WO2021135090A1 - 一种用于筛选晶体生成条件的超高通量平台及筛选方法 - Google Patents

一种用于筛选晶体生成条件的超高通量平台及筛选方法 Download PDF

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WO2021135090A1
WO2021135090A1 PCT/CN2020/096718 CN2020096718W WO2021135090A1 WO 2021135090 A1 WO2021135090 A1 WO 2021135090A1 CN 2020096718 W CN2020096718 W CN 2020096718W WO 2021135090 A1 WO2021135090 A1 WO 2021135090A1
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droplet
chip
crystal
droplets
screening
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French (fr)
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周建华
苏振宁
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中山大学
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
    • B01L3/50—Containers for the purpose of retaining a material to be analysed, e.g. test tubes
    • B01L3/502—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures
    • B01L3/5027—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/20—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by using diffraction of the radiation by the materials, e.g. for investigating crystal structure; by using scattering of the radiation by the materials, e.g. for investigating non-crystalline materials; by using reflection of the radiation by the materials
    • G01N23/20008—Constructional details of analysers, e.g. characterised by X-ray source, detector or optical system; Accessories therefor; Preparing specimens therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/14—Investigating or analyzing materials by the use of thermal means by using distillation, extraction, sublimation, condensation, freezing, or crystallisation
    • G01N25/147—Investigating or analyzing materials by the use of thermal means by using distillation, extraction, sublimation, condensation, freezing, or crystallisation by cristallisation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8477—Investigating crystals, e.g. liquid crystals

Definitions

  • the invention relates to the technical field of microfluidic screening, in particular to an ultra-high-throughput platform and a screening method for screening crystal formation conditions.
  • Crystals are the focus of research in many fields, such as drug crystals, inorganic crystals, organic metal framework material crystals, organic crystals, etc.
  • the physical and chemical properties of crystals of different crystal types are very different.
  • the crystallization of crystals is often affected by many parallel combination conditions, such as multiple combined solvents in different proportions (the classic single crystal culture solvent combination is: dichloromethane/toluene/n-hexane), in addition to temperature, pH, and induction reagents And other influencing factors.
  • the number of experiments required for the selection of crystal formation conditions may reach tens of thousands or even 100,000 times or more, which undoubtedly brings great difficulty to the selection of crystal formation conditions.
  • Conventional screening methods not only have the disadvantages of low throughput, high cost, and time-consuming, but also when there are many screening components (three components or more), its throughput is difficult to meet the needs of screening; Different fields (such as drugs, non-/organic substances, etc.) have higher and higher requirements for the properties of crystals, and the conditions for crystal formation have become more and more complex. It is more difficult to meet the needs of screening methods with high cost, low efficiency and low throughput.
  • the purpose of the present invention is to provide an ultra-high-throughput platform for screening crystal formation conditions in view of the defects or deficiencies in the prior art.
  • This ultra-high-throughput platform makes full use of the advantages of low reagent consumption, fast experiment speed, and no cross-contamination between droplets of the ultra-throughput droplet generation technology of microdevices, and the image recognition-artificial intelligence processing technology has the advantages
  • the powerful function of automated analysis provides a good solution for the rapid screening of crystal formation conditions.
  • the purpose of the present invention is also to provide a screening method for screening crystal production conditions based on the above platform.
  • the screening method can quickly screen out excellent and stable crystallization conditions, which is convenient and accurate.
  • An ultra-high-throughput platform for screening crystal formation conditions including a microfluidic droplet chip, a microdroplet collection chip, and a characterization-analysis system;
  • the microfluidic droplet chip is used for ultra-high-throughput generation of crystalline droplets; the microdroplet collection chip is used for collecting crystalline droplets generated by the microfluidic droplet chip; the microdroplet collection chip After collecting the crystal droplets, the characterization-analysis system characterizes and recognizes the morphology of the crystals in the crystal droplets, and analyzes the morphology of the crystals identified by the characterization to select the crystallization conditions corresponding to the best crystal morphology.
  • the micro-droplet collection chip is a micro-fluidic chip with an array of grooves; the micro-droplet collection chip can automatically collect the crystal droplets generated by the micro-fluidic droplet chip, and press the grooves.
  • the groove array is arranged in an array of droplets.
  • the micro-droplet collection chip is arranged at the outlet end of the micro-fluidic droplet chip so that it can move freely in a horizontal plane through a moving stage.
  • the characterization-analysis system includes a characterization and identification device and an analysis and processing terminal; the characterization and identification device is connected to the analysis and processing terminal; the characterization and identification device is used to characterize and identify the crystals collected by the microdroplet collection chip For the morphology of the crystal in the droplet, the analysis processing terminal analyzes and processes the morphology information recognized by the characterization and recognition device.
  • Microfluidic technology has attracted wide attention for its advantages such as low reagent and sample dosage, simple operation and less time-consuming, controllable reaction conditions, large-scale ultra-high throughput, and independent droplet reaction unit; characterization and identification
  • the device has the function of image recognition, and the analysis and processing terminal is artificial intelligence.
  • Image recognition and artificial intelligence have powerful fast data processing capabilities, which can realize automatic analysis of experimental results in a short time.
  • the combination of the two can promote an ultra-high-throughput screening platform Build.
  • the characterization and identification device includes an optical microscope, a differential scanning calorimeter (Differential Scanning Calorimeter, DSC), an X-ray diffractometer (X-Ray Diffractomer, XRD), and a thermogravimetric analyzer (Thermal Gravimetric Analyzer, TGA). ) More than one kind.
