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EP1668363A1 - Procede et systeme d'analyse d'interactions moleculaires - Google Patents

Procede et systeme d'analyse d'interactions moleculaires

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Publication number
EP1668363A1
EP1668363A1 EP04775458A EP04775458A EP1668363A1 EP 1668363 A1 EP1668363 A1 EP 1668363A1 EP 04775458 A EP04775458 A EP 04775458A EP 04775458 A EP04775458 A EP 04775458A EP 1668363 A1 EP1668363 A1 EP 1668363A1
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EP
European Patent Office
Prior art keywords
curves
fit
data set
binding
kinetic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
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EP04775458A
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German (de)
English (en)
Inventor
Karl Andersson
Peter Borg
Annica ÖNELL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cytiva Sweden AB
Original Assignee
Biacore AB
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Filing date
Publication date
Priority claimed from SE0302525A external-priority patent/SE0302525D0/xx
Application filed by Biacore AB filed Critical Biacore AB
Publication of EP1668363A1 publication Critical patent/EP1668363A1/fr
Ceased legal-status Critical Current

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    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/272—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration for following a reaction, e.g. for determining photometrically a reaction rate (photometric cinetic analysis)
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/55—Specular reflectivity
    • G01N21/552—Attenuated total reflection
    • G01N21/553—Attenuated total reflection and using surface plasmons
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
    • 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
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N21/03—Cuvette constructions
    • G01N2021/0346—Capillary cells; Microcells
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/21—Polarisation-affecting properties
    • G01N21/211—Ellipsometry
    • G01N2021/212—Arrangement with total internal reflection
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G01N21/45—Refractivity; Phase-affecting properties, e.g. optical path length using interferometric methods; using Schlieren methods
    • G01N2021/458—Refractivity; Phase-affecting properties, e.g. optical path length using interferometric methods; using Schlieren methods using interferential sensor, e.g. sensor fibre, possibly on optical waveguide
    • 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
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N21/03—Cuvette constructions
    • G01N21/05—Flow-through cuvettes
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/21—Polarisation-affecting properties
    • 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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G01N21/45—Refractivity; Phase-affecting properties, e.g. optical path length using interferometric methods; using Schlieren methods
    • 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
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/648—Specially adapted constructive features of fluorimeters using evanescent coupling or surface plasmon coupling for the excitation of fluorescence
    • 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
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
    • 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
    • G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
    • G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
    • G01N21/7703—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator using reagent-clad optical fibres or optical waveguides

Definitions

  • the present invention relates to a method of analysing molecular binding interactions at a sensing surface, and more particularly to an at least partially automated method for determining kinetic parameters from the resulting data describing the molecular interactions.
  • the invention also relates to an analytical system including means for such automated kinetic evaluation as well as to a computer program for performing the method, a computer program product comprising program code means for performing the method, and a computer system containing the program.
  • Analytical sensor systems that can monitor interactions between molecules, such as biomolecules, in real time are gaining increasing interest. These systems are often based on optical biosensors and usually referred to as interaction analysis sensors or biospecif ⁇ c interaction analysis sensors.
  • a representative such biosensor system is the BIACORE® instrumentation sold by Biacore AB (Uppsala, Sweden) which uses surface plasmon resonance (SPR) for detecting interactions between molecules in a " sample and molecular structures immobilized on a sensing surface. As sample is passed over the sensor surface, the progress of binding directly reflects the rate at which the interaction occurs. Injection of sample is followed by a buffer flow during which the detector response reflects the rate of dissociation of the complex on the surface.
  • a typical output from the BIACORE® system is a graph or curve describing the progress of the molecular interaction with time.
  • This binding curve which is usually displayed on a computer screen, is often referred to as a "sensorgram”.
  • the present invention provides a computer- implemented method of determining at least one kinetic parameter for the interaction of an analyte in solution with an immobilized ligand from a data set comprising a plurality of different binding curves, each of which represents the progress of the interaction of the analyte with the ligand with time, which method comprises the steps of: a) performing at least one fit of the whole data set or subsets thereof to a predetermined kinetic model for the interaction; b) based on the result of the fit or fits performed in step a), identifying and excluding binding curves of unacceptable quality; c) performing a final fit to the remaining data set; and d) obtaining therefrom the kinetic parameter or parameters.
