate the broom in a clockwise direction five times.Rinse……the broom as quickly as possible into the PreservCyt ®Solution vial by pushing the broom into the bottom of the vial 10 times, forcing the bristles apart. As a final step, swirl the broom vigorously to further release material. Discard the collection device.Tighten……the cap so that the torque line on the cap passes the torque line on the vial.Record……the patient’s name and ID number on the vial.…the patient information and medical history on the cytology requisition form.Place……the vial and requisition in a specimen bag for transport to the laboratory .Broom-Like Device Protocol。
蛋白质修饰
• posttranslational modifications: alter their interaction with DNA and nuclear proteins. H3 & H4: long tails; can be modified at several places, including methylation, acetylation, phosphorylation, ubiquitination, sumoylation, citrullination and ADPribosylation. The core of the histones H2A and H3 can also be modified. • Histone Code: hypothesized to be a code consisting of covalent histone tail modifications → epigenetic code
– modification site may be a targeting signal – modification may be a membrane anchor
• Degradation
– identify the protein for degradation
• ……
For more information, see /wiki/Posttranslational_modification
Non-histone Acetylation/Deacetylation
a significant post-translational regulatory mechanism
莫里莫里泡沫胶带产品说明书
Table.Spearman Correlations between CRISS and individual components at12months and Comparison of ABA and PBO using CRISS index and individual components at12 months;Outcome ABAN=44PBON=44TreatmentDifference(ABA-PBO)P-value^ACR CRISS(0.0-1.0) median(IQR)0.68(1.00)0.01(0.86)0.03 SpearmanCorrelationLSmean(SE)LSmean(SE)LS mean(SE)P-value^^D mRSS(0-51)-0.75*-6.7(1.30)-3.8(1.23)-2.9(1.75)0.10D FVC%predicted0.36*-1.4(1.30)-3.1(1.20)1.7(1.72)0.32D PTGA(0-10)-0.17-0.50(0.392)-0.30(0.385)-0.20(0.557)0.73D MDGA(0-10)-0.47*-1.34(0.282)-0.18(0.284)-1.16(0.403)0.004D HAQ-DI(0-3)-0.21-0.11(0.079)0.11(0.076)-0.22(0.108)0.05^p-value for treatment comparisons based on Van Elteren test^^p-value for treatment comparisons based on ANCOVA model with treatment,duration of SSc and baseline value as covariates*p<0.01using Spearman correlation coefficientNegative score denotes improvement,except for FVC%where negative score denotes worsening;LS mean=least squares mean;SE=standard errorFRI0328BRANCHED CHAIN AMINO ACIDSIN THE TREATMENTOF POLYMYOSITIS AND DERMATOMYOSITIS:RESULTSFROM THE BTOUGH STUDYNaoki Kimura,Hitoshi Kohsaka.Tokyo Medical and Dental University(TMDU), Department of Rheumatology,Tokyo,JapanBackground:Muscle functions of patients with polymyositis and dermato-myositis(PM/DM)remain often impaired even after successful control of the immune-mediated muscle injury by immunosuppressive therapy.The only effort at the present to regain muscle functions except for the immu-nosuppression is rehabilitation,which is carried out systematically in lim-ited institutes.No medicines for rebuilding muscles have been approved. Branched chain amino acids(BCAA)promote skeletal muscle protein syn-thesis and inhibit muscle atrophy.They thus have positive effects on muscle power,but have never been examined for the effects on PM/DM patients.Objectives:To assess the efficacy and safety of BCAA in the treatment of PM/DM for official approval of their use in Japan.Methods:Untreated adults with PM/DM were enrolled in a randomized, double-blind trial to receive either TK-98(drug name of BCAA)or pla-cebo in addition to the conventional immunosuppressive agents.One package of TK-98(4.15g)contained L-isoleucine952mg,L-leucine 1904mg,and L-valine1144mg(molar ratio is1:2:1.35),and6packages were administered daily in3divided doses.After12weeks,patients with average manual muscle test(MMT)score less than9.5were enrolled in an open label extension study for12weeks.The primary end point was the change of the MMT score at12weeks.The secondary end points were the disease activity evaluated with myositis disease activity core set (MDACS)and the change of functional index(FI),which evaluates dynamic repetitive muscle functions.Results:Forty-seven patients were randomized to the TK-98(24patients [12with PM and12with DM])and placebo(23patients[11with PM and12with DM])groups.The baseline MMT scores were equivalent (7.97±0.92[mean±SD]in the TK-98group and7.84±0.86in the placebo group).The change of MMT scores at12weeks were0.70±0.19(mean ±SEM)and0.69±0.18,respectively(P=0.98).Thirteen patients from the TK-98group and12from the placebo group were enrolled in the exten-sion study.The MMT scores in both groups improved comparably throughout the extension study.The increase of the FI scores of the shoulder flexion at12weeks was significantly larger in the TK-98group (27.9±5.67and12.8±5.67in the right shoulder flexion[P<0.05],27.0±5.44and13.4±5.95in the left shoulder flexion[P<0.05]).The improvement rate of the average FI scores of all tested motions(head lift,shoulder flexion,and hip flexion)through the first12weeks was larger in the TK-98group.No difference was found in the disease activ-ity throughout the study period.Frequencies of the adverse events until 12weeks were comparable.Conclusion:Although BCAA exerted no effects in the improvement of the muscle strength evaluated with MMT,they were effective in the improve-ment of dynamic repetitive muscle functions in patients with PM/DM with-out significant increase of adverse events.Disclosure of Interests:None declaredDOI:10.1136/annrheumdis-2019-eular.5235FRI0329ANALYSIS OF11CASES OF ANTI-PL-7ANTIBODYPOSITIVE PATIENTS WITH IDIOPATHIC INFLAMMATORYMYOPATHIES.MALIGNANCY MAY NOT BE UNCOMMONCOMPLICATION IN ANTI-PL-7ANTIBODY POSITIVEMYOSITIS PATIENTSTaiga Kuga,Yoshiyuki Abe,Masakazu Matsushita,Kurisu Tada,Ken Yamaji, Naoto Tamura.Juntendo University School of Medicine,Department of Internal Medicine and Rheumatology,Tokyo,JapanBackground:Various autoantibodies are known to be related to idiopathic inflammatory myopathies(IIM).Anti-PL-7antibody is anti-threonyl-tRNA synthetase antibody associated with antisynthetase syndrome(ASS).Since anti-PL-7antibody is rare(mostly1-4%of myositis,while a Japanese study reported17%),little is known as to clinical