Method and system for analysing analyte-ligand interaction

Abstract

In various aspects, the present invention relates to a method 700, 800 and a system 900 for determining one or more interaction parameters associated with the interaction between an analyte and a ligand. The method comprises steps 704, 804 of contacting a sensor surface having immobilized ligands with one or more samples containing the analyte, steps 706, 806 of registering a sensor response indicative of binding of the analyte to the binding site of the ligand, and steps 716, 808 of fitting the registered sensor response to an interaction model to determine one or more interaction parameters associated with the interaction between the analyte and the ligand, the fitting steps being constrained by predetermined upper and lower limits that apply to analyte saturation parameters associated with the interaction model.

Description

[Technical Field]

[0001] The present disclosure relates to the analysis of interactions between analytes and ligands on a sensor surface, and more particularly to systems and methods for enabling improved determination of interaction parameters associated with such interactions. [Background technology]

[0002] There has been growing interest in analytical sensor systems capable of monitoring interactions between molecules, such as biomolecules, in real time. Such systems typically allow for the determination of one or more of the binding, kinetics, affinity, specificity, and concentration of molecules ("analytes") contained in a sample solution. Optical biosensors are particularly useful for this purpose and are commonly referred to as interaction analysis sensors or biospecific interaction analysis sensors. A representative example of such a biosensor system is the BIACORE® instrument sold by Cytiva, which uses surface plasmon resonance (SPR) to detect interactions between molecules at a sensing surface without the need for labeling. As each sample passes over the sensor surface, the progress of binding can be measured, providing a direct reflection of the rate at which the molecular interaction is occurring.

[0003] A typical output from a system such as the BIACORE® system is a graph or curve describing the progression of a molecular interaction over time, including an association phase portion and a dissociation phase portion. This binding curve, typically displayed on a computer screen, is often called a "sensorgram." Thus, the BIACORE® system (and similar sensor systems) allows for the real-time determination of not only the presence and concentration of a specific analyte in a sample, without the use of labels and often without purifying the substances involved, but also additional interaction parameters, including the binding (association) and dissociation kinetic constants in the molecular interaction, as well as the affinity of the interaction being evaluated. The binding rate constant (k a) and dissociation rate constant (k d The affinity (binding equilibrium constant K) can be obtained by fitting the kinetic data obtained for one or preferably several different sample analyte concentrations to a mathematical description of the interaction model in the form of a differential equation. A or the dissociation equilibrium constant K D The binding rate constant (expressed as ) can be calculated from the association and dissociation rate constants.

[0004] However, in many cases, weak binding can make it difficult to obtain definitive kinetic data, and therefore it is usually more reliable to measure analyte affinity by equilibrium binding analysis, which involves determining the level of binding at equilibrium or steady state, presumably reached at or near the end of the association phase of the binding interaction, for a range of analyte concentrations.

[0005] However, determining affinity by equilibrium binding analysis in this way is problematic in that it traditionally relies on obtaining sensor response data over a wide range of analyte concentrations. Specifically, traditional analytical techniques rely on the dissociation equilibrium constant, K D This requires that sensor response values ​​for the analyte at concentrations close to K be available. However, in practice, such data can be difficult to obtain due to practical limitations, such as the fact that above a certain concentration, the analyte begins to precipitate or aggregate, making accurate readings of the sensor response impossible. As a result, K D It is not always possible to obtain unaffected sensor response values ​​for analyte concentrations close to K. Instead, response values ​​for only a limited range of analyte concentrations may be available. Such situations may arise frequently during drug candidate screening, to take one example. In such cases, the interaction parameter (e.g., K) D , K. ATraditional methods for calculating σ are inadequate and produce unrealistic and inaccurate estimates of these values. Some progress has been made to provide alternative solutions, but these fall short of providing completely accurate data, as discussed in more detail below.

[0006] As can be seen, therefore, existing processes for determining analyte-ligand interaction parameters suffer from significant drawbacks. It would be advantageous to provide systems and methods that address one or more of these problems, either singly or in combination. Summary of the Invention

[0007] This summary introduces concepts that are described in more detail in the detailed description, and is not intended to identify essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0008] According to one aspect of the present disclosure, there is provided a method for determining one or more interaction parameters associated with an interaction between an analyte and a ligand, the method comprising contacting a sensor surface having an immobilized ligand with one or more samples containing the analyte, and registering a sensor response indicative of binding of the analyte to the binding site of the ligand.

[0009] Preferably, the sensor surface is contacted with three or more samples containing different concentrations of the analyte. This injection of a series of samples with different analyte concentrations, called a concentration series, provides more reliable results than using a single analyte concentration.

[0010] Preferably, to improve the accuracy of the results, the registered response is the response level for each analyte sample recorded at equilibrium (or as close to equilibrium as possible), which in practice is assumed to be typically the end of the binding phase.

[0011] The method further comprises fitting the registered sensor response to an interaction model to determine one or more interaction parameters associated with the interaction between the analyte and the ligand, advantageously the fitting being constrained by predetermined upper and lower limits applied to an analyte saturation parameter associated with the interaction model.

[0012] Fitting registered sensor responses to an interaction model is a known method for determining interaction parameters. However, the inventors have surprisingly determined that the accuracy of this approach can likely be significantly improved by constraining the fitting as described above and in further detail herein. In particular, by applying upper and lower bounds to the analyte saturation parameters associated with the interaction model, the fitting procedure is constrained to produce values ​​within specific boundaries. The inventors believe that this constraint results in more accurate estimates of the interaction parameters than any existing method. In particular, the inventors have determined that the disclosed approach effectively provides a middle ground between previous approaches that either rely too heavily on registered response data or, at the other extreme, use fixed estimates of the analyte saturation parameters, thereby giving too little weight to the actual registered response data. The approach disclosed herein represents a balance between these competing approaches and, surprisingly, the inventors believe it provides more accurate affinity determinations.

[0013] The one or more interaction parameters determined using the disclosed methods may include the binding equilibrium constant, K, of the interaction. A and the dissociation equilibrium constant K of the interaction D These interaction parameters provide valuable insight into the binding behavior of the analyte in the presence of a ligand, particularly with respect to the affinity of the analyte for the ligand in question. This data can be used to inform a variety of important assays and analyses, for example, in candidate drug screening, antibody analysis, and quality control.

[0014] As noted above, the fitting of the sensor response data to the interaction model is constrained by predetermined upper and lower limits that are applied to the analyte saturation parameters associated with the interaction model. In one example, the analyte saturation parameters to which the upper and lower limits are applied are the analyte saturation response values ​​R associated with the interaction model used in the fitting procedure. max It can be. R max denotes the maximum possible sensor response of the analyte at a given concentration, i.e., the response expected if the analyte were bound to all available binding sites of the ligand on the sensor surface. As will be explained in more detail below, the inventors have surprisingly found that R max By constraining the fitting with upper and lower bounds that apply to D and K. A It was identified that the accuracy of determining interaction parameters such as

[0015] R max Fitting the sensor response data to an interaction model based on T is not the only option. An alternative is to use an interaction model based on target occupancy To. In that case, the analyte saturation parameters to which upper and lower limits apply are the target occupancy values ​​at saturation To. sat Target occupancy can be thought of as expressing the analyte response in terms of a percentage (or fraction) of the response expected at saturation. Thus, the sensor response is R max At saturation, the target occupancy To sat is 100% (or 1 as a fraction) because the response is greatest when there is maximum analyte-ligand binding (i.e., at saturation). When fitting sensor response data to an interaction model based on target occupancy, we use the target occupancy at saturation, To sat The use of upper and lower bounds on K D and K. A It was again confirmed that this is likely to result in better prediction of interaction parameters such as

[0016] In one example, the upper and lower limits applied to the analyte saturation parameter can be set by a user based on knowledge of the particular assay. Alternatively, however, the disclosed method can include determining an estimate of the analyte saturation parameter and setting at least one of the upper and lower limits based on this estimate of the analyte saturation parameter. In other words, an initial estimate of the analyte saturation parameter can be determined, and this estimate can then be used as a starting point for setting the upper and lower limits. This approach means that the fitting procedure is anchored to the best estimate of the analyte saturation parameter previously estimated from either theory or experimental data. This approach improves the likelihood of making an accurate determination of the interaction parameter compared to simply relying on the user to provide an estimate based on knowledge of the analyte and ligand's potential interactions.