  • the analysis and processing terminal includes a central processing unit or a computer; and the analysis and processing terminal has python language (python language) processing, SQL language processing (Structured Query Language), machine learning, and natural language processing (natural language processing). )Features.
  • python language python language
  • SQL language processing Structured Query Language
  • machine learning machine learning
  • natural language processing natural language processing
  • An ultra-high-throughput screening method for screening crystal formation conditions using the ultra-high-throughput platform described in any one of the above for screening, includes the following steps:
  • the crystallization conditions include more than one of solvent type, solvent quantity, solvent concentration ratio, impurity type, impurity quantity, mixing speed, crystallization temperature, crystallization pH value, and crystallization time.
  • the flux of the microfluidic droplet chip to generate the crystal droplets is at least 69 in 1 minute.
  • the crystal droplets are micro droplets containing crystals, including physical crystal droplets or chemical reaction crystal droplets.
  • the physically crystallized droplet includes a droplet containing a crystalline drug.
  • the droplets crystallized by the chemical reaction include droplets containing inorganic chemical reactants.
  • the continuous phase of the droplets of the physical crystals and the droplets of the chemical reaction crystals is: droplet paraffin mixed with 2 vol% Span 80, or simethicone mixed with 2 vol% Span 80.
  • the screening method of the present invention is not only suitable for the screening of drug crystal formation conditions and inorganic chemical reaction crystallization conditions, but also for the screening of other crystal crystallization conditions, and is used to explore the crystal growth kinetics of other crystals under different crystallization conditions, including Inorganic crystals, protein crystals or metal organic framework material crystals.
  • the present invention has the following advantages and beneficial effects:
  • the ultra-high-throughput platform of the present invention makes full use of the advantages of low reagent consumption, fast experiment speed, and no cross-contamination between droplets of the ultra-throughput droplet generation technology of microdevices, and image recognition-
  • the artificial intelligence processing technology has the powerful function of automated analysis, and provides a good solution for the rapid screening of crystal formation conditions, which can realize ultra-high throughput screening of crystal formation conditions (up to 100,000 times per day), and then screen out Optimal conditions for crystal formation.
  • the screening method of the present invention is based on the described ultra-high-throughput platform for crystallization condition screening.
  • the consumption of reagents and samples is low, the cost is low, the screening range of crystal formation conditions is large, and ultra-high-throughput screening can be provided, combined with images.
  • Recognition-artificial intelligence processing technology can process a large number of results in a short time, the screening is faster, and the screened results are verified and stable results; moreover, the screening method can be extended to more different kinds of substances that can produce crystals, and screen out The crystal form that meets the requirements.
  • Figure 1 is an overall schematic diagram of the ultra-high-throughput platform for screening crystal production conditions of the present invention in specific embodiments
  • FIG. 2 is a view of collecting crystal droplets by a micro-droplet collecting chip in a specific embodiment
  • Example 3 is a schematic diagram of the microfluidic droplet chip generating crystalline droplets in Example 1;
  • Figure 4 Views of indomethacin drug crystals with different morphologies generated under different crystal formation conditions screened in Example 1;
  • Example 5 is a view of indomethacin drug crystals with different morphologies generated under different crystal formation conditions screened in Example 1;
  • Figure 6 is an XRD spectrum of the linear crystals of indomethacin selected in Example 1;
  • Fig. 7 is a schematic diagram of crystal droplets generated by the microfluidic droplet chip in Example 2.
  • Example 8 is a view of inorganic calcium carbonate crystals with different morphologies generated under different crystal formation conditions screened in Example 2;
  • FIG. 9 is a view of inorganic calcium carbonate crystals with different morphologies generated under different crystal formation conditions screened in Example 2.
  • FIG. 9 is a view of inorganic calcium carbonate crystals with different morphologies generated under different crystal formation conditions screened in Example 2.
  • Fig. 1 is an ultra-high-throughput platform for screening crystal formation conditions of the present invention.
  • the platform includes a microfluidic droplet chip 1, a microdroplet collection chip 2, and a characterization-analysis system.
  • the characterization- The analysis system includes a characterization and recognition device 3 and an analysis and processing terminal 4; the characterization and recognition device 3 is connected to the analysis and processing terminal 4.
  • the microfluidic droplet chip 1 is used for ultra-high-throughput generation of crystalline droplets; the microdroplet collection chip 2 is used for collecting the crystalline droplets generated by the microfluidic droplet chip 1; After the micro-droplet collection chip 2 collects the crystal droplets, the characterization and recognition device 3 in the characterization-analysis system characterizes and recognizes the morphology of the crystals in the crystal droplets, and the analysis processing terminal therein 4 Analyze the morphology of the crystals identified by the characterization and screen out the crystallization conditions corresponding to the best crystalline morphology.
  • the characterization and identification device 3 includes at least one of an optical microscope, a differential scanning calorimeter, an X-ray diffractometer, and a thermogravimetric analyzer.
  • the analysis processing terminal includes a central processing unit or a computer; and the analysis processing terminal 4 has python language processing, SQL language processing, machine learning and natural language processing functions.
  • the micro-droplet collection chip 2 is a microfluidic chip with an array of grooves.
  • the micro-droplet collection chip 2 is set on a mobile stage, and the mobile stage can move in the XY direction in the horizontal plane under the control of the online software. By moving the stage, the micro-droplet collection chip can be on the horizontal plane.
  • the inside is freely movably arranged at the outlet end of the microfluidic droplet chip 1, so that the microdroplet collection chip 2 can automatically collect the crystal droplets generated by the microfluidic droplet chip 1, and press
  • the groove array is arranged into a droplet array.
