Patent ID: 12195793

DESCRIPTIONS OF CERTAIN EMBODIMENTS

The presently disclosed technique was demonstrated using the Aptima™ HIV-1 Quant Dx assay from Hologic, Inc., (Marlborough, MA) as a model system. This viral load monitoring assay is both highly sensitive and specific, and can be used to assess responses to antiretroviral treatment by monitoring changes in the concentration of HIV-1 RNA. The assay is an in vitro polynucleotide amplification test for the detection and quantification of human immunodeficiency virus type 1 (HIV-1) RNA groups M, N, and O that can be performed on the fully automated Panther™ system (Hologic, Inc.). The system running the viral load assay is calibrated to output a concentration of virus, measured in copies/ml using a 500 μl test sample. For example, the model assay can be used for monitoring the effect of antiviral treatment by measuring changes in the concentration of HIV-1 RNA in plasma. There would be a benefit for accurately correlating quantitative results obtained using DBS samples and plasma samples using the same calibration as well as reagents, and protocol for target capture, amplification and detection.

The model assay involves three main steps, which all take place in a single tube on the automated Panther system for polynucleotide analysis: target capture, target amplification by Transcription Mediated Amplification, and detection of the amplification products (amplicon) by the fluorescently labeled hybridization probes (torches). During target capture, the specimen is treated with a detergent to solubilize the viral envelope, denature proteins, and release viral genomic RNA. Capture oligonucleotides hybridize to highly conserved regions of the HIV-1 genome, if present, in the test sample. The hybridized target is then captured onto magnetic microparticles that are separated from the specimen in a magnetic field. Wash steps remove extraneous components from the reaction tube. Target amplification then occurs via TMA, which is a transcription-mediated polynucleotide amplification method that utilizes two enzymes, MMLV (Moloney murine leukemia virus) reverse transcriptase and T7 RNA polymerase. The reverse transcriptase is used to generate a DNA copy (containing a promoter sequence for T7 RNA polymerase) of the target sequence. T7 RNA polymerase produces multiple copies of RNA amplicon from the DNA copy template. The model assay utilizes the TMA method to amplify two regions of HIV-1 RNA (pol and LTR). Amplification of these specific regions is achieved using specific primers which are designed to amplify HIV-1 groups M, N, and O. The primer design and dual target approach ensure accurate detection and quantitation of HIV-1. Detection is achieved using single-stranded polynucleotide torches that are present during the amplification of the target and that hybridize specifically to the amplicon in real-time. Each torch has a fluorophore and a quencher. When the torch is not hybridized to the amplicon, the quencher is in close proximity to the fluorophore, and so suppresses fluorescence. When the torch binds to the amplicon, the quencher is moved farther away from the fluorophore and emits a signal at a specific wavelength when excited by a light source. A higher fluorescent signal is generated as more torches hybridize to amplicon. The time taken for the fluorescent signal to reach a specified threshold is proportional to the starting HIV-1 concentration. Each reaction has an internal calibrator/internal control (IC) that coamplifies with the HIV-1 analyte and controls for variations in specimen processing, amplification, and detection. The concentration of a sample is determined by the Panther system software using the HIV-1 and IC signals for each reaction and comparing them to calibration information. Determined concentrations are calibrated for HIV-1 in plasma samples, and not for reconstituted DBS samples. The determined concentration here can alternatively be referred to as an “observed” result or a “measured” result.

There are different types of DBS, and each can be used for performing the quantitative technique described herein. Blood samples can be obtained from infants using standard heal stick or finger stick techniques. Here the skin surface of the infant is disinfected and then pricked with a sterile needle or lancet. Next, 3-5 drops of blood can be added to each of a plurality (e.g., 5) of spots on a DBS “card,” ensuring that the entire surface of the circle is completely filled. The finger stick technique also can be used with adults to obtain DBS samples. Whole blood conveniently may be stored for up to 24 hours at 2° C. to 30° C. prior to application to the DBS cards. In this instance, 70 μl of stored whole blood can be applied to the center of a filter circle on a DBS card, for example using a calibrated 200 μl pipette. Spotted blood samples, however obtained, can be dried at ambient temperature for 4-24 hours. Individual cards harboring the dried samples can be placed into an envelope (e.g., a glassine envelope) for storage or transport. Multiple glassine envelopes can be packaged into a resealable plastic bag with one or more desiccant packs. However they are packaged, DBS samples can be held or shipped at ambient temperatures for subsequent processing.

