Patent Publication Number: US-2020292510-A1

Title: Techniques for targeted compound analysis

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62/819,022, filed on Mar. 15, 2019, the entire contents of which are incorporated by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments herein generally relate to processing analytical data and, more specifically, analytical data obtained from mass analysis of a sample. 
     BACKGROUND 
     Mass analysis techniques, such as mass spectrometry (MS) or ion mobility spectrometry (IMS), are widely used for identifying and quantifying compounds within a sample. For example, manufacturers and/or research organizations may use MS methods to determine expected and unexpected species in samples of interest. Such determinations are critical in evaluating manufacturing processes. For instance, pharmaceutical manufacturers may use MS methods to perform quality control (QC) processes, which may include verifying the presence of critical quality attributes (CQAs) and the absence of unwanted species in the products resulting from their manufacturing processes. However, conventional QC processes typically require multiple QC assays such as, for instance, ELISA and liquid chromatography, to evaluate a product. In addition, the processing of QC analysis data is a major bottleneck in the manufacturer&#39;s workflow to obtain regulatory approval and/or to certify the product for patient use. Accordingly, entities using mass analysis techniques to evaluate products may benefit from processes capable of quantifying compounds using methods that are more efficient and effective than conventional systems. 
     SUMMARY 
     In accordance with various aspects of the described embodiments is an apparatus that may include at least one memory and logic, coupled to the at least one memory, operative to implement a targeted compound detection process. The logic to receive raw data from analysis of a sample via an analytical device, generate cumulative data from the raw data, receive compound specification information associated with the sample, and determine quantified compound information via performing targeted compound detection based on the cumulative data and the compound specification information. 
     In some embodiments of the apparatus, the analytical device comprising at least one element of a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high-performance liquid chromatography (HPLC) system, a ultra-performance liquid chromatography (UPLC®) system, a ultra-high-performance liquid chromatography (UHPLC) system, or any combination thereof. 
     In various embodiments of the apparatus, the targeted compound detection process comprising a multi-attribute monitoring (MAM) process. In some embodiments of the apparatus, the logic to generate the cumulative data by selecting at least one alignment target and aligning the raw data based on the at least one alignment target. In various embodiments of the apparatus, the logic to generate the cumulative data by aggregate building to generate an aggregate set. In exemplary embodiments of the apparatus, the logic to generate the cumulative data by apex building the aggregate set. In some embodiments of the apparatus, the compound specification information comprising at least one critical quality attributes (CQA). In various embodiments of the apparatus, the logic to determine unknown compounds based on detected compounds not included in the compound specification information. 
     In accordance with various aspects of the described embodiments is a method to provide a targeted compound detection process include. The method may include receiving raw data from analysis of a sample via an analytical device, generating cumulative data from the raw data, receiving compound specification information associated with the sample, and determining quantified compound information via performing targeted compound detection based on the cumulative data and the compound specification information. 
     In some embodiments of the method, the analytical device comprising at least one element of a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high-performance liquid chromatography (HPLC) system, a ultra-performance liquid chromatography (UPLC®) system, a ultra-high-performance liquid chromatography (UHPLC) system, or any combination thereof. 
     In various embodiments of the method, the targeted compound detection process comprising a multi-attribute monitoring (MAM) process. In some embodiments of the method, comprising generating the cumulative data by selecting at least one alignment target and aligning the raw data based on the at least one alignment target. In various embodiments of the method, comprising generating the cumulative data by aggregate building to generate an aggregate set. In exemplary embodiments of the method, comprising generating the cumulative data by apex building the aggregate set. In some embodiments of the method, the compound specification information comprising at least one critical quality attributes (CQA). In various embodiments of the method, comprising determining unknown compounds based on detected compounds not included in the compound specification information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an embodiment of a first operating environment. 
         FIG. 2  illustrates an embodiment of a first logic flow. 
         FIG. 3  illustrates an embodiment of a second logic flow. 
         FIG. 4  illustrates an embodiment of a computing architecture. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments may generally be directed toward systems, methods, and/or apparatuses for processing data generated by analytical instruments. In particular, some embodiments may provide a sample analysis process operative to identify and/or quantify the components of a sample. In some embodiments, the sample analysis process may include a targeted compound detection process operative to determine whether certain known or expected compounds are present in analytical data. 
     In various embodiments, the sample analysis process may generate cumulative data based on raw analytical data. In various embodiments, cumulative data may include aggregate, aligned, and/or clustered data. For example, for MS data, aggregate data may include data aligned based on an alignment target and aggregated to generate an aggregated set of ions that are present in all analyzed injections. Embodiments are not limited in this context. 
