METHOD AND APPARATUS FOR ASSAYING A DRUG CANDIDATE TO ESTIMATE A PHARMACOKINETIC PARAMETER ASSOCIATED THEREWITH

Abstract: A method and apparatus for assaying a drug candidate with a biosensor having one or more sensing surface-bound biomolecules associated therewith are disclosed. The method comprises the steps of measuring the binding interaction between the drug candidate and the one or more sensing surface-bound biomolecules of the bisensor to obtain an estimate of at least one binding interaction parameter of the drug candidate, and then comparing the estimated binding interaction parameter against a mathematical expression correlated from binding interaction data associated with known drug compounds to determine an estimate of at least pharmacokinetic parameter of absorption, distribution, metabolism, or excretion (ADME) that is related to the drug candidate. The present invention allows for the simultaneous measurement of different pharmacokinetic parameters of the drug candidate, as well as an indication of the drug candidate's solubility, by use of a single analytical instrument. The pharmacokinetic data may be represented as a ADME characterization profile; such ADME profiles are of great utility for purposes of drug screening and lead optimization.

Description: METHOD AND APPARATUS FOR ASSAYING A DRUG CANDIDATE TO ESTIMATE A PHARMACOKINETIC PARAMETER ASSOCIATED THEREWITH TECHNICAL FIELD This invention is generally directed to a method and apparatus for assaying a drug candidate and, more specifically, to a method for measuring the binding interaction between a drug candidate and sensing surface-bound biomolecules of a biosensor to determine a binding interaction parameter of the drug candidate, and then comparing the binding interaction parameter against a predetermined drug correlation graph (e.g., a mathematical expression) to estimate at least one pharmacokinetic parameter. BACKGROUND OF THE INVENTION A variety of experimental techniques are currently used to determine chemical, physical and biological properties associated with low molecular weight substances, particularly in the context of drug discovery. For example, researchers are often concerned with deteirnining a variety of chemical, physical and biological properties associated with drug candidates for screening purposes. The determination of such properties often plays a pivotal role in the drug development and screening process. More specifically, it has long been recognized that the intensity and duration of the pharmacological effect of a systemically acting drug are functions not only of the mtrinsic activity of the drug, but also of its absorption, distribution, metabolism, and excretion (ADME) characteristics within the human body. These so-called ADME characteristics are all intimately related to the scientific discipline known as "pharmacokinetics." Pharmacokinetics is commonly referred to as the study of the time courses (i.e., kinetics) associated with the dynamic processes of ADME of a drug and/or its metabolites within a living organism, and is closely interrelated with the fields of biopharmaceutics, pharmacology, and therapeutics. Because the body delays the transport of drug molecules across membranes, dilutes them into various compartments of distribution, transforms them into metabolites, and eventually excretes them, it is often difficult to accurately predict the pharmacological effect of promising new drug candidates. Researchers, however, commonly use pharmacokinetic ADME studies as one method to predict the efficacy of a drug at a site of action within the body. Traditionally, researchers involved with pre-clinical ADME studies have used pharmacokinetic/mathematical models coupled with actual drug concentration data from blood (or serum or plasma) and/or urine, as well as concentration data from various tissues, to characterize the behavior and "fate" of a drug within living organisms. As is appreciated by those skilled in the art, the mathematical equations associated with pharmacokinetics are generally based on models that conceive the body as a multicompartmental organism. In such models it is presumed that the drug and/or its metabolites are equitably dispersed in one or several fluids/tissues of the organism. Any conglomerate of fluid or tissue which acts as if it is kinetically homogeneous may be termed a "compartment." Each compartment acts as an isotropic fluid in which the molecules of drug that enter are homogeneously dispersed and where kinetic dependencies of the dynamic pharmacokinetic processes may be formulated as functions of the amounts or concentrations of drug and metabolites therein. Stated somewhat differently, the conceptual compartments of the body are separated by barriers that prevent the free diffusion of drug among them; the barriers are kinetically definable in that the rate of transport of drug or metabolite across membrane barriers between compartments is a function of, for example, the amounts or concentrations of drug and metabolites in the compartments, the permeability of various membranes, and/or the amount of plasma protein binding and general tissue binding. More specifically, pharmacokinetic/mathematical models are commonly used by pharmacokineticists to represent drug absoφtion, distribution, metabolism, and excretion as functions of time within the various tissues and organs of the body. In such models, the movement of the aclministered drug throughout the body is concisely described in mathematical terms (e.g., a set of differential equations). The predictive capability of such models lies in the proper selection and development of mathematical functions that parameterize the essential factors governing the kinetic process under consideration. For example, a drug that is adrninistered by intravenous injection may be assumed to distribute rapidly in the bloodstream. A pharmacokinetic/mathematical model that describes this situation may be a tank containing a volume of fluid that is rapidly equilibrated with the drug. Because a fraction of the drug in the body is continually eliminated as a function of time (e.g., excreted by the kidneys and metabolized by the liver), the concentration of the drug in the hypothetical tank may be characterized by two parameters: (1) the volume of fluid in the tank that will dilute the drug, and (2) the elimination rate of the drug per unit of time, both of which are generally considered to be constant. Thus, if a known set of drug concentrations in the tank is determined at various time intervals by, for example, sampling, then the volume of fluid in the tank and rate of drug elimination may be estimated. This information may then, in turn, be used for predicting the disposition of the drug within a human body. Theoretically, an unlimited number of models may be constructed to describe the kinetic processes of drug absoφtion, distribution, metabolism, and excretion within the various tissues and organs of the human body. In general, however, the number of useful models is limited due to practical considerations associated with blood, tissue and/or organ sampling. As a result, and as is appreciated by those skilled in the art, two major types of models have been developed by pharmacokineticists: (1) compartmental models; and (2) physiologic models. In pharmacokinetic compartmental models, the body is represented as a series of compartments that communicate reversibly with each other. Each compartment is not a real physiological or anatomic region; rather, each compartment is considered to be inclusive of all tissues that have similar blood flow and drug affinity . For example, a compartmental model may consist of one or more peripheral compartments representing tissue(s) connected to a central compartment representing the blood stream. Conceptually, the drug moves dynamically into and out of the central compartment and into and out of each of the peripheral compartments. As such, rate constants may be used to represent the overall rate process for the drug's disposition within each compartment. Compartment models are generally based on linear assumptions using linear differential equations, and are particularly useful when there is little information known about the tissues and their respective drug concentrations. In contrast, pharmacokinetic physiologic models are based on known anatomic and physiologic data, data which is kinetically described in view of the actual blood flow volumes responsible for distributing the drug to the various parts of the body. Because there are many tissue organs in the body, each tissue volume must be estimated and its drug concentration and rate of change described mathematically (tissues having similar blood perfusion properties, however, are typically grouped together). Unfortunately, much of the information required to adequately describe such pharmacokinetic physiologic models are often very difficult to obtain experimentally. Nevertheless, such physiologically based models are commonly used in conjunction with animal data and interspecies scaling techniques to predict the drug's disposition within a human body. More importantly, however, is that pharmacokinetic/mathematical models, and knowledge of their associated ADME parameters play an extremely important role in drug discovery and development. A typical example is a drug that is active following intravenous administration but is considerably less active after comparable oral doses. Having appropriate pharmacokinetic information may reveal (1) whether the drug was poorly absorbed to yield sub-therapeutic circulating levels, or (2) whether the drug experienced pre-systemic metabolism to an inactive metabolite. Such information may also provide guidance for subsequent decisions, such as (1) whether to improve drug absoφtion by altering the salt form or formulation, (2) whether to investigate the possibility of making prodrugs, or (3) whether to consider a parenteral route of administration. In addition to the foregoing, pharmacokinetic/mathematical models are also generally considered extremely useful for, among other things: (1) predicting plasma, tissue, and urine drug levels with any dosage regimen; (2) calculating the optimum dosage regimen for an individual patient; (3) estimating the possible accumulation of drugs and/or metabolites; (4) correlating drug concentrations with pharmacologic and toxicologic activity (i.e., pharmacodynamics); (5) evaluating differences in the rate or extent of availability between formulations (i.e., bioequi valence); (6) describing how changes in physiology or disease affect the absorption, distribution, and/or elimination of the drug; and (7) explaining drug-drug and food-drug interactions. Lastly, pharmacokinetic ADME data has also become an integral part of the pharmacological characterization process of promising new drug candidates. Regulatory agencies, such as the U.S. Food and Drug Administration, now require (1) a determination of pharmacokinetic ADME data in Phase I drug studies, and (2) a submission of pharmacokinetic ADME data as part of a New Drug Application. In this context, such pharmacokinetic ADME data is deemed essential for predicting the behavior and fate of the drug candidate within the human body. Accordingly, there is a need in the art for improved methods for determining one or more pharmacokinetic parameters associated with absoφtion, distribution, metabolism, and excretion of a drug candidate. There is also a need for apparatuses useful for carrying out such methods. The present invention fulfills these needs and provides further related advantages. SUMMARY OF THE INVENTION In brief, the present invention is directed to a method and apparatus for assaying a drug candidate. More specifically, this invention discloses a method for measuring the binding interaction between a drug candidate and sensing surface- bound biomolecules of a biosensor to determine a binding interaction parameter of the drug candidate, and then comparing the binding interaction parameter against a predetermined drug correlation graph to estimate at least one pharmacokinetic parameter of the drug candidate. The at least one pharmacokinetic parameter may, for example, be one or more of ADME. In another embodiment of the present invention, at least two pharmacokinetic parameters of the drug candidate are determined, and in yet another embodiment, at least one pharmacokinetic parameter and a solubility property of the drug candidate are determined. Such pharmacokinetic parameters and/or solubility property may be determined when the one or more sensing surface-bound biomolecules are selected from, for example, liposomes, plasma proteins, CYP 450 enzymes, metabolic enzymes, or transport proteins. The biosensor used in the practice of the present invention may utilize a mass- sensing technique, such as surface plasmon resonance. In addition, the biosensor may further employ a sensor chip, wherein the sensor chip comprises a free electron metal that includes a sensor surface, wherein the free electron metal is copper, silver, aluminum or gold. The sensor chip may further comprise a hydrogel coupled to the sensor surface, wherein the hydrogel has a plurality of functional groups, and wherein the one or more sensing surface-bound biomolecules are covalently bonded to the hydrogel. In a more specific embodiment, a sensor surface adopted for use with a biosensor is disclosed. The sensor surface comprises a hydrogel matrix coating coupled to a top surface of the sensor surface, wherein the hydrogel matrix coating has plurality of functional groups. At least two different liposomes are bonded to the plurality of functional groups at discrete and noncontiguous locations on the hydrogel matrix coating of the sensor surface. In one embodiment, the sensor surface is a sensor chip, and a free electron metal is inteφosed between the hydrogel matrix and the top surface of the sensor surface. In another embodiment of this invention, an apparatus is disclosed for assaying a drug candidate, wherein the apparatus comprises a biosensor having one or more sensing surface-bound biomolecules associated therewith and capable of measuring at least one binding interaction parameter of the drug candidate, and a computer memory containing a data structure for comparing the at least one binding interaction parameter against at least one mathematical expression correlated from binding interaction data associated with known drug compounds to determine an estimate of at least one pharmacokinetic parameter of the drug candidate. In yet a further embodiment, a computer memory containing a data structure useful for assaying a drug candidate in accordance with the methods of the present invention is disclosed (as well as a generated data signal conveying the same). The data structure may be used to determine an estimate of at least pharmacokinetic parameter of the drug candidate. These and other aspects of the present invention will be evident upon reference to the following detailed description and related Figures. