Patent Application
20100280986
According to the World Health Organization, near the end of 2008, an estimated 170 million people or 2.1% of the world population are currently infected with hepatitis C virus. This number of infections may be more than four times the number of people living with HIV. While no vaccine against hepatitis C is currently available, the symptoms of infection can be medically managed. Additionally, patients can be cleared of the virus by a course of anti-viral medicines. The National Institute of Health suggests the current standard of care for patients with chronic hepatitis C (CHC) can include the combination of pegylated interferon a with ribavirin for period of 24 or 48 weeks, depending on genotype.
Sustained “cure” or sustained virological response (SVR) of 75% or better can occur in people with genotypes HCV 2 and 3 in 24 weeks of treatment, about 50% in those with genotype 1 with 48 weeks of treatment and 65% for those with genotype 4 in 48 weeks of treatment. Overall rates of SVR, defined as undetectable HCV RNA 24 weeks after treatment completion, of up to 66% have been obtained with an optimal regimen of peginterferon α-2a plus ribavirin in treatment-naïve patients in large, randomized, multicentre trials.
As suggested above, patients infected with HCV genotype 1, which represent about 70% of CHC patients in the U.S., are less likely to achieve an SVR than genotype non-1 infected patients. Approximately 50% of HCV genotype 1 infected patients generally achieve an SVR when treated with peginterferon α-2a plus ribavirin, whereas approximately 80% of HCV genotype non-1-infected patients generally achieve an SVR despite receiving a shorter treatment duration and a lower ribavirin dose. Thus, HCV genotype 1 patients represent a population with an unmet medical need, and have the potential to achieve a higher SVR rate from an improved treatment.
A significant question in the care of patients with both acute and chronic viral infections, such as HCV/HBV, Dengue, or even avian flu, is the time waiting to determine whether patients are responding to a given treatment.
Many viral diseases are asymptomatic during acute infection, and thus the diagnosis of is rarely made until after a subject has become chronically infected. The hepatitis C virus (HCV), for example, is usually detectable in the blood within one to three weeks after infection, and antibodies against HCV are generally detectable within 3 to 12 weeks. It is thought that between 15-40% of persons infected with HCV clear the virus during the acute phase of infection (defined as within the first six months of infection, or spontaneous viral clearance[REF]). The remaining 60-85% of patients infected with HCV develop chronic hepatitis C.
While the diagnosis of acute HCV is difficult, the diagnosis of chronic HCV is also challenging due to the absence or lack of specificity of symptoms until advanced liver disease develops. Unfortunately, this frequently does not occur until decades into the disease. Anti-HCV antibodies indicate exposure to the virus, but do not determine if ongoing infection is present. Persons with positive anti-HCV antibody tests must also undergo additional testing for the presence of the hepatitis C viral nucleic acid to determine if they are actively infected. The presence of the virus can be tested for using molecular nucleic acid testing methods such as polymerase chain reaction (PCR), transcription mediated amplification (TMA), or branched DNA (b-DNA). These HCV nucleic acid molecular tests have the capacity to detect not only whether the virus is present, but also to measure the amount of virus present in the blood (the HCV viral load). The HCV viral load itself indicates neither disease severity nor the likelihood of disease progression, additionally, there may be some quantification limits utilizing existing measurement techniques. However, viral load can be an important factor in determining the probability of response to interferon-based therapy.
The inventors recognize that baseline viral load may be a potent indicator of response to therapy-particularly immunologically biased therapies such as those containing PEGASYS, as it serves to integrate the patient's ongoing immunologic response to viral infection together with the viruses capacity to evade the host immune responses. Accordingly, what is desired is to solve problems relating to tailoring acute and chronic viral infection treatments for predictive algorithms which utilize viral kinetics as the foundation for their clinical claims will be able to define subjects most likely to respond to treatment, and thus permit clinicians to tailor therapy on a more individualized basis.
In various embodiments, systems and methods are provided for increasing the likelihood of a sustained virological response or “cure” using a model of patient physiology incorporating a subjects race, gender, age, weight, concomitant medicines and disease state, immune response status, and responsiveness to drug therapies to simultaneously characterize the change in viral burden in the subject in terms of velocity of viral load decline. In an embodiment, once viral load in a subject is below a physical measurement limit, the model can extrapolate the subject's observed viral velocity toward a physiological target shown to be highly correlated with “cure.” In further embodiments, the model can be used for personalized medicine—“the right drug at the right dose for the right treatment duration for the right patient.” Accordingly, the model can provide optimal value for treatment and reducing the high cost of side effects.