characteristics of it(1). Objectives:To analyze clinical characteristics of anti-PL-7positive IIM patients.Methods:Anti-PL-7antibody was detected by EUROLINE Myositis Profile 3.IIM diagnosis was made by the2017EULAR/ACR classification criteria (2)and/or Bohan And Peter classification(3).Eleven anti-PL-7antibody positive adult patients(all female),age at onset(61.5±12.6years)were enrolled in this study between2009and2018.Clinical manifestations, laboratory and instrumental data were reviewed in this single centre retro-spective cohort.Results:Characteristic symptoms were identified at diagnosis:skin mani-festations(7/11cases,63.6%),muscle weakness(8/11cases,72.7%), arthralgia(5/11cases,45.5%)and Raynaud’s phenomenon(4/11cases, 36.4%).Myogenic enzymes were elevated in most cases(10/11cases, 90.9%).ILD was detected in all patients(11/11cases,100%)and2 patients(18.2%)developed rapidly progressive rgest IIM subtype was polymyositis(PM,5/11cases),followed by dermatomyositis(DM,3/ 11cases)and amyopathic dermatomyositis(ADM,3/11cases).Five patients(45.5%)complicated with malignancy within3years from the diagnosis of IIM.Though clinical manifestations and laboratory data showed any difference between malignancy group and non-malignancy group,all3ADM cases but no DM cases complicated with malignancy in this study.Conclusion:Anti-PL-7antibody positive IIM patients frequently complicated with ILD.Frequency of cancer in ASS patients within three years from diagnosis was 1.7%and not much different from the general population in previous report from France(4).Though this study only included IIM patients and may have selection bias,careful malignancy survey may be essential in Anti-PL-7antibody positive IIM patients.REFERENCES:[1]Y Yamazaki,et al.Unusually High Frequency of Autoantibodies to PL-7Associated With Milder Muscle Disease in Japanese Patients With Poly-myositis/DermatomyositisARTHRITIS&RHEUMATISM Vol.54,No.6, June2006,pp2004–2009[2]Lundberg IE,Tjärnlund A,Bottai M,et al.EULAR/ACR classification crite-ria for adult and juvenile idiopathic inflammatory myopathies and their Major Subgroups.Ann Rheum Dis.2017;76:1955–64.[3]Bohan A,Peter J.Polymyositis and dermatomyositis.N Engl J Med1975,292:344-347;403-407.[4]Hervier B,et al.Hierarchical cluster and survival analyses of antisynthe-tase syndrome:phenotype and outcome are correlated with anti-tRNA syn-thetase antibody specificity.Autoimmunity reviews.2012;12:210–217. Disclosure of Interests:Taiga Kuga:None declared,Yoshiyuki Abe:None declared,Masakazu Matsushita:None declared,Kurisu Tada Grant/ research support from:Eli Lilly,Ken Yamaji:None declared,Naoto Tamura Grant/research support from:Astellas Pharma Inc.,Asahi Kasei Pharma,AYUMI Pharmaceutical Co.,Chugai Pharmaceutical Co.LTD, Eisai Inc.,:Takeda Pharmaceutical Company Ltd.,Speakers bureau:Jans-sen Pharmaceutical K.K.,Bristol-Myers Squibb K.K.,:Mitsubishi Tanabe Pharma Co.DOI:10.1136/annrheumdis-2019-eular.4150846Friday,14June2019Scientific Abstractson December 25, 2023 by guest. Protected by copyright./Ann Rheum Dis: first published as 10.1136/annrheumdis-2019-eular.5235 on 27 May 2019. Downloaded from。
Defining drug disposition determinants
Drug disposition is influenced by drug metabolizing enzymes (DMEs), drug transport proteins (DTPs), serum binding proteins and transcription factors that regulate DME and DTP expression (BOX 1). Foreknowledge of the specific proteins that influence the disposition of a new chemical entity (NCE) is an important goal of preclinical and early clinical drug develop‑ment. Early availability of this information enables mathematical modelling of the drug interaction potential of an NCE using quantitative kinetic parameters of specific DMEs1. This can therefore lead to better projection of doses for subsequent studies. Although tools exist to assess the roleof most of these proteins for the disposi‑tion of an NCE, drug developers typically learn only about a limited number of them (that is, several cytochrome P450s (CYPs), a few additional DMEs and the DTPP‑glycoprotein), and generally do not know the relative clinical importance of most of the more than 170 drug disposition pathways (TABLES 1,2;BOX 2; Supplementary information S1 (table), S2 (table), S3 (table), S4 (table)). Much of the knowledge aboutdrug disposition determinants comes fromacademic laboratories after a drug is marketed.Such knowledge has been highly beneficial forpatients; for example, when the prevalence ofsevere adverse events in thiopurine S‑methyl‑transferase (TPMT) poor metabolizers dosedwith thiopurines was identified2,3. However,for many drugs, the pathways that determinepharmacokinetic variation remain unknowneven years after regulatory approval. Thislimits the ability of drug developers to identify,manage and understand the consequences ofpharmacokinetic variability for efficacy, safetyand drug–drug interactions. For example,statins were used by many millions of peoplebefore it was discovered that their pharmaco‑kinetic variability, drug interactions, efficacyand safety might be dependent on the DTPsolute carrier organic anion transport protein1B1 (OATP1B1)4–6.Association between a gene variantand drug pharmacokinetics implies amechanistic role of the gene product indrug disposition, and the potential outcomeof making such associations is to learn theextent to which any of a wide range offactors influences the disposition of a drug.Increased knowledge and more accessibletechnology should now make it easier fordrug developers to study which pathwaysare responsible for the disposition of adrug. In this article, after a brief overviewof the preclinical characterization of drugdisposition, we summarize current know‑ledge on pharmacogenetics and drug dispo‑sition. We then propose a new approachin which pharmacogenetic results derivedfrom early clinical studies can both feedback to additional targeted in vitro studiesand feed forward to optimize later‑stage,larger clinical trials for NCEs, contribute tomore informative drug labels, and therebypotentially enable better drug use.Preclinical drug disposition assessmentPreclinical characterization of drug dis‑position generally involves the assessmentof individual proteins