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1A illustrates a sensorgram reflective of the steady-state binding levels associated with a selected drug-biomolecule interaction. Figure IB illustrates an enlarged portion of the sensorgram of Figure 1 A and shows sensorgram irregularities. Figure 1C illustrates an enlarged portion of the sensorgram of Figure 1 A, wherein bulk-refractive index effects have been eliminated. Figure 2A illustrates a single "dip" depicting the reflected light intensity associated with a homogeneous sensor surface. Figure 2B illustrates a number of "dips" depicting the non- averaged reflected light intensities associated with a non-homogeneous sensor surface. Figure 2C illustrates a broadening of the "dip" depicting the averaged reflected light intensities associated with a non-homogeneous sensor surface. Figure 3 shows a high-level block diagram of an exemplary computer system for assaying a drug candidate in accordance with the methods of the present invention. Figure 4 depicts a correlation graph having known KD values for known compounds plotted along the abscissa (i.e., the x-axis) and corresponding measured KD values obtained via the biosensor plotted along the ordinate (i.e., the y-axis) for nine drugs with known levels of plasma protein binding Figure 5 depicts a correlation graph having respective binding levels at 100 μM (R 100 μM) divided by the molecular weight of known drug compounds' plotted along the abscissa (i.e., the x-axis) and corresponding human serum albumin bmding percentage, as measured by equilibrium dialysis, plotted along the ordinate (i.e., the y-axis). Figure 6 depicts reference subtracted sensorgram traces for each of three drug candidates A-C. Figure 7 depicts a correlation graph having known fraction absorbed in humans (FA%) plotted along the ordinate (i.e., the y-axis) and corresponding calibrated (i.e., reference subtracted) steady state binding levels for each drug at 500 μM plotted along the abscissa in a 10-logarithm scale (i.e., the x-axis). Figure 8 depicts a general principal for interaction analysis. A sensor chip surface is placed in contact with the IFC (Integrated Fluidic Cartridge) and the detection unit. Continuous buffer flows through the IFC and over the surface. Samples are injected over the surface, using the autoinjector, and refractive index changes are detected by the detection unit. Figures 9a-9d depict the following: (a) Injection of delavirdine over reference and HSA flowcell. A high response level was obtained since the DMSO concentration of sample and assay buffer was not exactly matched in sample preparation, (b) Reference data subtracted from HSA data, (c) A series of 8 calibration solutions (running buffer with different DMSO concentrations) were run at the beginning and end of each experiment. The responses of the calibration solutions, obtained from the reference surface, covered a range from - 500 to +1000 RU relative to the baseline. A DMSO calibration curve was created by plotting difference in response between HSA and reference flowcell (DMSO correction factor) versus response in reference flowcell. (d) Compound responses were corrected for DMSO bulk differences via the calibration curves. Figure 10 illustrates a dose-response curves of six compounds binding to HSA. In (a) and (b) warfarin and naproxen displays mono-phasic binding curves obtained by fitting data to a steady state model. In (c) and (d) binding of digitoxin and ritonavir appears to be biphasic. Quinine and rifampicin give linear binding curves, (e) and (f). Curves in (c-f) were obtained by four- parameter fits and do not represent Krj fits. Figure 11 depicts a biosensor data of compounds binding to HSA correlated to data obtained with other techniques, references in Table 7. (Literature data for each compound varies depending upon experimental conditions). Figure 12 illustrates a ranking of binding levels of 19 compounds. The same set of compounds were ranked on the same sensor surface, in duplicate, on the day of HSA immobilization (shown) and in duplicate a week later (not shown). Diazepam and warfarin were used as marker compounds to group HSA binding levels into Low, Medium or High. Warfarin was injected early, in the middle and late in each assay as a control of the HSA binding activity. L, M and H represent the classification into Low, Medium or High level binders based on reported literature levels of % bound. Figure 13 illustrates the biosensor data of AGP binding correlated to data obtained with other techniques, references last in Discussion. Prior to immobilisation AGP (Sigma G-9885) was modified with PDEA (2-(2- pyridmyldit o)ethaneamine hydrocloride from Biacore). AGP (200 ug/ml in 10 mM Na-citrate pH 3.6) was then immobilised to a level of 7000 RU using thiol coupling (Lδfas et al., "Methods for Site Controlled Coupling to Carboxymethyldextran Surfaces in Surface Plasmon Resonance Sensors," Biosensors & Bioelectronics, 70:813-822 (1995)). All other experimental conditions were the same as for the HSA correlation study in Figure 10. Figure 14 depicts CYP2D6 and CYP2C19 immobilized in flow cells 3 and 2 to 13000 and 11000 RU, respectively. Figure 15 depicts CYP2E1 and CYP3A4 immobilized in flow cells 2 and 3 to 8500 and 7300 RU, respectively. DETAILED DESCRIPTION OF THE INVENTION The present invention is directed to a method for assaying a drug candidate and, more specifically, to a method and apparatus for measuring the binding interaction between at least one drug candidate and sensing surface-bound biomolecules of a biosensor to determine a binding interaction parameter of the drug candidate, and then comparing the binding interaction parameter against a predetermined drug correlation graph (e.g., a mathematical expression fitted to a series of known data points) to determine an estimate of at least one pharmacokinetic parameter. Although many specific details of certain embodiments of the invention are set forth in the following detailed description and accompanying Figures, those skilled in the art will recognize that the present invention may have additional embodiments, or that the invention may be practiced without several of the details described herein. For purposes of clarity and to assist in understanding the full scope of the present invention, a brief review of the nomenclature associated with pharmacokinetics has been provided. As used within the context of the present invention, the following pharmacokinetic terms shall be construed broadly, and shall have their generally accepted meanings as set forth below. (As previously noted, "pharmacokinetics" refers to the study of the kinetics associated with the dynamic processes of absoφtion, distribution, metabolism, and excretion (ADME) of a drug and/or its metabolites within a living organism.) "Absoφtion" refers to the process of uptake of a drug compound from the site of acm inistration into the systemic circulation. The transfer of drug across the intestinal lumen is generally referred to as oral absoφtion, whereas the transfer of drug across an external physiological barrier is referred to general absoφtion. As disclosed herein, the pharmacokinetic parameter of absoφtion may be estimated from biosensor data associated with a sensor chip having, for example, a plurality of appropriate liposomes immobilized thereon. "Distribution" refers to the transfer of a drug compound from the site of administration to the total systemic circulation and then to extracellular and intracellular water and tissues. Drug distribution is usually a rapid and reversible process. As disclosed herein, the pharmacokinetic parameter of distribution may be estimated from biosensor data associated with a sensor chip having, for example, a plurality of appropriate plasma proteins, liposomes, and/or transport proteins immobilized thereon. "Metabolism" refers to the sum of all the chemical reactions for biotransformation of endogenous and exogenous substances which take place in the living cell. As disclosed herein, the pharmacokinetic parameter of metabolism may be estimated from biosensor data associated with a sensor chip having, for example, a plurality of appropriate metabolic enzymes immobilized thereon. "Excretion" refers to the final elimination or loss of a drug from the body. Drug excretion includes both passive diffusion and relative specific carrier mediated excretion. Drugs may be excreted, unchanged or as metabolites, in urine via the kidneys or in feces via the bile and/or the intestine. Volatile compounds are often excreted in expired air by the lungs. As disclosed herein, the pharmacokinetic parameter of excretion may be estimated from biosensor data associated with a sensor chip having immobilized thereon, for example, an antibody that specifically detects the drug, as well as other proteins/receptors having a high affinity/specificity against the drug candidate. Such antibodies and proteins/receptors may be used to quantify the concentration/amount of the drug in different body fluids (e.g., urine/feces) and tissues, using a direct binding assay. In addition to these ADME parameters, the solubility of a drug is also an important property that may be measured by the methods of the present invention. "Solubility" refers to the ability of two substances to form a homogeneous solution or mixture with each other. Solubility is important for the dissolution of drug given in solid dosage form. As disclosed herein, the solubility of a drug candidate may be estimated from sensorgram irregularities associated with reflectance minimum and dip-shape of biosensor data. Furthermore, and as used herein, the term "parameter" refers to a constant or variable term in a function (e.g., a mathematical expression) that determines the specific form of the function but not necessarily its general nature. For example, the constant term "a" in the function ic,) = ax, where V determines only the slope of the line described byf(x), is referred to as a parameter. As such, the term "binding interaction parameter" refers to those constant or variable terms that are related to the binding interaction between a drug candidate and a sensing surface-bound biomolecule and includes, for example, association and dissociation rate constants, as well as maximum binding capacity. Similarly, the term "pharmacokinetic parameter" refers to those constant and variable terms that are related to the disposition of the drug candidate within a living organism and includes, for example: volume of distribution; total clearance; protein binding; tissue binding; metabolic clearance; renal clearance; hepatic clearance; biliary clearance; intestinal absoφtion; bioavailability; relative bioavailability; intrinsic clearance; mean residence time; maximum rate of metabolism; Michaelis-Menten constant; partitioning coefficients between tissues and blood (or plasma) such as those partitioning coefficients associated with the blood brain barrier, blood placenta barrier, blood human milk partitioning, blood adipose tissue partitioning, and blood muscle partitioning; fraction excreted unchanged in urine; fraction of drug systemically converted to metabolites; elimination rate constant; half-life; and secretion clearance. The methods of the present invention are intended to be carried out by use of an affinity-based biosensor. As is appreciated by those skilled in the art, "biosensors" are analytical devices for analyzing minute quantities of sample solution having an analyte of interest, wherein the analyte is analyzed by a detection device that may employ a variety of detection methods. Typically, such methods include, but are not limited to, mass detection methods, such as piezoelectric, optical, thermo-optical and surface acoustic wave (SAW) device methods, and electrochemical methods, such as potentiometric, conductometric, amperometric and capacitance methods. With regard to optical detection methods, representative methods include those that detect mass surface concentration, such as reflection-optical methods, including both internal and external reflection methods, angle, wavelength or phase resolved, for example, ellipsometry and evanescent wave spectroscopy (EWS), the latter including surface plasmon resonance (SPR) spectroscopy, Brewster angle refractometry, critical angle refractometry, frustrated total reflection (FTR), evanescent wave ellipsometry, scattered total internal reflection (STIR), optical wave guide sensors, evanescent wave-based imaging such as critical angle resolved imaging, Brewster angle