     In exemplary embodiments, the sample analysis process may receive compound specification information. In general, compound specification information may include information relating to expected compounds in the sample. For example, expected compounds may include species that are anticipated to be present in the sample. In this manner, the sample analysis process may perform targeted quantification (or targeted clustering) of the sample by analyzing specific areas of the cumulative data based on the compound specification information. In various embodiments, the sample analysis process may determine quantified compound information relating to located compounds that correspond to the compound specification information (for instance, a listing of expected compounds located in the cumulative data and/or quantitative data relating thereto, such as concentration, and/or the like). In some embodiments, the sample analysis process may determine unknown compound information that includes located compounds that do not correspond to the compound specification information (for instance, a listing of unexpected compounds located in the cumulative data and/or quantitative data relating thereto). 
     Systems and techniques according to some embodiments may be used for pharmaceutical and/or biopharmaceutical developers to evaluate products of interest. Although pharmaceutical and biopharmaceutical applications may be used in some examples in this Detailed Description, embodiments are not so limited. In particular, embodiments may be used in any application involving quantification of a sample, including, without limitation, food and environment (F&amp;E) applications, toxicology (for example, forensic toxicology) applications, medical applications, clinical applications, environmental analysis applications, and/or the like. 
     For example, some embodiments may be used by regulated biopharmaceutical developers working in discovery, development, and QC laboratories. Such developers may require workflows for characterizing, monitoring, and/or performing QC for biotherapeutics. Accordingly, systems and techniques according to some embodiments may be or may include a GxP compliant biotherapeutic (for instance, peptide) analysis application. Systems and techniques according to some embodiments may provide efficient, effective, and user-friendly dedicated workflows, for peptide characterization, monitoring and QC, that guide users from the acquisition, processing, review and reporting of biotherapeutic LC-MS data. For example, some embodiments may provide a single software platform supporting high resolution instruments in Discovery and Development, and Acquity RDa, BioAccord, lower resolution instruments, and/or the like in QC with seamless transfer of methods and data between them. 
     In various embodiments, the sample analysis process may be or may include quality-by-design (QbD) analysis. A Multi-Attribute Monitoring (MAM) process may provide an optimized analytical solution, for instance, to focus on the attributes of a therapeutic molecule essential for function and implement QbD principles across process development, manufacturing, drug disposition, and/or the like. Some embodiments may provide a MAM processing pipeline or workflow. A non-limiting example of a MAM process may include processes as described in “A View on the Importance of ‘Multi-Attribute Method’ for Measuring Purity of Biopharmaceuticals and Improving Overall Control Strategy,” The AAPS Journal, Rogers et al. (November 2017), which is hereby incorporated by reference. 
     In general, MAM may allow for direct monitoring of relevant product quality attributes (or critical quality attributes (CQAs)), in contrast to conventional methods which use indirect measurement techniques. A CQA may generally include a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure a desired product quality. Biotherapeutics, particularly complex products such as monoclonal antibodies (mAbs), can have numerous quality attributes that can potentially impact safety and/or efficacy of a product. 
     Identifying CQAs for a biotherapeutic may be an initial and challenging step in implementation of a QbD and/or MAM for development and production of pharmaceuticals and/or biopharmaceuticals. CQAs that may be determined as part of a MAM workflow may include, without limitation, modification X on position Y, Lysine clipping, sequence variants, disulfide scrambling, contaminants (for instance, host-cell proteins), trisulfide bonds vs. native disulfide bonds vs. scrambled disulfides, and/or the like. Certain CQAs may be measured using mass analysis techniques, such as liquid chromatography-mass spectrometry (LC-MS). Non-limiting examples of CQAs that may be measured via LC-MS may include, without limitation, concentration, purity, PTMs, residual proteins, and/or sequence variants. MAM, which is an LC-MS based approach, may be preferred in the manufacture of pharmaceuticals and/or biopharmaceuticals instead of conventional methods because, among other things, only one method is required versus several assays, it is generally more robust and accurate than conventional methods, and provides improved control. 
     In this description, numerous specific details, such as component and system configurations, may be set forth in order to provide a more thorough understanding of the described embodiments. It will be appreciated, however, by one skilled in the art, that the described embodiments may be practiced without such specific details. Additionally, some well-known structures, elements, and other features have not been shown in detail, to avoid unnecessarily obscuring the described embodiments. 
     In the following description, references to “one embodiment,” “an embodiment,” “example embodiment,” “various embodiments,” etc., indicate that the embodiment(s) of the technology so described may include particular features, structures, or characteristics, but more than one embodiment may and not every embodiment necessarily does include the particular features, structures, or characteristics. Further, some embodiments may have some, all, or none of the features described for other embodiments. 
     As used in this description and the claims and unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc. to describe an element merely indicate that a particular instance of an element or different instances of like elements are being referred to, and is not intended to imply that the elements so described must be in a particular sequence, either temporally, spatially, in ranking, or in any other manner. 