[0017] In one example, at least one of the upper and lower limits is set as a percentage of the estimated value of the analyte saturation parameter. In other words, if the estimated value represents 100%, the fitting can be constrained between upper and lower limits defined as percentages of that initial estimate. The inventors have determined that a particularly suitable lower limit is 75% of the estimated analyte saturation parameter. This constraint has been found to provide highly accurate estimates of the interaction parameter. Similarly, the inventors have determined that a particularly suitable upper limit is 150% of the estimated analyte saturation parameter. This constraint has also been found to provide highly accurate estimates of the interaction parameter. Thus, in one particularly advantageous configuration, the fitting procedure is constrained to use values ​​between 75% and 150% of the previously determined estimate of the analyte saturation parameter. This constraint prevents the fitting procedure from considering values ​​for the analyte saturation parameter outside this interval, thereby constraining the fitting procedure within the predetermined boundaries. As discussed above, this approach has been found to improve the accuracy of interaction parameter determination. While limits of 75% and 150%, respectively, have been found to be particularly advantageous, accuracy benefits are still provided when the lower limit is between 75% and 99% of the estimated value, and when the lower limit is between 25% and 99% of the estimated value. Similarly, accuracy benefits are provided when the upper limit is between 101% and 150% of the estimated value, and when the upper limit is between 101% and 250% of the estimated value. Thus, the lower limit may be set as 75% and 99% of the estimated value, or 25% and 99% of the estimated value. The upper limit may be set as 101% and 150% of the estimated value, or alternatively, as 101% and 250% of the estimated value. All of these percentage ranges are believed to provide benefits with regard to determining accurate interaction parameters.

[0018] As already mentioned, when the upper and lower limits used to constrain the fitting process are based on a predetermined estimate of the analyte saturation parameter, the predetermined estimate can be determined in several ways. In one example, the estimate of the analyte saturation parameter is determined theoretically based on known models and equations for the relationship between the analyte and the ligand. In that case, the step of determining the estimate of the analyte saturation parameter comprises the following: Molecular weight of the ligand Mw lig , Molecular weight of the analyte Mw ana and Ligand response, R lig This may include calculating a theoretical estimate of the value based on

[0019] Ligand response R lig may also be referred to as the immobilization level of the ligand. The advantage of this theoretical approach is that an initial estimate of the analyte saturation parameter can be determined theoretically without the need for a control analyte or performing any experiments, saving time and cost.

[0020] In one example, the interaction model used in the fitting procedure is max If the parameter is based on R, then a given estimate of the analyte saturation parameter is max The estimated theoretical value of, i.e.

number

number

[0021] Alternatively, if the interaction model used in the fitting procedure is based on target occupancy (To), then the analyte saturation parameters (To sat A given estimate of the sample response (R) to analyte A can be defined as 1.A ) is calculated as the target occupancy (To A ) can be expressed in terms of

number

[0022] While theoretically determining an estimate of the analyte saturation parameter is one option, another option is to experimentally determine the value using a control analyte that is assumed to interact with the ligand in a manner similar to how the analyte of interest interacts. In that case, determining the estimate of the analyte saturation parameter may include contacting the sensor surface with the control analyte, registering a sensor response indicative of binding of the control analyte to the binding site of the ligand, and determining a control analyte saturation parameter for the control analyte. For example, R maxC can be determined.

[0023] Once the control analyte saturation parameter is determined, it can be converted to the corresponding saturation parameter for the analyte of interest. For example, R maxC (of control analyte) to R maxA (of the analyte of interest). Methods for making this conversion are explained in more detail below.

[0024] As already mentioned, the method can include accepting user input to set upper and lower limits. In other words, upper and lower limits around the analyte saturation parameter to which the fitting procedure is constrained can be set by the user. As mentioned above, in one example, the user can simply set the limits based on existing knowledge of the interaction being analyzed. Alternatively, the system can store a predetermined estimate of the analyte saturation parameter (e.g., determined by one of the theoretical or experimental methods described above). In that case, the predetermined estimate can serve as a default value, and user input can be used to set upper and lower limits relative to this predetermined estimate.

[0025] In one example, the user can enter upper and lower numerical values, for example, in units of response (RU) or target occupancy. For example, if a given estimate is an R of 14 RU, max For values ​​of R, user input can set the upper bound as +2RU relative to the estimated value and the lower bound as -2RU relative to the estimated value. In that case, the fitting procedure will max is constrained to fits that result in values ​​between 12 RU and 16 RU. In another example, the user can enter percentage values ​​that determine upper and lower limits for a given estimate of the analyte saturation parameter. For example, the user may set the upper limit to 150% of the estimate of the analyte saturation parameter and the lower limit to 75% of the estimate of the analyte saturation parameter. Thus, R max In the example given above, where the "default" value of is 14 RU, these percentage limits would restrict the fitting process to fits that result in values ​​between 10.5 and 22.5 RU. Another practical approach is to set the lower limit to the response value from the sample with the highest concentration of analyte.

[0026] It will be appreciated that once the upper and lower bounds for the fitting process are set, various fitting algorithms can be used to fit the sensor response data to the interaction model. The fitting procedure is typically performed using an algorithm such as R max and K. D This involves iteratively "guessing" values ​​of R and plotting a response curve using those guessed values ​​and an interaction model, the details of which are outlined below. It is then possible to determine how well each set of estimated values ​​fits the actual recorded response data. max The value of is constrained by the upper and lower bounds mentioned above. In one example, the fitting step includes applying a least chi-squared estimate to the registered sensor responses to determine the quality or closeness of the fit to the response data, i.e., to determine how well each iteration of the estimate fits the response data.

[0027] In one example, the predetermined interaction model used in the fitting procedure comprises the following equation:

number

[0028] Thus, in this example, the step of fitting the sensor response data to the interaction model is performed in R max and K. D (for the corresponding value of C) to determine the corresponding value of R eq This includes algorithmically fitting the sensor response data (which provides a value of R), as described above. max and K.D This involves iteratively varying both R and R to find the combination that gives the best fit as measured by a fitting parameter such as chi-square. As mentioned above, the fit may be outside of defined upper and lower bounds on R max Once the fitting is complete, i.e., once the best fit is found within the interaction model and the upper and lower constraints, the interaction parameter K D The best estimate of K is determined. A can also be calculated.

[0029] The sensor system used to carry out the disclosed methods may be a surface plasmon resonance (SPR) system, in which case the registration of a sensor response indicative of binding of an analyte to a binding site of a ligand may be based on surface plasmon resonance.

[0030] The sensor system used to perform the disclosed methods may be an evanescent wave sensing system, in which case registering a sensor response indicative of binding of an analyte to a binding site of a ligand may be based on evanescent wave sensing.

[0031] According to another aspect of the present disclosure, a sample analysis system for determining an interaction parameter is disclosed, the system comprising a sensing surface configured to detect a binding interaction between an analyte and a ligand at the sensing surface, and a computing device configured to perform any of the methods disclosed herein.

[0032] According to a further aspect of the present disclosure, a computer is disclosed, the computer configured to cause a sample analysis system to perform any of the methods disclosed herein.

[0033] According to a further aspect of the present disclosure, a computer-readable storage medium is disclosed that includes instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein in a sample analysis system.