  • An ultra-high-throughput screening method for screening crystal formation conditions of the present invention specifically adopts the above-mentioned ultra-high throughput screening method.
  • Screening on a high-throughput platform includes the following steps:
  • the characterization-analysis system performs a table of the crystals in the crystal droplets collected by the micro-droplet collection chip.
  • the crystallization conditions include more than one of solvent type, solvent quantity, solvent concentration ratio, impurity type, impurity quantity, mixing speed, crystallization temperature, crystallization pH value, and crystallization time, using an ultra-high-throughput screening platform, Screen out the crystallization conditions that produce crystals with specific morphologies.
  • the crystal droplets are micro droplets containing crystals, including physical crystal droplets or chemical reaction crystal droplets; the physical crystal droplets include droplets containing crystalline drugs; the chemical reaction Crystalline droplets include droplets containing inorganic chemical reactants.
  • the screening method and ultra-high-throughput screening platform of the present invention will be described in detail below in combination with specific drug crystal (indomethacin drug) crystallization condition screening and chemical reaction crystal (calcium carbonate) crystallization condition screening examples.
  • microfluidic droplet generating chip According to the co-flow focusing microchannel structure of the microfluidic droplet chip, the microfluidic droplet chip is fabricated, and the chip manufacturing method selects the molding method;
  • microfluidic droplet chip using PDMS (polydimethylsiloxane) and aluminum sheet as materials;
  • the cured PDMS is peeled off from the aluminum mold to obtain the PDMS chip with the droplet generation channels; the liquid PDMS is coated on the prepared glass substrate, and then the spin coater is used to Spread the PDMS evenly on the glass at 800 rpm, then gently paste the PDMS chip with the droplet generation channel on the glass, place it in an oven at 80°C for 1 hour, and cure to obtain a complete micro Flow control droplet chip.
  • indomethacin drug-ethanol saturated solution as component 1
  • indomethacin drug-acetone saturated solution as component 2
  • indomethacin drug-diethyl ether saturated solution as component 3.
  • Indomethacin drug-chloroform saturated solution is used as component 4
  • pH buffer solution phosphate buffer
  • anti-solvent water is used as component 6.
  • the channel port of the microfluidic droplet chip is connected to a silica gel pipe with an inner diameter of 0.5mm, and is connected to a syringe pump to adjust the perfusion rate.
  • the total perfusion rate of the oil phase is 900 ⁇ l/min, water
  • the total phase perfusion rate is 300 microliters/min, which makes it generate at least 69 stable and uniform droplets within 1 min.
  • Optical microscope Collect the droplets in the micro-droplet collection chip, place them on the optical microscope stage, turn on the microscope illumination, adjust the focus, use the microscope control software to automatically take pictures continuously, and automatically collect the crystal appearance morphology pictures, such as Figures 4 and 5 show the drug linear crystals and cube-shaped crystals in the droplets, which characterize the appearance of the drug crystals in the droplets.
  • X-ray powder diffraction Take 0.8 mg of indomethacin linear crystals with a medicine spoon and put them into the sample slot of the glass sample holder, then lightly press with ground glass to fill it, and then gently wipe off the excess crystal powder to make the sample surface In the same plane as the surface of the glass frame, it is placed in the X-ray powder diffraction test.
  • the XRD pattern corresponding to the linear crystals of indomethacin grown under the condition that the volume ratio of the drug-saturated acetone solution to the anti-solvent is 6:4, there are two strong features at 2 ⁇ at 12 and 15
  • the peak can be used as the identification characteristic peak of indomethacin linear crystal.
  • the crystal growth experiment was carried out under this crystallization condition, and the formation of droplet crystals was observed using an optical microscope, and the law of crystal changes in crystal dynamics was discussed.
  • the growth and extension of crystal nuclei and the formation of corresponding crystal morphologies were affected by the drug.
  • Preparation of S12, 0.5mol/L and 0.05mol/L calcium chloride solutions Weigh 5.55g of calcium chloride powder, place it in a 100mL volumetric flask, add water to the volume to 100ml, and place it in an ultrasonic cleaner for 10 minutes. Let the calcium chloride completely dissolve and prepare a 0.5mol/L calcium chloride solution. Weigh 0.555g of calcium chloride powder, place it in a 100mL volumetric flask, add water to make the volume to 100ml, and place it in an ultrasonic cleaner for 10 minutes to let the calcium chloride completely dissolve, and prepare a 0.05mol/L calcium chloride solution .
  • Preparation of S13, 0.5mol/L and 0.05mol/L sodium carbonate solution Weigh 5.3g calcium chloride powder, place it in a 100mL volumetric flask, add water to make the volume up to 100ml, and then place it in an ultrasonic cleaner for 10 minutes to sonicate it. Calcium chloride is completely dissolved and prepared into a 0.5mol/L calcium chloride solution. Weigh 0.53g of calcium chloride powder, place it in a 100mL volumetric flask, add water to make the volume up to 100ml, and place it in an ultrasonic cleaner and ultrasonicate for 10min to completely dissolve the calcium chloride to prepare a 0.05mol/L calcium chloride solution .
  • pure water is component 1 and the concentration of calcium chloride solution is further adjusted by changing the speed
  • 0.5mol/L calcium chloride solution is component 2
  • 0.05mol/L calcium chloride solution Component 3
  • 0.5mol/L sodium carbonate solution is component 4
  • 0.05mol/L sodium carbonate solution is component 5
  • pure water is then component 6 and the concentration of sodium carbonate solution is further adjusted by changing the speed.