for their role in thedisposition of the NCE. A summary ofcurrently available tools for such studies,including purified or recombinantproteins, selective substrates and inhibitors isprovided in Supplementary information S5(table).Although many purified or recombinantDMEs — particularly members of thehuman CYP, flavin monooxygenase (FMO),monoamine oxidase (MAO), UDP glucu‑ronosyltransferase (UGT), sulphotransferase(SULT), N‑acetyltransferase (NAT) andglutathione S‑transferase (GST) families— are commercially available, there aresubstantial gaps in the availability of purifiedor recombinant DMEs from other families.Several DTPs have been functionallyexpressed in recombinant cell‑based assays,but these are generally not commerciallyavailable. When purified or recombinantprotein is not available, selective inhibitors(if known and commercially available) canbe useful to deconstruct the dispositionprocess in perfused organs, tissue slices,isolated cells or subcellular fractions.However, sufficiently selective inhibitors thatdistinguish between closely related proteinshave only been established for a few DMEor DTP families. Even among the CYPs, it isstill not possible to fully distinguish betweeno P i n i o nDefining drug dispositiondeterminants: a pharmacogenetic–pharmacokinetic strategyDavid A. Katz, Bernard Murray, Anahita Bhathena and Leonardo SahelijoAbstract | In preclinical and early clinical drug development, information about thefactors influencing drug disposition is used to predict drug interaction potential,estimate and understand population pharmacokinetic variability, and selectdoses for clinical trials. However, both in vitro drug metabolism studies andpharmacogenetic association studies on human pharmacokinetic parameters havefocused on a limited subset of the proteins involved in drug disposition. Furthermore,there has been a one-way information flow, solely using results of in vitro studiesto select candidate genes for pharmacogenetic studies. Here, we propose a two-way pharmacogenetic–pharmacokinetic strategy that exploits the dramatic recentexpansion in knowledge of functional genetic variation in proteins that influencedrug disposition, and discuss how it could improve drug development.NATUrE rEvIEWS |drug discovery vOLUME 7 | APrIL 2008 |293PersPectIves©2008Nature Publishing Groupthe four CYP3A enzymes, and selective inhibitors for some CYPs (for example, CYP2C18, CYP2G, CYP2r1, CYP2S1) have not been identified. Indeed, little is known at all about these and a number of other DMEs. When a selective inhibitor is not available, it may be possible to take advantage of tissue‑selective expression of particular isoforms within a protein family to learn the extent to which each can influence NCE disposition (for example, the roles of FMO1, FMO2 and FMO3 can be assessed separately in kidney, lung and liver using the same inhibitor). When neither recombinant protein nor selective inhibitor for a DME or DTP is available, observing that an NCE is a competitive inhibitor (in perfused organs, tissue slices, isolated cells or subcellular fractions) towards a known selective sub‑strate (again, if known and commercially available) might indicate that a particular DME or DTP is important for the NCE’sdisposition. If a drug induces DME or DTPexpression by a transcription factor bindingin a heterologous transcription activation assay,it might autoinduce its own disposition aswell as that of other drugs.To provide a comprehensive in vitrosurvey of drug disposition determinants,a laboratory needs the capability to per‑form the diverse assay types mentionedabove, many of which must be developedin‑house. Because of the current lack of acomprehensive toolset and the resourcesrequired — and also because there hasnot been a regulatory imperative foradditional investigation — a typical pre‑clinical in vitro survey has covered onlyseveral DMEs (mainly CYPs) and the DTPP‑glycoprotein. Knowing whether thesefactors can influence the disposition ofan NCE provides valuable but far fromcomprehensive information to predictdrug interaction potential, estimate andunderstand population pharmacokineticvariability, and select doses for subsequentclinical trials. Elucidation of the specific setof disposition pathways that are importantfor a particular NCE’s disposition has notbeen achieved, mainly because the neces‑sary resources have not been available.There is a need for an improved toolsetto identify the most important proteins thatinfluence the disposition of an NCE. Thistoolset should be more comprehensive incoverage of DMEs, DTPs and other factors;less diverse in assay types; feasible duringpreclinical or early clinical development; andaffordable. We propose that a strategy basedon the growing knowledge of the influenceof pharmacogenetic factors on drug disposi‑tion (summarized in the following section)can help provide that toolset.Pharmacogenetics and drug dispositionA genetic component of pharmacokineticvariability was postulated more than 100years ago by Archibald Garrod in studies ofpatients with alkaptonuria7. Half a centurylater, several drugs were shown to haveindistinguishable disposition in mono‑zygotic twins, but often distinct dispositionin dizygotic twins (for example, phenyl‑butazone8). These results established drugdisposition as a heritable trait. Deficienciesof the DMEs NAT and butyrylcholin‑esterase (BCHE) were later identified asrisk factors for adverse effects of isoniazid9and succinylcholine10, respectively, and thegenetic basis for these11–15 and other DMEpoor metabolizer phenotypes (for example,CYP2D616–20, TPMT21–23) were discoveredaround 1990.By the 1990s, there was an understand‑able reticence in the pharmaceutical industryto develop drugs that were substrates ofthese few polymorphic DMEs because of thelikelihood of variable pharmacokinetic anddrug–drug interactions. However, therewere exceptions: atomoxetine, a sensitiveCYP2D6 substrate, was approved for usein 2002 (REF. 24). Also in the 1990s, drugdevelopers began to utilize pharmacogeneticstudies to learn the magnitude of pharmaco‑kinetic variability of NCEs that could beattributed to genetic variation. In general,these studies focused on the few