resolved imaging, SPR angle resolved imaging, and the like. Further, photometric methods based on, for example, evanescent fluorescence (TIRF) and phosphorescence may also be employed, as well as waveguide interferometers. In the detailed description and Examples that follow, the present invention is illustrated in the context of SPR spectroscopy. However, it is to be understood that the present invention is not limited to this detection method. Rather, any affinity-based detection method where an analyte binds to a ligand immobilized on a sensing surface may be employed, provided that a change in a property of the sensing surface is measured and quantitatively indicative of binding of the analyte to the immobilized ligand thereon. In the context of SPR spectroscopy, one exemplary class of biosensors is sold by Biacore AB (Uppsala, Sweden) under the trade name BIACORE® (hereinafter referred to as "the BIACORE instrument"). Such biosensors utilize a SPR based mass-sensing technique to provide a "real-time" binding interaction analysis between a surface bound ligand and an analyte of interest. The BIACORE instrument includes a light emitting diode, a sensor chip covered with a thin gold film, an integrated fluid cartridge and a photo detector. Incoming light from the diode is reflected in the gold film and detected by the photo detector. At a certain angle of incidence ("the SPR angle"), a surface plasmon wave is set up in the gold layer, which is detected as an intensity loss or "dip" in the reflected light. More particularly, and as is appreciated by those skilled in the art, the phenomenon of surface plasmon resonance (SPR) associated with the BIACORE instrument is dependent on the resonant coupling of light, incident on a thin metal film, to oscillations of the conducting electrons, called plasmons, at the metal film surface. These oscillations give rise to an evanescent field which extends from the surface into the sample solution. When resonance occurs, the reflected light intensity drops at a shaφly defined angle of incidence, the SPR angle, which is dependent on the refractive index within the reach of the evanescent field in the proximity of the metal surface. Stated somewhat differently, surface plasmon resonance is an optical phenomenon arising in connection with total internal reflection of light at a metal film-liquid interface. Normally, light traveling through an optically denser medium, e.g., a glass prism, is totally reflected back into the prism when reaching an interface of an optically less dense medium, e.g. , a buffer, provided that the angle of incidence is larger than the critical angle. This is known as total internal reflection. Although the light is totally reflected, a component of the incident light momentum called the evanescent wave penetrates a distance of the order of one wavelength into the buffer. The evanescent wave may be used to excite molecules close to the interface. If the light is monochromatic andjo-polarized, and the interface between the media is coated with a thin (a fraction of the light wave-length) metal film, the evanescent wave under certain conditions will interact with free oscillating electrons (plasmons) in the metal film surface. When surface plasmon resonance occurs, light energy is lost to the metal film and the reflected light intensity is thus decreased. The resonance phenomenon will only occur for light incident at a shaφly defined angle which, when all else is kept constant, is dependent on the refractive index in the flowing buffer close to the surface. Changes in the refractive index out to about 1 μm from the metal film surface can thus be followed by continuous monitoring of the resonance angle. A detection volume is defined by the size of the illuminated area at the interface and the penetration depth of the evanescent field. It should be noted that no light passes through the detection volume (the optical device on one side of the metal film detects changes in the refractive index in the medium on the other side). As noted above, the SPR angle depends on the refractive index of the medium close to the gold layer. In the BIACORE instrument, dextran is typically coupled to the gold surface, with the ligand being bound to the surface of the dextran layer. (Note a detailed discussion of matrix coatings for biosensor sensing surfaces is provided in U.S. Patent No. 5,436,161, which is incoφorated herein by reference in its entirety.) The analyte of interest is injected in solution form onto the sensor surface through the fluid cartridge. Because the refractive index in the proximity of the gold film depends upon (1) the refractive index of the solution (which is constant) and, (2) the amount of material bound to the surface, the binding interaction between the bound ligand and analyte can be monitored as a function of the change in SPR angle. A typical output from the BIACORE instrument is a "sensorgram," which is a plot of response (measured in "resonance units" or "RU") as a function of time. An increase of 1,000 RU corresponds to an increase of mass on the sensor surface of approximately 1 ng/mm^. As a sample containing the 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 sample flow is replaced by, for example, a buffer flow. This step is indicted on the sensorgram by a drop in RU over time as analyte dissociates from the surface-bound ligand. 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, which is incoφorated herein by reference in its entirety. In addition, a detailed discussion of the technical aspects of the biosensor sensor chips used in connection with the BIACORE instrument may be found in U.S. Patent No. 5,492,840, which is incoφorated herein by reference in its entirety. This patent discloses, among other tilings, that each sensor chip may have a plurality of sensing surfaces, and that such sensing surfaces may be arranged in series or parallel with respect to the fluid sample pathway of the fluid cartridge. This patent also discloses that each of the plurality of sensing surfaces of a single sensor chip may have bound thereto a unique type of ligand that is capable of interacting with an analyte in its own characteristic way. For example, and as disclosed herein, each of the four discrete sensing surfaces of the BIACORE instrument may have immobilized thereon biomolecules such as liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/or transport/efflux proteins. By immobilizing one or a selected combination of at least two different liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/or transport efflux proteins, onto the one or more discrete sensing surfaces of a sensor chip, one or more pharmacokinetic parameters associated with a drug candidate may be readily determined. More specifically, and in one embodiment of the present invention, it has been discovered that (1) an estimate of an "absoφtion" parameter of a drug candidate may be determined from the binding interactions between the drug candidate and one or more appropriate liposomes; (2) an estimate of a "distribution" parameter of a drug candidate may be determined from the binding interactions between the drug candidate and an appropriate set of plasma proteins (e.g., specific and nonspecific tissue binding and tissue permeability); (3) an estimate of a "metabolism" parameter of a drug candidate may be determined from the binding interactions between the drug candidate and an appropriate set of metabolic enzymes; and (4) an estimate of an "excretion" parameter of a drug candidate may be determined from the binding interactions between the drug candidate and an appropriate set of transport proteins. Within the context of the present invention, suitable liposomes for estimating an "absoφtion" parameter of a drug candidate include organic compounds originating from natural or synthetic lipid molecules such as glycerophospholipids, glyceroglycolipids, sphingophospholipids and sphingoglycolipids, and from the classes phosphatidyl choline, phosphatidyl etanolamine, phosphatidyl serine, phosphatidyl glycerol, phosphatidyl acid, phosphatidyl inositol, galactopyranoside, digalactopyranoside, ceramide- phosphatidyl choline, ceramide-phosphatidyl etanolamine, ceramide- phosphatidyl serine, ceramide-phosphatidyl glycerol, ceramide-phosphatidyl acid, ceramide-phosphatidyl inositol, sphingomyelin molecules, glucosylceramides, glucocerebrosides, galactoceramides, galactocerebrosides, gangliosides, monoacyl phosphatidyl choline, cardiolipin molecules, that may be linked to saturated or unsaturated fatty or fluorocarbon chains ranging from eight to twenty-four carbons in length where fatty chains attached to the head group can be the same or of different structure, cholesterol, lanosterol, ergosterol, stigmasterol, sitosterol and derivatives thereof capable of being incoφorated into lipid membranes, N,N-dimethyl-N-octadecyl-l-octadecanammonium chloride or bromide, (N-[l-(2,3 -dioleoyloxy)propyl]-N,N,N-trimethylammonium chloride, diacetyl phosphate, N-[2,3-dihexadecyloxy)prop-l-yl]-N,N,N- trimethylammonium chloride, bolaamphiphiles, polyglycerolmonoalkylethers, polyethoxymonoalkylethers, as well as liposome-forming molecules from the classes amphiphilic polymers, amino acids, crown ether compounds and di(acyloxy)dialkylsilanes. The liposomes of the present invention and the like may be immobilized onto the sensing surface by the technique disclosed in Example 1. Suitable plasma proteins for estimating a "distribution" parameter of a drug candidate include proteins such as Immunoglobulin G (7s-y-globulin), IgG; Immunoglobulin A, IgA; Secretory IgA, s IgA; Immunoglobulin M (19s-y- globulin) IgM; Immunoglobulin D, IgD; Immunoglobulin E, IgE; αl- Antitrypsin, αl PI, αl A; αl-Antichymotrypsin, (αlX-Glycoprotein) αl X; Inter-α-Trypsin inhibitor, lαTI; Antithrombin III, (heparin cofactor) AT III; α- Thiol Proteinase inhibitor (LMW kininogen) αTPI; Cl-Inactivator, Cl esterase inhibitor (α2-neuraminoglycoprotein) Cl INA: α2-Macroglobulin, α2 M; α2- Antiplasmin, α2AP; Cystatin C (Post-y-globulin; y-Trace protein); Clq (US protein); Clr; Cls; C2; C3 (βlC-globulin), C4 (β IE-globulin); C5 (βlF- globulin); C6; C7; C8; C9; Factor B, (C3-proactivator; β2-glycoprotein II; glycine-rich β-glycoprotein); Factor D (C3-proactivator convertase); Properdin, P; Factor I, (C3b inactivator); C4-binding Protein; Fibrinogen, FI, Prothrombin, F II; Factor V (proaccelerin); FV; Factor VII, (proconvertin), F VII; Factor VIII: C (antihemophilic factor) F VIII: C; Factor VIII - Related Antigen; FV III: Rag (von Willebrand factor) (VWF); Factor IX (Christmas factor) F IX; Factor X, (Stuart-Power factor) F X; Factor XI (plasma thromboplalstin antecedent) F XI; Factor XII (Hageman factor) F XII; Factor XIII (fibrin stabilizing factor) F XIII; high-molecular- weight (HMW) Kininogen (Fitzgerald factor); Prekallikrein (Fletcher factor); Plasminogen; Protein C; Protein S; Allbumin, ALB; Haptoglobin, HP, Hp 1-1, Hp 2-1, Hp 2-2; Prealbumin (transthyretin, thyroxine- binding prealbumin); Retmol-binding Protein RBP; Thyroxme-binding Globulin TBG; Transcortin (corticosteroid-binding globulin) CBG; Sex hormone-binding globulin (Steroid-binding β Globulin) SHBG; Vitamin D-binding Protein (Gc- globulin, group specific component) VDBP; Transcobalamin I, TC I; Trancobalamin II, TC II; Transferrin (siderophilin) TF; Ferritin; Hemopexin, HPX; Apolipoprotein A, Apo A-l, Apo A-II; Apolipoprotein B, Apo B-48, Apo B-100; Apolipoprotein C; Apo C-I, Apo C-II, Apo C-III; Apolipoprotein E, Apo E; Apolipoprotein (a), apo (a); Serum Amyloid A, SAA; α-Fetoprotein, AFP; αl-Acid Glycoprotein (orosomucoid) αl AG; Ceruloplasmin, CP; Serum Amyloid P protein (9.5S αl -glycoprotein; αl-macroglobulin) SAP; α2-HS Glycoprotein, α2 HS; Fibronectin (cold insoluble globulin) FN, C-reactive Protein, CRP; β2-microglobulin, β2 M; Pregnancy-specific βl -glycoprotein, SP1; αl-microglobulin, αl M. The plasma proteins of the present invention and the like may be immobilized onto the sensing surface through the use of known immobilization techniques as is appreciated by those skilled in the art. Suitable metabolic enzymes for estimating a "metabolism" parameter and/or an "excretion" parameter of a drug candidate include cytochrome P450-enzymes selected from the class of cytochrome P450 enzymes having an active site characterized by a protopoφhyrin IX-iron complex with thiolate from a cysteine of the enzyme serving as the fifth ligand to iron. Suitable CYP 450 enzymes include CYTOCHROME P450, CYP1A1, CYP1A2, CYP2A1, 2A2, 2A3, 2A4, 2A5, 2A6, CYP2B1, 2B2, 2B3, 2B4, 2B5, 2B6, CYP2C1, 2C2, 2C3, 2C4, 2C5, 2C6, 2C7, 2C8, 2C9, 2C10, 2C11, 2C12, CYP2D1, 2D2, 2D3, 2D4, 2D5, 2D6, CYP2E1, CYP3A1, 3A2, 3A3, 3A4, 3A5, 3A7, CYP4A1, 4A2, 4A3, 4A4, CYP4A11, CYP P450 (TXAS), CYP P450 11A (P450scc), CYP P450 17(P45017a), CYP P450 19 (P450arom), CYP P450 51 (P45014a), CYP P450 105A1, CYP P450 105B1. Other important metabolic enzymes include Glutathione-thioethers, Leukotriene C4,butyrylcholinesterase, human serum paraoxonase/arylesterase, N-Acetyltransferase, UDP-glucuronosyltransferase (UDPGT) isoenzymes, TL PST, TS PST, drug glucosidation conjugation enzyme, the glutathione-S-transferases (GSTs) (RX:glutathione-R-transferase), GSTl, GST2, GST3, GST4, GST5, GST6, alcohol dehydrogenase (ADH), ADH I, ADH II, ADH III, aldehyde dehydrogenase (ALDH), cytosolic (ALDH1), mitochondrial (ALDH2), monoamine oxidase, MAO: Ec 1.4.3.4, MAO A, MAOB, flavm-contøining monoamine oxidase, enzyme superoxide dismutase (SOD), Catalase, amidases, Nl,-monoglutathionyl spermidine, N1,N8- bis(glutathionyl) spermidine, Thioesters, GS-SG, GS-S-cysteine, GS-S- cysteinylglycine, GS-S-03H, GS-S-CoA, GS-S-proteins, S-carbonic anhydrase III, S-actin, Mercaptides, GS-Cu(I), GS-Cu(II)-SG, GS-SeH, GS-Se-SG, GS-Zn- R, GS-Cr-R, Cholin