  • steps a) and b) may be iterated until no binding curves with unacceptable quality are identified. If the remaining data set after step b) is identical to a data set (the whole data set or a data subset) to which a fit has been made in step a), step c) may be omitted and the kinetic parameter(s) may be obtained from the fit in step a) (when no binding curves are excluded, the "remaining" data set in step c) is, of course, identical to the whole data set in step a)).
  • the terms "analyte” and "ligand” as used herein are to be interpreted in a broad sense. Basically, ligand means an entity that has a known or unknown affinity for a given analyte.
  • the ligand may be a naturally occurring species or one that has been synthesized.
  • the ligand is usually a biomolecule.
  • Common analytes include biomolecules (such as proteins, peptides, DNA, RNA, and the like), chemicals purified from extracts of biological material (e.g. plant extracts), synthesized chemicals (including small molecules), cells and viruses.
  • the present invention provides an analytical system for studying molecular interactions, which comprises data processing means for performing the above method.
  • the present invention provides a computer program comprising program code means for performing the method.
  • the present invention provides a computer program product comprising program code means stored on a computer readable medium or carried on an electrical or optical signal for performing the method.
  • the present invention provides a computer system containing a computer program comprising program code means for performing the method.
  • Figure 5 shows (A) overlay sensorgrams for the interaction of a drug (CBSA) with a sensing surface, (B) the corresponding sensorgrams together with fitted binding curves and indicated outlier sensorgrams, and (C) the corresponding sensorgrams together with fitted binding curves after exclusion of outliers.
  • Figure 6 shows (A) overlay sensorgrams for the interaction of a drug
  • Figure 7 shows (A) overlay sensorgrams for the interaction of a drug (furosemide) with a sensing surface, (B) the corresponding sensorgrams together with fitted binding curves and indicated outlier sensorgrams, and (C) the corresponding sensorgrams together with fitted binding curves after exclusion of outliers.
  • the present invention relates to analytical sensor methods, particularly biosensor based methods, where molecular interactions are studied and the results are presented in real time, as the interactions progress, in the form of detection curves, often called sensorgrams. While biosensors are typically based on label-free techniques, detecting e.g. a change in mass, refractive index or thickness for the immobilized layer, there are also sensors relying on some kind of labelling.
  • Typical sensor detection techniques include, but are not limited to, mass detection methods, such as optical, thermo-optical and piezoelectric or acoustic wave methods (including e.g. surface acoustic wave (SAW) and quartz crystal microbalance (QCM) methods), and electrochemical methods, such as potentiometric, conductometric, amperometric and capacitance/impedance methods.
  • mass detection methods such as optical, thermo-optical and piezoelectric or acoustic wave methods (including e.g. surface acoustic wave (SAW) and quartz crystal microbalance (QCM) methods)
  • electrochemical methods such as potentiometric, conductometric, amperometric and capacitance/impedance methods.
  • representative methods include those that detect mass surface concentration, such as reflection-optical methods, including both external and internal reflection methods, angle, wavelength, polarization, or phase resolved, for example evanescent wave ellipsometry and evanescent wave spectroscopy (EWS, or Internal Reflection Spectroscopy), both of which may include evanescent field enhancement via surface plasmon resonance (SPR), Brewster angle refractometry, critical angle refractometry, frustrated total reflection (FTR), scattered total internal reflection (STIR), which may include scatter enhancing labels, optical wave guide sensors, external reflection imaging, evanescent wave-based imaging such as critical angle resolved imaging, Brewster angle resolved imaging, SPR-angle resolved imaging, and the like.
  • photometric and imaging/microscopy methods “per se” or combined with reflection methods, based on for example surface enhanced Raman spectroscopy (SERS), surface enhanced resonance Raman spectroscopy (SERRS), evanescent wave fluorescence (TIRF) and phosphorescence may be mentioned, as well as waveguide interferometers, waveguide leaking mode spectroscopy, reflective interference spectroscopy (RlfS), transmission interferometry, holographic spectroscopy, and atomic force microscopy (AFR).