  • Optical microscope Collect the droplets in the micro-droplet collection chip, place them on the optical microscope stage, turn on the microscope illumination, adjust the focus, use the microscope control software to automatically take pictures continuously, and automatically collect the crystal appearance morphology pictures, such as The square crystals of the inorganic reactant and the spherical crystals of the inorganic reactant in the droplets shown in Figs. 8 and 9 represent the appearance of the drug crystals in the droplets.
  • X-ray powder diffraction Take 0.8mg of the inorganic reactant crystal with a medicine spoon and put it into the sample slot of the glass sample holder, then lightly press it with ground glass to fill it, and then gently wipe off the excess crystal powder to make the sample surface and the glass holder The surface is in the same plane and placed in the X-ray powder diffraction test.
  • the crystal growth experiment was carried out, using an optical microscope and X-ray powder diffraction to continuously monitor, observe the formation of droplet crystals, explore the law of diffraction spectrum changes in crystal dynamics, and the growth, extension, and formation of crystal nuclei
  • the crystal morphology of this process is affected by the concentration of the drug from the reactant and the rate of the chemical reaction.

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Abstract

一种用于筛选晶体生成条件的超高通量平台及筛选方法。该平台包括微流控液滴芯片(1)、微液滴收集芯片(2)以及表征-分析系统；所述微流控液滴芯片(1)用于超高通量生成结晶液滴；所述微液滴收集芯片(2)用于收集所述微流控液滴芯片(1)生成的结晶液滴；所述微液滴收集芯片(2)收集结晶液滴后，由所述表征-分析系统表征识别所述结晶液滴内的结晶体的形貌，并对表征识别的结晶体形貌进行分析筛选出对应最佳结晶形貌的结晶条件。该超高通量平台为晶体生成条件的快速筛选提供了良好的解决方案；该筛选方法可快速筛选出优异、稳定的结晶条件，便捷、准确。

Description

一种用于筛选晶体生成条件的超高通量平台及筛选方法 技术领域
本发明涉及微流控筛选技术领域，具体涉及一种用于筛选晶体生成条件的超高通量平台及筛选方法。
背景技术
晶体在很多领域是重点研究的对象，比如药物晶体、无机物晶体、有机金属框架材料晶体、有机物晶体等等，其不同晶型的晶体其理化性质有很大的差异。然而，晶体的结晶常受诸多并行组合条件的影响，比如不同比例的多种组合溶剂(经典单晶培养溶剂组合为：二氯甲烷/甲苯/正己烷)，另外还有温度、pH、诱导试剂等影响因素。
根据数学排列组合法，晶体生成条件的筛选所需的实验次数可能达到上万次乃至上十万次甚至更多，这无疑给晶体生成条件的筛选带来巨大的难度。常规筛选方法不仅存在通量较低、成本高和费时等缺点，而且在筛选组分比较多(三种组分或以上)的时候，其通量难以满足筛选的需求；并且，随着人们在不同领域(如药物、无/有机物等)对晶体的性质要求越来越高，以及晶体生成条件也越来越复杂，高成本、低效率和通量低的筛选方法更加难以满足需求。
发明内容
本发明的目的在于针对现有技术中存在的缺陷或不足，提供了一种用于筛选晶体生成条件的超高通量平台。该超高通量平台充分利用了微装置超通量液滴生成技术所具备的试剂消耗量低、实验速度快、液滴之间无交叉污染等的优点，和图像识别-人工智能处理技术具有自动化分析的强大功能，为晶体生成条件的快速筛选提供了良好的解决方案。