esterase, lysosomal carboxypeptidase, Calpains, Retinol dehydrogenase, Retinyl reductase, acyl-CoA retinol acyltrunderase, folate hydrolases, protein phosphates (pp) 4 st, PP-1, PP-2A, PP-2Bpp-2C, deamidase, carboxyesterase, Endopeptidases, Enterokinase, Neutral endopeptidase E.C.3.4.24.11 , Neutral endopeptidase, carboxypeptidases, dipeptidyl carboxypeptidase, also called peptidyl-dipeptidase A or angiotensin-converting enzyme (ACE) E.C.3.4.15.1, carboxypeptidase M, g-Glutamyl transpeptidase E.C.2.3.2.2, Carboxypeptidase P, Folate conjugase E.C.3.4.12.10, Dipeptidases, Glutathione dipeptidase, Membrane Gly-Leu peptidases, Zinc-stable Asp-leu dipeptidase, Enterocytic intracellular peptidases, Amino tripeptidase E.C.3.4.11.4, Amino dipeptidase E.C.3.4.13.2, Prodipeptidase, Arg-selective endoproteinase; the family of brush border hydrolases, Endopeptidase-24.11, Endopeptidase-2(meprin), Dipeptidyl peptidase IV, Membrane dipeptidase GPI, Glycosidases, Sucrase-isomaltase, Lactase-glycosyl-ceraminidase, Glucoamylase-maltase, Trehalase, Carbohydrase enzymes, alfa-Amylase (pancreatic), Disaccharidases (general), Lactase-phhlorizin hydrolase, Mammalian carbohydrases, Glucoamylase, Sucrase-Isomaltase, Lactase-glycosyl ceramidase, Enzymatic sources of ROM, Xanthine oxidase, NADPH oxidase, Amine oxidases, Aldehyde oxidase, Dihydroorotate dehydrogenase, Peroxidases, Human pancreatic exocrine enzymes, Trypsinogen 1 , Trypsinogen 2, Trypsinogen 3, Chymotrypsinogen, proElastase 1, proElastase 2, Protease E,Kallikreinogen, proCarboxypeptidase Al, proCarboxypeptidase A2, proCarboxypeptidase Bl, proCarboxypeptidase B2, Glycosidase, Amylase, Upases, Triglycaride lipase, Collipase, Carboxyl ester hydrolase, Phospholipase A2, Nucleases, Dnase I, Ribonucleotide reductase (RNRs), Wistar rat exocrine pancreatic proteins, Label Protein IEP, Al Amylase 1, A2 Amylase 2, Lipase, CEL Carboxyl-ester lipase, PL Prophospholipase A, Tl Trypsinogen 1, T2 Trypsinogen 2, T3 Trypsinogen 3, T4 Trypsinogen 4, Cl Chymotrypsinogen 1, C2 Chymotrypsinogen 2, PE1 Proelastase 1, PE2 Proelastase 2, PCA ProCarboxypeptidase Al, PCA1 ProCarboxypeptidase A2, PCB1 ProCarboxypeptidase Bl, PCB2 ProCarboxypeptidase B2, R Ribonuclease, LS Lithostatin, Characteristics of UDPGT isoenzymes purified from rat liver, 4- nitrophenol UDPGT, 17b-Hydroxysteriod UDDPGT, 3-a-Hydroxysteroid UDPGT, Moφhine UDPGT, Billirubin UDPGT, Billirubin monoglucuronide, Phenol UDPGT, 5-Hydroxytryptarnine UDPGT, Digitoxigenin monodigitoxide UDPGT, 4-Hydroxybiphenyl UDPGT, Oestrone UDPGT, Peptidases, Aminopeptidase N, Aminopeptidase A, Aminopeptidase P, Dipeptidyl peptidase IV, b-Casomoφhin, Angiotensin-converting enzyme, Carboxypeptidase P Angiotensin II, Endopeptidase-24.11, Endopeptidase-24.18 Angiotensin I, Substance P (deamidated), Exopeptidase,l . NH2 terminus Aminopeptidase N (EC 3.4.11.2), Aminopeptidase A (EC 3.4.11.7), Aminopeptidase P (EC 3.4.11.9), Aminopeptidase W (EC 3.4.11.-), Dipeptidyl peptidase IV (EC 3.4.14.5), g-Glutamyl transpeptidase (EC 2.3.2.2), 2. COOH terminus Angiotensin-converting enzyme (EC 3.4.15.1), Carboxypeptidase P (EC 3.4.17.- ), Carboxypeptidase M (EC 3.4.17.12),3. Dipeptidase Microsomal dipeptidase (EC 3.4.13.19), Gly-Leu peptidase, Zinc stable peptidase,Endopeptidase Endopeptidase-24.11 (EC 3.4.24.11), Endopeptidase-2 (EC 3.4.24.18, PABA- peptide hydrolase, Meprin, Endopeptidase-3, Endopeptidase (EC 3.4.21.9), GST Al-1, Alpha,GST A2-2 Alpha, GST Mla-la Mu, GST Mlb-lb Mu, GST M2-2 Mu, GST M3-3 Mu, GST M4-4 Mu, GST M5-5 Mu, GST P 1 - 1 Pi, GST T 1 - 1 Theta, GST T2-2 Theta, Microsomal Leukotriene C4 synthase, UGT isozymes, UGT1.1, UGT1.6, UGT1.7, UGT2.4, UGT2.7, UGT2.11, Pancreatic enzymes, Elastase, Aminopeptidase (dipeptidyl aminopeptidase (IV), Chymotrypsin, Trypsin, Carboxypeptidase A, Methyltransferases, 0-methyltransferases, N- methyltransferases, S-methyltransferases, Catechol-O-methyltransferases, MN- methyltransferase, S-sulphotransferases, Mg2+-ATPase, Growth factor receptors Alkaline phosphatase, ATPases, Na, K+ATPase, Ca2+-ATPase, Leucine aminopeptidase, K + channel. The metabolic enzymes of the present invention and the like may be immobilized onto the sensing surface through the use of known immobilization techniques as is appreciated by those skilled in the art. Suitable transport proteins for estimating an "excretion" (as well as an efflux, absoφtion, distribution) parameter of a drug candidate include Glucose a. Na + - glucose cotransp GLUT 1, b. Facilitative transporter, GLUT 1-5, GLUT-2, Neutral amino acid transporter, Na + -independent system L amino acid transporter, cationic amino acid, Y + cationic L-amino acid transporter, Dipeptides H + contransport, Nucleosides Na + -dependent and facilitative, Taurine Na + and Cl" dependent, Bile acids Na + /bile acid cotransporter, Na + - independent bile acid transporter, ABC transporters, Prostaglandins facilitative transpoprter, Na + H + exchanger Antiporter, Phosphate Na + /Pi cotransporter, Sulfate Na + -cotransporter, Transporters for neurotransmitters, Norepinephrine Na + /Cl" cotransporter, Dopamine Na + /Cl" cotransporter, Serotonine Na + /Cl" cotransporter, GAB A, GAT-1, Na + /Cl ~ dependent, Glycine Na + /Cl" dependent, Glutamate Na + cotransporter, K + /OH _ counter-transport, ABC transporters, P- glycoprotein (MDR1 , MDR 2 OR MDR 3), Cl" channel (CFTR), Antigenic peptides, TAP1 and TAP2 heterodimer, Lung resistance protein (LRP), Multidrug resistance protein 1 (MRP1), Multidrug resistance protein 2 (MRP2 or cMOAT), Multidrug resistance protein 3 (MRP3), Multidrug resistance protein 4 (MRP4), Multidrug resistance protein 5 (MRP5), Multidrug resistance protein 6 (MRP6), mrp (mouse), EBCR (rabbit), C. elegans mφl (nematode), C. elegans mrp2 (nematode), MRP6 (human), YCFl (yeast), AtMRPl (Arabidopsis), SURl (human), sur2 (rat, mouse), YOR1/YRS1 (yeast), LtpgpA (leishmania), Hepatic amino acid transport system, Neutral amino acid transporters, MeAIB, Dicarboxylic amino acids, Neutral amino acids (branched), b-amino acids, Long chain fatty acids, Monoglycerides, L-Lysophosphatidylcholine, Transport proteins for bilirubin, Bilitranslocase (BTL), Organic anion binding protein (OABP), BSP/bilirubin binding protein, Signal receptor and transduction- hydrolases, ATPases, Na + dependent/independent bile acid transport, Bilirubin/BSP carrier (Cl-dependent), SO/OH exchanger Cl'channel Na + /H + exchanger, Na + /HCO^ cotransport, GSH-, GSSH-, GS conjugate carrier, Sθ4/HCθ3 exchanger, Na + -dependent amino acid transport, Dipolar amino acid transporter, Basic amino acids, Cystine, Imino acids Cl", b- Amino acids Cl", XAG Acidic amino acids K + , A Dipolar a-amino acids, three-and four-carbon dipolar amino acids, L Bulky, hydrophobic, dipolar amino acids, y+ Basic amino acids, folate transperter, cbl transport proteins, Na + -K" - ATPase, Bile acid transperter (BAT), protein kinase C, Na + /I" sympoter (NIS); Bile salt export pump(BSEP, cBAT, SPGP); Intestinal bile acid transporters; Purine selective Na + -dependent nucleoside transporter (hSPNTI); Pyrimidine selective Na + - dependent nucleoside transporter (cNTI); Mitoxantrone transporter (MXR1 and MXR2); Intestinal oligopeptide transporter(PepTl); Renal oligopeptide transporter(PepT2); Breast cancer resistance protein (BCRP). The transport/efflux proteins of the present invention and the like may be immobilized onto the sensing surface through the use of known immobilization techniques as is appreciated by those skilled in the art. As stated above, it has been discovered that the pharmacokinetic parameters of absoφtion, distribution, metabolism, and excretion (ADME) of a drug candidate may be determined from the binding interactions between the drug candidate and appropriate sensing surface-bound biomolecules (e.g., from different liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/or transport/efflux proteins, such as those as identified above) of a biosensor. That is, the binding interactions between a drug candidate and one or more sensing surface-bound biomolecules selected from liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and transport/efflux proteins, may be measured via the BIACORE instrument to determine a "binding interaction parameter" of the drug candidate. The estimated binding interaction parameter may then, in turn, be compared against a predetermined drug correlation graph to estimate one or more absoφtion, distribution, metabolism, and excretion "pharmacokinetic parameters" of the drug candidate. Moreover, the pharmacokinetic parameter of the drug candidate may be, for example, selected from the group consisting of volume of distribution; total clearance; protein binding; tissue binding; metabolic clearance; renal clearance; hepatic clearance; biliary clearance; intestinal absoφtion; bioavailability; relative bioavailability; intrinsic clearance; mean residence time; maximum rate of metabolism; Michaelis-Menten constant; partitioning coefficients between tissues and blood (or plasma) such as those partitioning coefficients associated with the blood brain barrier, blood placenta barrier, blood human milk partitioning, blood adipose tissue partitioning, and blood muscle partitioning; fraction excreted unchanged in urine; fraction of drug systemically converted to metabolites; elimination rate constant; half-life; and secretion clearance; any one of which may be determined from appropriate sensorgrams of the selected drug-biomolecule interaction (alternatively, they may be determined by extracting score vectors with principal component analysis from digitalized interaction profiles, as well as other known multivariate methods). For example, to estimate an apparent equilibrium constant between a drug candidate and selected sensing surface-bound biomolecules, the following procedure may be employed. First, a concentration series (e.g., 0, 20, 50, 100, 500, and 1,000 μM) of the drug candidate may be prepared, and sequentially injected into a biosensor having a sensor chip operatively associated therewith, wherein the sensor chip has a reference sensing surface and at least one sensing surface with surface-bound biomolecules. The relative responses at steady-state binding levels for each drug concentration level may then be measured. Because of bulk-refractive index contributions from solvent additives in the biosensor's running buffer, a correction factor may be calculated (via known calibration procedures) and applied to give corrected relative responses. The corrected relative responses for each drug concentration may then be mathematically evaluated as is appreciated by those skilled in the art to estimate the apparent equilibrium constant. More specifically, the apparent equilibrium constant (KD) between the drug candidate and the selected sensing surface-bound biomolecules may be calculated by fitting the measured equilibrium (R e q) data and known drug concentration (C) data to Equation (1): Req = C*R max /(C + KD) + offset (1) wherein R ma χ is the maximum binding capacity of the sensing surface. Alternatively, the apparent equilibrium constant (KD) may be calculated from as little as two concentrations (i.e., Cl and C2) of the drug candidate by use of Equation (2): D = (ReqCl Cl - ReqC2 C2)/(Req 2 " ReqCl) (2) The estimated apparent equilibrium constant (KD) or binding level at a specific molar drug concentration may then, in turn, be compared against a predetermined drug correlation graph to estimate one or more absoφtion, distribution, metabolism, and excretion parameters of the drug candidate. In the context of the present invention, a predetermined drug correlation graph refers to a mathematical expression or function that has been developed from binding interaction data associated with known drug compounds. For example, a correlation graph may be constructed wherein known binding interaction parameters (e.g., apparent equilibrium constants and/or binding levels at specific molar drug concentrations) for known compounds are plotted along the abscissa (i.e., the x-axis) and corresponding measured binding interaction parameters obtained via the biosensor are plotted along the ordinate (i.e., the y-axis), or vice versa. Stated somewhat differently, the correlation plot is a graphical representation of a mathematical expression that correlates known and measured affinity data. By comparing the estimated binding interaction parameter obtained from the selected drug-biomolecule interaction against the mathematical expression (i.e., correlation graph) correlated from known and measured affinity data, it has been suφrisingly discovered that an estimate of a pharmacokinetic ADME parameter related to the drug candidate may be accurately predicted. In addition, by immobilizing a selected combination of at least two different liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/or transport/efflux proteins, onto one or more discrete sensing surfaces of a sensor chip, it has been further surprisingly discovered that at least two pharmacokinetic parameters associated with a drug candidate may be readily determined. Moreover, by immobilizing a selected combination of different liposomes, plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/or transport/effiux proteins, onto the sensing surfaces of biosensor, such as in a predetermined line of different spots of biomolecules, an ADME pattern or profile, such as a drug candidate characterization matrix, may be readily developed. Such ADME profiles are of great utility for purposes of drug screening. In addition to determining one or more pharmacokinetic parameters by monitoring the refractive index changes of a biosensor as disclosed above, the solubility of a drug candidate may also be simultaneously determined (together with the one or more pharmacokinetic parameters) by