Preferred Polynucleotide Amplification Methods

Examples of in vitro polynucleotide amplification methods useful in connection with the present technique include, but are not limited to: Transcription Mediated Amplification (TMA), Single-Primer Nucleic Acid Amplification, Nucleic Acid Sequence-Based Amplification (NASBA), the Polymerase Chain Reaction (PCR), Strand Displacement Amplification (SDA), Self-Sustained Sequence Replication (3SR), DNA Ligase Chain Reaction (LCR) and amplification methods using self-replicating polynucleotide molecules and replication enzymes such as MDV-1 RNA and Q-beta enzyme. Methods for carrying out these various amplification techniques respectively can be found in U.S. Pat. No. 5,399,491, U.S. patent application Ser. No. 11/213,519, published European patent application EP 0 525 882, U.S. Pat. Nos. 4,965,188, 5,455,166, Guatelli et al.,Proc. Natl. Acad. Sci. USA87:1874-1878 (1990), International Publication No. WO 89/09835, U.S. Pat. No. 5,472,840 and Lizardi et al.,Trends Biotechnol.9:53-58 (1991). The disclosures of these documents which describe how to perform polynucleotide amplification reactions are hereby incorporated by reference. Thus, although the model system used for demonstrating the correction factor adjustment technique employed TMA as the amplification reaction mechanism, alternative amplification reaction mechanisms also can be used with equally good results.

Examples of Preferred Real-Time Quantitative Techniques

Generally speaking, real-time polynucleotide amplification and detection procedures involve monitoring production of amplification reaction products as the amplification reaction is occurring. As indicated above, any number of different amplification methods can be used to create amplification products. In some embodiments, synthesis of amplification products as a function of time or cycle number is indicated by detection of a fluorescent signal generated in the amplification reaction mixture. Examples of methods useful for calibrating instruments carrying out real-time amplification reactions are given in U.S. Pat. Nos. 9,932,628 and 9,976,175, the disclosures of these patents being incorporated by reference herein for all purposes. Success of these methods is independent of the manner in which run curves in the real-time procedures are obtained. Stated differently, different indicia of amplification can be used to establish when an amplification reaction has achieved a desired threshold level of amplification progress.

A variety of indicia of amplification can be used for quantifying analytes before the CF adjustment is applied to the data. Real-time amplification and detection for quantifying polynucleotide analytes is highly preferred for use in connection with the disclosed CF adjustment technique, and is subject to alternative data processing procedures with good results in each case. For example, mathematical and computing techniques that will be familiar to those having an ordinary level of skill in the art can be used to identify the time of occurrence of the maximum of the first derivative, or the time of occurrence of the maximum of the second derivative of a real-time run curve. Approaches for determining these features of a growth curve have been detailed by Wittwer et al., in U.S. Pat. No. 6,503,720, the disclosure of which is incorporated by reference herein. Other useful approaches involve calculating a derivative of a growth curve, identifying a characteristic of the growth curve, and then determining the threshold time or cycle number corresponding to the characteristic of the derivative. Such techniques have been disclosed in U.S. Pat. No. 6,783,934, the disclosure of which is incorporated by reference. Still other useful indicia of amplification include “TTime” and “TArc.” Different approaches for determining TArc values employ directionally similar vectors (i.e., resulting in a value identified simply by “TArc”), and directionally opposed vectors (i.e., resulting in a value identified as “OTArc”). Still other techniques involve identifying cycle threshold (e.g., “Ct”) values as the time or cycle number during a reaction at which a signal, preferably a fluorescent signal, equals a static threshold (e.g., a predetermined static threshold value).

Preferred Systems and Apparatus

The methods disclosed herein are conveniently implemented using a computer or similar processing device (“computer” hereafter). In different preferred embodiments, software or machine-executable instructions for performing an algorithm can be loaded or otherwise held in a memory component of a freestanding computer, or in a memory component of a computer linked to a device used for monitoring, preferably as a function of time, the amount of a product undergoing analysis. In a highly preferred embodiment, software for executing the correction factor adjustment procedure is held in a memory component of a computer that is linked to, or that is an integral part of a device capable of monitoring the amount of an amplicon present in a reaction mixture as a function of time. This includes a processing device component on an electronic circuit board (e.g., embedded software) of an automated nucleic acid analyzer.

In some embodiments, the computer can be in communication with, either by wired or wireless means, a fluorometer that detects fluorescent signals, where the fluorometer is arranged or configured to monitor fluorescent signals generated in one or more reaction vessels contained within a temperature-controlled incubator. The incubator can be a temperature-controlled block (e.g., a metal block configured for receiving and containing one or more tubes, or even a multi-well plate), or a chamber that exposes one or more reaction vessels to controlled temperature conditions.

In some embodiments, either or both of a controller system for controlling a real-time amplification device and/or the detection system of the real-time amplification device can be coupled to an appropriately programmed computer which functions to instruct the operation of these instruments in accordance with preprogrammed or user input instructions. The computer preferably also can receive data and information from these instruments, and interpret, manipulate and report this information to the user.