  • SERS surface enhanced Raman spectroscopy
  • SERRS surface enhanced resonance Raman spectroscopy
  • TIRF evanescent wave fluorescence
  • phosphorescence phosphorescence
  • waveguide interferometers waveguide leaking mode spectroscopy
  • RlfS reflective interference spectroscopy
  • transmission interferometry holographic spectroscopy
  • AFR atomic force microscopy
  • biosensors include the BIACORE® system instruments, marketed by Biacore AB, Uppsala, Sweden, which are based on surface plasmon resonance (SPR) and permit monitoring of surface binding interactions in real time berween a bound ligand and an analyte of interest.
  • SPR surface plasmon resonance
  • the phenomenon of SPR is well known, suffice it to say that SPR arises when light is reflected under certain conditions at the interface between two media of different refractive indices, and the interface is coated by a metal film, typically silver or gold.
  • the media are the sample and the glass of a sensor chip which is contacted with the sample by a microfluidic flow system.
  • the metal film is a thin layer of gold on the chip surface.
  • SPR causes a reduction in the intensity of the reflected light at a specific angle of reflection. This angle of minimum reflected light intensity varies with the refractive index close to the surface on the side opposite from the reflected light, in the BIACORE® system the sample side.
  • a schematic illustration of the BIACORE® system is shown in Fig. 1.
  • Sensor chip 1 has a gold film 2 supporting capturing molecules 3, e.g. antibodies, exposed to a sample flow with analytes 4 (e.g. an antigen) through a flow channel 5.
  • Monochromatic p-polarised light 6 from a light source 7 (LED) is coupled by a prism 8 to the glass/metal interface 9 where the light is totally reflected.
  • the intensity of the reflected light beam 10 is detected by an optical detection unit (photodetector array) 11 .
  • An optical detection unit photodetector array 11 .
  • a detailed discussion of the technical aspects of the BIACORE instrument and the phenomenon of SPR may be found in U.S. Patent No. 5,313,264. More detailed information on matrix coatings for biosensor sensing surfaces is given in, for example, U.S. Patents Nos. 5,242,828 and 5,436,161.
  • a detailed discussion of the technical aspects of the biosensor chips used in connection with the BIACORE® instrument may be found in U.S. Patent No. 5,492,840.
  • the full disclosures of the above-mentioned U.S. patents are incorporated by reference herein.
  • the concentration, and therefore the refractive index at the surface changes and an SPR response is detected. Plotting the response against time during the course of an interaction will provide a quantitative measure of the progress of the interaction. Such a plot is usually called a sensorgram.
  • the SPR response values are expressed in resonance units (RU).
  • One RU represents a change of 0.0001° in the angle of minimum reflected light intensity, which for most proteins and other biomolecules correspond to a change in concentration of about 1 pg/rnm ⁇ on the sensor surface.
  • association As sample containing an analyte contacts the sensor surface, the ligand bound to the sensor surface interacts with the analyte in a step referred to as "association.” This step is indicated on the sensorgram by an increase in RU as the sample is initially brought into contact with the sensor surface. Conversely, “dissociation” normally occurs when the sample flow is replaced by, for example, a buffer flow. This step is indicated on the sensorgram by a drop in RU over time as analyte dissociates from the surface-bound ligand. A representative sensorgram (binding curve) for a reversible interaction at the sensor chip surface is presented in Fig.
  • the sensing surface having an immobilized capturing molecule, for example an antibody, interacting with analyte in a sample.
  • the y-axis indicates the response (here in resonance units, RU) and the x-axis indicates the time (here in seconds).
  • buffer is passed over the sensing surface giving the baseline response A in the sensorgram.
  • an increase in signal is observed due to binding of the analyte.
  • This part B of the binding curve is usually referred to as the "association phase”.
  • association phase Eventually, a steady state condition is reached where the resonance signal plateaus at C.
  • the sample is replaced with a continuous flow of buffer and a decrease in signal reflects the dissociation, or release, of analyte from the surface.
  • This part D of the binding curve is usually referred to as the "dissociation phase".
  • the analysis is usually ended by a regeneration step (not shown in Fig. 2) where a solution capable of removing bound analyte from the surface, while (ideally) maintaining the activity of the ligand, is injected over the sensor surface. Injection of buffer restores the baseline A and the surface is then ready for a new analysis.
  • the profiles of the association and dissociation phases B and D respectively, provide valuable information regarding the interaction kinetics, and the height of the resonance signal represents surface concentration (i.e., the response resulting from an interaction is related to the change in mass concentration on the surface).