monitoring the minimum, maximum or centroid of the drug-biomolecule interaction signal (as is disclosed, for example, in PCT Publication No. WO 97/09618, which is incoφorated herein by reference in its entirety). More specifically, the solubility of the drug candidate may be estimated/identified from irregularities, as well as from reflectance minimum (Rmin) and dip-shape data, shown in the sensorgram of the drug-biomolecule interaction. In general, the concentration at which precipitation occurs is referred to as the solubility limit; this property may be important to measure because it may indicate that the drug candidate has an affinity greater than otherwise indicated. Solubility problems of the drug candidate may be detected because insoluble particles (i.e., precipitates) tend to bind to sensing surface-bound biomolecules, thereby causing the sensing surface to be non-homogenous. (A homogenous surface has the same surface concentration throughout, whereas a non- homogenous surface has concentration disruptions.) A non-homogenous sensing surface actually measures several refractive indices, which are all averaged together in the biosensor's detector. These multiple measurements from a single, but non-homogenous sensing surface, tend to result in an increase in reflectance minimum and a broadening of the dip associated therewith. An illustration of how sensorgram irregularities may be used to identify solubility problems (i.e., the presence of precipitates) is shown in Figures 1 A-C. More specifically, the steady-state binding levels associated with a selected drug- biomolecule interaction is generally shown in Figure 1 A. By enlarging or "zooming" in on the top area of the sensorgram, which is reflective of the steady- state binding, the sensorgram irregularities become more apparent as is shown in corresponding Figure IB. The sensorgram irregularities become even more apparent by using reference subtracted data (i.e., bulk-refractive index effects have been eliminated) as is shown in corresponding Figure lC. An illustration of how reflectance minimum (Rmin) and dip-shape data may be used to identify solubility problems (i.e., the presence of precipitates) is shown in Figures 2A-C. More specifically, a homogeneous surface has the same surface concentration throughout, and will thus result in a single "dip" with respect to the reflected light intensity as shown in Figure 2A. When precipitates bind to the sensing surface, the sensing surface becomes non-homogenous which tends to result in an increase in reflectance minimum and a broadening of the dip associated therewith as is shown in Figures 2B and 2C, respectively. Based on the foregoing methods for assaying a drug candidate, researchers may now simultaneously measure several different pharmacokinetic parameters of the drug candidate, as well as gauge the drug candidate's solubility, by using a single analytical instrument. The present invention simplifies and improves the rate of drug discovery and development because important pharmacokinetic data may now be readily obtained at a relatively early stage of the process. In addition to the foregoing methods, the present invention is also directed to apparatuses adapted to carrying out such methods. More specifically, the apparatuses of the present invention comprise a biosensor having a sensing surface associated therewith, and a computer system that facilitates the implementation of the steps associated with the methods disclosed herein. The computer includes a computer memory containing a data structure useful for assaying a drug candidate; the data structure comprises binding interaction data associated with known drug compounds such that the data structure may be used to determine an estimate of at least pharmacokinetic parameter of the drug candidate. The aspect of the present invention relating to a computer memory containing a data structure useful for assaying a drug candidate may be more fully illustrated in the context of a high-level computer block diagram as is depicted in Figure 3. As shown, such a computer system 300 contains one or more central processing units (CPUs) 310, input/output devices 320, and the computer memory containing a data structure useful for assaying a drug candidate(memory) 330. Among the input/output devices is a storage device 321, such as a hard disk drive, and a computer-readable media drive 322, which may be used to install software products, where the software products are provided on a computer- readable medium, such as a CD-ROM. The input/output devices also include a network connection 323, through which the computer system 300 may communicate with other connected computer systems, such as networks. The input/output devices may also contain a display 324 and a data input device 325. The memory 330 preferably contains an operating system 331, such as MICROSOFT WINDOWS, for providing to other programs access to resources of the computer system. The memory 330 preferably further contains software 332. While the computer memory containing a data structure useful for assaying a drug candidate is preferably implemented on a computer system configured as described above, those skilled in the art will recognize that it may also be implemented on computer systems having different configurations. In a related aspect, the present invention is also directed to a generated data signal conveying a data structure useful for assaying a drug candidate. As above, the data structure comprises binding interaction data associated with known drug compounds, such that the data structure may be used to determine an estimate of at least pharmacokinetic parameter of the drug candidate. In still a further embodiment, a sensor surface adapted for use with a biosensor is disclosed. The sensor surface has a hydrogel matrix coating coupled to the top surface of the sensor surface, wherein the hydrogel matrix coating has a plurality of functional groups, and at least two different types of liposomes are bonded to the plurality of functional groups at discrete and noncontiguous locations on the hydrogel matrix coating of the sensor surface (such as disclosed in Example 1). In the context of the BIACORE instrument as described above, the sensor surface is preferably in the form a sensor chip, wherein the sensor chip has a free electron metal inteφosed between the hydrogel matrix and the top surface of the sensor chip. Suitable free electron metals in this regard include copper, silver, aluminum and gold. The sensor surface may have a lipophilic substance inteφosed between the different types of liposomes and the plurality of functional groups, wherein the lipophilic substance is covalently bonded to the plurality of functional groups. Representative lipophilic substances comprise an alkyl chain having from 12 to 24 carbon atoms, such as stearylamine. Similarly, representative liposomes include l,2-dimyristol-sn-glycero-3-phosphocholine (DMPC) and l-palmitoyl-2- oleoyl-s/ι-glycero-3-phosphocholine (POPC). The sensor surfaces of this invention may also have non-liposome biomolecules associated therewith. For example, human serum albumin, CYP 450 enzyme, a metabolic enzyme, and/or a transport protein may be bonded to the plurality of may be bonded to the plurality of functional groups at discrete and noncontiguous locations on the hydrogel matrix coating of the sensor surface. For purposes of illustration and not limitation, the following examples more specifically disclose various aspects of the present invention. EXAMPLES EXAMPLE 1 SIMULTANEOUS MEASUREMENT OF SOLUBILITY, PLASMA PROTEIN BINDING, LIPOPHILICΠΎ AND INTESTINAL ABSORPTION FOR THREE DRUG CANDIDATES A- C This example discloses how combined information from each of four different flow-cells of a biosensor may be represented in a characterization matrix where each of the three drug candidates A-C illustrates a quality pattern (i.e., HSA% Bound, Predicted Lipophilicity, Solubility, and Predicted FA%) which is useful for the selection of lead drug compounds. Preparation of Sensor Chip Three of the four discrete sensing surfaces of a CM5 Sensor Chip (Biacore AB, Uppsala, Sweden) were modified such that the CM5 Sensor Chip had surface-bound biomolecules as depicted below in Table 1. TABLE 1 SURFACE-BOUND BIOMOLECULES OF CM5 SENSOR CHIP More specifically, and to achieve covalent attachment of stearylamine to surface/cell nos. 3 and 4, the following procedure was employed. A CM5 Sensor Chip was first inserted into a BIACORE 3000 biosensor (Biacore AB, Uppsala, Sweden). The flow of ranning buffer (isotonic phosphate buffer pH 7.0 (9.6 g Na2HP04 - 2H2O, 1.7 g KH2PO4, 4.1 g NaCl to 1 liter)) with 2% dimethylsulfoxide (DMSO) was directed to flow-cells 3 and 4 by using appropriate software commands. A mixture of 0.2 M N-ethyl-N'-(3- diethylaminopropyl)-carbodiimide and 50 mM N-hydroxysuccmimide in water was then injected into the biosensor so as to flow over flow-cells 3 and 4 for a period of 10 minutes. The CM5 Sensor Chip was then washed with running buffer, removed from the biosensor, and placed in a petri dish on top of a few layers of tissue paper that had been moistened with ethanol. The exposed sensing surfaces were then treated with 30 μl of a 10 mM stearylamine solution in 99% ethanol. After another 45 minute period, the CM5 sensor chip was gently washed with ethanol and then with water. Next, the exposed sensing surfaces were fiirther treated with 50 μl of a IM ethanolamine solution at pH 8.5. Finally, and after another 10 minute period, the sensing surfaces were washed with HBS- buffer. Following covalent attachment of stearylamine to surface/cell nos. 3 and 4 - and after the CM5 Sensor Chip that had just been washed with HB S-buffer was blown dry with nitrogen and reinserted into the biosensor - human serum albumin (HSA), DMPC-liposomes, and POPC-liposomes (available from Sigma) were then captured onto surface/cell nos. 2, 3 and 4, respectively. The following procedures were employed to capture these biomolecules. To capture human serum albumin the flow was initially directed to flow-cell 2 by using appropriate software commands. The sensing surface of flow-cell 2 was then activated for a period of 7 minutes with EDC/NHS. Next, human serum albumin was injected at 15 μg/ml in lOmM acetate buffer pH 5.2 for a period of 7 minutes; then IM ethanolamine pH 8.5 was injected for another period of 7 minutes. This procedure resulted in the immobilization of 9-12 kRU of human serum albumin to the sensing surface of surface/cell no. 2. To capture the two different liposomes the flow was first directed to flow-cell 3, and 0.5- 1 mM DMPC liposome was injected until 5-7 kRU of the DMPC liposome had been captured. The flow was then directed to flow-cell 4, and 0.5- 1 mM POPC liposome was injected until 5-7 kRU of the POPC liposome had been captured. Finally, the flow-system of the biosensor was washed with lOOmM NaOH, and the autoinjector tubing was washed with 0.5% SDS and 50 nM glycine pH 9.5 to remove trace lipids. Calibration Procedure In order to improve data quality by reducing bulk-refractive index contributions from the DMSO solvent additive, a calibration procedure was employed. (Note the calibration procedure required varying concentrations of DMSO.) Because the running buffer contained 2% DMSO, a series of 8 to 10 calibration solutions were made that had varying DMSO concentrations ranging from 1.5% to 3.0%. Each calibration solution was sequentially injected over each of the four sensing surfaces by using the serial injection mode of the biosensor, and the respective steady-state binding levels were measured. The relative responses of the reference sensing surface of flow-cell 1 (FC1 having unmodified CM5 as a reference surface) were then subtracted from the corresponding calibration responses of the target sensing surfaces of flow-cells 2, 3, and 4, respectively, and plotted as functions of the relative responses. From these plots, calibration functions were calculated for each of the respective sensing surfaces. The calibration functions were then used to calculate appropriate correction factors for the samples containing the three drug candidates A-C. That is, for the samples containing the drug candidates, the relative responses of the steady state binding levels of the reference sensing surface (i.e., signals from flow-cell 1) were measured and the respective calibration functions were used to calculate correction factors appropriate for each drug candidate. The correction factors were then applied (i.e., subtracted) to the differences between the reference responses and the sample responses to give a responses where the bulk-refractive index contributions from the DMSO solvent additive had been eliminated. Corrected Relative Responses of Drug Candidates A-C The modified CM5 Sensor Chip (having surface-bound biomolecules as depicted above in Table 1) was used to measure the binding interaction of the three drug candidates A-C. Concentration series (i.e., 0, 20, 50, 100, 500, and 1,000, μM) of each drug candidate were prepared, and sequentially injected in serial mode into the biosensor such that four sensorgrams corresponding to each flow-cell were developed. Corrected relative responses at steady-state binding levels for each drug concentration were determined for each of the target sensing surfaces/flow-cells; such data is presented below in Tables 2-4. TABLE 2 DRUG CONCENTRATION DATA FOR HUMAN SERUM ALBUMIN TABLE 3 DRUG CONCENTRATION DATA FOR POPC LIPOSOME TABLE 4 DRUG CONCENTRATION DATA FOR DMPC LIPOSOME Analysis of Data The corrected relative responses for each drug concentration as depicted above in Tables 2-4 were, among other things, adjusted for differences in molecular weight of the drug candidates, as well as transformed to increase