In some embodiments, the computer also can include appropriate software for receiving user instructions, either in the form of user input into a set of parameter fields, or in the form of preprogrammed instructions (e.g., preprogrammed for a variety of different specific operations). The software then converts these instructions to appropriate language for instructing the operation of the real-time amplification controller to carry out the desired operation. Preferably, the computer also is capable of receiving data from one or more sensors/detectors included within the system, and interprets the data in accordance with the programming. The system preferably includes software that correlates a feature of a growth curve representing the quantity of amplified copies of the polynucleotide of interest as a function of time, as detected by the detector, to the number of copies of the polynucleotide of interest present in a test sample.

Preferably, when the computer used for executing the disclosed CF determination and adjustment procedure is an integral component of an apparatus for performing and analyzing real-time polynucleotide amplification reactions, the apparatus preferably comprises a temperature-controlled incubator, a detection device for collecting signals (e.g., a fluorometer), and an analyzing device (e.g., a computer or processor) for analyzing signals. The apparatus optionally can further include an output device for displaying data obtained or generated. The analyzing device may be connected to the temperature-controlled incubator through an input device known in the art, and/or connected to an output device known in the art for data display. In one embodiment, the temperature-controlled incubator is capable of temperature cycling.

Generally speaking, the various components of an apparatus for performing the real-time polynucleotide amplification useful in connection with the disclosed methods will be conventional components that will be familiar to those having an ordinary level of skill in the art. The temperature-controlled incubator used to perform and analyze real-time polynucleotide amplification may be of a conventional design which can hold a plurality of reaction tubes, or reaction samples in a temperature-controlled block in standard amplification reaction tubes or in wells of a multiwell plate. In one aspect, the detection system is suitable for detecting optical signals from one or more fluorescent labels. The output of the detection system (e.g., signals corresponding to those generated during the amplification reaction) can be fed to the computer for data storage and manipulation. In one embodiment, the system detects multiple different types of optical signals, such as multiple different types of fluorescent labels and has the capabilities of a microplate fluorescence reader. The detection system is preferably a multiplexed fluorimeter containing an excitation light source, which may be a visible light laser or an ultraviolet lamp or a halogen lamp, a multiplexer device for distributing the excitation light to the individual reaction tubes and for receiving fluorescent light from the reaction tubes, a filtering means for separating the fluorescence light from the excitation light by their wavelengths, and a detection means for measuring the fluorescence light intensity. Preferably, the detection system of the temperature-controlled incubator provides a broad detection range that allows flexibility of fluorophore choice, high sensitivity and excellent signal-to-noise ratio. Optical signals received by the detection system are generally converted into signals which can be operated on by the processor to provide data which can be viewed by a user on a display of a user device in communication with the processor. The user device may comprise a user interface or may be a conventional commercially available computer system with a keyboard and video monitor. Examples of data which can be displayed by the user device include amplification plots, scatter plots, sample value screens for all the tubes or reaction vessels in the assembly and for all labels used, an optical signal intensity screen (e.g., fluorescent signal intensity screen), final call results, text reports, and the like.

Computer Program Products

Included within the scope of the invention are software-based products (e.g., tangible embodiments of software for instructing a computer to execute various procedural steps) that can be used for performing the data processing method. These include software instructions stored on computer-readable media, such as magnetic media, optical media, “flash” memory devices, and computer networks or cloud storage. As well, the invention embraces a system or an apparatus that amplifies polynucleotides, detects polynucleotide amplification products, and processes results to indicate a quantitative result for target in a test sample. Although the various components of the apparatus preferably function in a cooperative fashion, there is no requirement for the components to be part of an integrated assembly (e.g., on a single chassis). However, in a preferred embodiment, components of the apparatus are connected together. Included within the meaning of “connected” are connections via wired and wireless connections.

Particularly falling within the scope of the invention is an apparatus or system that includes a computer linked to a device that amplifies polynucleotides and monitors amplicon synthesis as a function of cycle number or time, where the computer is programmed to execute the quantitative algorithm disclosed herein. An exemplary system in accordance with the invention will include a temperature-controlled incubator, and a fluorometer capable of monitoring and distinguishing at least two wavelengths of fluorescent emissions. These emissions may be used to indicate target amplicon synthesis, and IC amplicon synthesis.

In connection with computer-implemented or software-implemented embodiments of the disclosure, a result can be recorded or stored in a “non-transient” format where it can be accessed for reference at a later time than when the data analysis to be recorded was carried out or performed. For example, a computed result can be recorded in a non-transient format by printing on paper, or by storing on a computer-readable memory device (e.g., a hard drive, flash memory device, file in cloud storage, etc.).