[0034] Exemplary implementations of the present disclosure will now be described, by way of example only, with reference to the drawings, in which: [Brief explanation of the drawings]

[0035] [Figure 1] FIG. 1 is a schematic diagram of an SPR-based biosensor system. [Figure 2] FIG. 1 shows a representative sensorgram illustrating detector response versus time for interactions between molecules occurring on the sensor surface. [Figure 3] FIG. 1 shows the equilibrium binding response values ​​versus analyte concentration over a wide range of concentrations. [Figure 4] FIG. 4 shows a curve generated by fitting the data points of FIG. 3 to an interaction model. [Figure 5] FIG. 1 shows the values ​​of equilibrium binding response versus concentration of analyte over a narrow range of concentrations. [Figure 6] FIG. 6 shows a curve generated by fitting the data points of FIG. 5 to an interaction model. [Figure 7] FIG. 1 shows a method for determining the saturation response RmaxA of an analyte based on a prior determination of the saturation response RmaxC of a control analyte. [Figure 8] FIG. 1 illustrates a method for determining one or more interaction parameters associated with an interaction between an analyte and a ligand according to the present disclosure. [Figure 9] A schematic diagram of a Biacore T200 from Cytiva is shown, which is an example of a sample analysis system that can be used to carry out the disclosed methods. [Figure 10]FIG. 1 illustrates components of an exemplary computing device that can be used to implement the methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0036] Like reference numbers refer to like features throughout the description and drawings.

[0037] This detailed description explains the basic principles of biosensors (also called sample analysis systems) and their methods of operation with reference to Figures 1 and 2. Shortcomings of existing methods for determining interaction parameters using such systems are illustrated with reference to Figures 3-7. An improved method for interaction parameter determination is described with reference to Figure 8. Finally, with reference to Figures 9 and 10, components of an exemplary sample analysis system and an exemplary computer device that can be used to implement the methods described herein are described.

[0038] The methods disclosed herein generally involve measuring the binding (K) associated with the interaction between the analyte and the ligand. A ) equilibrium constant or dissociation (K D ) equilibrium constant. As noted above, problems with conventional approaches for determining such parameters arise because conventional methods are reliable only when data are available for a wide range of analyte concentrations. More specifically, such methods involve determining one or more interaction parameters, such as the analyte dissociation equilibrium constant, K D These methods rely on having response data for a range of analyte concentrations that covers (and preferably exceeds) 100 s. When only a limited range of data is available (as can occur in various cases where high concentrations of analyte cannot be obtained or reliably studied), these existing methods fall short because the fitting procedures result in unrealistic and inaccurate estimates of the interaction parameters.

[0039] One proposed solution to this drawback has been to utilize a control analyte. Such an approach is described in European Patent No. 2507618 in the name of GE Healthcare Bio-Sciences AB, the contents of which are incorporated herein by reference in their entirety. In this approach, R maxC A steady-state binding assay is first performed on a control analyte to obtain the saturation response of the control analyte, denoted by R. This control parameter is then used to calculate the corresponding parameter of the analyte of interest (R maxA (denoted as R) can be estimated. maxA Once estimated, this value is used as a fixed value in the interaction model, and the interaction parameter of interest (e.g., K D , K. A ) can be determined.

[0040] This approach offers an improvement over previous methodologies that simply rely on free fitting of response data to interaction models without further constraints or restrictions on analyte saturation parameters. However, the improved method of EP 2507618 is not without its own problems. First, the approach requires finding a control analyte, which is not always straightforward. The control analyte is often a soluble analyte, such as a soluble analyte, which is often a soluble analyte. maxC From R maxA For the extrapolation to be valid, the control analyte must be similar to the analyte of interest in terms of its binding properties. Finding such a suitable control analyte can require significant experimental work, as suitable control analyte candidates must be identified and tested. Often, control analyte candidates perform poorly and are not as effective as predicted by theory, meaning that many candidates must be tested frequently. In practice, finding a suitable control analyte can take weeks or even months, even in a sophisticated laboratory environment. This poses a significant barrier to performing large-scale assays, as research to find a suitable control analyte must be conducted before many assays. In some cases, a suitable control analyte is not available.

[0041] Even if a suitable control analyte can be found, we have maxC From R maxA It has been observed that the conversion to σ is not always remarkably successful, as it depends on many assumptions about the similarity of the control analyte and the analyte of interest. In practice, these assumptions may not hold as securely as in theory, which is why, when using this method, the subsequently determined interaction parameters (e.g., K D , K. A ) is not always particularly accurate.

[0042] The disclosed invention seeks to address the above-mentioned shortcomings. In particular, the inventors have identified that alternative possibilities exist for determining the interaction parameters of an interaction. The inventors have surprisingly found that fitting response data to an interaction model can yield analyte saturation parameters (e.g., R) associated with the interaction model. max We believe that a more accurate estimate of the interaction parameter can be obtained if it is constrained by an interval (i.e., upper and lower bounds) that applies to .

[0043] The inventors have determined that this method effectively serves as a middle ground between the traditional approach (free fitting of data to an interaction model with no constraints on analyte saturation parameters) and the approach of EP 2507618, which constrains the fit to fixed analyte saturation parameters determined using a control analyte. Advantageously, the disclosed methodology is believed to retain the advantages of the EP 2507618 method, namely, its ability to provide accurate estimates of interaction parameters even when response data are available for only a limited range of analyte concentrations. Importantly, however, the disclosed methodology, in contrast to the EP 2507618 method, does not rely on the use of a control parameter. Most importantly, by striking a balance between the traditional approaches and performing a fitting procedure that is constrained by an interval (i.e., upper and lower bounds) rather than a fixed value, the inventors believe that more accurate estimates of interaction parameters can be obtained than are possible using either of the aforementioned methodologies.

[0044] To aid understanding, each of the above approaches will be described in more detail. First, the principles of the underlying sensor technology for interaction analysis will be explained. This will be followed by an overview of the traditional free-fitting approach, which fits response data to an interaction model without constraints on analyte saturation parameters. Next, the saturated response R max We describe an alternative approach to EP 2507618 that uses a fixed value for . Finally, we outline the novel approach of the present disclosure, which improves on both of the above approaches, and contrast it with the conventional approach.

[0045] Background Technology - Underlying Sensor Technology As mentioned above, this disclosure relates to the evaluation of binding response data obtained for multiple concentrations of an analyte, typically at equilibrium (steady state). From this, one or more interaction parameters for the interaction can be determined. Typically, experimental binding data is obtained by sensor-based technologies that study molecular interactions and provide results in real time as the interaction progresses. To aid in understanding, some brief background information regarding such sensor-based technologies is provided here.

[0046] Chemical or biosensors (also referred to herein as "sample analysis systems") are typically based on label-free technology, detecting changes in sensor surface properties such as the mass, refractive index, or thickness of the immobilized layer, although some sensors rely on labels of some kind. Typical sensor detection techniques include, but are not limited to, mass detection methods such as optical, thermo-optical, and piezoelectric or acoustic methods (including, for example, surface acoustic wave (SAW) and quartz crystal microbalance (QCM) methods), and electrochemical methods such as potentiometry, conductometry, amperometry, and capacitance / impedance methods. With respect to optical detection methods, representative methods include those that detect mass surface concentrations, e.g., reflective optical methods, including both external and internal reflectance methods, which may be angle, wavelength, polarization, or phase resolved, such as evanescent wave ellipsometry and evanescent wave spectroscopy (EWS or internal reflectance spectroscopy), both of which may include evanescent field enhancement by surface plasmon resonance (SPR), Brewster angle refractometry, critical angle refractometry, frustrated total internal reflection (FTR), scattered total internal reflection (STIR) (which may include scattering-enhanced labels), optical waveguide sensors, and evanescent wave-based imaging such as external reflectance imaging, critical angle resolved imaging, Brewster angle resolved imaging, SPR angle resolved imaging, etc. Further examples include photometry and imaging / microscopy based on, for example, surface-enhanced Raman spectroscopy (SERS), surface-enhanced resonance Raman spectroscopy (SERRS), evanescent wave fluorescence (TIRF) and phosphorescence, either "by itself" or in combination with reflectance methods, as well as waveguide interferometry, waveguide leaky mode spectroscopy, reflection interference spectroscopy (RIfS), transmission interferometry, holographic spectroscopy, and atomic force microscopy (AFR).