correlation with other properties. For example, for each target sensing surface/flow-cell, an apparent equilibrium constant (KD) was calculated by fitting the measured equilibrium ( eq) data and known drug concentration (C) data to Equation (3): Req = C*R m aχ/(C + KD) + offset (3) wherein R max is the maximum binding capacity of the sensing surface. In the fitting procedure, Rmax > KD, and the offset were calculated. Alternatively, the apparent equilibrium constant (KD) may have been calculated from two concentrations of the drug candidate, Cl and C2, by use of Equation (4): KD = (ReqCl/Cl - ReqC2/C2)/(R e qC2 - ReqCl) (4) In either case, the KD values obtained via the biosensor were subsequently correlated with known KD values calculated from reported human serum albumin binding percentages (i.e., reported binding percentages that have been reported for known drug compounds), thereby enabling a prediction of the degree of protein binding for each of the three drug candidates A-C. In other words, a correlation graph as shown in Figure 4 was constructed having known KD values for known compounds plotted along the abscissa (i.e., the x-axis) and corresponding measured KD values obtained via the biosensor plotted along the ordinate (i.e., the y-axis). This correlation graph was then used to predict the degree of plasma protein binding for each of the three drug candidates A-C (see below). In addition, for each target sensing surface/flow-cell, a molecular weight adjusted response at a single concentration level was used as a threshold value for ranking the drug candidates A-C. More specifically, a correlation graph as shown in Figure 5 was constructed, wherein respective binding levels at 100 μM (R 100 μM) divided by the known drug compounds' molecular weight were plotted along the abscissa (i.e., the x-axis) and corresponding human serum albumin binding percentage, as measured by equilibrium dialysis, were plotted along the ordinate (i.e., the y-axis). This correlation graph was then used to discriminate between strong and weak human serum albumin binders for each of the three drug candidates A-C. Furthermore, for each target sensing surface/flow-cell, reference subtracted sensorgram traces (e.g., FC2 minus FC1) were developed to detect the presence of precipitates (caused by low solubility). More specifically, reference subtracted sensorgram traces for each of the three drug candidates A-C, as shown in Figure 6, were constructed. (Note that when precipitates form and then bind/associate with the sensing surface, the response signal will decrease in a non-continuous way.) By analyzing the concentration series for each of the three drug candidates A-C (as shown in Figure 6), it was determined that drug candidate A precipitated and thus had relatively low solubility, whereas drug candidates B-C did not precipitate and thus had relatively high solubilities. Finally, and as shown below in Table 5, the combined information from each target sensing surface/flow cell was tabulated into a reduced characterization matrix where each drug candidate A-C received a quality pattern that was useful for selection of the lead drug compounds. (Note that the predicted lipophilicity and fraction absorbed are prophetic.) TABLE 5 CHARACTERIZATION MATRIX FOR DRUG CANDIDATES A-C Based on the forgoing characterization matrix, it was determined that drug candidate B was the preferred compound due to its low plasma protein binding level (HSA %bound <90), medium lipophilicity (predicted lipophilicity = 2), no identified solubility problems (solubility = OK), and acceptable intestinal absoφtion (predicted fraction absorbed >90). EXAMPLE 2 DEMONSTRATED CORRELATION BETWEEN BIOSENSOR DATA AND FRACTION ABSORBED IN HUMANS (FA%) This example discloses a correlation between biosensor data obtained form the BIACORE instrument (i.e., BIACORE 3000) and known data for the fraction absorbed in humans (FA%) for a number of different drugs, wherein the correlation graph is useful for drug candidate absoφtion predictions. More specifically, a correlation graph as shown in Figure 7 was constructed having known fraction absorbed in humans (FA%) plotted along the ordinate (i.e., the y- axis) and corresponding calibrated (i.e., reference subtracted) steady state binding levels for each drug at 500 μM plotted along the abscissa in a 10-logarithm scale (i.e., the x-axis). In this example, the sensing surfaces of the target flow-cells each had 6,000 RU of POPC-GM3 ganglioside (available from Sigma) captured on stearylamine tiles. (Note that an unmodified sensing surface of a CM5 Sensor Chip was used as the reference.) As shown in Figure 7, there is high degree of correlation between the biosensor data and the known fraction absorbed in humans (FA%) data, as is evidenced by the mathematical expression/function that has been fitted to the various data points. Moreover, the correlation graph also shows a classification of the various substances, wherein absoφtion of the substances using the passively transported trans-cellular route through the intestine are depicted by h = high, m = medium, and 1 = low, and wherein absoφtion using active transport is depicted by h-act = high, and wherein absoφtion of substances with molecular weights <200 using the para-cellular route is depicted by h-para = high and m-para = medium, respectively. (Note the Sulfasalazine is a pre-drug which rapidly decomposes in the intestine, which may explain its outlier properties; Piroxicam has a very low solubility ««500 μM, which may explain its lack of correlation.) EXAMPLE 3 BIOSENSOR ANALYSIS OF THE INTERACTION BETWEEN IMMOBILIZED HUMAN SERUM ALBUMIN AND DRUG COMPOUNDS FOR PREDICTION OF HSA Many compounds bind reversibly to human serum albumin (HSA), αj-acid glycoprotein (AGP) and other serum components. The affinity of a drug toward plasma proteins is an important issue when determining its overall pharmacokinetic profile. A high level of protein binding reduces the free concentration and therefore the physiological activity of the drug. Circulating protein-drug complex also serve to replenish the free drug concentration as free drug is removed from the body by various elimination processes, and thereby prolong the duration of drug action. Thus, the level of protein binding is an important factor in the delicate balance between intended physiological activity and potential side effects of the drug. Ideally the free concentration of each drug candidate should be estimated in the blood from the patient for which the drug is intended. Early estimates of the protein binding level, as well as of other ADME parameters, are now becoming more important in the drug discovery process. Therefore, simplified approaches for the determination of protein binding are being considered. A wide variety of methods and experimental conditions are used for this purpose. Methods include equilibrium dialysis, ultrafiltration, ultracentrifugation, spectroscopic methods, affinity and size exclusion chromatography and electrophoretic methods as reviewed by Oravcova (Oravcova et al., "Drug-Protein Binding Studies. New Trends in Analytical and Experimental Methodology," Chromatogr. 677: 1-28 (1996)) and Hage (Hage et al., "Review. Recent Advances in Chromatographic and Electrophoretic Methods for the Study of Drug-Protein Interactions," J. Chromatogr., 699:499- 525 (1997)). These methods rely either on a separation of bound and free drug or protein, or on a change in intrinsic parameters such as the mobility of protein or the spectroscopic properties of a drug-protein complex. To simplify the studies of drug protein interactions it is also common to use single proteins, HSA and/or α i -acid glycoprotein, instead of blood or plasma. This approach is particularly useful when many compounds have to be screened for protein binding. Furthermore it has also been demonstrated that the use of immobilised HSA in high performance liquid chromatography (HPLC) correctly reflects the binding of drugs to free HSA. (Domenici et al., "Immobilized Serum Albumin: Rapid HPLC Probe of Stereoselective Protein Binding Interactions," Chirality 2:263- 268 (1990) and Domenici et al., "Use of a Human Serum Albumin-Based Chiral Stationary Phase for High-Performance Liquid Chromatography for the Investigation of Protein Binding: Detection of the Allosteric Interaction Between Warfarin and Benzodiazepine Binding Sites," J. Pharm. Sci., 80, 164-169 (1991)). These results encouraged us to investigate the use of biosensor technology for affinity analysis and for ranking of the binding of drug compounds to surface immobilised HSA. The main advantages of biosensor technology (here a surface plasmon resonans, SPR, sensor) are that binding is monitored directly without the use of labels, sample consumption is low and the analysis is rapid and automated. ((Jonsson et al., "Real-Time Biospecific Interaction Analysis. The Integration of Surface Plasmon Resonance Detection, General Biospecific Interface Chemistry and Microfluidics into one Analytical System," Advances in Biosensors, 2:291- 336 (1992) and Szabo et al., "Surface Plasmon Resonance and its Use in Biomolecular Interaction Analysis," (BIA). Curr. Opin. Struct. Biol. 5:699-705 (1995)). In this Example, we present a new method, based on the injection of compounds at a single concentration, for classifying compounds as high, intermediate or low HSA binders by comparing their binding levels to that of selected reference compounds . Materials and methods Instrumentation: BIACORE 3000 from Biacore AB, Uppsala, Sweden, was used, Figure 8. Interaction analysis was performed at 25°C. Buffers: 67 mM isotonic phosphate buffer with 5 % dimethyl sulphoxide (DMSO) pH 7.4 (9.6 g Na2HP04 ■ 2 H2O, 1.7 g KH2PO4, 4.1 g NaCl to 1 litre) was used as running buffer during the assay. Immobilisation of HSA: Sensor chip CM5 was used for analysis. The sensor chip consists of a carboxymethylmodified dextran polymer linked to a gold covered glass support. (Johnsson et al., "Immobilization of Proteins to a Carboxymethyldextran Modified Gold Surface for Biospecific Interaction Analysis in Surface Plasmon Resonance Sensors," Anal. Biochem., 198:268-277 (1991).) HSA (Sigma, essentially fatty acid and globulin free, A 3782) at 1 mg/ml was diluted to 15 ug/ml in 10 mM sodium acetate, pH 5.2 and immobilised to the sensor chip, using amine coupling. Running buffer without DMSO was used during immobilisation. The surface was activated by injecting a solution containing 0.2 M N-ethyl-N '-dimethyl aminopropyl carbodiimide (EDC) and 50 mM N-hydroxysuccinimide (NHS) for 7 minutes. HSA was injected during 7 minutes and the surface was then blocked by injecting 1 M ehanolamine at pH 8.5 for 7 minutes. 50 mM NaOH was injected in three 30 seconds pulses to wash off non covalently bound HSA and to stabilise the baseline. Final immobilisation levels were between 9500 and 11000 RU (Resonance Units). The immobilisation procedure was followed by several washes with buffer to equilibrate the HSA surface in the DMSO running buffer. Unmodified dextran was used as a reference surface. To ensure highest sensitivity the SPR detector response was normalised before running the assay, by calibrating the detector at various light intensities under conditions of total internal reflection (automised procedure). Compounds: naproxen, sulphadimethoxin, warfarin, digitoxin, diazepam, naproxen, pyrimethamine, coumarin, salicylic acid, rifampicin, phenytoin, prednisone, quinine, salbutamol, dipyridamole, timolol, acebutolol, atenolol, alprenolol, pindolol, metoprolol and propranolol were from Sigma. Ketanserin was from Fluka. Ritonavir was a gift from Bjδrn Claeson, Medivir. Delavirdine, tolterodine, 5-HM and tipranivir were gifts from Pharmacia & Upjohn. Compounds were selected to: a) cover a wide affinity range towards HSA, b) include binders to major binding sites, c) include different classes of compounds, d) show a range in molecular weight e) if possible be commercially available, see also Table 7. Compounds were diluted from 20 mM DMSO stock solutions to their final concentration in running buffer. Dilution was done in such a way that the pH and the concentrations of both DMSO and buffer substances in samples and running buffer were carefully matched. Calibration of DMSO bulk differences between reference and HSA flowcells The SPR signal reflects changes in refractive index (RI) at the sensor surface. RI changes as a consequence of binding events close to the sensor surface and are related to the increase of mass on the surface (Stenberg et al., "Quantitative Determination of Surface Concentration of Protein with Surface Plasmon Resonance using Radiolabeled Proteins," J. Colloid Interface Sci., 143:513-526 (1991)). An additional signal is obtained if the injected sample has a RI that differs from that of the running buffer. This signal is referred to as a solvent effect. When the solvent effect is small (in the order of a hundred RU) it can normally be eliminated from the total signal by subtraction of the signal from the reference surface. (Karlsson et al., "Surface Plasmon Resonance Detection and Multi-Spot Sensing for Direct Monitoring of Interactions Involving Low Molecular Weight Analytes and for Determination of Low Affinities," Anal. Biochem., 225:274-280 (1995)). However, the introduction of a high refractive index solvent such as DMSO can give rise to large shifts in refractive index during the injection, and mere subtraction of the data from the reference flowcell is no longer sufficient. To correct for these effects a DMSO calibration procedure was adopted (Figure 9c). Buffer solutions with varying concentrations of DMSO (4.5 - 6 %) were injected in sequence over reference and HSA surfaces. This was done at the beginning and end of each experiment, using