具有生物活性物质的分子设计

null具有生物活性物质的分子设计具有生物活性物质的分子设计（药物分子设计）课程内容课程内容1、绪论 2、先导化合物的产生 3、生物活性相关的理论基础 4、生物等排关系 5、先导化合物的优化策略 6、定量构效关系 7、受体结构未知的设计（间接设计） 8、受体结构已知的设计（直接设计） 9、量子药理学的分子设计 10、组合化学和高通量筛选 11*、另外可能还要加一些有关分子设计部分的 内容：Molecule Modelingnull 注意：许多材料，包括幻灯以及素材等都可能会引用网上下载的内容。并且可能不会注明出处，在此向原著作者示感谢和歉意！第一章 绪论第一章 绪论null一、本课程特点：介绍知识 创制与仿制的最大的区别在于新药发现体系。 农药：涉及到有机化学、计算机化学、分子生物学、生物化学、结构化学等。 二、创制新农药的意义 农药与人类生活密切相关、环境要求。 今后农药发展的方向：绿色农药、对环境友好的农药、与环境相容的农药。 同时农药本身的抗性问题也使得我们对新农药的创制比较关注。null三、二十一世纪新农药的特点 1、与环境相容性好——毒性、残留 2、活性高（70年代150g/亩，现在10g甚至几克）。 3、安全性好：药本身、生产过程 4、市场潜力大——面积大和经济作物 5、难度大、耗资大四、新农药开发过程四、新农药开发过程随即合成筛选类同合成天然活性物质模型生物合理设计先导（母体）产生N 次先导化合物二次先导化合物一次先导化合物先导优化最佳候选物1先导展开先导展开先导优化先导优化最佳候选物2最佳候选物n应用开发安全性评价商品化品种新农药研究开发程序新农药研究开发程序link此图引自陈万义等《新农药研究与开发》五、计算机辅助的药物分子设计五、计算机辅助的药物分子设计目的：让计算机代替人脑进行重复、复杂的计算。 要弄清受授体的关系：锁钥关系。 定量构效关系（QSAR）： 2D-QSAR：logP=Aπ+bσ+·······+C 结构参数：疏水性、电性、立体性 3D-QSAR：CoMFA Comparative Molecular Field Analysis 原理：药物分子与受体之间可逆性的相互作用主要是通过非共价键作用力如范德华力、静电、氢键等。作用于同一受体的一系列药物分子它们与受体之间的相互作用力场应该有一定的相似性。这样，在不了解受体的三维结构的情况下，研究药物分子周围的这三种作用力，把它们与药物分子的生物活性定量的联系起来，既可以推测受体的特性，又可以建立模型指导化合物的设计，并定量推测新化合物分子间的药效强度。Drug DiscoveryDrug DiscoveryTrends in Drug DiscoveryTrends in Drug DiscoveryAnimal screening assays (pre ‘70) Target ID and compound optimization (’70) Medicinal, Peptide Chemistry, Molecular Biol. Rational Drug Discovery (‘80s) Computational Chemistry, Structural BiologyTrends in Drug DiscoveryTrends in Drug DiscoveryScreening (particularly HTS; ‘80s-’90s) Animal models Combinations (today) HTS, rational, theoretical, animal models…Development ConsiderationsDevelopment ConsiderationsClinical Trials Animal Models Market Segments Patient Compliance Issues Combination Therapies ProtectionSteps in Drug DiscoverySteps in Drug DiscoveryTarget identification/validation Hit (pre-lead) identification Lead optimization Pre-clinical development Clinical candidate Clinical trialsTarget identification/validationTarget identification/validationIs the target critically involved in disease Is the target critically involved in normal biology Can the target be effectively studied Is the target amenable to HTS Does manipulation lead to desired effectnullHit (pre-lead) identificationHit (pre-lead) identificationIs it effective at reasonable concentrations Does it possess desirable chemical properties Is it relevant for lead optimization Can it be protectedLead optimizationLead optimizationSusceptibility to degradation Routs of metabolism Solubility Formulation considerations Pre-clinical developmentPre-clinical developmentAll aspects of ADME PK/PD considerations Toxicology (very unique in cancer) Animal model efficacy Target clinical markets Side-effect profilesClinical candidateClinical candidateRouts of administration Formulation considerations Manufacturing cost Stability testing Clinical trialsClinical trialsPhase I, II, III Phase IV Compassionate Trials Orphan Indications Drug Discovery & DevelopmentDrug Discovery & DevelopmentIdentify diseaseIsolate protein involved in disease (2-5 years)Find a drug effective against disease protein (2-5 years)Preclinical testing (1-3 years)FormulationHuman clinical trials (2-10 years)Scale-upFDA approval (2-3 years)File INDFile NDATechology is impacting this processTechology is impacting this processIdentify diseaseIsolate proteinFind drugPreclinical testingGENOMICS, PROTEOMICS & BIOPHARM.HIGH THROUGHPUT SCREENINGMOLECULAR MODELINGVIRTUAL SCREENINGCOMBINATORIAL CHEMISTRYIN VITRO & IN SILICO ADME MODELSPotentially producing many more targets and “personalized” targetsScreening up to 100,000 compounds a day for activity against a target proteinUsing a computer to predict activityRapidly producing vast numbers of compoundsComputer graphics & models help improve activityTissue and computer models begin to replace animal testing1. Genomics, Proteomics & Biopharmaceuticals1. Genomics, Proteomics & BiopharmaceuticalsUnderstanding the link between diseases, genetic makeup and expression of