In one embodiment, a method for treating subjects having viral infections is provided to increase the probability of a predetermined clinical outcome. An antiviral drug can be administered to a subject having a viral infection, such as that of the liver in the case of hepatitis C (HVC), hepatitis B (HBV), dengue fever, avian flu, or the like. Administration of the antiviral drug may form part of a combination of treatments. Measurements may be obtained of viral load in the subject before, during, or after the administration of the antiviral drug. In an embodiment, viral load of the subject may be obtained during a first time period where at least one time point in the first time period occurs subsequent to administering the antiviral drug.
Velocity of viral load decline in the subject can be determined for the first time period. A determination or prediction can be made whether viral load in the subject after a second time period subsequent to the first time period passes a cure threshold based on a non-linear mixed-effects model of the viral disease. The model may model or otherwise predict viral load in the subject for the second plurality of time points using the velocity of viral load decline in the subject for the first time period. A dosing regimen associated with the antiviral drug can then be altered for the subject based on whether viral load in the subject passes the cure threshold.
In an embodiment, the velocity of viral load decline in the subject for the first time period may be determined by calculating the rate at which the viral load in the subject decreases at the at least one time point occurring subsequent to administering the antiviral drug. The model further may be used to determine viral load in the subject during one or more time points in the second plurality of time points occurring when viral load in the subject fails to satisfy a limit of quantification. In a further embodiment, viral kinetics (including infection activity and liver activity) may be simulated using the model to determine viral load in the subject during one or more time points in the second plurality of time points. In some embodiment, a profile may be determined for the subject using the model. The profile for the subject may be compared to a clustering of members of a population represented by the model.
In further embodiments, the non-linear mixed-effects model may represent the hepatitis C virus (HCV) or the hepatitis B virus (HBV). Altering the dosing regimen associated with the antiviral drug for the subject may include one or more of modifying dose of the antiviral drug, modifying a dose schedule for the antiviral drug, modifying a treatment duration, modifying a treatment combination, or removing the antiviral drug from the treatment of the subject and administering a different antiviral drug to the subject.
In various embodiments, a computer-readable storage medium may be configured to store one or more software programs which when executed by a information processing device or computer system cause the information processing device to perform the steps recited in the above method.
In yet another embodiment, a method is provided for assisting in the treatment of subjects having liver viral infections. Data for a subject infected with a virus attacking the liver may be received at an information processing device or computer system. In an embodiment, the data can may specify at least viral load in the subject during a first time period where at least one time point in the first time period occurs subsequent to administration of an antiviral drug. A rate may be then determined at which viral load in the subject decreases for the first time period. Viral load in the subject can be predicted or estimated for a second time period that occurs subsequent to the first time period when viral load passes a physical degree of detection based on simulating subject physiology and virus patho-physiology using a non-linear mixed-effects model. A clinical outcome may be suggested in response to a correlation provided by the model between the rate at which viral load in the subject declines and when viral load in the subject for the second time period satisfies a predetermined threshold.
In an embodiment, an information processing device or computer system may generated information suggesting the clinical outcome. The information about the clinical outcome may include information indicative of a sustained viral response, information indicative of a partial viral response, information indicative of a null viral response, information indicative of a breakthrough response, or information indicative of a relapse.
A further understanding of the nature of and equivalents to the subject matter of this disclosure (as wells as any inherent or express advantages and improvements provided) should be realized by reference to the remaining portions of this disclosure, any accompanying drawings, and the claims in addition to the above section.
In order to better describe and illustrate embodiments and/or examples of any innovations presented within this disclosure, reference may be made to one or more accompanying drawings. The additional details or examples used to describe the one or more accompanying drawings should not be considered as limitations to the scope of any of the claimed inventions, any of the presently described embodiments and/or examples, or the presently understood best mode of any innovations presented within this disclosure.