DMEs inwhich there were known polymorphisms,and were undertaken only when they wereconsidered necessary. That is, when in vitroresults indicated that the NCE was asubstrate of the polymorphic DME(hypothesis‑based experiments).P e r s P e c t i v e s294 | APrIL 2008 | vOLUME 7 /reviews/drugdisc©2008Nature Publishing GroupUntil recently, limited knowledge about functional genetic variation in DMEs (and none about polymorphism in other drugdisposition factors) substantially limited the scope of pharmacogenetics–pharmaco‑kinetics research. However, in the past few years, substantial knowledge in this field has been accumulated, and a review of the literature reveals that there are now over 170 gene products known or expected to have a role in drug disposition (BOX 3). These include not only numerous DMEs and DTPs, but also abundant serum binding proteins and regulatory (transcription) factors that control the expression of DMEs and DTPs. More than half of the corresponding genes are known to be poly‑morphic (TABLES 1,2;BOX 2; Supplementary information S1 (table)); most that are not known to contain common functional poly‑morphisms (Supplementary information S2 (table), S3 (table), S4 (table)) have not been adequately studied to state with certainty that they do not.The 16 proteins involved in drug disposition for which consistently replicated associations between variants in the corresponding genes and the human pharmacokinetics of at least one drug havebeen published are shown in TABLE 1. Withthe exception of FMO3, for which only tworeports showing relationship to sulindacpharmacokinetics were found, there werethree or more consistent reports for at leastone drug for each gene. FMO3 was includedin this group because the established associ‑ation of variants in this gene with fish-odoursyndrome25 is additional evidence supportingits relevance for xenobiotic disposition.Nearly all the genes listed in TABLE 1 encodeDMEs, although there is also a gene thatencodes a DTP (OATP1B1). There is robustscientific evidence showing that each genecontains common variants (combinedminor allele frequency of variants sharinga phenotype ≥5%), which have substantialeffects on human pharmacokinetics of oneor more drugs (see TABLE1 for references).However, this does not mean that the rele‑vance of these variants for clinical practicehas been established; in fact, dose adjust‑ments or contraindications based on onlyCYP2D6, CYP2C9, TPMT and UGT1A1 arecurrently included in US drug labels26.TABLE 2 shows the 18 genes for whichcommon variants are likely to have a role indrug disposition, having been shown to beassociated with the human pharmacokineticsof one or more drugs, albeit in single studies.Of these, 15 encode DMEs, 2 encode DTPsand 1 encodes a serum binding protein(α‑1 acid glycoprotein, gene = ORM1).The preponderance of DMEs in TABLE 1 andTABLE 2 reflects that DMEs were the solefocus of pharmacogenetics research relatedto drug disposition until recently. Many ofthe studies cited in TABLE 2 are relativelyrecent, and might therefore be replicatedin the near future. As this occurs, the rangeof genes established as polymorphic deter‑minants of human pharmacokinetics willexpand. In addition to pharmacokinetics,several of these genes have also beenassociated with drug efficacy or safety.Common variants of at least 55 genesencoding drug disposition factors have func‑tional effects on protein activity or expression(Supplementary information S1 (table);BOX 2), but have not yet been associated withhuman pharmacokinetics for any drug. It iscurrently unknown how many of these geneswill be found to have meaningful influenceon human pharmacokinetics. Several ofthem encode DMEs such as CYP3A4 and thetable 1 | Consistently replicated associations between genotype and clinical pharmacokineticsOnline Mendelian Inheritance in Man (OMIM) database web site: /sites/entrez?db=omim.P e r s P e c t i v e sNATUrE rEvIEWS |drug discovery vOLUME 7 | APrIL 2008 |295©2008Nature Publishing GroupP e r s P e c t i v e stable 2 | Associations between genotype and clinical pharmacokinetics*Online Mendelian Inheritance in Man (OMIM) database web site: /sites/entrez?db=omim. NsAID, non-steroidal anti-inflammatory drug; sNP, single nucleotide polymorphism.296 | APrIL 2008 | vOLUME 7 /reviews/drugdisc©2008Nature Publishing GroupUGTs, which are known to metabolize a wide range of drugs27,28, and these may be among those most likely to be found to be relevant for human pharmacokinetic variability. Finally, DMEs, DTPs and regulatory factors for which the relevance of genetic variation to drug disposition has not been established are shown in Supplementary information S2 (table), S3 (table) and S4 (table).Proposal for a new PG–PK strategyThe increase in knowledge about phar‑macogenetics and drug disposition sum‑marized above — coupled with technology advancements that have made genotyping more affordable (with costs of less than US$1 per genotype for multiple available technologies) in the past decade — have made broad application of pharmacogenet‑ics in most drug development programmes more feasible. Several platforms have been developed to screen the known functional variants that might influence drug disposi‑tion in the context of early clinical trials from which high‑quality pharmacokinetic data are also available.The approach we propose includes a broad evaluation of genotype–pharmaco‑kinetic relationships during early drug development, together with in vitro studies, to first generate and then confirm hypoth‑eses about the pathways that are majordisposition determinants of an NCE.Our core strategy for incorporation in NCE development comprises five steps (FIG. 1). The first step is to conduct in vitro experiments before clinical trials to assess whether, and to what extent, selected DMEs and DTPs might influence disposition of the NCE. This activity provides valuable information about the potential role of proteins that are known to influence the disposition of many drugs and to mediate a number of clinically important drug inter‑actions, such as CYP3A and P‑glycoprotein. These experiments have demonstrated utility in drug development planning and decision‑making. Additional