proteinsGenomicsGenomicsGenomics is fast-forwarding our understanding of how DNA, genes, proteins and protein function are related, in both normal and disease conditions Human genome project has mapped the genes in human DNA Hope is that this understanding will provide many more potential protein targets Allows potential “personalization” of therapiesATACGGAT TATGCCTAfunctionsGene ChipsGene Chips“Gene chips” allow us to look for changes in protein expression for different people with a variety of conditions, and to see if the presence of drugs changes that expression Makes possible the design of drugs to target different phenotypescompounds administeredpeople / conditionse.g. obese, cancer, caucasianexpression profile (screen for 35,000 genes)BiopharmaceuticalsBiopharmaceuticalsDrugs based on proteins, peptides or natural products instead of small molecules (chemistry) Pioneered by biotechnology companies Biopharmaceuticals can be quicker to discover than traditional small-molecule therapies Biotechs now paring up with major pharmaceutical companies2. High-Throughput Screening2. High-Throughput ScreeningScreening perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease proteinHigh-Throughput ScreeningHigh-Throughput ScreeningDrug companies now have millions of samples of chemical compounds High-throughput screening can test 100,000 compounds a day for activity against a protein target Maybe tens of thousands of these compounds will show some activity for the protein The chemist needs to intelligently select the 2 - 3 classes of compounds that show the most promise for being drugs to follow-upInformatics ImplicationsInformatics ImplicationsNeed to be able to store chemical structure and biological data for millions of datapoints Computational representation of 2D structure Need to be able to organize thousands of active compounds into meaningful groups Group similar structures together and relate to activity Need to learn as much information as possible from the data (data mining) Apply statistical methods to the structures and related information3. Virtual Screening3. Virtual ScreeningBuild a computational model of activity for a particular target Use model to score compounds from “virtual” or real libraries Use scores to decide which to make, or pass through a real screen Computational Models of ActivityComputational Models of ActivityMachine Learning Methods E.g. Neural nets, Bayesian nets, SVMs, Kahonen nets Train with compounds of known activity Predict activity of “unknown” compounds Scoring methods Profile compounds based on properties related to target Fast Docking Rapidly “dock” 3D representations of molecules into 3D representations of proteins, and score according to how well they bind Present molecules to modelPresent molecules to modelWe may want to virtual screen All of a company’s in-house compounds, to see which to screen first A compound collection that could be purchased A potential combinatorial chemistry library, to see if it is worth making, and if so which to make Model will come out with with either prediction of how well each molecule will bind, or a score for each molecule4. Combinatorial Chemistry4. Combinatorial ChemistryBy combining molecular “building blocks”, we can create very large numbers of different molecules very quickly. Usually involves a “scaffold” molecule, and sets of compounds which can be reacted with the scaffold to place different structures on “attachment points”. Example Combinatorial LibraryExample Combinatorial LibraryNHR1R2R3Scaffold“R”-groupsR1 = OH OCH3 NH2 Cl COOH R2 = phenyl OH NH2 Br F CN R3 = CF3 NO2 OCH3 OH phenoxy ExamplesNHOHCF3OHNHOHOCH3NHCOHOHOCF3NHCOHOHOOFor this small library, the number of possible compounds is 5 x 6 x 5 = 150 Combinatorial Chemistry IssuesCombinatorial Chemistry IssuesWhich R-groups to choose Which libraries to make “Fill out” existing compound collection? Targeted to a particular protein? As many compounds