本发明的目的还在于提供基于上述平台的用于筛选晶体生成条件的筛选方法。该筛选方法可快速筛选出优异、稳定的结晶条件，便捷、准确。
本发明的目的通过如下技术方案实现。
一种用于筛选晶体生成条件的超高通量平台，包括微流控液滴芯片、微液滴收集芯片以及表征-分析系统；
所述微流控液滴芯片用于超高通量生成结晶液滴；所述微液滴收集芯片用于收集所述微流控液滴芯片生成的结晶液滴；所述微液滴收集芯片收集结晶液滴后，由所述表征-分析系统表征识别所述结晶液滴内的结晶体的形貌，并对表征识别的结晶体形貌进行分析筛选出对应 最佳结晶形貌的结晶条件。
优选的，所述微液滴收集芯片为具有凹槽阵列的微流控芯片；所述微液滴收集芯片可自动收集所述微流控液滴芯片生成的结晶液滴，并按所述凹槽阵列排布成液滴阵列。
更优选的，所述微液滴收集芯片通过移动载物台可在水平面内自由移动的设置在所述微流控液滴芯片的出口端。
优选的，所述表征-分析系统包括表征识别装置和分析处理终端；所述表征识别装置与所述分析处理终端连接；所述表征识别装置用于表征识别所述微液滴收集芯片收集的结晶液滴内的结晶体的形貌，所述分析处理终端对所述表征识别装置识别的形貌信息进行分析处理。
微流控技术，以其试剂及样品用量少、操作简单耗时少、反应条件可控且可提供大规模超高通量，并独立的液滴反应单元等优点受到广泛的关注；表征识别装置具有图像识别功能，而分析处理终端为人工智能，图像识别和人工智能具有强大的数据快速处理能力，能够在短时间内实现自动化分析实验结果，两者结合能促成的超高通量筛选平台的搭建。
更优选的，所述表征识别装置包括光学显微镜、差示扫描量热仪(Differential Scanning Calorimeter，DSC)、X射线衍射仪(X-Ray Diffractomer，XRD)和热重分析仪(Thermal Gravimetric Analyzer，TGA)中的一种以上。
更优选的，所述分析处理终端包括中央处理器或计算机；且所述分析处理终端具有python语言(蟒蛇语言)处理、SQL语言处理(Structured Query Language)、机器学习以及自然语言处理(natural language processing)功能。
一种用于筛选晶体生成条件的超高通量筛选方法，采用上述任一项所述的超高通量平台进行筛选，包括如下步骤：
S1、向所述微流控液滴芯片内灌注形成含有结晶内容物的液滴，并在不同结晶条件下，在所述微流控液滴芯片内使结晶内容物结晶，生成结晶液滴；
S2、由所述微液滴收集芯片收集所述结晶液滴；
S3、由所述表征-分析系统对所述微液滴收集芯片收集的结晶液滴内的结晶体进行表征、分析；
S4、筛选出对应最佳结晶形态的结晶条件；
S5、在S4筛选出的结晶条件下，重复S1-S3，进行晶体生长动力学验证。
优选的，所述结晶条件包括溶剂种类、溶剂数量、溶剂浓度配比、杂质种类、杂质数量、混合速度、结晶温度、结晶pH值以及结晶时间中的一种以上。
优选的，所述微流控液滴芯片生成所述结晶液滴的通量为1min内至少69颗。
优选的，所述结晶液滴为内含结晶体的微液滴，包括物理结晶的液滴或化学反应结晶的液滴。
更优选的，所述物理结晶的液滴包括内含结晶药物的液滴。
更优选的，所述化学反应结晶的液滴包括内含无机化学反应物的液滴。
更进一步优选的，所述物理结晶的液滴和所述化学反应结晶的液滴的连续相为：混合有2vol％Span80的液滴石蜡，或混合有2vol％Span80的二甲基硅油。
由于不同形貌的晶体在药物领域、无机物领域均能影响其理化性质，进而影响药物的临床疗效和无机物的密度、分散性等，同时晶体生成条件复杂多样，所以有必要筛选出合适的药物晶体和无机物晶体。因此，将该超高通量筛选平台应用于药物、无机物晶体生成条件的筛选，对药物晶体及无机物晶体的应用具有重要意义。
本发明的筛选方法，不仅适用于药物晶体生成条件和无机物化学反应结晶条件的筛选，还适用于其它晶体结晶条件的筛选，用于探讨其它晶体在不同结晶条件下的晶体生长动力学，包括无机物晶体、蛋白质晶体或金属有机框架材料晶体。
与现有技术相比，本发明具有如下优点和有益效果：
(1)本发明的超高通量平台充分利用了微装置超通量液滴生成技术所具备的试剂消耗量低、实验速度快、液滴之间无交叉污染等的优点，和图像识别-人工智能处理技术具有自动化分析的强大功能，为晶体生成条件的快速筛选提供了良好的解决方案，可实现晶体生成条件的超高通量筛选(可每日达10万次以上)，进而筛选出晶体生成的最优条件。
(2)本发明的筛选方法基于所述的超高通量平台进行结晶条件筛选，试剂和样品的消耗量少、成本低、晶体生成条件筛选范围大、可提供超高通量筛选，结合图像识别-人工智能处理技术可以在短时间处理大量的结果，筛选更加快速，筛选出的结果为经过验证的稳定结果；而且，该筛选方法可推广到更多不同种类能产生晶体的物质，筛选出满足要求的晶体晶型。
附图说明
图1为具体实施例中本发明的用于筛选晶体生成条件的超高通量平台的整体示意图；
图2为具体实施例中微液滴收集芯片收集结晶液滴的视图；
图3为实施例1中微流控液滴芯片生成结晶液滴的示意图；
图4实施例1中筛选的不同晶体生成条件下生成的不同形貌的吲哚美辛药物晶体的视图；