  • Equation (3) A more convenient method is, however, fitting of the integrated function (4), or numerical calculation and fitting of the differential Equation (3), preferably by means of a computer program as will be described below.
  • Analysis of kinetic data produced by the Biacore® instruments is usually performed using the dedicated BIAevaluation software (supplied by Biacore AB, Uppsala, Sweden) using numerical integration to calculate the differential rate equations and non-linear regression to fit the kinetic parameters. Basically, such software-assisted data analysis is performed as follows. After subtracting background noises, an attempt is made to fit the above-mentioned simple 1 : 1 Langmuir binding model as expressed by equations (4) and (6) above to the measurement data.
  • the binding model is fitted simultaneously to multiple binding curves obtained with different analyte concentrations C (or with different levels of surface derivatization Rmax)- B ase d on the sensorgram data such a "global" fitting establishes whether a single global k ass or k ( ji ss will provide a good fit to all the data.
  • the results of the completed fit is presented to the operator graphically, displaying the fitted curves overlaid on the original sensorgram curves.
  • the closeness of the fit is also presented by the chi-squared ( ⁇ 2) value, a standard statistical measure. For a good fitting, the chi-squared value is in the same magnitude as the noise in RU ⁇ .
  • “residual plots” are also provided which give a graphical indication of how the experimental data deviate from the fitted curve showing the difference between the experimental and fitted data for each curve. The operator then decides if the fit is good enough. If not, the sensorgram or sensorgrams exhibiting the poorest fit are excluded and the fitting procedure is run again with the reduced set of sensorgrams. This procedure is repeated until the fit is satisfactory. Sometimes, the above-mentioned 1:1 binding reaction model will not be valid, which requires the data set to be reanalysed using one or more other reaction models.
  • Such alternative models may include, for example, a one to one reaction influenced by mass transfer, two parallel independent one to one reactions, two competing reactions, and a two state reaction.
  • the method of the invention provides for an automated curve fitting and assessment procedure that, without intermediate decisions by the operator, excludes bad sensorgrams, reiterates the fit on the reduced data set, and presents the calculated kinetic constants to the operator, preferably together with information on the goodness of the fit.
  • the method comprises the following steps: a) performing at least one fit on the whole or parts of the data set, b) from the result of step a), identifying and excluding unacceptable binding curves from the data set, c) performing a final fit on the remaining binding curves of the data set, and d) presenting the results. Steps a) and b) may be iterated until no more binding curves with unacceptable quality are identified. If more than one data set is handled simultaneously, the results from step c) are preferably presented in order of quality.
  • the fit or one of the fits performed in step a) may be acceptable, and no final fit will, of course, then be necessary. This is, for example, the case when a fit has been made to the whole data set and the result is acceptable without exclusion of any binding curves, or when a binding curve or curves have been excluded but the remaining data set is identical to a data subset to which a fit has already been made in step a).
  • binding curve as used herein is to be interpreted in a broad sense.
  • binding curve may refer not only to the whole response curve but also to only a part thereof, such as e.g. the association part (or a part thereof) or the dissociation part (or a part thereof). Also, in e.g.
  • a ligand-supporting surface is sequentially contacted with different analyte solutions, e.g. stepwise changed analyte concentration, without intermediate regeneration or renewal of the immobilized ligand.
  • the response curve for the total experiment may be said to consist of a plurality of consecutive "binding curves", one for each analyte solution (e.g. analyte concentration).
  • a basic feature of the invention is the automated assessment and selection of binding curves that are acceptable to be included in the final fit.
  • a cross-validation type procedure is used.
  • Cross- validation which is well known to the skilled person, is, for example, described in Wold S., Technometrics, 20 (1978) 397-406 (the relevant disclosure of which is incorporated by reference herein).
  • the cross-validation may be performed either as a full cross-validation or a segmented cross-validation.
  • one binding curve is successively excluded at a time, and a fit is performed to the remaining curves and the result of the fit, e.g. expressed as the association rate constant or dissociation rate constant, is compared with that of the excluded curve.
  • a kinetic analysis is to be made of binding data obtained for multiple analyte-ligand interactions, using, for example, an array (one- or two-dimensional) with a number of spots with different immobilized ligands and corresponding specific analytes to the ligands.