as possible? Computational profiling of libraries can help “Virtual libraries” can be assessed on computer5. Molecular Modeling5. Molecular Modeling 3D Visualization of interactions between compounds and proteins “Docking” compounds into proteins computationally 3D Visualization3D VisualizationX-ray crystallography and NMR Spectroscopy can reveal 3D structure of protein and bound compounds Visualization of these “complexes” of proteins and potential drugs can help scientists understand the mechanism of action of the drug and to improve the design of a drug Visualization uses computational “ball and stick” model of atoms and bonds, as well as surfaces Stereoscopic visualization available“Docking” compounds into proteins computationally“Docking” compounds into proteins computationallyDocking with a Genetic AlgorithmDocking with a Genetic AlgorithmPut a compound in the approximate area where binding occurs Genetic algorithm encodes orientation of compound and rotatable bonds Optimize binding to protein Minimize energy Hydrogen bonding Hydrophobic interactions Can be used for “virtual screening”6. In Vitro & In Silico ADME models6. In Vitro & In Silico ADME modelsTraditionally, animals were used for pre-human testing. However, animal tests are expensive, time consuming and ethically undesirable ADME (Absorbtion, Distribution, Metabolism, Excretion) techniques help model how the drug will likely act in the body These methods can be experemental (in vitro) using cellular tissue, or in silico, using computational models In Vitro ADME ModelsIn Vitro ADME ModelsBased around real tissue samples, which have similar properties to those in the body Example: CACO-2 tissue closely resembles the lining of the stomach, so if a molecule passes through CACO-2 it likely will also pass through the stomach lining, and thus be a candidate for oral delivery Cuts down animal tests, by acting as a “pre-screen” Enables ADME data to be discovered on many more compoundsIn Silico ADME ModelsIn Silico ADME ModelsComputational methods can predict compound properties important to ADME, e.g. LogP, a liphophilicity measure Solubility Permeability Cytochrome p450 metabolism Means estimates can be made for millions of compouds, helping reduce “atrittion” – the failure rate of compounds in late stageProperty calculation on the webProperty calculation on the webhttp://www.molinspiration.com/cgi-bin/propertiesDraw in structure…Draw in structure…Returned results…Returned results…LogPTotal Polar Surface Area# of atomsMol. Wt.Rule-of-5 violations# Rot. bondsDruglikeness (N/A)Some of the challenges...Some of the challenges...Huge increase in the volume of information Genomics & High-throughput screening How do we use it to make better decisions (earlier) Immature technology and informatics Experimental hardware is changing rapidly Computing needs to meet complex, changing analysis needs “Fuzzy” science Even our understanding of the underlying science is constantly changing第二章 先导化合物的产生和发现第二章 先导化合物的产生和发现一、引言一、引言先导化合物（lead compound）：通过生物筛选，从众多化合物中发现和选出具有某种生物活性功能的新化合物；一般具有新颖的化学结构，有衍生化和改变结构的发展潜力，可以用做起始模型，经过结构优化开发出受专利保护的新药品种。 lead generation, structure modifying, lead optimization, lead development二、化学的或者物理的假设二、化学的或者物理的假设例如三唑类杀菌剂的开发，首先是Bayer公司根据Buchel提出的理论：能在生物体内生成对生物体有一定 活性的碳正离子的化合物，它的活性应当较高。三、生物化学或药理学假设三、生物化学或药理学假设1、作用机制 乙酰胆碱酯酶：有机磷 靶标的能量代谢：鱼藤酮、三硝基酚等 昆虫表皮形成：几丁质合成抑制剂、苯甲酰基脲、醌的合成抑制剂等 激素：保幼激素 神经系统 2、抗性机制：药物从体外进入体内是利用渗透作用，但体内酶的作用会使得药物失去活性，（因此应当增加药物的渗透性）。 解毒：微粒体多功能氧化酶mfo，谷光甘肽转移酶GSH-t，水解酶等null3、代谢机制3、代谢机制激活、解毒：氧化、水解、断裂、轭合nullnullnullnull四、天然活性物质四、天然活性物质1、植物体内 例如：乙烯=>乙烯利也就是Y-CH2-CH2-X式结构，X：离去基团2、植物次生代谢物 引入一个名词：克生物质(allello chemicals)，实际上就是指植物体内的一种次生物质，在生物体内不参与代谢，但干扰某些代谢过程。nullnull异株克生（allelopathy）：某种作物对另一种作物的抑制生长作用。 一个明显的例子：核桃树的树下是无草的，就是因为其中含有草的克生物质。银胶菊里的香豆素与核桃树中的一种物质（胡桃醌）相结合，使草无法生存。 香豆素和胡桃醌：nullnullnull3、拟除虫菊酯单一体直接使用时没有效果，原因？两个问题：A、哪一个对映体效力最高？B、分解问题如何解决？如何对结构进行改造？ 从三个方面：酯部分（用苯环代替现有结构）、双键部分（用卤素代替）以及三元环结构。