In various embodiments, systems and methods are provided for increasing the likelihood of a sustained virological response or “cure” using a model of patient physiology incorporating a subjects race, gender, age, weight, concomitant medicines and disease state, immune response status, and responsiveness to drug therapies to simultaneously characterize the change in viral burden in the subject in terms of velocity of viral load decline. In an embodiment, once viral load in a subject is below a physical measurement limit, the model can extrapolate the subject's observed viral velocity toward a physiological target shown to be highly correlated with “cure.” In further embodiments, the model can be used for personalized medicine—“the right drug at the right dose for the right treatment duration for the right patient.” Accordingly, the model can provide optimal value for treatment and reducing the high cost of side effects.
In one embodiment, a viral kinetic model, such as for hepatitis C (HCV), hepatitis B (HBV), dengue fever, or avian flu, can be used to explain the complexity and diversity of individual viral kinetic profiles of subjects as measured during and after treatment with a drug or combination of drugs. In comparison to many other disease states, viral infection frequently presents a uniquely efficient biomarker: Viral Load. In HCV, for example, there is thought to be no cellular reservoir outside of the hepatocyte cytoplasm. Additionally, the half-life of the viral RNA and viral particle is such that the virus requires ongoing efficient replication to maintain its presence in a host. As a result, in some embodiments, viral load can be used to provide an integrated “read out” of the combination of viral replication efficiency and host response.
In various embodiments, changes to either viral replication efficiency and host response can be used to alter the final viral load. Thus, treatment with a direct acting antiviral with profound viral suppressive properties can produce a change in viral load, just as immuno-modulatory therapy with weaker direct viral effects. As discussed further herein, a sustained viral decline can be a positive predictive marker of recovery from disease. Accordingly, with this decline in viral load, patient long term clinical outcomes can be dramatically improved (manifest, for example, by lower rates of cirrhosis, and hepatocellular carcinoma).
Before, during, or after administration of an antiviral drug or other therapy for treatment 120, one or more diagnostics may be performed to determine clinical outcome 130. For example, one or more diagnostics may be performed in an attempt to determine what therapy to provide, whether a give therapy is effective, whether the patient has been cured, or the like. For example, the patient may be given a series of tests or other diagnostics before beginning treatment to determine the type and extent of the infection. The patient may be treated based on the test results with a single therapy or a combination of drugs, therapies, or the like. In another example, the patient may be given a series of tests or other diagnostics during a treatment to determine effects of the treatment on the patient, such as measuring one or more biomarkers of disease activity.
In various embodiments, techniques associated with viral dynamics may be used during treatment 120 to increase the likelihood that one or more therapies will result in a sustained virological response (SRV) or “cure” as clinical outcome 130. For example, viral dynamics may be modeled or simulated during a therapy to provide important insights into the life cycle of a given virus elucidating the kinetic parameters governing viral infection and death of infected cells, the antiviral effects of interferons, and how anti-viral drugs (e.g., ribavirin) impact specific treatments. In some embodiments, models of viral kinetics may provide a means to compare different treatment regimens with the clinical outcomes, such as a partial response or a sustained response, in different patient populations.
In various embodiments, systems and methods are provided for increasing the likelihood of a sustained virological response or “cure” using a model of patient physiology incorporating a subjects race, gender, age, weight, concomitant medicines and disease state, immune response status, and responsiveness to drug therapies to simultaneously characterize the change in viral burden in the subject in terms of velocity of viral load decline. In an embodiment, once viral load in a subject is below a physical measurement limit, the model can extrapolate the subject's observed viral velocity toward a physiological target shown to be highly correlated with “cure.” In other embodiments, differences between the different clinical outcomes can be studied for personalized medicine allowing “the right drug at the right dose for the right treatment duration for the right patient.” Accordingly, the model can provide optimal value for treatment and reducing the high cost of side effects.
In step 320, information about a subject is obtained. The information may include a past medical history, family history, medicine allergies, drug/alcohol/tobacco use, dietary and social histories, a review of systems, history of present illness, genotype, phenotype, exam results, diagnostics and other test results, pathology, or the like. In step 330, a dosing regime is determined based on the information about the subject. The dosing regime may consider timing of administration of an drug or combination of drugs, amount of the drug or combination of the drug, or other factors.
In step 340, subject response is measured. In some embodiments, various diagnostics or other tests may be performed to determine virological response of the subject. In further embodiments, measurements of biomarkers relevant to disease activity may be taken. For example, viral load in the subject can be measured. In an embodiment, viral load in the subject measured over time can be used to determine whether the clinical outcome of the dosing regime is predicted as a cure in step 350. If the clinical outcome of the dosing regime is not predicted as a cure, in step 360, the dosing regime is adapted to increase likelihood of a cure. Method 300 then may repeat until the clinical outcome of the dosing regime is predicted as a cure in step 350. Method 300 ends in step 370 if the clinical outcome of the dosing regime is predicted as a cure in step 350.