  • a curve quality control is first performed to exclude sensorgrams with instrument-related defects (e.g. base-line slope, air spikes, carry-over between measurements), using the automated process described in our aforementioned international patent publication WO 03/081245 (the disclosure of which is incorporated by reference herein).
  • the particular analytes and immobilized ligand spots to be analysed are then selected by the operator, causing the relevant binding data for the kinetic analysis to be automatically extracted.
  • the first step (30) of the algorithm defines, for each data set or series (i.e. each group of sensorgrams corresponding to a particular analyte-ligand combination), the association and dissociation phases for the data series, or more particularly, the parts of the group of sensorgrams that are to be included in the analysis.
  • Background noise is corrected for by subtracting a sensorgram describing a sample injection of a liquid with analyte concentration 0 (zero) from all sensorgrams describing a sample injection of a liquid with analyte concentration greater than 0 (zero). This procedure is referred to as zero subtraction.
  • step (31) a simple quality control is performed by excluding curves with obviously erroneous kinetic data, such as e.g. sensorgrams with a positive dissociation slope.
  • step (32) a cross-validation procedure is performed by dividing each data series, or group of sensorgrams, into several subseries or subgroups. Start guesses (k ass , k ( ij ss , R m a ⁇ ) are calculated for each subseries, and for each data series, the subseries are then fit to a kinetic model for the interaction, in the illustrated case 1:1 binding with mass transfer limitation (MTL). The results of the fit from all subseries of a data series are put together (33).
  • the results are considered to be acceptable, and a final fit is done by fitting the kinetic model to all accepted sensorgrams with start guesses taken from the cross-validation results (34). If, on the other hand, there are large differences, a second quality control is performed by analysing the data series to find out if there is one or more sensorgrams that cause the bad result (35). If so, this or these sensorgrams are excluded and a final fit to the model is performed (34). When this has been performed for all the data series (i.e. all combinations of analytes and immobilized ligands), the measuring results are presented (36) so that they maybe sorted with regard to quality, e.g.
  • the "goodness" of fit such as the above- mentioned chi-squared (chi2) or chi2/(R max )2.
  • several different goodness measures may be provided.
  • the operator may now view all the fits and accept or reject results of the automatic evaluation performed.
  • Another (non-limiting) embodiment of the invention based on residual analysis is described below with reference to Fig. 4.
  • the first step (40) of the algorithm defines the association and dissociation phases and makes a zero subtraction for each data series (each combination of analyte and ligand), and a simple quality control is performed in the second step (41).
  • a global fit of each data series is made to a kinetic model for the interaction (here 1 : 1 binding with mass transfer limitation), and a residual analysis is made, i.e. using the kinetic parameters obtained in the global fitting. Fitted curves are produced for all sensorgrams, and the closeness of the fit to each curve is determined by residual values.
  • the residual values are then evaluated (43), and if all values are sufficiently small, i.e. below a predetermined level, the data series, and thereby the results of the fit, are accepted.
  • the quality of the fit, the reliability of the kinetic parameters and, optionally, other measures are determined, and the results are presented to the operator for examination and assessment (44).
  • the data series is analysed (45) to identify and exclude individual sensorgrams having too great residuals (outliers). It is understood that the exclusion criteria in this step (45) may be different from those used in step (43) above.
  • a new fit to the kinetic model is then made on the modified data series. Quality descriptors/measures are then determined and results are presented as described above (44). After examination of the results presented in step (44), additional (bad) sensorgrams may optionally be excluded, and the modified data series be refitted, whereupon the final results may be presented.
  • the above described procedure for automated determination of kinetic parameters is readily reduced to practice in the form of a computer system running software which implements the steps of the procedure.
  • the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the quality assessment procedure of the invention into practice.
  • the carrier may be any entity or device capable of carrying the program.
  • the carrier may comprise a storage medium, such as a ROM, a CD ROM or a semiconductor ROM, or a magnetic recording medium, for example a floppy disc or a hard disk.
  • the carrier may also be a transmissible carrier, such as an electrical or optical signal which may be conveyed via electrical or optical cable or by radio or other means.
  • the carrier may be an integrated circuit in which the program is embedded.