in vitro assays may become standard on the basis of new information about drug disposition and interaction factors. For example, recent results from our group29 and others30–33 have demonstrated that the hepatic uptake DTP OATP1B1 (SLCO1B1) is a meaningful determinant of drug disposition for statins and other drugs, and that OATP1B1 inhibi‑tors may have drug interaction potential. recombinant OATP1B1 can be expressed in cell culture, and selective substrates and inhibitors are available (Supplementary information S5 (table)). Learning whetheran NCE is an OATP1B1 substrate or inhibi‑tor could become an ordinary preclinicalactivity.The second step is to conduct a broadsearch for associations of pharmacokineticswith genotypes during the first‑in‑humanstudy (BOX 4). The scope of this search couldinclude all relevant genes for which thereis reasonable expectation that a positiveresult can be obtained (based on studypower) and readily interpreted based oncurrent information about functionalvariants. This includes genes for which thereare well‑established (TABLE 1) or observed(TABLE 2) associations between genetic vari‑ants and human pharmacokinetics, as wellas genes for which in vitro evidence indicatesthat common variants alter the activityor expression of the gene product (BOX 2;Supplementary information S1 (table)).Defining the search scope in this way willinclude over half of known or suspecteddrug disposition determinants. For genes inwhich common variants are known but theirfunction is not (Supplementary informationS2 (table)), associations between genotypeand human pharmacokinetics might notbe readily interpreted without subsequentexperimentation to define the phenotype ofthe associated variant(s). These genes mightbe screened if there is preclinical evidencethat the NCE is a substrate or inhibitor,and perhaps not otherwise. Because first‑in‑human studies are generally not large,the likelihood of finding an associationbetween an uncommon functional variant(Supplementary information S3 (table))and human pharmacokinetics is low. Ifthere is preclinical evidence that the NCEis a substrate or inhibitor, a special study todetermine the clinical relevance of genotypetherein may be a better approach to assessthe effect of uncommon functional variantsthan the general strategy described here.Genes for which there is no publishedinformation regarding common geneticvariation (Supplementary information S3(table)) might be screened using singlenucleotide polymorphisms (SNPs) foundin online databases. Including these SNPswould enable some assessment of essentiallyevery known or expected drug dispositiondeterminant.P e r s P e c t i v e sNATUrE rEvIEWS |drug discovery vOLUME 7 | APrIL 2008 |297©2008Nature Publishing GroupThe third step is to replicate any observed associations during the multiple rising‑dose study (BOX 4). As large numbers of genes may be screened, replication of any observed association from a first‑in‑human study is essential to minimize the risk of making a decision based on false‑positive results. The fourth step is to confirm the gene product’s role using an in vitro assay (if the role was not already known from preclinical work) before or concurrently with Phase II clinical studies (BOX 4). When functional genetic variants affect protein activity (rather than expression), the activities of those variants towards the NCE should also be measured in the in vitro assay, as some variants have substrate‑dependent effects. For example, the *17 allele of CYP2D6 has normal catalytic activity towards codeine but reduced activity towards dextromethorphan and debrisoquin34. If different variants in the same gene were used jointly to classifyindividuals (for example, into poor metabo‑lizer or non‑poor metabolizer) for clinicalpharmacogenetic–pharmacokinetic associa‑tions, the classification should be confirmedby showing that the different variants sharea similar phenotype toward the compound(for example, SLCO1B1 and atrasentan29).The fifth step is to estimate the magni‑tude of genotype effect in a populationpharmacokinetics model during Phase IIclinical studies. At this point, genotype isused as a covariate in the population phar‑macokinetics model, just as sex or weightmight be used. It is often not feasible to usegenotype as a population pharmacokineticsmodel covariate before Phase II, becausethe number of subjects in Phase I clinicalstudies (BOX 4) is generally not large enoughto ensure an accurate estimate of the magni‑tude of genetic effect in a diverse population.Sufficient numbers of individuals havingrare genotypes might not be dosed with anNCE until Phase III clinical studies (BOX 4),or it might be necessary to conduct anenriched clinical study to appropriatelyinform the model.Knowledge about genetic variability rele‑vant to drug disposition can be applied inseveral ways, and these are discussed in thefollowing section.Applications of PG–PK knowledgePotent inhibitors or inducers of a polymor‑phic DME or DTP are likely to have effectsthat are proportional to the magnitude ofeffect of genotype (for example, CYP2C19and ticlopidine35). Thus, associationsbetween human pharmacokinetics of anNCE and genetic variation identifies apotential pathway for drug–drug inter‑actions, and hence the need for specific andadditional drug–drug interaction studies.By contrast, little or no effect of a well‑established genetic variant might show thatcertain drug–drug interaction studies arenot necessary. For example, a CYP2D6 inhib‑itor is unlikely to have a meaningful effecton a CYP2D6 substrate if that substrate’spharmacokinetics are not meaningfullyinfluenced by CYP2D6 genotype. Prioritizingdrug–drug interaction studies on the basis ofclinical pharmacogenetic effects will improveon the current approach of doing so on thebasis of in vitro experiments alone1.CYP2D6 can be used to exemplifyanother application of clinical pharmaco‑genetics: to increase confidence thatpharmacokinetic outliers are not likely tobe an issue in later development. Lack of asignificant effect of any genotype suggeststhat multiple distribution pathways arecontributing to drug disposition equally, andthat genetic pharmacokinetic outliers are atmost