In various embodiments, techniques associated with viral dynamics may be used, for example in the prediction step 350 of
In one example, a model of HCV infection was originally proposed by Neumann et al. in “Hepatitis C Viral Dynamics in Vivo and the Antiviral Efficacy of Interferon-a Therapy,” Science, Vol. 282, pages 103-107, October 1998 which is incorporated herein by reference for all purposes. In general, the Neumann model describes typical early therapy outcome characterized by an initial rapid viral decline followed by a second slower decline until HCV RNA becomes undetectable. The Neumann model has therefore been frequently used to describe viral load profiles after short-term treatment.
However, after long-term treatment with current standards of care, the HCV virus is generally not eradicated in approximately 50% of HCV genotype 1 patients and in approximately 20% of HCV genotype non-1 infected patients. In these patients, viral load either rebounds to pretreatment levels during therapy (breakthrough response 210 of
In various embodiments, population models can be provided as developed by nonlinear mixed effects analysis that simultaneously describe the individual long-term HCV kinetic profiles of subjects treated with peginterferon α-2a alone or in combination with ribavirin. In an embodiment, a population model may account for the ribavirin effect, the natural turnover and proliferation of hepatocytes, and the viral eradication. The population model further may account for HCV RNA measurements below the LLOQ. Population models can be adapted for hepatitis B virus (HBV) infection, dengue fever, avian flu, or the like, based on host response and viral replication for the given disease.
In an embodiment, treatment dose can be used as an input into model 400 where the treatment effect is assumed to decrease according to:
ε−kt (1)
HCV viral kinetic model 400 is extended with a density dependent proliferation of hepatocytes [r]. HCV viral kinetic model 400 further includes the effect of peginterferon α-2a [1−ε] on the virion production (p), and the effect of ribavirin rendering a fraction of newly produced virions non-infectious [ρ], formed the basis of the current population analysis:
Infectious HCV virions (VI) infect target cells (uninfected hepatocytes) [T] creating productively infected cells (I) at a rate β·V1·T. Uninfected hepatocytes are produced at rate s and die at rate d. Infected hepatocytes die at rate δ. Infectious (VI) and non-infectious (VNI) virions are produced at rate p and cleared at rate c. The measured viral load (V) can be expressed in IU/mL, representing the sum of infectious and non-infectious virions V=VI+VN. Model 400 can further be extended with Emax dose-response models describing the dose-dependent effects of peginterferon α-2a and ribavirin:
In this example, DosePEG is the weekly subcutaneous dose of peginterferon α-2a and ED50
In general, the maximum number of hepatocytes present in an individual liver is assumed to be 2.5·1011 hepatocytes. As HCV RNA is distributed in plasma and extracellular fluids with a volume of approximately 13500 mL, the maximum number of hepatocytes in model 400 (e.g., Tmax) is assumed to be 18.5·106 cells/mL. In an embodiment, assuming a hepatocyte turnover in a healthy liver of 300 days, the death rate of target cells (d) can be set to 1/300 day−1, and the production of new hepatocytes in the absence of liver disease (s) can be assumed to be 61.7·103 cells·mL−1 days−1.
Non-linear mixed effects models generally include of a combination of fixed and random effects. In one example, individual parameters (PARi) in such model disclosed herein can be described by:
PARi=θ·eη
In this example, the subscript i denotes individual, the fixed effects parameter θ represents the mean (typical) value of the parameter in the population, and ηi, is the random effect accounting for the individual difference from the typical value. The ηi values are assumed to be normally distributed in the population with a mean of zero and an estimated variance of ω2. Individual parameter estimates are used to predict the viral load in an individual i at a certain point in time j (Vpred,ij). The measured viral load (Vobs,ij) differ from the predicted where:
V
obs,ij
=V
pred,ij·exp(εij) (9)
The εij values are assumed to be normally distributed with a mean of zero and an estimated variance σ2. The ω2 quantifies the inter-individual variability (IIV) and the σ2 quantifies the residual variability. Individual parameter estimates (PARi) are assumed to be lognormally distributed, whereas the residual error was assumed to be multiplicative. Finally, the measured viral load data were log 10-transformed for one exemplary analysis.