图5为实施例1中筛选的不同晶体生成条件下生成的不同形貌的吲哚美辛药物晶体的视图；
图6为实施例1中筛选出的吲哚美辛药物线状晶体的XRD谱图；
图7为实施例2中微流控液滴芯片生成结晶液滴的示意图；
图8为实施例2中筛选的不同晶体生成条件下生成的不同形貌的无机物碳酸钙晶体的视图；
图9为实施例2中筛选的不同晶体生成条件下生成的不同形貌的无机物碳酸钙晶体的视图。
具体实施方式
以下结合具体实施例对本发明的技术方案作进一步详细的描述，但本发明的保护范围及实施方式不限于此。除非另有定义，本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。本文中在本发明的说明书中所使用的术语只是为了描述具体的实施方式的目的，不是旨在于限制本发明。在具体实施例中，除非另有解释说明，所使用的技术手段均按照本发明技术领域的技术人员采用的常规手段。
参见图1所示，为本发明的用于筛选晶体生成条件的超高通量平台，该平台包括微流控液滴芯片1、微液滴收集芯片2以及表征-分析系统，所述表征-分析系统包括表征识别装置3和分析处理终端4；所述表征识别装置3与所述分析处理终端4连接。
在工作时，所述微流控液滴芯片1用于超高通量生成结晶液滴；所述微液滴收集芯片2用于收集所述微流控液滴芯片1生成的结晶液滴；所述微液滴收集芯片2收集结晶液滴后，由所述表征-分析系统中的表征识别装置3表征识别所述结晶液滴内的结晶体的形貌，并由其中的所述分析处理终端4对表征识别的结晶体形貌进行分析筛选出对应最佳结晶形貌的结晶条件。
具体的，所述表征识别装置3包括光学显微镜、差示扫描量热仪、X射线衍射仪和热重分析仪中的一种以上。而所述分析处理终端包括中央处理器或计算机；且所述分析处理终端4具有python语言处理、SQL语言处理、机器学习以及自然语言处理功能。
其中，参见图2所示，在本发明中，所述微液滴收集芯片2为具有凹槽阵列的微流控芯片。并且，所述微液滴收集芯片2设置在移动载物台上，移动载物台在联机软件控制下在水平面内可沿XY方向移动，通过移动载物台使微液滴收集芯片可在水平面内自由移动的设置在所述微流控液滴芯片1的出口端，从而使所述微液滴收集芯片2可自动收集所述微流控液滴芯片1生成的结晶液滴，并按所述凹槽阵列排布成液滴阵列。
本发明的一种用于筛选晶体生成条件的超高通量筛选方法，具体采用了上述的超
高通量平台进行筛选，包括如下步骤：
S1、向所述微流控液滴芯片内灌注形成含有结晶内容物的液滴，并在不同结晶条件下，
在所述微流控液滴芯片内使结晶内容物结晶，生成结晶液滴；且生成所述结晶液滴的通量
为1min内至少69颗，实现超高通量；
S2、由所述微液滴收集芯片收集所述结晶液滴；
S3、由所述表征-分析系统对所述微液滴收集芯片收集的结晶液滴内的结晶体进行表
征、分析；
S4、筛选出对应最佳结晶形态的结晶条件；
S5、在S4筛选出的结晶条件下，重复S1-S3，进行晶体生长动力学验证。
其中，所述结晶条件包括溶剂种类、溶剂数量、溶剂浓度配比、杂质种类、杂质数量、混合速度、结晶温度、结晶pH值以及结晶时间中的一种以上，利用超高通量筛选平台，筛选出产生特定形貌晶体的结晶条件。
而且，所述结晶液滴为内含结晶体的微液滴，包括物理结晶的液滴或化学反应结晶的液滴；所述物理结晶的液滴包括内含结晶药物的液滴；所述化学反应结晶的液滴包括内含无机化学反应物的液滴。
以下结合具体的药物晶体(吲哚美辛药物)结晶条件筛选和化学反应结晶晶体(碳酸钙)的结晶条件筛选实施例，对本发明的筛选方法及超高通量筛选平台进行详细描述说明。
实施例1
药物晶体-吲哚美辛药物晶体的结晶条件筛选，具体步骤如下：
S1、微流控液滴超高通量液滴生成实验
S11、微流控液滴生成芯片的制作：根据微流控液滴芯片的共流聚焦微通道结构，制作微流控液滴芯片，芯片的制作方法选择模塑法；
微流控液滴芯片的制备:以PDMS(聚二甲基硅氧烷)和铝片为材料；
(1)利用SolidWorks2017软件设计液滴生成的共流聚焦微通道结构，再利用高分辨率高功率激光雕刻机将通道结构雕刻在铝片上，通过把握雕刻时间控制通道的雕刻深度，雕刻完了后进行抛光处理，使铝片表面粗糙度降低，以获得光亮、平整的表面。
(2)将铝片模具用洗衣粉清洗3遍，然后依次放置于200ml丙酮、200ml乙醇、200ml纯水中分别超声20min以洗掉铝片模具表面的赃物，用氮气吹干，再放置烘箱中60℃进一步烘干。
(3)将PDMS和交联剂(SYLGARDTM184 Silicone Elastomer Curing Agent)以10：1的质量比混合搅拌均匀，浇注于制作好的铝片模具上，置于80℃的真空干燥箱中抽真空，然后进行固化。
(4)最后将已固化的PDMS从铝片模具上剥离下来，即可得到刻印有液滴生成通道的PDMS芯片；将液态的PDMS涂在已准备好的玻璃基片上，然后使用旋涂仪在800rpm的转速下将PDMS均匀涂在玻璃上，再将刻印有液滴生成通道的PDMS芯片轻轻地粘贴在玻璃上， 置于然后放入80℃的烘箱中1h，固化，即得完整的微流控液滴芯片。
S12、吲哚美辛药物-有机溶剂饱和溶液制备：取6个2ml的试管，分别称取0.2g吲哚美辛药物并置于其中，再分别加入200μL乙醇、200μL丙酮、200μL乙醚和200μL氯仿，超声震荡2min，直至药物刚溶解至剩有一点点沉淀，6000rpm离心10min，除去沉淀，取上清液待用。