very rare (as they would have to carrymultiple rare genotypes). Pharmacogenetic–pharmacokinetic relationships can help todistinguish between ordinary populationvariability and true outliers in a limiteddataset — a relevant factor in decisions ofhow (or whether) to move a programmeforward. For example, in one study we iden‑tified that unusual pharmacokinetics of aCYP2D6 substrate was observed in a personwhose CYP2D6 genotype had a populationfrequency of <0.4% and was thus reasonablyconsidered an outlier36. In other situations,learning that one or more apparent outliersshare genetic constitution with other subjectssuggests a greater level of inter‑individualvariation rather than the existence of twoseparate populations.Box 3 | Survey of genetic variants in drug disposition factorsA search of the scientific literature and online resources was conducted to provide an overview ofthe variation in genes that encode drug metabolizing enzymes (DMEs), the drug transport proteins(DTPs), abundant plasma binding proteins, and factors that regulate DME and DTP expression.For each gene, at least the following sources were searched:• Online Mendelian Inheritance in Man (OMIM) (/entrez/query.fcgi?db=OMIM)• Medline (accessed through Dialog DataStar)was searched using the OMIM gene symbol.If this simple search yielded >100 articles, it was limited by applying the condition AND(pharmacogenetics OR polymorphism-genetic).• The Pharmacogenetics and Pharmacogenomics Knowledge Base ()genes were categorized as follows:• Consistently replicated association of variants with human pharmacokinetics of at least one drug(TABLE 1).• Association of variants with human pharmacokinetics of one or more drugs, but withoutconsistent replication for any drug (TABLE 2).• Functionality of common variants (≥5% combined frequency of variants of similar phenotype)demonstrated by in vitro methods, but no published association with human pharmacokinetics(BOX 2; see Supplementary information S1 (table)).• Common variants have been published, but functionality or association with humanpharmacokinetics have not (see Supplementary information S2 (table)).• Functionality of one or more rare (or unreported frequency) variants demonstrated (seeSupplementary information S3 (table)).• No information on variant functionality or association related to human pharmacokinetics;rare mutations in some of these genes have been linked to Mendelian metabolic disorders (seeSupplementary information S4 (table)).The aim of this overview is to provide a sense of the diversity of pharmacogenetics–pharmacokinetics knowledge. Although extensive, the tables are not necessarily comprehensive.We generally did not include reports of association unrelated to the pharmacokinetics of aspecific drug, even when a phenotype (such as cancer susceptibility) might be related toxenobiotic disposition. Literature citations are either to our choice of review articles orto primary literature supporting what we considered to be the most important sort of informationavailable. For example, if variants in a gene were associated with human pharmacokinetics in aclinical study we do not also cite work showing the molecular phenotype of those variants; if thephenotype of common variants in a gene is understood we do not also cite work concerning thephenotype of rare variants. We apologize in advance to any scientists whose relevantpublications are not cited.P e r s P e c t i v e s298 | APrIL 2008 | vOLUME 7 /reviews/drugdisc©2008Nature Publishing GroupNature Reviews | Drug DiscoveryAssess safety and tolerabilityin healthy volunteersDose verification andproof of conceptLong-term safetyand efficacy The use of genotype–pharmacokinetic associations can also enhance the design of special population or regional bridging studies (BOX 4). If a drug’s pharmacokinetic properties are sensitive to a polymorphism in a gene, success in special population studies may depend on including that gene in the design. How this is most efficiently done may vary between situations, depend‑ing in part on the strength of the effect and frequency of the genotype. For some studies, retrospective determination of whether an unbalanced representation of genotypes contributed to group differences may be sufficient. Sometimes, recruitment of geno‑type‑balanced cohorts or separate matched cohorts of different genotypes, or excluding individuals of a certain genotype, may be desirable to address the key clinical phar‑macology question. These approaches can be particularly relevant in regional bridging studies, as genetic variant frequencies are known to differ substantially betweenpopulations of different geographic origins (for example, CYP2C9 (REF . 37), CYP2C19 (REF . 38), CYP2A6 (REF . 39), UGT1A1 and UGT1A9 (REF . 40), NAT2 (REF . 41), OATP1B1 (REF . 42)).For example, suppose that pharmaco‑genetics–pharmacokinetics research in both first‑in‑human and multiple rising‑dose studies showed that, on average, individu‑als heterozygous for a low activity allele of CYP2A6 (intermediate metabolizers ) had higher levels of an NCE than those homozygous for the wild‑type allele of the gene (extensive metabolizers ). Furthermore,in vitro experiments conducted subsequently showed that the NCE is a CYP2A6 substrate and population pharmacokinetic analysis of Phase II clinical trial results confirm the influence of the CYP2A6 genotype on the pharmacokinetic properties of the NCE. To facilitate global development of the NCE, a pharmacokinetic bridging study between Japanese and Caucasians is a next step 2. Successful conduct of this study could elimi‑nate the need for a separate full development programme in Japan 43. Y et, because of highly different variant frequencies, individuals homozygous for low activity alleles (poor metabolizers) are common among Japanese but not in Caucasians. Hence, random recruitment of Japanese and Caucasians (the standard practice for regional bridging studies) is almost certain to fail to showequivalent pharmacokinetics between the two groups. There are several possible regional bridging trial designs that might provide the evidence to avert a requirement to conduct similar full development programmes in both