the same flow rate as for the assay. The responses of the calibration solutions, obtained from the reference surface, covered a range no larger than -500 to +1000 RU relative to the baseline. A DMSO calibration curve was created by plotting the difference in response between HSA and reference flowcells versus the response in the reference flowcell. This curve was used for correcting response levels obtained during sample injection. Assay design. Compounds were injected over the reference and HSA flowcell during one minute at a flow rate of 30 μl/min. Each cycle consisted of a one minute waiting period for monitoring of the baseline stability, a one minute injection of compound, a 35 seconds undisturbed dissociation phase, and a wash of the flow system, except the sensor surface, with a 1 : 1 mixture of DMSO and water. Compound responses in the reference flowcell were within the range of the DMSO calibration curve, extrapolation of the calibration curve was not used. An injection of running buffer was made between each sample to check for carry over effects. Eight DMSO calibration solutions were injected sequentially, each during 30 seconds, at the beginning and end of each experiment. Data evaluation: Data obtained in the reference flowcell was subtracted from that obtained in the HSA flowcell. Responses from injections of drug compounds and calibration solutions were extracted between report points set at 10 seconds before injection start and 10 seconds before the end of injection. These responses were further corrected for DMSO effects by use of the calibration curves (Figure 9) and the final response values were used in Krj determinations and in ranking experiments. To get an estimate of Kf_> and response values at site saturation (R max ), measured response values (Req) obtained with different concentrations (C) of the compound were fitted to the equation: Req/mw = CxR max /(C+K D ) (5) This equation was derived previously for the general case of an analyte binding to an immobilised partner (Karlsson et al., "Kinetic Analysis of Monoclonal Antibody- Antigen Interactions with a New Biosensor Based Analytical System," J. Immunol. Methods, 145:229-240 (1991)) and here provides the affinity and the saturation response for a compound that interacts with a single site of immobilised albumin. To facilitate a comparison of responses obtained with compounds of varying molecular weights R q/mw i.e., the steady state response divided with the molecular weight of the compound was used in the fitting procedure instead of R q- By using this ratio, the saturation level R m ax was considered identical for all compounds that bind to a single site of albumin and dose response curves from several compounds were therefore analysed simultaneously with R m aχ as a global parameter i.e., common to all compounds and Kj) as a local parameter i.e., unique for each compound. To compare Kj) calculated by this method with reported levels of % bound the equilibrium equation was used, [HSA] [Drug]/[HSA-drug complex]=Kj). A concentration of 0.68 mM of HSA in serum was assumed. With x as the total concentration of compound and p as the part bound (0.68-xxp) x (x-xxp)/xχp = Kj). Following an input of p (p is equal to % bound/100) Kj} values were calculated for total drug concentrations ranging from 0.1 nM to 1 mM. Calculated KQ values were largely independent of the total drug concentration. Values for selected compounds are shown in Table 6. In ranking experiments responses obtained at 80 μM concentration were divided by the molecular weight of the compound to allow comparison of binding levels. RESULTS Sensorgrams and calibration of solvent effects. All compounds tested bound reversibly to immobilised HSA. During injection a steady state response was reached within a few seconds. After the end of the injection drug compounds dissociated very rapidly and the signal returned to baseline almost immediately (Figures 9a and 9b). Of the compounds investigated only tipranivir remained bound to the HSA surface for more than two minutes. To reduce errors in reference subtraction, solvent effects were compensated for by applying a DMSO calibration procedure (Figure 9c). Solvent effects from samples typically ranged from -100 and 500 RU leading to correction factors of -20 to 10 RU. In previous studies remaining solvent related errors have been shown to be low and between +/- 3 RU when solvent effects were as large as 2000 RU (Karlsson et al., "BIACORE Analysis of Drug Target Interactions. Direct and Competitive Binding Assays for Investigation of Interactions between Thrombin and Thrombin Inhibitors," Anal. Biochem., 275:1-13 (2000)). Activity and stability of immobilised HSA. The major drug binding sites on HSA remained accessible after immobilisation of HSA to the sensor surface via its amine groups. This was demonstrated by the binding of warfarin (Zatόn et al., "Binding of Coumarins to Site I of Human Serum Albumin. Effect of the Fatty Acids," Chemico-Biol. Int. 97:\69-\74 (1995)) to Site I (the warfarin site), naproxen (Cheruvallath et al., "A Quantitative Circular Dichroic Investigation of the Binding of the Enantiomers of Ibuprofen and Naproxen to Human Serum Albumin," J. Pharm. Biomed. Anal, 75:1719-1724 (1997)) to Site II (the benzodiazepin site) and by binding of digitoxin (Kragh-Hansen, U., "Relations between High- Affinity Binding Sites of Markers for Binding Regions on Human Serum Albumin," Biochem. J., 225:629- 638 (1985)) to its site (Figures lOa-lOc). As expected saturation levels were related to the molecular weight of the compounds. Digitoxin with a mw of 765 Da, warfarin with mw 308 Da and naproxen with mw of 230 Da approached saturation levels close to 70, 35 and 15 RU respectively. These values indicated that mainly single sites were occupied at these concentrations with the exception of ritonavir where the response indicated a stoichiometry of more than one ritonavir molecule/HSA molecule (Figure lOd). In ranking experiments warfarin was used as a positive control of HSA activity. Warfarin was injected early, in the middle and late in each experiment. Reference subtracted and calibrated responses of warfarin from an experiment performed on the day of HSA immobilisation and from an experiment conducted seven days later on the same HSA surface are presented in Table 7. The warfarin response was stable and on this surface response levels ranged from 37.1 to 41.8 RU with a standard deviation of 1.6 RU. KQ determinations Dose response curves obtained for warfarin, naproxen, digitoxin, ritonavir, quinine, and rifampicin are shown in Figure 10. For warfarin and naproxen binding appeared monophasic and approached saturation levels at low concentrations. For ritonavir and to some extent digitoxin response levels indicated a biphasic interaction. Quinine and rifampicin gave linear dose response curves over a wide concentration range and it was not possible to estimate saturation levels. Given the dose response curves in Figure 10, Kj) analysis was problematic. To obtain an estimate of affinity the analysis was simplified by assuming 1) that HSA binding sites were equally available 2) that only high affinity sites were populated at concentrations below 40 μM and 3) that response levels were normalised by dividing the measured response (RU proportional to g/m^) with the molecular weight (RU/mw proportional to mole/m^). The KQ data obtained (from eqnl) with these assumptions showed that compounds with low and medium binding to HSA gave much higher Krj values than compounds with high level of binding to HSA and suggested a good correlation between reported % bound and j but with limited resolution of compounds with binding levels above 97 % bound (Table 8). Ranking of binding levels, comparison of biosensor data with conventional techniques. For ranking of compounds we used a simpler approach by measuring the degree of binding at a concentration high enough to give a reasonable signal but low enough to avoid misinteφretation of compounds binding to several binding sites. The binding levels of 19 drugs with molecular weights between 138 and 823 Da and with reported HSA binding levels between 8 and 99.9% were studied through duplicate injections of a single concentration (80 μM) of each drug. Identical experiments were run on the day of HSA immobilisation and a week later on the same HSA surface. Figure 11 shows the correlation between binding levels obtained on the day of HSA immobilisation and the reported degree of HSA binding. To increase the resolution of the high affinity compounds, values were put on a logarithmic scale and 2-logι n(100-%bound) was plotted as a function of biosensor data. In a screening situation it may be useful to include compounds as markers for medium and high level binders. The data set in Figure 11 can then be re plotted using only the biosensor data (Figure 12). Using diazepam with a reported binding level of 86.1% and warfarin with reported binding of 98% as marker compounds drugs could be divided into Low, Medium and High level binders. When tested on the same HSA surface a week later the 19 compounds were all classified into the same groups, except for salicylic acid which changed from Medium to Low on the second analysis. The individual ranking within the group ofHigh level bmders were the same as on the day of immobilization. Within the Medium and Low group some compounds shifted places but only salicylic acid switched group from Medium to Low. Discussion Early estimates of pharmacokinetic properties can be used to complement data found on the interaction between a compound and its pharmacological target. The combined data can provide a broader understanding of the functional aspects of the compounds and this may be useful for developing them into drug candidates. With respect to protein binding it is of particular interest to identify compounds that are highly bound to plasma proteins. We have developed a screening method to rank the binding of compounds to immobilised HSA, based on biosensor technology. The use of different methodologies and experimental conditions for determination of HSA binding levels vary considerably and for some compounds used in this Example reported binding data was sometimes conflicting, making a fair correlation study difficult. Examples of variations in reported binding levels included Warfarin 98.0% - 99.6% (Ferrer et al., "The Binding of Benzopyranes to Human Serum Albumin. A Structure- Affinity Study," J. Protein. Chem. 77:115-119 (1998); and Vestberg et al., "High-Affinity Binding of Warfarin, Salicylate and Diazepam to Natural Mutants of Human Serum Albumin Modified in the C-Terminal End,". Biochem. Pharmacol. 44:1515-1521 (1992)), Diazepam 86.1% - 98.2% (McNamara et al., "The Protein Binding of Phenytoin, Propranolol, Diazepam and AL01576 (an Aldose Reductase Inhibitor) in Human and Rat Diabetic Serum," Pharm. Res., 5:261-265 (1988); and Viani et al., "Binding of Diazepam, Salicylic Acid and Digitoxin to Albumin Isolated from Fetal and Adult Serum," Dev. Pharmacol. Ther., 77:100-108 (1991)) and Salicylic Acid 81.7% - 90.5% (Gahramani et al., "Pharmacokinetics and Drug Disposition; Protein Binding of Aspirin and Salicylate Measured by In Vivo Ultrafiltration," Clin. Pharmacol. Ther., 63:285-295 (1998); and Viani et al., "Binding of Diazepam, Salicylic Acid and Digitoxin to Albumin Isolated from Fetal and Adult Serum," Dev. Pharmacol. Ther., 77:100-108 (1991)). For other compounds only one literature value was found, but most likely a similar variation would be found if these compounds were assayed by several methodologies. In spite of this it was possible to correlate biosensor data to HSA binding levels determined with other techniques. The concentration 80 μM was chosen for the ranking experiments with the purpose of demonstrating ranking of compounds over the entire affimty range. At this concentration the responses obtained for compounds with binding levels lower than 60% were still small, but since many compounds may be of limited solubility and since it was desired to reduce the influence of secondary binding sites, higher concentrations were not used. During preparation of this manuscript we have seen indications that the concentration may be reduced to 10-40 μM, when focusing mainly on identification of high level binders. To discriminate between low affinity binders an alternative approach with an inhibition study with HSA in solution and the drug target immobilised could be used. By comparing dose response curves and considering experimental errors of a few RU molecular weight adjusted R ma χ values obtained with compounds binding to different sites were similar. This led to the assumption that the binding sites were equally available. Dramatically different site availability would have resulted in erroneous ranking of binding levels. By using warfarin and diazepam as reference compounds, classification into high (>95%) or low (<80%) level binding was facilitated. Inspection of Figure 12 demonstrates that of the reported high level binders sulfadimethoxin and ritonavir were classified as medium level binders and digitoxin was on the border between the medium and the high level binders. The reported value of 99% bound for ritonavir was obtained in plasma (Vacca, J. and Condra, J., "Clinically Effective HIV-1 Protease Inhibitors," DDT, 2:261-272 (1997)) and it has further been reported (Lazdins et al., "7Ϊ Vitro Effect of Alpha 1- Acid Glycoprotein on the Anti-Human Immunodeficiency Virus (HIV) Activity of the Protease Inhibitor CGP 61755: A Comparative Study with other Relevant HIV Protease Inhibitors," J. Infect. Dis. , 175: 1063- 1070 (1997)) of a 10-20 fold reduction in plasma binding at the addition of AGP (αl- acid glycoprotein), indicating a significant part of the