null总的来说，先导化合物的发现可以归纳为四类途径：经典的是随机合成筛选法，其他的则是类同合成法，天然活性物质模型法以及生物合理设计法。 1、随机合成筛选法 Random synthesis screening：经验合成，也称为bluesky。包括新化合物的设计、合成和筛选。 主要思路：1、引入过去农药中少见的元素，包括金属和非金属元素（F，Si，Sn等）；2、设计新颖的杂环结构；3、利用大吨位的有机化工产品的副产物出发，从原料来源考虑；4、立体化学结构；5、模拟天然生物活性化合物的化学结构，通过全合成、半合成进行衍生、改变。null2、类同合成：analogue synthesis 也称衍生合成，从某个已开发新农药的分子结构出发，谋求开发同一系列衍生物新品种，或者以该化合物为先导物，进行结构改变，期望能发现二次先导。 优点：方向明确。 缺点：一哄而上。 设计思路：一、低层次：衍生；二、高层次：1、亚结构连接（活性基团拼接法）：把两种或更多的已知有效结构进行各种方式的连接；2、生物等排。 生物等排：当有机化合物结构中改变某些特定原子间的排列时，由于电子分布或构型仍保持一定的相似性，结果新的结构与原来化合物之间可能具有某些共性，包括生物性质的相似性。null改变结构的方法：1、饱和环开裂；2、饱和侧链环合；3、饱和环或者芳环上的取代；4、饱和环或者芳环上的缩合；5、同系化合物的衍生化；6、引入或者消除双键；7、芳环改变成杂环或者反之；8、饱和环改变成芳环或者杂环；9、引入立体结构等等。 3、天然活性物质模型（natural bioactive substance model） 4、生物合理设计（biorational design）nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullCombinatorial Chemistry (1)Combinatorial Chemistry (1)Combinatorial Chemistry within drug designCombinatorial Chemistry within drug designLead DiscoveryLead OptimisationDevelopment CandidateDrugTherapeutic TargetCombinatorial Chemistry impacts hereTraditional Medicinal ChemistryTraditional Medicinal ChemistryTraditionally lead structures for medicinal chemistry have originated from: Natural Products (Plant extracts) (Fermentation broths) (Animal sources) Pre-existing compound collections Both these lead discovery strategies depend on screening large numbers of compounds, in the hope that one of them would fit the biological targetHigh Throughput ScreeningHigh Throughput ScreeningThe advent of high throughput automated assays has made possible the robotic screening of in excess of 100,000’s of individual compounds per year per drug target. The challenge is to be able to supply the screening process with compounds.Traditional Drug DiscoveryTraditional Drug DiscoveryOnce a lead was discovered, a team of medicinal chemists would prepare a series of very similar molecules (analogues) These analogues would be biologically evaluated. The biological results would determine the next generation of analogues to be synthesised. The process was repeated until biological activity was optimised.The Pharmaceutical LotteryThe Pharmaceutical LotteryOnly 1 out 10,000 chemical entities reaches the production pipeline. Each entity costs ~ £7,500 Until recently, ~ 25 test compounds a year was considered a good work rate for an organic chemist in the pharmaceutical industry As a result 400 organic chemists would be required to make only 1 drug candidate per year.Economics and PoliticsEconomics and PoliticsAs can be seen traditional drug discovery can be very expensive. Governmental pressure for cheaper drugs coupled to consolidation within the pharmaceutical industry demands a more efficient method of drug discovery. Compound screening is already very efficient. A method of making more analogues more quickly, OR, better still, a method of avoiding analogues altogether needed to be found.Rational Drug Design (1)Rational Drug Design (1)In the 80’s advances in biochemistry and computing made it possible to accurately model both compounds and their biological targets (ie. enzymes). This offered the prospect of rationally designing molecules to fit a given enzyme or receptor. Designing molecules would avoid expensive and time consuming rounds of analogue synthesis.Rational Drug Design (2)Rational Drug Design (2)Unfortunately this approach is dependent on a knowledge of receptor site/enzyme topography. Currently this information is not available for the vast majority of biological targets. As a result rational drug design may be viewed to have failed to fulfil it’s early promise.Drug Discovery: A Pre -Industrial Process?Drug Discovery: A Pre -Industrial Process?The process of drug discovery had depended on the hand crafted, serial synthesis and testing of individual chemical entities. The perceived failure of the rational drug design approach gave the pharmaceutical industry little option but to explore the mass production of analogues for high throughput screening.A New