S13、液滴的连续相选择:混有体积比为1vol％、2vol％、3vol％、4vol％Span80的液滴石蜡(购于Aladdin公司)；不加表面活性剂的液滴石蜡(购于Aladdin公司)。
S14、根据图3所示，将吲哚美辛药物-乙醇饱和溶液做为组分1、吲哚美辛药物-丙酮饱和溶液作为组分2、吲哚美辛药物-乙醚饱和溶液作为组分3、吲哚美辛药物-氯仿饱和溶液作为组分4、pH缓冲溶液(磷酸缓冲液)作为组分5，以及反溶剂水作为组分6。
S15、含有药物溶液的液滴实验：微流控液滴芯片的通道端口连接内径为0.5mm的硅胶管道，并连接注射泵，调节灌注速度，油相总灌注速度为900微升/分钟，水相总灌注速度为300微升/分钟，使其在1min内至少生成69颗稳定、粒径均匀的液滴。
通过增加或更改分散相，以及改变分散相灌注速度，设置不同药物浓度(0.01mg/mL、0.02mg/mL、0.03mg/mL、0.04mg/mL、0.05mg/mL、0.06mg/mL)、不同极性溶剂(乙醇、丙酮、乙醚和氯仿)、调节pH缓冲液进而改变不同pH环境(pH＝2、4、6、7、8、10)。进而超高通量生成对应不同结晶条件的液滴，再将液滴收集到微液滴收集芯片的通道内(收集结果参见图2所示)。
S2、液滴内药物晶体的表征
S21、光学显微镜：将液滴收集于微液滴收集芯片中，置于光学显微镜载物台上，打开显微镜照明，调节焦距，利用显微镜控制软件自动连续拍照功能，自动收集晶体外观形态图片，如图4和图5所示液滴内的药物线状晶体和立方体状晶体，表征液滴内药物晶体的外观形态。
S22、XRD：分为X-射线粉末衍射法；
X-射线粉末衍射：用药勺取0.8mg吲哚美辛线状晶体放入玻璃样品架样品槽内，然后用毛玻璃轻压，使之充满，再轻轻抹去多余的晶体粉末，使样品表面与玻璃架表面在同一平面内，置于X-射线粉末衍射测试。参见图6所示，药物饱和丙酮溶液与反溶剂体积之比为6:4的条件下生长的吲哚美辛线状晶体对应的XRD图谱，2θ为12和15处有两个较强的特征峰，可以作为吲哚美辛线性晶体的鉴别特征峰。
S3、药物晶体表征结果的图像识别-大数据分析处理
利用计算机，将光学显微镜、扫描电镜得到晶体形貌图片利用python语言、mobilenetv2 网络图像识别技术对药物晶体晶型识别处理，将XRD等表征手段得到的实验数据利用大数据技术(Structured Query Language)和分析技术(机器学习)进行数据结果筛选，进而筛选出吲哚美辛线性晶体的最佳条件。
S4、晶体生长动力学实验
利用晶体生成条件筛选的超高通量平台，单因素或多因素控制变量法重复药物结晶条件，筛选出产生线状晶体的结晶条件：pH＝7，吲哚美辛丙酮溶液(1mg/μL)与反溶剂水体积之比为6：4。
并在该结晶条件下进行晶体生长实验，利用光学显微镜，观察液滴晶体的生成，探讨晶体动力学中的晶体变化规律，晶核的生长，延伸，形成相应的晶体形貌，这过程受到药物从溶剂中析出速率的影响。
实施例2
无机物晶体-碳酸钙晶体的结晶条件筛选，具体步骤如下：
S1、微流控液滴超高通量液滴生成实验
S11、微流控液滴生成芯片的制作：制作方法与实施例1的S11相同。
S12、0.5mol/L和0.05mol/L氯化钙溶液的制备：称取5.55g氯化钙粉末，置于100mL的容量瓶中，加水定容至100ml，再放置于超声清洗仪中超声10min让氯化钙完全溶解，配制成0.5mol/L的氯化钙溶液。称取0.555g氯化钙粉末，置于100mL的容量瓶中，加水定容至100ml，再放置于超声清洗仪中超声10min让氯化钙完全溶解，配制成0.05mol/L的氯化钙溶液。
S13、0.5mol/L和0.05mol/L碳酸钠溶液的制备：称取5.3g氯化钙粉末，置于100mL的容量瓶中，加水定容至100ml，再放置于超声清洗仪中超声10min让氯化钙完全溶解，配制成0.5mol/L的氯化钙溶液。称取0.53g氯化钙粉末，置于100mL的容量瓶中，加水定容至100ml，再放置于超声清洗仪中超声10min让氯化钙完全溶解，配制成0.05mol/L的氯化钙溶液。
S14、液滴的连续相选择:混有体积比为1vol％、2vol％、3vol％、4vol％Span80的液滴石蜡(购于Aladdin公司)；不加表面活性剂的液滴石蜡(购于Aladdin公司)。
S15、根据图7所示，纯水为组分1并通过速度的改变来进一步调节氯化钙溶液的浓度，0.5mol/L氯化钙溶液为组分2，0.05mol/L氯化钙溶液组分3，0.5mol/L碳酸钠溶液为组分4，0.05mol/L碳酸钠溶液为组分5，纯水再为组分6并通过速度的改变来进一步调节碳酸钠溶液的浓度。
S16、化学反应生成碳酸钙晶体的液滴实验：微流控液滴芯片的通道端口连接内径为0.5mm的硅胶管道，并连接注射泵，调节灌注速度，油相总灌注速度为900微升/分钟，水相总灌注速度为300微升/分钟，使其生成连续、稳定、粒径均匀的液滴。
通过增加或更改分散相，以及改变分散相灌注速度，设置不同氯化钙和碳酸钠的浓度(0.5mg/mL、0.4mg/mL、0.3mg/mL、0.25mg/mL、0.1mg/mL、0.05mg/mL)等影响为无机碳酸钙晶体结晶的条件，进而超高通量生成对应不同结晶条件的液滴，同样将其收集到微液滴收集芯片的通道中(收集结果参见图2所示)。