In one embodiment, estimated fixed effects parameters of model 400 can include basic reproduction number (R0), p, c, δ, liver proliferation rate r, ED50
Population parameters of HCV viral kinetic model 400 can be estimated using the SAEM algorithm, as implemented in the MATLAB language using a software tool MONOLIX, available on the author's website (www.monolix.org). In one example, version 2.4 of MONOLIX and MATLAB version 7.6 running under Windows XP were used to estimate the fixed effects parameters and the variance of the random effects as well as the residual variability. In the example, S-PLUS version 6.2 was used for data file creation and goodness of fit assessments. Goodness of fit assessments revealed that individual viral load profiles of subjects in a population can be well described by model 400.
In step 520, data for biomarker activity is obtained at a plurality of time points during an observable time period. For example, measurements of viral load in a subject may be taken at various points in time. In general, the observable time period includes those measurements above a given detection limit, lower limit of detection, or LOD (limit of detection) when the lowest quantity of a substance can be distinguished from the absence of that substance within a predetermined confidence limit or threshold (e.g., 1%). In step 530, a change in biomarker activity is determined for the observable time period. Some examples of change can include a null change in biomarker activity, a decrease in biomarker activity, an increase in biomarker activity, or some combination.
In step 540, biomarker activity is modeled during a non-observable time period based on the change determined during the observable time period to predict a clinical outcome. The non-observable time period can include when the lowest quantity of a substance cannot be distinguished from the absence of that substance within a stated confidence limit. In one example, mathematical technique may be used that involve the simulation of missing data between 0 and a LLOQ, and then fitting observed data and simulated scatter data using information from direct observations. In an embodiment, a model such as model 400 is used to simulate a disease to determine biomarker activity that cannot be observed by measurement. The model can provide a link or correspondence between the change in biomarker activity determined during the observable time period and a clinical outcome using the simulated biomarker activity during the non-observable time period.
In step 550, information is generated indicative of the predicted clinical outcome. For example, a patient profile may be created indicating a predicting clinical outcome of a null response, a partial response, a breakthrough response, a relapse, or a sustained response or cure.
In one sampling, a selection of 12 individual viral load profiles shows that HCV viral kinetic model 400 can not only describe the initial decreases in viral load over the first month, but also the typical phenomena observed after long-term therapy.
Inspection of individual parameter estimates in patients experiencing a breakthrough during therapy indeed showed that the administered drug therapy failed to decrease the basic reproduction number (R0) below 1. According to our modeling assumptions, a treatment with either higher doses or a combination treatment with new drugs may be an option in these patients in order to try to get the R0 below 1. The situation in patients having a relapse after the end of treatment may be twofold: i) on the one hand, relapsing patients may have had a R0<1 during treatment, but were not treated long enough so that the viral load quickly returned back to baseline at the end of therapy, or ii) drug therapy may have failed to decrease the R0 below 1. Extended treatment duration at the same drug combination, dose and schedule in relapsing patients may therefore be an option in the former situation but not in the latter. Based on these hypotheses, individual treatments may be optimized using model 400 when the individual R0 and drug effect are known.
Continuing the previous example, parameters may be estimated with good precision.
In
In various embodiment, no significant correlation was found between c and HCV genotype. In contrast, the infected cell death rate (δ) appeared to be dependent on HCV genotype, and the typical value was estimated to be 0.139 days in genotype-1 infected patients and 0.192 days in patients infected with HCV genotype non-1. These estimates appear to be in line with reported values of δ. In an embodiment, the higher δ in HCV genotype non-1 infected patients may indicate an enhanced immunological response. This can confirm a finding that a fast viral decay early in treatment is correlating with SVR. Also, the typical value of the ED50
A comparison of the individual parameter estimates between patients with and without an SVR reveals that the R0 and ED50
In an embodiment, a relatively low R0 prior to treatment and a relatively high treatment effect will increase the likelihood of R0<1 during treatment and will thus increase the likelihood of SVR. In a model-based analysis, the free virion clearance rate (c) did not appear to be a prognostic factor for SVR, whereas the death rate of infected cells (δ) was found to be higher in SVR patients indicating these patients may have an enhanced immunological response and thus a higher likelihood of viral eradication.