Curve Fitting Procedures

In accordance with the disclosed method of creating a curve, plot, or fitted equation for determining correction factors, the relating procedure or step preferably involves obtaining one or more equations optimized to fit a data set. The data set comprises calculated CF values as a function of a result produced by a nucleic acid analyzer calibrated for determining the amount of analyte in a known volume of liquid sample. This can be accomplished by applying standard mathematical curve fitting techniques to each of the data sets to result in a fitted equation that defines a curve associated therewith. In some embodiments, one or more linear equations can be used for determining an appropriate CF from the output of a nucleic acid analyzer calibrated to deliver quantitative results for samples of a type (e.g., plasma samples) other than a reconstituted DBS sample. In other embodiments, the equation used in the curve fitting procedure preferably is a non-linear equation that contains no less than two, more preferably no less than three, and more preferably no less than four coefficients that can be optimized or determined during the curve fitting procedure. Some highly preferred equations have exactly four coefficients, while other highly preferred equations have exactly five coefficients. Optimizing an equation to fit the measured indicia of amplification can easily be accomplished using a commercially available software package, such as the SOLVER program which is available as an EXCEL add-in tool for finding an optimal value for a formula, and equation solving from Microsoft Corporation (Redmond, WA). Certain curves generated by this procedure can be shaped such that increasing levels of the polynucleotide analyte input into a reaction correlate with reduced CF values.

Although other equations can be used in the curve fitting procedure, the methods described below employed a four-parameter logistic (4-PL) equation having the following form:

CF=c+d-c(1+exp^(b*(x-e)))(Eq⁢1)
In this equation, the CF dependent variable represents the correction factor as a function of the observed or measured concentration (x) in logarithmic scale of the polynucleotide analyte. Again, the “observed” or “measured” concentration is the quantitative output of an assay calibrated for detecting the HIV-1 polynucleotide analyte in a test sample, but need not be calibrated for quantifying the analyte in a reconstituted DBS sample. The four coefficients in the equation that can be optimized by standard procedures are identified as b to e. The “exp” constant is the base of the natural logarithm (i.e., about 2.7183). Of course, it is to be understood that success in using the present invention does not require the use of any particular equation.
Alternative Equations for Performing Curve Fitting

Notably, although a 4-PL equation was used for illustrating the disclosed technique, other mathematical functions can also be used in the procedure to simulate the trend of CF values versus measured outputs.

Those having an ordinary level of skill in the art will appreciate that numerous types of equations may be used in the procedures disclosed herein. Examples of symmetric transition functions include, but are not limited to: Sigmoid, Gaussian Cumulative, Lorentzian Cumulative and Cumulative Symmetric Double Sigmoidal. Examples of asymmetric transition functions include, but are not limited to: Logistic Dose Response (LDR), Log Normal Cumulative, Extreme Value Cumulative, Pulse Cumulative, Pulse Cumulative with Power Term, Weibull Cumulative, Asymmetric Sigmoid, Asymmetric Sigmoid Reverse Asymmetry, Cascade Formation, and Cumulative Exponentially Modified Gaussian. Additionally, simple linear and non-linear equations, such as multiple order polynomials, power, exponential and logarithmic functions can be used to model real-time data with subsequent adjustment of the baseline coefficient, as detailed herein. Kinetic functions with baseline coefficients can also be used in the same manner. Exemplary basic kinetic equations containing baseline coefficients include but are not limited to: Half Order Decay and Formation, First Order Decay and Formation, Second Order Decay and Formation, Second Order Decay and Formation (Hyperbolic Forms), and Third Order Decay and Formation, Variable Order Decay and Formation. Exemplary complex kinetic equations containing baseline coefficients include but are not limited to: Simultaneous First and Second Order Decay and Formation, First Order Sequential Formation, Two Component First Order Decay, Two First Order Independent Decay and Formation, Two Second Order Independent Decay and Formation, and First and Second Order Independent Decay and Formation. Exemplary kinetic equilibrium equations containing baseline coefficients include but are not limited to: Simple Equilibrium (Forward and Reverse Rate), Simple Equilibrium (Net Rate and Equilibrium Concentration), Complex Equilibrium A=B+C, and Complex Equilibrium A+B=C+D. Exemplary intermediate kinetic equations containing baseline coefficients include but are not limited to: First Order Intermediate and First Order Intermediate with Equilibrium. One of ordinary skill in the art will readily understand that success of the disclosed CF adjustment method does not depend on the use of any particular equation for performing the curve fitting step. Indeed, it is believed that any equation having coefficients that can be optimized in a curve fitting procedure for the disclosed CF adjustment procedures.

All of the above-listed equation types can be used to carry out the disclosed methods. This is because success of the procedure depends not on the particular equation used, but on its ability to fit the data optimally.