[0047] Commercially available biosensors include the aforementioned BIACORE® System instrument manufactured and sold by Cytiva of Uppsala, Sweden, which is based on surface plasmon resonance (SPR) and allows for real-time monitoring of surface binding interactions between a binding ligand and an analyte of interest. In this context, a "ligand" is a molecule with known or unknown affinity for a given analyte, including any collector or capture agent immobilized on the sensor surface, while an "analyte" includes any specific binding partner therefor that is typically injected and flowed over or past the ligand on the sensor surface.

[0048] While the detailed description that follows of the present invention is presented in the context of SPR spectroscopy, and more specifically the BIACORE® system, it should be understood that the present invention is not limited to this detection method. Rather, any affinity-based detection method in which an analyte binds to a ligand immobilized on a sensing surface can be used, provided that a change in the sensing surface that quantitatively indicates binding of the analyte to the immobilized ligand thereon can be measured.

[0049] The phenomenon of SPR is well known; it occurs when, under certain conditions, light is reflected at the interface between two media of different refractive indexes, and the interface is coated with a metal film, typically silver or gold. In the BIACORE® instrument, the media are the sample and the glass of the sensor chip, which contacts the sample via a microfluidic flow system. The metal film is a thin layer of gold on the chip surface. SPR reduces the intensity of the reflected light at a specific reflection angle. This angle of minimum reflected light intensity varies depending on the refractive index of the surface adjacent to the sample, which in the BIACORE® system is opposite the reflected light.

[0050] A schematic diagram of the BIACORE® system is shown in Figure 1. The sensor chip 101 has a gold film 102 supporting capture molecules (ligands) 103, e.g., antibodies, and is exposed to a sample flow with analyte 104, e.g., antigens, passing through a flow path 105. Monochromatic p-polarized light 106 from a light source 107 (e.g., an LED) is coupled by a prism 108 to a glass / metal interface 109, where the light is totally internally reflected. The intensity of the reflected light beam 110 is detected by an optical detection unit 111 (e.g., a photodetector array).

[0051] A detailed description of the technical aspects of the BIACORE® instrument and the phenomenon of SPR can be found in U.S. Patent No. 5,313,264. More detailed information regarding matrix coatings on biosensor sensing surfaces is provided, for example, in U.S. Patent Nos. 5,242,828 and 5,436,161. Additionally, a detailed description of the technical aspects of biosensor chips used in connection with BIACORE® instruments can be found in U.S. Patent No. 5,492,840. The above publications, as well as any other publications, patent applications, patents, or other references mentioned in this disclosure, are incorporated by reference in their entirety.

[0052] When molecules (analytes) in a sample bind to capture molecules (ligands) on the sensor chip surface, the concentration at the surface, and therefore the refractive index, changes, and an SPR response is detected. Plotting the response versus time over the course of the interaction provides a quantitative measure of the progress of the interaction. Such plots, i.e., kinetic curves or binding curves (binding isotherms), are typically called sensorgrams, and are sometimes referred to in the art as "affinity traces" or "affinograms." In the BIACORE® system, SPR response values ​​are expressed in resonance units (RU). 1 RU represents a 0.0001° change in the angle of minimum reflected light intensity, which for most proteins and other biomolecules corresponds to approximately 1 pg / mm on the sensor surface. 2This corresponds to a change in the concentration of analyte. When a sample containing the analyte contacts the sensor surface, capture molecules (ligands) bound to the sensor surface interact with the analyte in a step called "association." This step is indicated on the sensorgram by an increase in response (RU) when the sample first contacts the sensor surface. Conversely, "dissociation" typically occurs when the sample flow is replaced by, for example, a buffer flow. This step is typically indicated on the sensorgram by a decrease in response (RU) over time as the analyte dissociates from the surface-bound ligand.

[0053] A representative sensorgram (binding curve) of a reversible interaction on a sensor chip surface is shown in Figure 2. The sensorgram represents an interaction involving an immobilized capture molecule (ligand), e.g., an antibody, interacting with a binding partner (analyte) in the sample. Binding curves generated by biosensor systems based on the other detection principles mentioned above have a similar appearance. The vertical axis (y-axis) represents the response (here, in resonance units, or RU), and the horizontal axis (x-axis) represents time (here, in seconds). Initially, buffer solution is passed over the sensing surface, giving a baseline response, K, in the sensorgram. During sample injection, an increase in signal is observed due to binding of the analyte to the ligand. This portion, L, of the binding curve is typically referred to as the "binding phase." Eventually, a steady-state condition is reached at or near the end of the binding phase, where the resonance signal plateaus at M (although this is not always achieved). Note that the term "steady state" is used synonymously with the term "equilibrium" herein (although in other contexts the term "equilibrium" may be used restrictively to describe an ideal interaction model, since in reality binding can be constant over time even when the system is not at equilibrium). At the end of sample injection, 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 portion N of the binding curve is usually referred to as the "dissociation phase." When complete dissociation in buffer becomes impractical, the analysis may optionally be terminated by a regeneration step in which the sensor surface is injected with a solution that can remove bound analyte from the surface while (ideally) maintaining ligand activity. This is shown in portion O of the sensorgram. The buffer injection restores the baseline K, and the surface is ready for a new analysis.

[0054] The respective profiles of the binding phase L and dissociation phase N provide information about the binding and dissociation kinetics. The height of the resonance signal at M represents the affinity (the response resulting from the interaction related to changes in mass concentration on the surface), which will now be described in more detail below in the context of various aforementioned techniques for determining the interaction parameters of analyte-ligand interactions at the sensor surface.

[0055] Determination of Interaction Parameters - Background To aid understanding, we first provide some background derivation of the interaction model underlying the methods of this disclosure.

[0056] First, we assume a reversible reaction between an analyte A and a surface-bound (immobilized) capture molecule, i.e., a ligand B, which is not diffusion- or mass-transfer-limited and follows pseudo-first-order kinetics. A+B⇔AB

[0057] This interaction model (commonly referred to as the Langmuir model) assumes that the analyte (A) is both monovalent and homogeneous, the ligand (B) is homogeneous, and all binding events are independent. These assumptions have been established to be applicable in most cases in practice, and are therefore valid assumptions in both the free-fitting methodology and the approach of the present disclosure.

[0058] The rate of change of the surface concentration of analyte A during analyte injection (which is equal to the rate of change of the concentration of the formed complex AB) is the sum of the rates of binding and dissociation of analyte A,

number

[0059] After time t, the concentration of unbound ligand B on the surface is [BT]-[AB], where [BT] is the total or maximum concentration of ligand B. Inserting into equation (1) gives:

number

[0060] In terms of the detector response unit (where AB is detected), this can be expressed as:

number

[0061] Rearranging equation (3), we get

number

number

[0062] Now, according to equation (4), when dR / dt is plotted against the bound analyte concentration R, the slope is -(k a C+k d ) and the vertical intercept is k a CR max The bulk concentration C is known and the saturated response R max Once k has been determined (e.g., by saturating the surface with a large excess of analyte), the binding rate constant k a and the dissociation rate constant k d can be calculated.

[0063] However, a more convenient method is the fitting of the integrated function (5) or the numerical calculation and fitting of the differential equation (4), preferably by a computer program. This is because R max provides an alternative method for determining

[0064] k dcan also be determined as follows: The dissociation rate can be expressed as:

number

number

[0065] Equation (6) can be linearized to

number

[0066] Affinity is the binding equilibrium constant, K A =k a / k d , or the dissociation equilibrium constant (also simply called the equilibrium constant) K D =k d / k a It is expressed as:

[0067] Alternatively, the coupling constant K A may be determined from equation (3) where dR / dt=0 at equilibrium, giving

number

number

[0068] If the binding reaction is performed at multiple concentrations, the K can be determined by nonlinear curve fitting of the data. A (and the extension K D Alternatively, for example, if the kinetic data is unreliable or the association and dissociation are too rapid to be accurately measured, R eq / C to R eq can be plotted against the slope = -K A (Scatchard plot) is obtained.