groups, including recruitment of genotype‑matched cohorts of Japanese and Caucasians. However, there is currently a lack of public experience, and hence uncer‑tainty, as to whether this or other designs will be acceptable to regulatory agencies.Another way to use this information is in dose selection for pivotal studies (BOX 4). Understanding that the dose–exposure relationship differs between identifiable groups may lead to a decision to moveforward with a dose or doses that differ from what might have been selected considering a homogeneous population. For example, it may be desirable to increase the pivotal study dose to enhance efficacy among individuals who can be expected to have lower exposures (FIG. 2). The drug levels of some leading antidepressants (for example, paroxetine, venlafaxine) or antipsychotics (for example, olanzapine, aripiprazole) are moderately influenced by CYP2D6 genotype 44. These drugs are generally safe at a range of doses 45–48; however, there have been reports of poor efficacy related to low drug levels in CYP2D6 ultrarapid metabolizers 49–51. FIGURE 2a illustrates how pharmacogenetics might have been applied during the development of these drugs. Here, the ‘default’ dose represents the lowest dose that showed efficacy in pivotal studies and is generally the recommended starting dose in the drug’s labels. Suppose that the association between CYP2D6 genotype and pharmacokinetics had been established during Phase I and II clinical studies. Then, the developers of these drugs could have predicted that using a somewhat higher dose (the pharmacogenetics‑based dose) in pivotal studies would improve the efficacy profile in ultrarapid metabolizers and not meaningfully diminish the safety profile in other groups (including poor metabolizers). Perhaps they would have selected the phar‑macogenetics‑based doses for their pivotal studies. Because the drug label indicates doses studied in controlled efficacy trials, such a decision would have been reflected in the label and perhaps improved clinical practice using these drugs.Figure 1 | Flow chart of the proposed pharmacogenetic–pharmacokinetic strategy. In vitro experiments are conducted before clinical trials to assess whether, and to what extent, selected drug metabolizing enzymes (DMes) and drug transport proteins (DtPs) might influence the disposition of a new chemical entity (Nce). then, during the first-in-human study, a broad search for associations of pharmacokinetic (PK) properties with genotypes is conducted. Any observed associations are replicated during the multiple rising-dose study. Before or concurrently with Phase IIclinical studies, the gene product’s role is confirmed using an in vitro assay(if the role was not already known from preclinical work). the magnitude of genotype effect in a population pharmacokinetics model is then estimated during Phase II clinical studies.P e r s P e c t i v e sNATUrE rEvIEWS | drug discoveryvOLUME 7 | APrIL 2008 | 299© 2008Nature Publishing Group。
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Identities from Weighted2-Motzkin Paths William Y.C.Chen1,Sherry H.F.Yan2and Laura L.M.Yang31,2,3Center for Combinatorics,LPMC-TJKLCNankai UniversityTianjin300071,P.R.China2Department of MathematicsZhejiang Normal UniversityJinhua,Zhejiang321004,P.R.China1chen@,2hfy@,3yanglm@Abstract.Based on a weighted version of the bijection between Dyck paths and 2-Motzkin paths,wefind combinatorial interpretations of two identities related to the Narayana polynomials and the Catalan numbers,in answer to two problems recently proposed by Coker.AMS Classification:05A15,05A19Keywords:Narayana number,Catalan number,2-Motzkin path,weighted2-Motzkin path,multiple Dyck path,bijection.Suggested Running Title:Weighted2-Motzkin PathsCorresponding Author:William Y.C.Chen,Email:chen@1.IntroductionIn answer to two problems recently proposed by Coker[5],wefind combinatorial inter-pretations of two identities on the Narayana polynomials and the Catalan numbers,by using a weighted version of the well-known bijection between Dyck paths and2-Motzkin paths.The Catalan numbers are defined byC n=1n+12nn,n≥0.The Narayana numbers are defined asN(n,k)=1nnknk+1,n≥1,with N(0,0)=1and N(0,k)=1for k≥1.The Narayana numbers are listed as sequence A001263in[15],see also[8,13,16,17,22].The Narayana polynomials aregiven byN n(x)=n−1k=0N(n,k)x k,n≥1,which have been studied by Bonin,Shapiro,Simion[2],Coker[5],and Sulanke[18,19].We will be concerned with the following two combinatorial identities due to Coker [5].For n≥1,n−1 k=01nnknk+1x k=(n−1)/2k=0C kn−12kx k(1+x)n−2k−1,(1.1)n−1 k=01nnknk+1x2k(1+x)2(n−1−k)=n−1k=0C k+1n−1kx k(1+x)k.(1.2)The above identities are derived by using generating functions,and Coker proposed the problems offinding combinatorial interpretations of these two identities.Our work was motivated by the work of Chen,Deutsch and Elizalde[4]on plane trees and2-Motzkin paths.However,our combinatorial proofs of(1.1)and(1.2)in Section3are based on the bijection between Dyck paths and2-Motzkin paths,which wasfirst dis-covered by Delest and Viennot[6],together with the fact that the numbers of evenly positioned up steps on Dyck paths of length2n are distributed with respect to the Narayana numbers as described in Lemma3.3.2.Coker’s ProblemsThe aforementioned two identities arose from the study of multiple Dyck paths.Recall that a multiple Dyck path is a path that starts at the origin,never runs below the horizontal axis,and uses steps in the set{(h,0):h>0}∪{(0,h):h>0}.Coker[5] proposed the following problems:Problem2.1Find a bijective proof of the identityn k=11nnknk−14n−k=(n−1)/2k=0C kn−12k4k5n−2k−1.(2.1)Problem2.2Find a combinatorial interpretation of the identityn k=11nnknk−1x2k(1+x)2n−2k=x2n−1k=0C k+1n−1kx k(1+x)k.(2.2)Thefirst identity is a special case of(1.1).Note that identity(1.1)can be derived from the following identity due to Simion and Ullman[14],see also[3]:1 nnknk−1=k−1r=0n−12rn−2r−1k−1−rC r.