WORKING EXAMPLES

As stated above, the disclosed technique improved the quantitative capacity of assays carried out using dried bodily fluid samples by delivering reliable results that correlated with concentrations of polynucleotide analyte in the starting sample that was used to create the dry sample. The Examples presented below are intended to be illustrative, and are not intended to limit the disclosure in any way.

Those having an ordinary level of skill in the art will appreciate that the lower limit of quantification (“LLOQ”) in an assay is the lowest concentration of an analyte that can be quantified with a certain level of accuracy and precision, and have at least 95% reactivity. Likewise, those having an ordinary level of skill in the art will appreciate that the limit of detection (“LOD”) in an assay is the lowest concentration of analyte that can be consistently detected in at least 95% of tested samples.

The LLOQ of an assay is the minimum concentration at which the following two requirements are met: (1) reactivity should be at least 95%; and (2) total error (TE) should meet specifications for assay accuracy. In the case of the model viral load assay used for illustrating the present CF adjustment technique, the TE specification is ≤1 log accuracy at the LLOQ. Two different “total error” assessment approaches were used to gauge the impacts of different correction factor approaches at the lower limit of quantification (LLOQ). These approaches were the CLSI EP-17-A2 guideline recommended Westgard, and the root mean square (RMS) models for calculation of LLOQ. The procedure involved determining accuracy and precision of quantification at low HIV concentrations using reconstituted DBS samples as the source of analyte. Stocks of a diluted WHO HIV standard having an assigned concentration value, which served as the “gold standard” for quantification, were used to create DBS samples. More particularly, various amounts of HIV WHO standard stock were spiked into different aliquots of whole blood, and the resulting dilutions used to prepare DBS samples.

In accordance with the above-cited Westgard model, the TE can be calculated using the following equation.
Bias+(2×Std Dev)≤1 log  (Eq 2)

In accordance with the above-cited RMS model, the TE can be calculated using the following equation.
√{square root over ((Bias)2+(Std Dev)2)}≤1 log  (Eq 3)

In the context of these equations, “bias” is the difference between the expected (i.e., actual) and the “recovered” assay result. As used herein, a “recovered” result has been adjusted using a CF multiplier, and so differs from a measured result, which has not been adjusted using a CF multiplier. Simply stated, a recovered result can be calculated by multiplying a measured result by a CF. The CF adjustment is able to improve assay quantification at low analyte concentrations by reducing bias (improving accuracy) and improving precision (by reducing Std. Dev.).

An initial approach to improve DBS quantification involved the use of static (i.e., constant) CF value multipliers. More specifically, pre-selected constants in the range of from 15 to 33 were multiplied by the measured value of a real-time polynucleotide amplification assay that was calibrated to deliver quantitative results for a 500 μl liquid sample (e.g., plasma). It is important for assays that measure HIV viral load to meet accuracy goals at concentrations ≤1,000 copies/ml. This is because the WHO recommended medical decision point for monitoring effectiveness of antiretroviral treatment is 1,000 copies/ml. Therefore, clinical sensitivity and specificity of the assay was calculated at the medical decision point of 1,000 copies/ml using recovered assay results for DBS calculated using different static CFs. No significant difference in assay sensitivity or specificity was seen when CFs ranging from 25 to 33 were used. According to one approach, the LOD determined for reconstituted DBS samples (i.e., 873 copies/ml) was divided by the LLOQ for plasma samples (i.e., 30 copies/ml) for the same assay chemistry to establish a constant CF value of 29.1 for use as a multiplier. Thus, an assay conducted using a 500 μl aliquot of a reconstituted DBS sample (e.g., a filter having been spotted with 70 μl of whole blood and then dried, and subsequently reconstituted with 1 ml of buffer) that yielded a “measured” or observed output of 35 copies/ml would be multiplied by 29.1 to give a corrected (i.e., “recovered”) result of 1,019 copies/ml.

Example 1 describes a real-time polynucleotide amplification assay that quantified HIV-1 polynucleotides using reconstituted DBS samples. The automated nucleic acid analyzer used in the procedure was calibrated to deliver results measured in copies/ml for plasma samples.