[0069] Rearranging equation (10), we get

number

number

[0070] Usually, equation (12) is modified as follows:

number

[0071] Equations (11) and (12) can be modified by introducing a steric interference factor n, which specifies how many binding sites are blocked on average by one analyte molecule.

number

number

[0072] Response data may be normalized prior to further analysis to improve visualization of low response data.

[0073] Free fitting using software-assisted analysis As mentioned above, one conventional approach to determining interaction parameters relies on recording the sensor response to various analyte concentrations and then fitting this response data to a predetermined interaction model, such as one of the models described above, without further constraints on the parameters. Within the context of the present disclosure, this approach relies on determining the analyte saturation parameter (R max or To sat It is known as a "free fitting" technique in that no constraints are applied to the saturation response R. Rather, the goal is simply to find the best fit between the sensor response data and the interaction model and to directly read off the resulting values ​​of the analyte saturation parameters produced by that fit. Thus, from this fit, the saturation response R max and the interaction parameter K D and K. A Various parameters can be determined, such as: The improved methodology of the present disclosure is based on this conventional free-fitting method, which will now be described in more detail.

[0074] Software for the analysis of kinetic and affinity data is commercially available. Thus, for example, evaluation of kinetic and affinity data generated by a BIACORE® instrument typically involves using the dedicated BIACORE Insight Evaluation® software (supplied by Cytiva, Uppsala, Sweden) to calculate differential rate equations using numerical integration and fit the kinetic and affinity parameters using nonlinear regression to find the parameter values ​​that give the best fit, minimizing the sum of squared residuals.

[0075] Thus, in one example, the free-fitting approach involves determining affinity constants from measured steady-state binding levels using BIACORE Insight Evaluation® software via the following steps: (i) For various analyte concentrations, the steady-state binding level (R) is calculated from the reported points on the sensorgram in the steady-state region of the curve (typically, region M in Figure 2, where the sensorgram response is substantially parallel to the x-axis). eq ), (ii) R for C eq creating a plot of (iii) This plot is fitted to an interaction model, such as a general "steady-state affinity" fitting model (e.g., Equation 13 or Equation 14), to obtain the K A / K D and R max Steps to get

[0076] Step (iii) specifically represents a "free fitting" approach in which the sensor response data is fitted to the interaction model (in this case Equation 13 or Equation 14) without any constraints on the analyte saturation parameters.

[0077] An example of this free fitting procedure is shown in connection with Figures 3 and 4. Figure 3 shows the equilibrium response (R) of analyte at various concentrations (C).eq ) is shown. In this example, the binding behavior of the analyte is stable over a wide range of concentrations, so data is available for concentrations ranging from approximately 10 μM to 10 mM. Figure 4 shows the curve obtained from fitting the data points of Figure 3 to an interaction model, in this example Equation 13 above. The inflection point of the curve is K D provides the value of R, which in this example is equal to approximately 0.5 mM as indicated by the vertical dashed line. The asymptote of the fitted curve is R max , i.e., the maximum response value obtainable for this analyte. In the example shown, R max is found to be approximately 55 RU as indicated by the horizontal dashed line.

[0078] In this example, data are available for a wide range of analyte concentrations, so the fit shown in Figure 4 is reliable and the K determined from the fit. D and R max The values ​​of K are accurate and reproducible. However, it is not always possible to obtain data over such a wide range of analyte concentrations. In particular, the binding behavior of many analytes becomes unstable at higher concentrations due to solubility issues. For example, some analytes have a K D In such cases, only limited response data can be obtained at low analyte concentrations. An example of response data for such an analyte is shown in Figure 5. To aid comparison, Figure 5 shows only the first five data points of the plot in Figure 3. However, such a data set is representative of an analyte that exhibits solubility problems at higher concentrations.

[0079] Figure 6 shows the resulting curves from fitting the data points of Figure 5 to the same interaction model used above for Figure 4, namely the model exemplified by Equation 13. As can be seen, the K determined from this fit D and R maxThe value of K is very different from that obtained from Figure 4, where many more data points were available. D was determined to be only about 0.1 mM (see 0.5 mM in Figure 4), and R max can be seen to be around 22.5 RU (see 55 RU in Figure 4). In other words, the additional constraint or analyte saturation parameter (in this case R max The traditional approach to fitting an interaction model to response data without the constraint of D and R max In many cases, the opposite is true, and fitting can be tricky when fitting from limited data. D and / or R max In either case, K D , R max , and K. A The resulting values ​​of any additionally derived interaction parameters such as σ, ...

[0080] Therefore, as can be seen, this traditional free-fitting approach is ineffective when only limited data are available, especially when the analyte affinity is low and the K D It is unreliable when data are only available for concentrations below 0. This is problematic because many useful applications and assays, such as fragment-based drug design and fragment affinity analysis, involve low-affinity analytes, which are unlikely to be stable at high analyte concentrations. Furthermore, even if it is theoretically possible to obtain data at sufficiently high concentrations, this may result in very expensive assays due to the amount of analyte required at higher concentrations. This high cost may mean that experiments are not practically feasible in large quantities and at high throughput.

[0081] Use of fixed analyte saturation parameter values As mentioned above, one approach that has been proposed to address this issue in free-fitting techniques is to use the analyte saturation response R maxThe idea is to reduce the number of variables in the fitting procedure by estimating a fixed value for σ and using that fixed value during the fitting procedure. In other words, rather than a free fitting procedure, the fitting is constrained by setting one of the key parameters to a fixed value. This approach, exemplified in EP 2507618, relies on the use of a control analyte to first estimate the control analyte saturation parameter R maxC Then, this control parameter R maxC , the corresponding value of the analyte of interest, R maxA can be converted to R maxA can be used as a fixed value during fitting. This method is briefly explained below.

[0082] A method 700 for determining a fixed value of a saturation control parameter is shown generally in FIG. 7. In block 702, a sensor surface with immobilized ligand is provided. This can be considered a setup or configuration step that can be performed manually or automatically. The following steps (i.e., blocks 704-716) then feature a self-contained method that can be computer-implemented and executed in an automated manner by a sample analysis system. In block 704, the sensor surface is contacted with a control analyte. In block 706, the sensor response from binding of the control analyte to the binding sites of the ligand is registered. In block 708, a control saturation response (R) to the interaction between the control analyte and the ligand is calculated. maxC ) is determined.

[0083] In block 710, the relative molar response contributions of the analyte and control analyte are used to calculate the control saturation response (R maxC ) is the analyte saturation response (R maxA) in block 712. In block 712, the sensor surface is contacted with one or more samples containing different concentrations of the analyte of interest. In block 714, the sensor response from the binding of the analyte to the binding sites is registered. Finally, in block 716, the registered sensor response is converted to the analyte saturation response (R maxA ) are fitted to a predetermined interaction model (e.g., Equation 13 above).

[0084] Block 702, providing a ligand-immobilized sensor surface, involves any suitable method for immobilizing a ligand on the sensor surface within the particular biosensor being used. The immobilized ligand provides binding sites for the control analyte and the analyte being investigated. In block 704, a suitable control analyte is contacted with the sensor surface. The control analyte is preferably contacted with the sensor surface in block 708 to provide a control saturation response (R maxC ) can be easily determined. For example, the control analyte should have a low affinity K compared to the analyte under investigation. D In one suitable example, the control analyte should have a higher solubility with respect to the control saturation response (R maxC Alternatively, the control analyte may be provided at a concentration that is capable of occupying all binding sites of the ligand on the sensor surface to provide a direct determination of the control saturation response (R) from the non-steady-state interaction between the control analyte and the ligand. maxC The concentration range is provided such that the .alpha.-hydroxybenzoate (H2O) can be determined.