(2.3)The identity(1.1)has many consequences as pointed out by Coker[5].For example,it implies the classical identity of Touchard[20]when x=1,C n= (n−1)/2k=0C kn−12k2n−2k−1,and implies the following identity on the little Schr¨o der numbers s n when x=2,see [12,19]:s n= (n−1)/2k=0C kn−12k2k3n−2k−1.Coker’s interest in the evaluation of N n(t)at t=4lies in the fact that N n(4)equals the number d n of multiple Dyck paths of length2n.Thefirst few values of d n for n≥0 are as follows1,1,5,29,185,1257,8925,65445,which form the sequence A059231in[15].Coker[5]proved this fact from the well-known interpretation of Narayana numbers as counting Dyck paths of length2n with k+1peaks. The enumeration of multiple Dyck paths has also been studied independently by Sulanke [18]and Woan[21].Identity(2.2)was established from the enumeration of multiple Dyck paths of length 2n with a given number of steps.Letλn,j be the number of multiple Dyck paths of length2n and j steps,and P n(x)be the polynomialP n(x)=2nj=2λn,j x j.Coker[5]derived the following formulaP n(x)=nk=11nnknk−1x2k(1+x)2n−2k,which can be restated asP n(x)=x2n N n((1+x−1)2).On the other hand,P n(x)can be considered as a variant of the polynomial R n(x)which was defined by Denise and Simion[7].Then(2.2)can be deduced from the formulaR n(x)=n−1k=0(−1)k C k+1n−1kx k(1−x)k,and the relationP n(x)=x2R n(−x).The combinatorial proofs of the above identities will be given in the next section.ttice Path ProofsIn this section,we present combinatorial interpretations of(1.1)and(1.2)by using a weighted version of the bijection between Dyck paths and2-Motzkin paths.In general, for a nonnegative integer c,a c-Motzkin path is a lattice path starting at(0,0),ending at(n,0),and never going below the x-axis,with possible steps(1,1),(1,0)and(1,−1), where the level steps,or horizontal steps,(1,0)can be colored by one of c colors.When c=1,we have a common Motzkin path and we use U,D,and H to denote an up step (1,1),a down step(1,−1)and a level step(1,0),respectively.When c=0,there are no level steps and such a path reduces to a Dyck path.When c=2,we use B(R)to denote a blue(red)level step.The length of a path is defined to be the number of steps. The notion of2-Motzkin paths may have originated in the work of Delest and Viennot [6]and has been studied by others,including[1,9].Let D n denote the set of Dyck paths of length2n;it is well-known that|D n|=C n. Let M n denote the set of Motzkin paths of length n,and let CM n denote the set of 2-Motzkin paths of length n.For a Dyck path P=p1p2...p2n,we say that a step p i is in an even position if i is even.Let EU(P)denote the number of U steps in even positions on a Dyck path P.From[6,10,11,13,16,22]one canfind that the statistic EU is distributed by the Narayana numbers:Lemma3.3For n≥1,the number of Dyck paths P of length2n with EU(P)=k is given by the Narayana number N(n,k).Here we recall a well-known bijection between Dyck paths and2-Motzkin paths,first introduced by Delest and Viennot[6].DefineΨ:D n→CM n−1,where P=p1p2...p2n∈D n is mapped to Q=q1q2...q n−1∈CM n−1such thatp2i p2i+1=UU if and only if q i=U,=DD···=D,=UD···=B,=DU···=R.From the above bijection we see that for n≥1,the number of2-Motzkin paths of length n−1equals the Catalan number C n.For a2-Motzkin path P,we use UB(P)to denote the total number of U and B steps on P.Then we have the following relation concerning the Narayana numbers and the statistic UB.Lemma3.4For n≥1,the number of2-Motzkin paths P of length n−1with UB(P)=k is given by the Narayana number N(n,k).Combinatorial proof of identity (1.1):As usual,the weight of a path is the product of the weights of its steps,and the weight of a path set is the sum of the weights of the paths.For the left-hand side of (1.1),let us consider the set CM n −1,where we assign the weight x to each U or B step and the weight 1to any other step.Then,by Lemma 3.4the left-hand side equals the weight of CM n −1.For the right-hand side of (1.1),we consider the weight of the subset of CM n −1consisting of paths having exactly k up steps.The weight of this subset equalsC k n −1n −1−2kx k (1+x )n −1−2k ,since (i)there are (1+x )n −1−2k ways to arrange the bi-colored level steps among them-selves reflecting the weight assignment that a blue step has weight x and a red step has weight 1,(ii)there are n −1n −1−2k ways to intersperse the level steps in a Dyck path of length 2k to form a path of CM n −1,and (iii)there are C k such Dyck paths.This completes theproof.Combinatorial proof of identity (1.2):For the left-hand side of (1.2),if we assign the weight x 2to each U or B step and the weight (1+x )2to any other step,then the left-hand side equals the weight of CM n −1.For the right-hand side,we first let S (k )denote any subset of CM n −1where each path has k up steps and has the up and down steps in given positions.Since the U ’s and D ’s can be matched on any path,and since x 2·(1+x )2=(x (1+x ))2,there is no change in the total weight if we reassign the weight x (1+x )to all U and D steps.Thus the weight of S (k )is(x (1+x ))2k (x 2+(1+x )2)n −1−2ksince a blue step has weight x 2and a red step has weight (1+x )2.Let T M n −1denote the set 3-Motzkin paths of length n −1having level steps B ,R ,and G .Assign the weight x (1+x )to each of the U ,D ,B ,and R steps and the weight 1to each G step.Let S (k )denote any subset of T M n −1where each path has k up steps and has the up and down step steps in given positions.Similarly,the weight of S (k )equals(x (1+x ))2k (1+x (1+x )+x (1+x ))n −1−2k .Since S (k )and S (k )have the same weight,it remains to show that the weight of T M n −1coincides with the right-hand side of (1.2).To construct a path of T M n −1with (n −1−k )G steps,we may insert the G steps into bi-colored paths of CM k where each U ,D ,B ,and R step has the same weight x (1+x ).Since there are n −1n −1−k = n −1k ways to insert the G steps and since |CM k |=C k +1,the weight ofthe subset of T M n −1consisting of paths with (n −1−k )G steps equals C k +1 n −1k x k (1+x )k ,which is the summand of the right-hand side of (1.2).This completes theproof.Acknowledgments.The authors wish to thank Professor Robert Sulanke for directing their attention to the lemmas of Section 3and for valuable suggestions leading to animprovement of an earlier version.This work was supported by the973Project,the PCSIRT Project of the Ministry of 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