Example 1

Static Correction Factor Quantifies Polynucleotide Analyte with Excessive Error

DBS samples harboring known quantities of HIV-1 polynucleotides were prepared using laboratory procedures that will be familiar to those having an ordinary level of skill in the art. Whole blood was spiked with HIV-1 from the value assigned WHO standard virus stock to produce samples having concentrations in the range of from 50 copies/ml to 1,200 copies/ml. Whole blood samples (70 μl each) of the different HIV concentrations were separately applied to standard filter paper cards, and then allowed to dry. Dried blood spots were punched from the cards and each DBS was combined with 1 ml of a buffered detergent solution (i.e., DBS extraction buffer). One-half of each sample (500 μl) was then used for testing in the Aptima HIV-1 Quant Dx real-time viral load assay on the Panther automated nucleic acid analyzer (Hologic, Inc.; Marlborough, MA). At least 90 replicates of DBS samples tested using different HIV-1 reagent lots on the platform yielded essentially equivalent outcomes. Table 1 presents illustrative results obtained using one of the reagent lots. Columns 1 and 2 list the actual stock concentrations of analyte in whole blood that were used for creating the DBS samples. Column 3 (“Reactivity”) indicates the percentage of trials yielding positive results (i.e., HIV-1 analyte detected). Column 4 (“Avg. Recovered”) indicates the averaged product of multiplying the static CF by the measured concentration of analyte outputted by the automated analyzer. Column 5 (“Bias”) indicates the magnitude of deviation of the average recovered concentration result from the actual analyte concentration. Column 6 (“Std Dev Log Copies”) indicates the standard deviation among recovered results presented in column 4. Column 7 (“Total Error (Westgard)”) presents results calculated in accordance with a standard Westgard analysis protocol. Column 8 (“Total Error (RMS)”) presents results calculated in accordance with the above-cited RMS analysis protocol.

TABLE 1Quantitative Adjustment Using a Static CFLogAvg.StdTargetTargetRecoveredDevTotal(copies/(copies/Reac-(logLogTotal ErrorErrorml)ml)tivitycopies/ml)BiasCopies(Westgard)(RMS)9002.9597%2.020.930.532.001.071,0003.0097%2.001.000.522.041.131,2003.0897%2.210.870.531.921.01

The results presented in Table 1 indicated that Total Error, regardless of the method used for making the determination, undesirably exceeded the acceptable 1.0 threshold goal. Although not shown, different constant CF values substituted in place of 29.1 also gave unacceptable results.

Example 2 presents experimental results showing that a single (i.e., constant) CF cannot be used for quantifying analyte over the dynamic range of the assay, particularly at lower analyte concentrations. As will be apparent from the results presented below, lower concentration values outputted by the nucleic acid analyzer calibrated for processing plasma samples had to be multiplied by higher CFs to recover correct starting concentrations used to prepare the DBS samples. Likewise, higher outputted values had to be multiplied by lower CFs to recover correct starting concentrations used to prepare the DBS samples.

Example 2

The Correction Factor is not Constant Across the Dynamic Range of the Quantitative Real-Time Assay

Procedures essentially described under Example 1 were followed to prepare DBS samples using whole blood spiked with different levels of the HIV-1 analyte. The DBS samples were processed as described above, and eluted polynucleotides amplified and detected using the Aptima HIV-1 Quant Dx real-time viral load assay on the automated Panther nucleic acid analyzer (Hologic, Inc.). The target HIV-1 concentration used for DBS preparation was compared to measured concentration in the assay to calculate the appropriate CF for each HIV-1 concentration input according to equation Eq 4.

CF=Analyte⁢concentration⁢in⁢blood⁢used⁢to⁢prepare⁢the⁢DBSConcentration⁢result⁢measured⁢by⁢nucleic⁢acid⁢analyzer(Eq⁢4)

TABLE 2Correction Factor Needed for Adjustment Varies as aFunction of Measured Target ConcentrationMeasuredTarget (logTargetConc.Calculated CFcopies/ml)(copies/ml)(copies/ml)(Eq 4)2.705001.14552.887504.01883.001,00028363.382,40033734.0010,000175575.00100,0001,546656.001,000,00027,347376.705,011,872122,119417.3019,952,623543,574377.6039,810,7171,229,82132

The results presented in Table 2 clearly indicated that a single, fixed or static CF value could not be used for correctly quantifying polynucleotide analyte over the dynamic range of the assay. The final column in the table generally reveals a trend where higher CF values were required for correctly quantifying samples having lower concentrations of the polynucleotide analyte.

Example 3 describes development of a quantitative approach using a CF value that varied as a function of the measured result calibrated for a liquid sample (e.g., plasma) different from the liquid sample undergoing testing (i.e., an extracted DBS sample). This type of variable CF is sometimes referred to as “non-static.”