[0085] Control saturation response (R maxC ) as the analyte saturation response (R maxA ) in block 710, the saturation response (R max The fact that the saturation response (R ) represents the response when all binding sites are occupied is used for the conversion. max), ideally, the saturated response (R maxS ) and (R maxT ) each represent the response resulting from the same number of molecules bound to the sensor surface. Therefore, the saturation response (R maxS ) and (R maxT ) gives the relative molar response contribution of the two analytes. Therefore, given that the relative molar response contributions of the two analytes are known or can be estimated, then the saturation response (R max ) is the saturation response (R max ) can be calculated (estimated) from the control saturation response (R maxC ) as the analyte saturation response (R maxA )

[0086] As mentioned above, there are several biosensors available based on several different detection technologies, and therefore the relative molar response of two different analytes will depend on the biosensor used, but the principle remains valid. In one example, the relative molar response contribution between the analyte and the control analyte in block 710 is approximated by the molar weight ratio between the analyte and the control analyte. This approach is valid, or at least a good approximation, for any biosensor technology that directly or indirectly registers the mass of molecules bound to the sensor surface. Mathematically, this approach is characterized by Equation 16 below:

number

[0087] In one example, the dissociation rate of the control analyte is high enough to reach complete dissociation within a reasonable time frame after exchanging the control analyte for a sample, e.g., buffer, that does not contain an analyte capable of binding to the sensor surface. Alternatively, a regeneration step may be required to free all binding sites between the analytes. In some examples, the saturation response (R) calculated for each analyte is used as a saturation response (R) as shown at reference numeral 718 in Figure 7. maxA ), steps 704-708 may be repeated after steps 710-716 have been performed for a predetermined number of analytes to detect and / or correct for possible degradation of the sensor surface over time, as shown by reference numeral 720 in FIG.

[0088] The method 700 of FIG. 7 offers an improvement over conventional free-fitting methods. Nevertheless, as noted above, the inventors have determined that this method is still far from perfect. One drawback of this method is that it is entirely dependent on finding a control analyte that can act as a good surrogate for the analyte of interest. Finding a good control analyte is often impossible, or at least requires a lot of experimental work. Furthermore, even if a good control analyte could be found, the inventors have found that the estimated interaction parameters (e.g., K) determined by this method are not reliable. D , K. A ) is not always completely reliable in practice. We believe that this inaccuracy is due to the fixed parameters determined, i.e., R used in the fitting procedure of block 716. maxA We recognized that this was due to the method in Figure 7, which relies too heavily on a fixed value of R maxA is R maxC This approach allows for the determination of the correspondence between analyte and control analyte retention, as well as R maxCThe accuracy of the R is highly dependent on the precision and accuracy of the experiments performed to determine R (i.e., blocks 702-708 of FIG. 7). If there is inaccuracy in any of these aspects, i.e., with respect to the correspondence (or lack thereof) between the analyte and the control analyte, and / or R maxC If there is uncertainty regarding the precision and reliability of the experiment to determine R maxA This will directly affect the decision of K D and K. A This makes the subsequent determination of interaction parameters such as

[0089] Considering these issues, neither the free-fitting method nor the more tightly controlled method of Figure 7 achieves satisfactorily accurate measurements in all cases. We have identified a new method that is believed to improve the reliability of interaction parameter estimation and does not rely on a control analyte. The details of this method are described here.

[0090] New method: Fitting with upper and lower bounds We have determined that the interaction parameters are likely to be determined more accurately if the fitting process is constrained by intervals, i.e., upper and lower limits or boundaries, applied to the analyte saturation parameters used in the fitting. This approach is significantly different from the free fitting approach (where there are no constraints on the analyte saturation parameters) and the method in Figure 7 (which uses the analyte saturation parameter R maxA7 ) (which is tightly constrained by a fixed value of ). We believe, surprisingly, that having a "looser" constraint on the fitting procedure compared to the approach of FIG. 7 provides a more accurate estimate of the interaction parameters. This is because, as we have determined, the fitting algorithm is able to more effectively balance fitting to the actual response data with taking into account known or estimated approximations of the analyte saturation parameters. Our approach is able to balance these competing requirements in a way that is not possible using either the free-fitting approach or the approach of FIG. 7 .

[0091] The inventors also found that, advantageously, the disclosed approach is generalizable and the analyte saturation parameter can be calculated using the analyte saturation response (R max ) and the analyte saturation parameter is the target occupancy at saturation (To sat We confirmed that the method can be used in both cases, where the saturation response is expressed in terms of σ (σ), and where the saturation response is expressed in terms of σ (σ). This enhances the versatility of the method, allowing it to be applied whether the results of the experimental assay and associated interaction model are expressed in terms of saturation response or target occupancy. Furthermore, the disclosed approach does not rely on the use of a control analyte, since theoretical values ​​of the analyte saturation parameters can be used instead if a control analyte is not readily available (see Figure 7).

[0092] The disclosed novel methodology is shown schematically in FIG. 8, which describes a method 800 for determining one or more interaction parameters associated with the interaction between an analyte and a ligand. In block 802, a sensor surface with immobilized ligands is provided. This can be considered a setup or configuration step that can be performed manually or automatically. The following steps (i.e., blocks 804-808) then characterize a self-contained method that can be computer-implemented and executed in an automated manner by a sample analysis system. In block 804, the sensor surface is contacted with one or more samples containing the analyte. If only a single sample is used, the concentration of the sample is determined by the K of the analyte to enable an effective fitting procedure. D This is because the affinity of the antibody is high (low K D ) is possible for most interactions with D is still a manageable low concentration from the standpoint of solubility.

[0093] However, preferably block 804 involves contacting the sensor surface with three or more samples of analyte, each sample having a different analyte concentration. This is beneficial in terms of improving reliability because K D Low affinity (high K) analyte concentrations approaching (let alone double the amount) are difficult or impossible to achieve. D ) can be more effectively studied. In instances where three or more samples of analyte are provided, block 804 involves contacting at least three concentrations of analyte (provided in the three or more samples) with the sensor surface to generate concentration-series data. The inventors have determined that using between 5 and 11 concentrations (provided in between 5 and 11 samples) is advantageous in balancing the need to generate useful data with practical considerations and cost.

[0094] In block 806, a sensor response indicative of analyte binding to the ligand's binding site is registered. In other words, a sensor response is recorded for each analyte concentration. Typically, the recorded response is the maximum response, i.e., equilibrium (steady-state) response, of each sample contacted with the sensor, which can be obtained (readout) when the sensorgram response for that concentration is substantially parallel to the x-axis. The considerations and implementation details discussed above in the general description of sensors and analyte-ligand interactions, as well as related comments related to blocks 702 through 706 of method 800 of FIG. 7, apply equally to blocks 802 through 806 of FIG. 8.

[0095] In block 808, the registered sensor response is fitted to an interaction model (e.g., Equation 13 above) to determine one or more interaction parameters associated with the interaction between the analyte and the ligand. In accordance with the disclosed invention, the fitting is constrained by predetermined upper and lower bounds associated with the interaction model and applied to the analyte saturation parameters. As noted above, this is achieved by fitting K D and K. A It is believed that this provides improved accuracy in determining interaction parameters such as R. The fitting procedure of block 808 is similar to the fitting procedure of block 716, and the fitting of block 808 max or To sat The same considerations apply, except that the method is constrained by upper and lower limits of the analyte saturation parameters such as , and does not use fixed values. It will be appreciated that the method may include an additional step of accepting said upper and lower limits, for example as user input. This step may occur at any point in the process of FIG. 8.

[0096] In one implementation, the fitting procedure of block 808 involves applying a best-fit estimate, e.g., a least chi-square estimate, to the registered sensor response within the constraints set by the interaction model and upper and lower limits. For example, if the interaction model is represented by Equation 12 or Equation 13 above, the response data represents the equilibrium response at each analyte concentration, and the fitting procedure finds a curve that best fits the response data. In other words, the response data showing the response at equilibrium (on the y-axis) versus concentration (on the x-axis) can be plotted and fitted to Equation 12 or Equation 13 above. More specifically, R max and K. D The values ​​of σ can be iteratively "guessed" and each set of guessed values ​​can be fitted to the interaction model described in the above equation. The analyte saturation parameter (in this case, R max ) and the fitting procedure considers the upper and lower bounds set for R max Only fittings that are test values ​​of σ are permitted to be considered. The quality of the fitting (or "closeness") of each set of estimates can then be determined, for example, using chi-squared estimation as described above. The best fitting value can then be determined to be the best estimate of the interaction parameter in question.