The predictive performance of model 400 was assessed by an external model evaluation procedure predicting the SVR rate of a large clinical trial not included in the model building dataset. This SVR rate was then compared with the observed SVR rate.
Accordingly, HCV viral kinetic model 400 was able to adequately describe all individual long-term viral load profiles of 2100 CHC patients receiving chronic treatment of peginterferon α-2a alone or in combination with ribavirin. In an embodiment, model 400 provides new insights and explanations for typical phenomena observed in the clinic such as breakthrough during therapy and relapse after stopping therapy. In another embodiment, model 400 may help to better understand current treatment success and failure, and can also be used to predict and evaluate the efficacy of alternative treatment options (e.g. alternative doses, durations and new drug combinations) in an overall patient population.
Bus subsystem 1120 provides a mechanism for letting the various components and subsystems of computer system 1100 communicate with each other as intended. Although bus subsystem 1120 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple busses.
Storage subsystem 1130 may be configured to store the basic programming and data constructs that provide the functionality of the present invention. Software (code modules or instructions) that provides the functionality of the present invention may be stored in storage subsystem 1130. These software modules or instructions may be executed by processor(s) 1110. Storage subsystem 1130 may also provide a repository for storing data used in accordance with the present invention. Storage subsystem 1130 may comprise memory subsystem 1140 and file/disk storage subsystem 1150.
Memory subsystem 1140 may include a number of memories including a main random access memory (RAM) 1142 for storage of instructions and data during program execution and a read only memory (ROM) 1144 in which fixed instructions are stored. File storage subsystem 1150 provides persistent (non-volatile) storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a Compact Disk Read Only Memory (CD-ROM) drive, a DVD, an optical drive, removable media cartridges, and other like storage media.
Input devices 1160 may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a barcode scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and mechanisms for inputting information to computer system 1100.
Output devices 1170 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), or a projection device. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system 1100.
Network interface subsystem 1180 provides an interface to other computer systems, devices, and networks, such as communications network 1190. Network interface subsystem 1180 serves as an interface for receiving data from and transmitting data to other systems from computer system 1100. Some examples of communications network 1190 are private networks, public networks, leased lines, the Internet, Ethernet networks, token ring networks, fiber optic networks, and the like.
Computer system 1100 can be of various types including a personal computer, a portable computer, a workstation, a network computer, a mainframe, a kiosk, or any other data processing system. Due to the ever-changing nature of computers and networks, the description of computer system 1100 depicted in
Various embodiments of any of one or more inventions whose teachings may be presented within this disclosure can be implemented in the form of logic in software, firmware, hardware, or a combination thereof. The logic may be stored in or on a machine-accessible memory, a machine-readable article, a tangible computer-readable medium, a computer-readable storage medium, or other computer/machine-readable media as a set of instructions adapted to direct a central processing unit (CPU or processor) of a logic machine to perform a set of steps that may be disclosed in various embodiments of an invention presented within this disclosure. The logic may form part of a software program or computer program product as code modules become operational with a processor of a computer system or an information-processing device when executed to perform a method or process in various embodiments of an invention presented within this disclosure. Based on this disclosure and the teachings provided herein, a person of ordinary skill in the art will appreciate other ways, variations, modifications, alternatives, and/or methods for implementing in software, firmware, hardware, or combinations thereof any of the disclosed operations or functionalities of various embodiments of one or more of the presented inventions.
The disclosed examples, implementations, and various embodiments of any one of those inventions whose teachings may be presented within this disclosure are merely illustrative to convey with reasonable clarity to those skilled in the art the teachings of this disclosure. As these implementations and embodiments may be described with reference to exemplary illustrations or specific figures, various modifications or adaptations of the methods and/or specific structures described can become apparent to those skilled in the art. All such modifications, adaptations, or variations that rely upon this disclosure and these teachings found herein, and through which the teachings have advanced the art, are to be considered within the scope of the one or more inventions whose teachings may be presented within this disclosure. Hence, the present descriptions and drawings should not be considered in a limiting sense, as it is understood that an invention presented within a disclosure is in no way limited to those embodiments specifically illustrated.
Accordingly, the above description and any accompanying drawings, illustrations, and figures are intended to be illustrative but not restrictive. The scope of any invention presented within this disclosure should, therefore, be determined not with simple reference to the above description and those embodiments shown in the figures, but instead should be determined with reference to the pending claims along with their full scope or equivalents.