S2、液滴内无机反应物晶体的表征
S21、光学显微镜：将液滴收集于微液滴收集芯片中，置于光学显微镜载物台上，打开显微镜照明，调节焦距，利用显微镜控制软件自动连续拍照功能，自动收集晶体外观形态图片，如图8和图9所示液滴内的无机反应物方块状晶体和无机反应物球状晶体，表征液滴内药物晶体的外观形态。
S22、XRD：分为X-射线粉末衍射法；
X-射线粉末衍射：用药勺取0.8mg无机反应物晶体放入玻璃样品架样品槽内，然后用毛玻璃轻压，使之充满，再轻轻抹去多余的晶体粉末，使样品表面与玻璃架表面在同一平面内，置于X-射线粉末衍射测试。
S3、无机反应物晶体表征结果的图像识别-大数据分析处理利用计算机，将光学显微镜、扫描电镜得到晶体形貌图片利用p y t h o n语言、mobilenetv2网络图像识别技术对药物晶体晶型识别处理，将XRD等表征手段得到的实验数据利用大数据技术(Structured Query Language)和分析技术(机器学习)技术进行数据结果筛选，进而筛选出碳酸钠方块状晶体结晶的最佳条件。
S4、晶体生长动力学实验
利用本超高通量结晶条件筛选平台，单因素或多因素控制变量法重复无机物碳酸钙结晶条件，筛选出产生方块状碳酸钙晶体的结晶条件为：pH＝7，0.05mol/L碳酸钠溶液与0.25mol/L氯化钙溶液的体积之比为5:2。
并在该结晶条件下进行晶体生长实验，利用光学显微镜、X-射线粉末衍射持续监控，观察液滴晶体的生成，探讨晶体动力学中的衍射光谱变化规律，晶核的生长，延伸，形成相应的晶体形貌，这过程受到药物从反应物的浓度、化学反应速率的影响。
以上实施例仅为本发明的较优实施例，显示和描述了本发明的基本原理、主要特征和优点，但本发明的具体实施方式及保护范围不限于此。本行业的技术人员应该了解，上述实施 例不以任何形式限制本发明，凡采用等同替换或等效变换的方式所获得的技术方案，或者其他的任何未背离本发明的精神实质与原理下所作的改变、修饰、替代、组合、简化等，均应为等效的置换方式，都包含在本发明的保护范围之内。

Claims (10)

  1. 一种用于筛选晶体生成条件的超高通量平台，其特征在于，包括微流控液滴芯片、微液滴收集芯片以及表征-分析系统；
    所述微流控液滴芯片用于超高通量生成结晶液滴；所述微液滴收集芯片用于收集所述微流控液滴芯片生成的结晶液滴；所述微液滴收集芯片收集结晶液滴后，由所述表征-分析系统表征识别所述结晶液滴内的结晶体的形貌，并对表征识别的结晶体形貌进行分析筛选出对应最佳结晶形貌的结晶条件。
  2. 根据权利要求1所述的用于筛选晶体生成条件的超高通量平台，其特征在于，所述微液滴收集芯片为具有凹槽阵列的微流控芯片；所述微液滴收集芯片可自动收集所述微流控液滴芯片生成的结晶液滴，并按所述凹槽阵列排布成液滴阵列。
  3. 根据权利要求2所述的用于筛选晶体生成条件的超高通量平台，其特征在于，所述微液滴收集芯片通过移动载物台可在水平面内自由移动的设置在所述微流控液滴芯片的出口端。
  4. 根据权利要求1所述的用于筛选晶体生成条件的超高通量平台，其特征在于，所述表征-分析系统包括表征识别装置和分析处理终端；所述表征识别装置与所述分析处理终端连接；所述表征识别装置用于表征识别所述微液滴收集芯片收集的结晶液滴内的结晶体的形貌，所述分析处理终端对所述表征识别装置识别的形貌信息进行分析处理。
  5. 根据权利要求4所述的用于筛选晶体生成条件的超高通量平台，其特征在于，所述表征识别装置包括光学显微镜、差示扫描量热仪、X射线衍射仪和热重分析仪中的一种以上。
  6. 根据权利要求4所述的用于筛选晶体生成条件的超高通量平台，其特征在于，所述分析处理终端包括中央处理器或计算机；且所述分析处理终端具有python语言处理、SQL语言处理、机器学习以及自然语言处理功能。
  7. 一种用于筛选晶体生成条件的超高通量筛选方法，其特征在于，采用权利要求1-6任一项所述的超高通量平台进行筛选，包括如下步骤：
    S1、向所述微流控液滴芯片内灌注形成含有结晶内容物的液滴，并在不同结晶条件下，在所述微流控液滴芯片内使结晶内容物结晶，生成结晶液滴；
    S2、由所述微液滴收集芯片收集所述结晶液滴；
    S3、由所述表征-分析系统对所述微液滴收集芯片收集的结晶液滴内的结晶体进行表征、分析；
    S4、筛选出对应最佳结晶形态的结晶条件；
    S5、在S4筛选出的结晶条件下，重复S1-S3，进行晶体生长动力学验证。
  8. 根据权利要求7所述的筛选方法，其特征在于，所述结晶条件包括溶剂种类、溶剂数量、 溶剂浓度配比、杂质种类、杂质数量、混合速度、结晶温度、结晶pH值以及结晶时间中的一种以上。
  9. 根据权利要求7所述的筛选方法，其特征在于，所述微流控液滴芯片生成所述结晶液滴的通量为1min内至少69颗。
  10. 根据权利要求7所述的筛选方法，其特征在于，所述结晶液滴为内含结晶体的微液滴，包括物理结晶的液滴或化学反应结晶的液滴；所述物理结晶的液滴包括内含结晶药物的液滴；所述化学反应结晶的液滴包括内含无机化学反应物的液滴。
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