Example 3

Development of a Non-Static Correction Factor

A total of 747 DBS samples were prepared using whole blood stocks having different known analyte HIV-1 concentrations that spanned the quantification range of the model real-time quantitative assay. For completeness, the known analyte HIV-1 concentrations used for creating the DBS samples were the same as presented in the first column of Table 2. The DBS samples were reconstituted with 1 ml of a buffered detergent solution (i.e., DBS extraction buffer), and 500 μl of the resulting solution was used for nucleic acid isolation and target amplification and detection with the model real-time quantitative assay. Target (i.e., actual) HIV-1 concentration was compared to measured concentration in the assay to calculate the appropriate CF for each HIV-1 concentration using equation Eq 4. The CF multiplier required for adjusting HIV-1 measured analyte concentration values to equal known input analyte concentrations were then plotted as a function of measured copy values. The resulting data was then used for optimizing a non-linear equation according to standard mathematical curve-fitting techniques that will be familiar to those having an ordinary level of skill in the art. Although many different non-linear equations can be used for this purpose, the technique is illustrated inFIG.1using a fitted 4-PL equation. It will be recognized that curve-fitting using 4-PL equations are frequently used for processing data exhibiting biphasic or sigmoid curve properties.

The results presented inFIG.1graphically confirmed that the CF values were not static or constant, but instead varied as a non-linear function of the measured concentration value outputted by the nucleic acid analyzer calibrated for processing of plasma samples. Clusters of data points appearing as spaced-apart crescent-shapes demonstrate variability among calculated CF results for single-level input amounts. Stated differently, a collection of DBS samples harboring substantially the same amount of polynucleotide analyte (i.e., the dried blood spots having been prepared using a single stock of diluted analyte) naturally yielded a range of CF values. Coefficients for the optimized 4-PL equation (i.e., Eq 1) shown as the fitted curve inFIG.1were as follows: b=3.705178; c=47.21279; d=486.6657; and e=0.506889. Notably, the fact that the data in the present case did not particularly conform to a sigmoid shape did not prevent usefulness of the 4-PL equation, as demonstrated in the following Example.

Example 4 demonstrates use of CF values determined by a fitted non-linear curve. More specifically, the determined CF value was multiplied by the measured quantitative result outputted from a real-time nucleic acid analyzer (measured in copies/ml) to indicate the analyte concentration in the liquid sample used to prepare the dried blood spot.

Example 4

Correction Factor Calculated from Non-Linear Curve Fit Improved Analyte Quantification

Procedures essentially described under Example 1 were followed to prepare DBS samples using whole blood spiked with different levels of the HIV-1 analyte. The DBS samples were processed as described above, and eluted polynucleotides amplified and detected using the Aptima HIV-1 Quant Dx real-time viral load assay on the Panther automated nucleic acid analyzer. Outputted (i.e., measured) quantitative results were multiplied by CF values taken from the fitted curve shown inFIG.1. More specifically, the equation for the fitted curve shown in the figure was solved to determine a CF value using the outputted quantitative result on the horizontal axis as the independent variable (i.e., x-value) in the equation. The determined CF value was then multiplied by the same outputted quantitative result (i.e., x-value) to calculate a “recovered” (i.e., adjusted) concentration. Results are presented in Table 3.

TABLE 3Quantitative Adjustment Using a Calculated CFTargetStdTarget(logRecoveredDevTotal(copies/copies/Reac-(logLogTotal ErrorErrorml)ml)tivitycopies/ml)BiasCopies(Westgard)(RMS)9002.9597%2.790.170.471.100.501,0003.0097%2.780.220.461.150.511,2003.0897%2.910.170.370.910.41

The results presented in Table 3 confirmed that use of the CF calculated from the fitted non-linear equation substantially improved the quantitative capacity of the assay. As indicated under the final two columns of the table, TE values were substantially reduced when compared with results presented in Table 1. Stated differently, use of the CF calculated from a fitted non-linear equation yielded significant improvements when compared with a similar process employing a fixed value (i.e., CF=29.1). The LLOQ of the assay in this Example was 813 copies/ml (i.e., 2.91 log copies/ml). Among all results obtained using three different reagent lots, the highest LLOQ determined using calculated CF values taken from the fitted curve shown inFIG.1was 883 copies/ml (i.e., 2.95 log copies/ml).

Example 5

Use of Correction Factor Equation Improves Assay Precision and Accuracy

Procedures disclosed herein were used to prepare DBS samples from whole blood stocks spiked with an HIV-1 analyte at 900 copies/ml, 1,000 copies/ml, or 1,200 copies/ml. Polynucleotides eluted from the samples were amplified using the Aptima HIV-1 Quant DX real-time viral load assay on the Panther automated nucleic acid analyzer. Reported results were obtained using procedures carried out in our own laboratories with the intention of analyzing performance around the medically relevant decision point of 1,000 copies/ml. Averaged results obtained using three different reagent lots are presented in Table 4.