Industrial Revolution?A New Industrial Revolution?A major theme of the industrial revolution was the replacement of the highly expensive individualised mode of manufacturing by the concept of mass production. Combinatorial chemistry has been adopted by the pharmaceutical industry to mass produce compounds for testing. Combinatorial Chemistry - DefinitionCombinatorial Chemistry - DefinitionThe systematic and repetitive covalent connection of a set of building blocks to each other to yield a large array of structurally diverse molecules.Combinatorial Chemistry - How does it differ from traditional synthesis ?Combinatorial Chemistry - How does it differ from traditional synthesis ?A + BABA A1 A2 A3 A4 . . . AnB B1 B2 B3 B4 . . . BnA1-nB1-nTraditional SynthesisCombinatorial SynthesisCombinatorial Chemistry and Evolution (1)Combinatorial Chemistry and Evolution (1)By employing a building block collection and systematically assembling the blocks in many combinations using chemical, biological and biosynthetic procedures it is possible to create combinatorial libraries consisting of vast populations of molecules. Combinatorial Chemistry and Evolution (2)Combinatorial Chemistry and Evolution (2)This approach is thought to be similar to the processes that occurred millions of years ago on Earth when complex biological molecules, oligo -nucleotides, carbohydrates and peptides were generated through the combination of nucleosides, sugars and amino acid building blocks.Numbers (1)Numbers (1)The number of molecular entities (N) generated by an ideal combinatorial synthesis is given by: N = bx Where b is the number of building blocks available and x is the number of synthetic stepsNumbers (2) - A Set of 20 Building Blocks (e.g. Natural Amino Acids)Numbers (2) - A Set of 20 Building Blocks (e.g. Natural Amino Acids)Numbers (3) - Set of 100 building BlocksNumbers (3) - Set of 100 building BlocksNumbers (4)Numbers (4)As can be seen only a few combinatorial experiments would suffice to create more chemical entities than currently exist in the compound libraries of the world wide pharmaceutical industry.Chemical LibrariesChemical LibrariesChemical libraries are intentionally created collections of differing molecules which can be prepared by synthesis or biosynthesis and screened for biological activity in a variety of formats.Library FormatsLibrary FormatsSoluble Molecules Molecules tethered to beads Silica (silicon?) chips Recombinant peptide libraries on viral coat proteins (intact viruses) Virtual librariesPeptide Libraries (1)Peptide Libraries (1)For historical reasons peptide libraries have been the first to be generated through combinatorial chemistry. This was due to the availability of a large and structurally diverse array of amino acid (natural and unnatural) building blocks. The existence of a highly refined generic coupling chemistry to join the building blocks. And the importance of polypeptides as biological molecules.Peptide Libraries (2) - Problems of Peptide Libraries (2) - Problems of While peptide libraries offer a number of advantages they suffer from the important drawback that polypeptides in themselves do not necessarily make good drug candidates. Not least due to their instability in the stomach.Combinatorial Organic Synthesis (COS) (1)Combinatorial Organic Synthesis (COS) (1)Currently, therefore interest is considerable in the generation of small molecule libraries through the rapid synthesis of a vast number of structurally diverse, low molecular weight, non-polymeric organic molecules.Combinatorial Organic Synthesis (COS) (2)Combinatorial Organic Synthesis (COS) (2)The importance of COS to the development of combinatorial chemistry cannot be over stressed. Since the vast majority of drugs currently on the market are low molecular weight, non-polymeric compounds.Challenges (1)Challenges (1)Generating large numbers of molecules in itself will not generate new drug products. One mu