[0097] As mentioned above, the method of the present disclosure involves R max In some cases, the analyte saturation response is instead calculated as the target occupancy at saturation, To sat As mentioned above, R A =R max In this case, To sat is the target occupancy and R max and To A are linked by the following:

number

number

[0098] When using this interaction model, response data showing target occupancy (on the y-axis) versus concentration (on the x-axis) can be expressed as Equation 18 and To sat can be plotted and fitted according to the methods of the present disclosure based on upper and lower bounds applied to

[0099] As briefly discussed above, the upper and lower bounds that constrain the fitting can be set in a variety of ways. In one example, the system accepts user input indicating the upper and lower bounds. The user input can take a variety of forms. In one implementation, the interaction model used for fitting is R max The biosensor system can be configured to record response values ​​in RU for different concentrations of the analyte. The user may then set the upper and lower limits as point (e.g., integer) values ​​in RU. For example, the user may determine from past experience that the R max The user may know that the maximum occupancy is likely to be approximately 20 RU. Thus, the user may, for example, set the upper limit to a point value of 25 RU and the lower limit to a point value of 15 RU. The same applies if the system is configured for target occupancy, and the user may enter upper and lower limits for the target occupancy, for example, 110% (or 1.1 as a decimal) for the upper limit and 90% (or 0.9 as a decimal) for the lower limit.

[0100] Alternatively, the system can assist the user in setting upper and lower limits by providing initial default values ​​for the analyte saturation parameters. In this case, user input determines how far the fitting process is allowed to deviate from the default parameter values. In other words, the user specifies values ​​lower than the default parameters (i.e., lower limits) and higher than the default parameters (i.e., upper limits), which then constrain the fitting in the manner described above. The upper and lower limits can be defined in unit values ​​(e.g., + / - a certain number of RU units) or percentages. For example, if the system determines that the R max R max Consider a case where the preset default value for R is 20RU. User input can then indicate upper and lower limits for this default value. For example, the user can set the upper limit as +2RU and the lower limit as -2RU. In this example, the fitting process is max The fitting is constrained to fittings where the RU is between 22 and 18 RU. Alternatively, input may be provided as a percentage. For example, the user may set the upper limit to 120% and the lower limit to 80%. In this example, the fitting process will constrain fittings where the RU is between 24 RU and 16 RU. max These values ​​represent 120% and 80% of the default value of 20 RU, respectively. A similar approach can be applied when the system is operated with respect to a target occupancy.

[0101] In implementations where default analyte saturation parameters are used as starting points around which upper and lower limits are set, various mechanisms for determining said default values ​​are available. Two primary means for determining default parameter values ​​are available: theoretical estimation and experimental estimation. As explained below, either approach can be used to determine default starting points for analyte saturation parameters, and both approaches are suitable for different situations.

[0102] The first mechanism for determining a default value for the analyte saturation parameter is to determine a theoretical estimate. This can be done based on the molecular weight of the analyte and ligand, as well as the immobilization level of the ligand. In particular, in one example, R max A theoretical estimate of can be calculated based on

number

[0103] The theoretically estimated analyte saturation parameter, i.e., R max_theo is used as a default value to which the upper and lower limits are set in the above method. The advantage of this theoretical determination of the default or starting value of the analyte saturation parameter is that there is no requirement for a control analyte to be used. Only the molecular weights of the ligand and analyte and the level of immobilization need to be determined. R max_theo can also be expressed in terms of target occupancy, as discussed above.

[0104] A second mechanism for determining default values ​​for analyte saturation parameters is to determine estimates experimentally using control analytes. In this case, R max is first determined for the control analyte. The control analyte value is then converted to the corresponding value for the analyte of interest. Details of how this procedure can be performed are described above in connection with Figure 7. The determined R maxA Values ​​can also be expressed in terms of target occupancy as described above (e.g., To satA ). R maxA or To satA Once determined, this value is used as the default analyte saturation parameter value to which upper and lower limits are set.

[0105] In various implementations, embodiments of the present invention may include free fitting parameters and / or constant parameters (e.g., R max ) instead of one or more models that use constrained bounds (e.g., constrained R max Techniques may be provided that enable the use of models (having

[0106] However, other alternatives are that the model controls are of low quality or the theoretical parameters (e.g., R max ), if you must use (e.g., R max (about) provides a possible workflow in which a wide range of such parameters can be set. For samples that do not provide good results, this can be done by systematically adjusting the K D This helps avoid erroneous determination of the parameter range, but samples that provide good results may have results closer to those of the free-fitting model. Furthermore, if the model control is of sufficiently high quality, the parameter range can be set narrower than the previous wide parameter range. In this way, various such embodiments provide the advantage that the user does not need to evaluate which fitting procedure to use for each series of samples to be analyzed, but simply sets the range for the entire evaluation.

[0107] Various such ranges may be predetermined, automatically generated (e.g., dynamically), or user-defined as needed. Thus, automated software switching between various models (e.g., free-fitting, constrained, or using constant parameters) can be provided depending on the sample being analyzed, to provide optimal analysis without having to manually determine which model is best in a given situation.

[0108] For example, a constant model generally yields an expected R max It performs better when is accurately determined, especially for low responses, but the expected R max R maxExpected R as determined from control / input or ligand levels max The question remains as to how accurately R can be determined. max Users can be encouraged to use controls / inputs. max Normal variation in controls / inputs (e.g., R max About K D When determining the R max Analysis has shown that using R is as good as or better than using a constrained model with low response. However, max When there are outliers in the inputs, there can be >>20% of the correct values, in which case the use of a constrained model is preferable. Therefore, switching between different models used can be a function of the response and / or R max It may be triggered depending on the value etc.

[0109] The above-described methods of the present disclosure can be advantageously implemented by a sample analysis system, such as a biosensor. An exemplary sample analysis system that can be used to implement the disclosed methods is the BIACORE® T200 instrument manufactured by Cytiva. This instrument is shown in FIG. 9. It will be understood that this is a non-limiting example of a biosensor system, and that the components described below can be generalized to other forms of biosensor systems. More generally, the disclosed methods can be incorporated into and applied to a wide range of other biosensor systems that operate under similar principles and are not limited to implementation in a BIACORE® system.

[0110] The analysis system 900 includes a sample compartment 902, shown in a closed position in FIG. 9 . The sample compartment 902 can be opened to allow a sample solution (typically held in one or more sample reservoirs, such as vials) to be inserted into the compartment 902. When sealed, an observation window 906 allows the sample solution in the compartment 902 to be viewed from the outside. During analysis, a sample delivery system (also called a fluid handling system) delivers the sample solution from the sample reservoir held in the sample compartment 902 to the sensor surface. At its simplest, the sample delivery system comprises at least one pump and a network of fluid channels for transporting sample and running buffer through the analysis system 900 to the sensor. The sensor can be inserted and removed before and after analysis via a sensor chip port 904. A continuous running buffer can be provided from a buffer reservoir 908 via a buffer pump (not shown) housed in a buffer pump compartment 910. Waste can be collected in a waste reservoir 912. The methods disclosed herein can be performed by a computer system (not shown) housed within or connected to the analysis system 900.

[0111] 10, which shows a schematic and simplified representation of a computing device 1001 that can be used to perform the methods described herein, alone, in combination with other computing devices, or as part of a "cloud" computing configuration. For example, computing device 1001 may form part of analysis system 900 or may be connected to analysis system 900 (via a wireless or wired connection) and configured to cause analysis system 900 to perform the various methods disclosed herein. Computing device 1001 may also perform analysis of readings or information generated by sensors of analysis system 900.