  • any suitable computer language may be used to implement the present invention, it is currently preferred to use a suite of MATLABTM module files (The MathWorks, Inc., Natick, MA, U.S.A.). While the invention is generally applicable to the evaluation of kinetic data obtained in e.g. real-time biointeraction analysis, an example of a particular application is for quality control in the production of protein drugs, i.e. for testing that different batches of the same protein exhibit the same kinetics when binding to its target. The invention will be further illustrated by the following non-limiting Example.
  • EXAMPLE A BIACORE® S51 (Biacore AB, Uppsala, Sweden) was used to generate sensorgram raw data for the interaction of three drugs, CBS A (4-carboxybenzene- sulfonamide), indapamide and furosemide with carbonic anhydrase immobilized to Sensor Chip CM5 (Biacore AB, Uppsala, Sweden) (all reagents were from in-house sources, Biacore AB, Uppsala, Sweden). Each drug was injected at a number of different concentrations. The resulting sensorgram data are shown as sensorgram overlays "A" in Figs. 5, 6 and 7, respectively.
  • the sensorgram raw data were then subjected to an automated kinetic evaluation for determining association rate constants, k a , and dissociation rate constants, k ( j, by running a simple embodiment of the algorithm of the present invention in MATLAB 5.3.1.29215a (Rl 1.1) (The MathWorks, Inc., Natick, MA, U.S.A.), using a PC with Windows NT 4.0.
  • the program used is shown below.
  • str ⁇ 'indapamide' 'cbsa' 'furosemid' ⁇ ;

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Abstract

L'invention se rapporte à un procédé mis en oeuvre sur ordinateur qui permet de déterminer au moins un paramètre cinétique associé à l'interaction d'un analyte en solution avec un ligand immobilisé à partir d'un ensemble de données comportant une pluralité de courbes de liaison différentes, chacune de ces courbes représentant la progression temporelle de l'interaction de l'analyte avec le ligand. Ce procédé comprend les étapes suivantes: a) mise en oeuvre d'au moins une adaptation de l'ensemble entier des données ou de sous-ensembles de celui-ci à un modèle cinétique prédéterminé associé à l'interaction; b) sur la base du résultat de l'adaptation ou des adaptations effectuées dans l'étape a), identification et exclusion des courbes de liaison de qualité inacceptable; c) mise en oeuvre d'une adaptation finale à l'ensemble des données restantes; et d) obtention subséquente du paramètre ou des paramètres cinétiques. L'invention se rapporte également à un système analytique permettant la mise en oeuvre de ce procédé, ainsi qu'à un programme informatique, à un produit logiciel et à un système informatique permettant la mise en oeuvre dudit procédé.
EP04775458A 2003-09-24 2004-09-22 Procede et systeme d'analyse d'interactions moleculaires Ceased EP1668363A1 (fr)

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SE0302525A SE0302525D0 (sv) 2003-09-24 2003-09-24 Method and system for interaction analysis
PCT/SE2004/001356 WO2005029077A1 (fr) 2003-09-24 2004-09-22 Procede et systeme d'analyse d'interactions moleculaires

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WO2006135309A2 (fr) * 2005-06-13 2006-12-21 Biacore Ab Procede et systeme d'analyse par affinite
DE602005017148D1 (de) * 2005-08-30 2009-11-26 Perkinelmer Cellular Technolog Verfahren zum Nachweis einer biochemischen Interaktion
US7473916B2 (en) * 2005-12-16 2009-01-06 Asml Netherlands B.V. Apparatus and method for detecting contamination within a lithographic apparatus
EP2507618B1 (fr) * 2009-11-30 2015-05-27 GE Healthcare Bio-Sciences AB Procédé et système d'analyse d'interaction
EP3071946B1 (fr) 2013-11-19 2022-04-06 Cytiva Sweden AB Procédé de détermination de cinétiques d'interaction avec dissociation rapide
GB201516992D0 (en) * 2015-09-25 2015-11-11 Ge Healthcare Bio Sciences Ab Method and system for evaluation of an interaction between an analyte and a ligand using a biosensor
GB201705280D0 (en) 2017-03-31 2017-05-17 Ge Healthcare Bio Sciences Ab Methods for preparing a dilution series
GB201914063D0 (en) 2019-09-30 2019-11-13 Ge Healthcare Bio Sciences Ab Method for classifying monitoring results from an analytical sensor system arranged to monitor molecular interactions

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