plasma binding being to AGP. It could therefore be argued that in this respect only sulfadimethoxine was an outlier. Our ranking of Ketanserin as a low level binder is in conflict with the reported value of 93.2 % (Meuldermans et al., "Plasma Protein Binding of Ketanserin and its Distribution in Blood," Arzneimittelforschung, 35:794-800 (1988)). Others have reported a lower binding, 90.6% to plasma (Trenk et al., "Pharmacokinetics and Pharmacodynamics of the 5-HT2 Receptor Antagonist Ketanserin in Man," J. Cardiovasc. Pharm., 5:1034-1039 (1983)) which suggests a lower level of binding to HSA, but still the reason for the discrepancy is unknown at this point. Along with these results, the excellent repeatability of the warfarin data and the stability of the HSA surface makes the biosensor method well suited for use in the prediction of protein binding levels. A particular advantage of the present method was that binding levels obtained with a single injection of the compound could be used for the correlation. One 96 well plate of compounds can be assayed in 24 hours. The method is practical, it provides the necessary information and complicated analysis is avoided. Furthermore ranking experiments can be complemented by competition studies in order to determine site specificity. In such experiments compounds with known site specificity are conjugated with another molecule to increase its mass (and therefore the signal). This marker is mixed with other compounds and a competition for binding site is achieved (Karlsson, R., "Real Time Competitive Kinetic Analysis of Interactions between Low Molecular Weight Ligands in Solution and Surface Immobilised Receptors," Anal. Biochem., 227:142-151 (1994)). Initial attempts to determine KD values from the biosensor data showed that some information on the number of binding sites on HSA could be obtained. To calculate data for low affinity binders when the concentration was well below the Krj concentration R q/mw was used in the fitting procedure. R max was then used as a global parameter to constrain the fit. This made Krj values more consistent than individual calculations of R max values for each compound. But the analysis was also complex due to difficulties in determimng Kj) for compounds with multiple binding sites. In spite of the complexity in affinity determination, the inteφretation of dose response curves for high affinity binders holds considerable potential for obtaining stoichiometric information. Since SPR technology measures refractive index it is natural that future experiments include investigation of how differences in the refractive indices of compounds affect analysis. Some of the halogens are known to have high refractive index and we have noticed that compounds containing for instance bromine may give higher response values than anticipated from the molecular weight. Thus further studies along this line may be used to fine tune the HSA binding data. The possibility of studying in similar manner interactions with other plasma proteins such as AGP is ongoing in our laboratory. The first results from these studies are shown in Figure 13. Biosensor data were correlated against published AGP binding levels of tolterodine (Pahlman et al., "Serum Protein Binding of Tolterodine and its Major Metabolites in Humans and Several Animal Species," Biopharm. DrugDispos., 20:91-99 (1999)), quinine (Wanwimolruk et al., "Plasma Protein Binding of Quinine: Binding to Human Serum Albumin, Alpha 1-Acid Glycoprotein and Plasma from Patients with Malaria," J. Pharm. Pharmacol., 44:806-811 (1992)), seven β-adrenoceptor blocking drugs (McDonnell et al., "Using Capillary Electrophoresis/Frontal Analysis to Screen Drugs Interacting with Human Serum Proteins," Electrophoresis, 79:448-454 (1998); and Belpair et al., "Binding of b-Adrenoceptor Blocking Drugs to Human Serum Albumin, to al-Acid Glycoprotein and to Human Serum," Eur. J. Clin. Pharmacol., 22:253-256 (1982)) and dipyridamole (MacGregor, T. and Sardi, E., "In Vitro Protein Behaviour of Dipyridamole," J. Pharm. Set , 80: 119- 120 (1991)). A good correlation of binding was obtained with the exception of quinine which obtained a higher binding level compared to other techniques. All experimental conditions were the same as for the HSA correlation experiments, except the immobilisation chemistry of AGP (Figure 13). It would therefore be possible to immobilise HSA and AGP in two different flowcells, inject compounds and obtain binding data to both proteins simultaneously. TABLE 6 COMPOUNDS, MOLECULAR WEIGHTS AND LITERATURE REFERENCES TO HSA BINDING LEVELS Compound Molecular Reported Reported binding Literature weight binding classified as High, reference of (Da) to HSA (%) Medium ' or Low HSA binding Tipranivir 603 99.9 H 8 Naproxen 230 99.7 H 9 Ritonavir 721 99 to plasma H 10 Sulfadimethoxin 310 98.7 H 11 Warfarin 308 98 H 12 Delavirdine 457 96.7 H 13 Digitoxin 765 96.6 H 14 Ketanserin 395 93.2 M 1,5 Pyrimethamine 249 86.5 M 16 Diazepam 285 86.1 M 17 Coumarin 146 83.9 M 12 Salicylic acid 138 81.7 to plasma M 18 Rifampicin 823 73.3 L 19 Phenytoin 252 63.4 L 17 Prednisone 358 53.7 L 20 Tolterodine 325 42 L 21 Quinine 325 34.6 L 22 5-HM 341 23 L 21 Salbutamol 239 8 to plasma L 23 TABLE 7 DATA FROM TWO RANKING EXPERIMENTS ON THE SAME SURFACE, THE FIRST ON THE DAY OF HSA IMMOBILIZATION AND THE SECOND 7 DAYS LATER. WARFARIN (80 μM) WAS INJECTED IN DUPLICATE EARLY, IN THE MIDDLE AND LATE IN EACH ASSAY TO CHECK HSA BINDING ACTIVITY. Response (RU) Warfarin day 1 Warfarin day 7 early 37.1 38.4 38.2 38.8 middle 38.4 41.3 38.9 41.1 late 39.1 41.8 39.3 41.8 average: 38.5 40.5 standard 08 L5 deviation: TABLE 8 COMPARISON OF LITERATURE DATA OF % BOUND AND THE CORRESPONDING Kn FROM THE EQUILIBRIUM EXPRESSION, WΓTH Kp OBTAINED WITH BIOSENSOR — ANALYSIS. ~ Compound From Literature Biosensor Analysis % bound α K D (μM)* K D (μM) Naproxen 99.7 2 26 Ritonavir 99 7 • 21 Warfarin 98 14 9 Digitoxin 97.6 17 28 Rifampicin 73.3 218 195 Quinine 34.6 1290 556 a Literature references in Table 6. b Calculated from % bound as described in data evaluation EXAMPLE 4 POSSIBILITY OF IDENTIFYING THE CYTOCHROME P450 ISOZYME INVOLVED IN THE BINDING OF A TEST SUBSTANCE Experimental Procedure The experiments were carried out on a Biacore 3000 instrument using a CM5 sensor chip. The running buffer contained 0.1 M Na/K-phosphate (pH 7.5), 20% glycerol, and 0.05 mM EDTA. The temperature was maintained at 15 °C throughout the experiment. Surface preparation The chip surface was activated by injection of 35 μl of solution containing 5.75 g/1 N-hydroxysuccinimide and 37.5 g/1 N-ethyl-N'-(3- dinιethylammopropyl)carbom ' imide hydrochloride. The proteins were then immobilized by injecting 35 μl of 10 μg/ml solution in a buffer containing 5 mM K-phosphate (pH 6.8), 20% glycerol, 0.05 mM EDTA, and 0.1% Emulgen 913. Unreacted surface groups were then deactivated by injection of 50 μl 0.1 M Tris- HC1 (pH 8.0), 0.15 M KC1, and 20% glycerol. The flow rate during surface preparation was 5 μl min. Surface in the reference cell was not activated. Analysis The substances dissolved in the running buffer at indicated concentrations and injected at a flow rate of 30 μl/rnin. Figure 14. CYP2D6 and CYP2C19 were immobilized in flow cells 3 and 2 to 13000 and 11000 RU, respectively. Flow cell 1 was used as a reference. Figure 15. CYP2E1 and CYP3A4 were immobilized in flow cells 2 and 3 to 8500 and 7300 RU, respectively. Flow cell 1 was used as a reference. Cytochromes P450 (CYP) is a group of enzymes involved in the metabolism of many drug substances. The experiments show the possibility of identifying the cytochrome P450 isozyme involved in the binding of a test substance. This is an important pharmacokinetic parameter since it allows a conclusion that (i) the substance has a potential to affect the pharmacokinetics of other drugs known to be metabolized by the same isozyme and (ii) the substance may itself be metabolized by that isozyme. The substances used in the described experiment, ketoconazole and quinidine, are known to be specific inhibitors of CYP3A4 and CYP2D6, respectively. The results obtained demonstrate that BIA assay is able to detect the interaction of these substances with their targets in a specific way. While the present invention has been described in the context of the embodiments illustrated and described herein, the invention may be embodied in other specific ways or in other specific forms without departing from its spirit or essential characteristics. Therefore, the described embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims: CLAIMS 1. A method for assaying a drug candidate with a biosensor having one or more sensing surface-bound biomolecules associated therewith, comprising the steps of: measuring a binding interaction between the drug candidate and the one or more sensing surface-bound biomolecules of the biosensor to obtain at least one binding interaction parameter of the drug candidate; and comparing the at least one binding interaction parameter against at least one mathematical expression correlated from binding interaction data associated with known drug compounds having known pharmacokinetic parameters to determine an estimate of at least one pharmacokinetic parameter of the drug candidate. 2. The method of claim 1 wherein the at least one pharmacokinetic parameter is an absoφtion parameter, a distribution parameter, a metabolism parameter, or an excretion parameter. 3. The method of claim 1 wherein the at least one pharmacokinetic parameter is volume of distribution, total clearance, protein binding, tissue binding, metabolic clearance, renal clearance, hepatic clearance, biliary clearance, intestinal absoφtion, bioavailability, relative bioavailability, intrinsic clearance, mean residence time, maximum rate of metabolism, Michaelis-Menten constant, partitioning coefficients between tissues and blood or plasma, fraction excreted unchanged in urine, fraction of drug systemically converted to metabolites, elimination rate constant, half-life, or secretion clearance. 4. The method of claim 3 wherein the partitioning coefficients between tissues and blood or plasma are partitioning coefficients associated with the blood brain barrier, blood placenta barrier, blood human milk partitioning, blood adipose tissue partitioning, or blood muscle partitioning. 5. The method of claim 1 wherein an estimate of at least two pharmacokinetic parameters of the drug candidate are determined. 6. The method of claim 1 further comprising determining an estimate of a solubility property of the drug candidate. 7. The method of claim 1 wherein the biosensor utilizes a mass- sensing technique. 8. The method of claim 7 wherein the mass-sensing technique involves surface plasmon resonance. 9. The method of claim 1 wherein the at least one mathematical expression correlated from binding interaction data associated with known drug compounds is a function correlated to a plurality of data points plotted on a Cartesian coordinate system. 10. The method of claim 1 wherein the plurality of sensing surface- bound biomolecules are selected from liposomes, plasma proteins, CYP 450 enzymes, metabolic enzymes, or transport proteins. 11. The method of claim 1 wherein the biosensor utilizes a sensor chip comprising: a hydrogel coupled to the sensor surface, wherein the hydrogel has a plurality of functional groups, and wherein the one or more sensing surface-bound biomolecules are bonded to the hydrogel. 12. The method of claim 11 wherein the sensor chip further comprises: a free electron metal that includes a sensor surface, wherein the free electron metal is selected from the group consisting of copper, silver, aluminum and gold. 13. The method of claim 12 wherein the biosensor is capable of detecting surface plasmon resonance associated with the free electron metal. 14. The method of claim 12 wherein the hydrogel is a polysaccharide or a water-swellable organic polymer. 15. The method of claim 14 wherein the polysaccharide is dextran. 16. The method of claim 11 wherein the plurality of functional groups of the hydrogel of the sensor chip include one or more of a hydroxyl, carboxyl, amino, aldehyde, carbonyl, epoxy or vinyl functional group. 17. The method of claim 11 wherein the step of measuring comprises detecting a signal associated with a reflected light beam with respect to time, wherein the reflected light beam establishes a surface plasmon resonance with the free electron metal. 18. The method of claim 17 wherein the signal associated with the reflected light beam defines a resonance curve of the surface plasmon resonance. 19. The method of claim 17 wherein the signal associated with the reflected light beam defines a reflectance πήnimum of the surface plasmon resonance. 20. A computer memory containing a data structure useful for assaying a drug candidate in accordance with the method of claim 1, the data structure comprising binding interaction data associated with known drug compounds such that the data structure may be used to determine an estimate of at least one pharmacokinetic parameter of the drug candidate. 21. A generated data signal for conveying a data structure useful for assaying a drug candidate in accordance with claim 1, the data structure comprising binding interaction data associated with known drug compounds such that the data structure may be used to determine an estimate of at least one pharmacokinetic parameter of the drug candidate.

Inventors: HAEMAELAEINEN MAIKKU, KARLSSON ROBERT, LOEFAAS STEFAN, HAEMAELAEINEN, MAIKKU, KARLSSON, ROBERT, LOEFAAS, STEFAN
Applicants: BIACORE AB, HAEMAELAEINEN MAIKKU, KARLSSON ROBERT, LOEFAAS STEFAN, HAEMAELAEINEN, MAIKKU, KARLSSON, ROBERT, LOEFAAS, STEFAN
Application year: 2000
Main classification: G01N33
IPC codes: G01N21, G01N33