TABLE 4Use of the CF Equation Increased Accuracy and PrecisionAvg.Avg.PrecisionPrecisionAdjustedAdjustedStd. Dev.Std. Dev.HIV logHIV logLogLogTargetLog Targetcopies/mlcopies/mlcopies/mlcopies/mlConcConcusing CF ofusing CFfor CF offor CF(copies/ml)(copies/ml)29.1Equation29.1Equation9002.952.212.890.480.421,0003.002.182.910.490.321,2003.082.383.010.460.27

The results presented in Table 4 show that the CF calculated from the nonlinear equation, when multiplied by the result measured in copies/ml for a plasma sample, advantageously yielded higher accuracy in quantitative assignments with greater precision. Columns 1 and 2 in the table indicate HIV-1 target concentrations of stock samples used to create the DBS samples. Columns 3 and 4 show adjusted HIV concentrations determined by multiplying a CF (29.1 for column 3; calculated value using the equation from the fitted curve inFIG.1for column 4) by an outputted result from the model viral load assay that had been calibrated for quantifying plasma samples, and not DBS samples. The difference between the values presented under column 2 and the values presented under columns 3 and 4 reflect accuracy of assays employing the different correction factor approaches. In every instance, the magnitude of the difference was lower when the CF equation was used instead of the static CF. These smaller differences indicate more accurate quantification. Columns 5 and 6 show measures of precision (i.e., standard deviations among measured concentrations for replicates). Again, in every instance the standard deviation was lower when the CF equation was used instead of the static CF. This indicated use of the CF equation was associated with greater precision in the quantitative results.

Example 5 presents clinical data demonstrating how improved assay quantification resulting in higher clinical sensitivity at the medical decision point of 1,000 copies/ml for HIV-1 was achieved by employing a CF multiplier calculated using an equation for the fitted curve shown inFIG.1. Notably, the data used to obtain the fitted curve was not the same clinical data that was processed in the Example. This further demonstrated how a fitted curve (or the equation therefor) could be prepared using one data set, and then used for determining CF values and processing a different data set (e.g., a data set obtained using a different instrument to perform the assay).

Example 5

Improvement in Clinical Performance at the WHO Recommended Medical Decision Point of 1,000 Copies/ml for HIV-1 Viral Load Monitoring

Paired plasma and DBS specimens were collected from HIV-1 positive patients on antiretroviral therapy. Two replicates were tested for the plasma specimen, and viral load results obtained using the procedures described herein were then averaged and used as a reference. Approximately 5 reconstituted DBS samples were also tested from each patient, and the measured quantitative results multiplied either by a static CF (i.e., 29.1) or a CF calculated using the non-linear equation for the fitted curve shown inFIG.1to obtain recovered concentration values. Results were then compared to the plasma reference standard at the medically relevant decision point of 1,000 copies/ml. It should be noted that 90% of results in this study had plasma viral load <10,000 copies/ml, making this dataset ideal for assessment of clinical performance around 1,000 copies/ml of HIV.

As supported by the results presented in Tables 5 and 6, assay sensitivity at 1,000 copies/ml improved from 79.83% to 90.56% when the CF was determined from the non-linear equation compared to the static value. The change from one CF value to the other did not have a significant negative impact on specificity. This will be evident from the specificity of 94.30% obtained using the static CF of 29.1 versus 91.36% obtained using the CF equation. The close correspondence between these latter values indicated minimal impact on specificity.

TABLE 5Results Processed Using a Static Correction FactorHIV DBS viralHIV viral load in copies/ml plasmaload in copies/ml(2 replicate average)(CF 29.1)<1,000>1,000Grand Total<1,0001,157471,204>1,00070186256Grand Total1,2272331,460Total Agreement91.99%Sensitivity (Pos Agreement)79.83%Specificity (Neg Agreement)94.30%

TABLE 6Results Processed Using a Correction Factor Calculated froma Non-Linear EquationHIV DBS viralHIV viral load in copies/ml plasmaload in copies/ml(2 replicate average)(CF Equation)<1,000>1,000Grand Total<1,0001,121221,143>1,000106211317Grand Total1,2272331,460Total Agreement91.23%Sensitivity (Pos Agreement)90.56%Specificity (Neg Agreement)91.36%

While the present disclosure has been described and shown in considerable detail with reference to certain illustrative embodiments, including various combinations and sub-combinations of features, those skilled in the art will readily appreciate other embodiments and variations and modifications thereof as encompassed within the scope of the present disclosure. Moreover, the descriptions of such embodiments, combinations, and sub-combinations is not intended to convey that the disclosure requires features or combinations of features other than those expressly recited in the claims. Accordingly, the present disclosure is deemed to include all modifications and variations encompassed within the spirit and scope of the following numbered embodiments.

Although various embodiments of the present disclosure have been illustrated and described in detail, it will be readily apparent to those skilled in the art that various modifications may be made without departing from the present disclosure or from the scope of the appended claims.