[0112] The computing device 1001 in the illustrated example comprises various data processing resources, such as a processor 1002 (particularly a hardware processor), coupled to a central bus structure. Additional data processing resources, such as a memory 1004, are also connected to the bus structure. A display adapter 1006 connects a display device 1008 to the bus structure. One or more user input device adapters 1010 connect user input devices 1012, such as a keyboard and / or mouse, to the bus structure. One or more communications adapters 1014 are also connected to the bus structure to provide connectivity to other computing systems 1001 and other networks. The computing device 1001 may be a local computer or a server. It may be a standalone element or part of existing computing hardware for use in a sample analysis laboratory.

[0113] During operation, the processor 1002 of the computer system 1001 executes a computer program including computer-executable instructions, which may be stored in the memory 1004. When executed, the computer-executable instructions cause the computer system 1001 to perform one or more of the methods described herein in the sample analysis system 900. Results of processing (e.g., a sensorgram, as shown in FIG. 2 ) may be displayed to a user via the display adapter 1006 and the display device 1008. User input for controlling the operation of the computer system 1001 may be received from a user input device 1012 via a user input device adapter 1010.

[0114] It will be apparent that some features of the computer system 1001 shown in Figure 10 may not be present in certain cases. For example, one or more of the computer devices 1001 may not require a display adapter 1006 or a display device 1008. This may be true, for example, for certain computer devices 1001 that are used solely for their processing power and do not need to directly display information to a user. Similarly, the user input device adapter 1010 and the user input device 1012 may not be required. In its simplest form, the computer device 1001 comprises a processor 1002 and a memory 1004.

[0115] As mentioned above, the described methods can be implemented using computer-executable instructions. A computer program product or computer-readable medium can comprise or store the computer-executable instructions. The computer program product or computer-readable medium can comprise a hard disk drive, flash memory, read-only memory (ROM), CD, DVD, cache, random access memory (RAM), and / or any other storage medium in which information is stored for any period of time (e.g., long-term, permanent, short-term, temporary buffering, and / or caching of information). A computer program can comprise the computer-executable instructions. A computer-readable medium may be a tangible or non-transitory computer-readable medium. The term "computer-readable" encompasses "machine-readable."

[0116] Although various specific combinations of components and method steps are described, these are merely examples. Components and method steps can be combined in any suitable arrangement or combination. Components and method steps may be omitted to leave any suitable combination of components or method steps.

[0117] The singular terms "a" and "an" should not be construed to mean "only one." Rather, unless otherwise specified, they should be construed to mean "at least one" or "one or more." The word "comprising" and its derivatives, including "comprises" and "comprise," includes each of the features listed but does not exclude the inclusion of one or more additional features.

[0118] The above-described implementations have been described by way of example only, and the described implementations are to be considered in all respects only as illustrative and not restrictive. It will be understood that modifications can be made to the described implementations without departing from the scope of the present disclosure. It will also be apparent that there are many variations not described but which are within the scope of the appended claims. [Explanation of symbols]

[0119] 101 Sensor Chip 102 Gold film 103 Ligand 104 Analyte 105 Flow path 106 Monochromatic p-polarized light 107 Light source 108 Prism 109 Glass / metal interface 110 Reflected Light Beam 111 Optical detection unit 700 Methods, Techniques 800 ways 900 Analysis System 902 Sample compartment 904 Sensor chip port 906 Observation window 908 Buffer Reservoir 910 Buffer Pump Compartment 912 Waste Reservoir 1001 Computer equipment, computer systems 1002 processor 1004 memory 1006 Display Adapter 1008 Display Device 1010 User Input Device Adapter 1012 User Input Devices 1014 Communication adapter

Claims

1. 1. A method (700) for determining one or more interaction parameters associated with an interaction between an analyte and a ligand, comprising: contacting the ligand-immobilized sensor surface with one or more samples containing an analyte (702); registering (706) a sensor response indicative of binding of the analyte to the binding site of the ligand; fitting (716) the registered sensor responses to an interaction model to determine one or more interaction parameters associated with the interaction between the analyte and the ligand, a step (716) in which the fitting is constrained by predetermined upper and lower limits applied to analyte saturation parameters associated with the interaction model; A method (700) comprising:

2. 10. The method (700) of claim 1, wherein the sensor surface is contacted with three or more samples containing different concentrations of analyte.

3. The one or more interaction parameters are: The binding equilibrium constant K Ａ and The dissociation equilibrium constant K of the interaction Ｄ 3. The method (700) of claim 1 or 2, comprising one or more of:

4. The analyte saturation parameter is an analyte saturation response value R ｍａｘ The method (700) of any one of claims 1 to 3, wherein:

5. The analyte saturation parameter is the target occupancy value at saturation, To ｓａｔ The method (700) of any one of claims 1 to 3, wherein:

6. determining (708) an estimate of the analyte saturation parameter; setting at least one of the upper and lower limits based on the estimate of the analyte saturation parameter; The method (700) of any one of claims 1 to 5, further comprising:

7. 7. The method (700) of claim 6, wherein at least one of the upper and lower limits is set as a percentage of the estimated value of the analyte saturation parameter.

8. The method (700) of claim 7, wherein the lower limit is set to between 75% and 99% of the estimated analyte saturation parameter.

9. The method (700) of claim 7, wherein the lower limit is set between 25% and 99% of the estimated analyte saturation parameter.

10. The method (700) of claim 7, 8 or 9, wherein the upper limit is set between 101% and 150% of the estimated analyte saturation parameter.

11. The method (700) of claim 7, 8 or 9, wherein the upper limit is set between 101% and 250% of the estimated analyte saturation parameter.

12. determining an estimate of the analyte saturation parameter; The molecular weight Mw of the ligand ｌｉｇ , The molecular weight Mw of the analyte ａｎａ and Ligand response, R ｌｉｇ 12. The method (700) of any one of claims 6 to 11, comprising the step of calculating a theoretical estimate of said value based on:

13. determining an estimate of the analyte saturation parameter; contacting the sensor surface with a control analyte (704); registering (706) a sensor response indicative of binding of the control analyte to the binding site of the ligand; determining (708) a control analyte saturation parameter for said control analyte; calculating the estimate of the analyte saturation parameter based on the determined control analyte saturation parameter and the molar weight ratio between the analyte and the control analyte; 12. The method (700) of any one of claims 6 to 11, comprising:

14. The method (700) of any one of claims 1 to 13, further comprising accepting a user input to set the upper and lower limits.

15. 15. The method (700) of any one of claims 1 to 14, wherein the fitting step comprises applying a least chi-squared estimate to determine closeness of the fit to the registered sensor responses.

16. The predetermined interaction model is formula [Equation 1] Including, where: R ｅｑ is the registered sensor response at equilibrium, C is the analyte concentration, R ｍａｘ is the analyte saturation parameter, K Ｄ is the dissociation constant of the interaction, 16. The method (700) of any one of claims 1 to 15.

17. 17. The method (700) of claim 16, wherein the predetermined interaction model further comprises an offset term to compensate for parallel baseline displacement due to refractive index errors in the system.

18. 18. The method (700) of any one of claims 1 to 17, wherein the step of registering a sensor response indicative of binding of the analyte to the binding site of the ligand is based on surface plasmon resonance (SPR).

19. 18. The method (700) of any one of claims 1 to 17, wherein the step of registering a sensor response indicative of binding of the analyte to the binding site of the ligand is based on evanescent wave sensing.

20. A sample analysis system (900) for determining an interaction parameter, comprising: a sensing surface configured to detect a binding interaction between an analyte and a ligand at the sensing surface; a computing device configured to cause the sample analysis system to perform the method of any one of claims 1 to 19; A sample analysis system (900) comprising:

21. A computer configured to cause a sample analysis system to carry out the method of any one of claims 1 to 19.

22. A computer readable storage medium comprising instructions that, when executed by a computer, cause the computer to cause a sample analysis system to perform the method (700) of any one of claims 1 to 19.

23. 20. A computer program comprising instructions that, when said program is executed by a computer, cause said computer to cause a sample analysis system to perform the method (700) of any one of claims 1 to 19.

Images