       FIG. 1  illustrates an example of an operating environment  100  that may be representative of some embodiments. As shown in  FIG. 1 , operating environment  100  may include an analysis system  105  operative to manage analytical information  132  associated with analytical devices  115   a - n . In some embodiments, analytical devices  115   a - n  may be or may include a chromatography system, a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass detector system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high-performance liquid chromatography (HPLC) system, a ultra-performance liquid chromatography (UPLC®) system, a ultra-high performance liquid chromatography (UHPLC) system, an ultraviolet (UV) detector, a visible light detector, a solid-phase extraction system, a sample preparation system, a sample introduction system, a pump system, a capillary electrophoresis instrument, combinations thereof, components thereof, variations thereof, and/or the like. Although LC, MS, and LC-MS are used in examples in this detailed description, embodiments are not so limited, as other analytical instruments capable of operating according to some embodiments are contemplated herein. 
     In some embodiments, analytical device  115   a - n  may operate to perform an analysis and generate analytical information  132 . In various embodiments, analytical information  132  may include information, data, files, charts, graphs, images, and/or the like generated by an analytical instrument as a result of performing an analysis method. For example, for an LC-MS system, analytical device  115   a - n  may separate a sample and perform mass analysis on the separated sample according to a specified method to generate analytical information  132  that may include raw, native, or otherwise unprocessed data, chromatograms, spectra, peak lists, mass values, retention time values, concentration values, compound identification information, and/or the like. 
     The term raw data may refer to mass spectral data acquired in an elevated energy mode and/or a low energy mode may identify a plurality of scan times and, for each of said scan times, may identify one or more masses each of an ion detected during said each scan time and, for each of the one or more masses, may identify an intensity denoting an intensity of a detected ion having said each mass. The raw data may represent experimental data that has not been further processed to onvert the scan times to corresponding retention or drift times. For example, the raw data may represent data that has not been post processed by other software that performs peak detection, maps or translates raw scans and scan times to corresponding retention times and/or drift times, software which performs retention time alignment and associates precursor and fragment or product ions as originating from a same originating molecule based on common retention times of such ions, and the like. The raw data may be unfiltered data. 
     In various embodiments, analysis system  105  may include computing device  110  communicatively coupled to analytical device  115   a - n  or otherwise configured to receive and store analytical information  132  associated with analytical device  115 . For example, analytical device  115   a - n  may operate to provide analytical information  132  directly to computing device  110  and/or to a location on a network  150  (for instance, a database, a cloud computing environment, a storage device, a storage network, a server, and/or the like) accessible to computing device  110 . In some embodiments, computing device  110  may be operative to control, monitor, manage, or otherwise process various operational functions of analytical device  115   a - n . In some embodiments, computing device  110  may be operative to provide analytical information  132  to a location on a network  150  through a secure or authenticated connection. In some embodiments, computing device  110  may be or may include a stand-alone computing device, such as a personal computer (PC), server, tablet computing device, cloud computing device, mobile computing device (for instance, a smart phone, tablet computing device, and/or the like), data appliance, and/or the like. In various embodiments, computing device  110  may be or may include a controller or control system integrated into analytical device  115   a - n  to control operational aspects thereof. 
     Although only one computing device  110  is depicted in  FIG. 1 , embodiments are not so limited. In various embodiments, the functions, operations, configurations, data storage functions, applications, logic, and/or the like described with respect to computing device  110  may be performed by and/or stored in one or more other computing devices. A single computing device  110  is depicted for illustrative purposes only to simplify the figure. 
     As shown in  FIG. 1 , computing device  110  may include processor circuitry  120 , a memory unit  130 , and a transceiver  160 . Processor circuitry  120  may be communicatively coupled to memory unit  130  and/or transceiver  160 . 
     Processor circuitry  120  may include and/or may access various logic for performing processes according to some embodiments. For instance, processor circuitry  120  may include and/or may access analytical services logic  122 , data processing logic  124 , and/or compound identification logic  126 . Processing circuitry  120  and/or analytical services logic  122 , data processing logic  124 , and/or compound identification logic  126 , and/or portions thereof, may be implemented in hardware, software, or a combination thereof. As used in this application, the terms “logic, “component,” “layer,” “system,” “circuitry,” “decoder,” “encoder,” and/or “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture  400 . For example, a logic, circuitry, or a layer may be and/or may include, but are not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, a computer, hardware circuitry, integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), a system-on-a-chip (SoC), memory units, logic gates, registers, semiconductor device, chips, microchips, chip sets, software components, programs, applications, firmware, software modules, computer code, combinations of any of the foregoing, and/or the like. 
     Although analytical services logic  122  is depicted in  FIG. 1  as being within processor circuitry  120 , embodiments are not so limited. In addition, although data processing logic  124  and compound identification logic  126  are depicted as being a logic of analytical services logic  122 , embodiments are not so limited, as data processing logic  124 , data system logic  128 , and/or system controller logic may be separate logics and/or may not be standalone logics but, rather, a part of analytical services logic  122 . For example, analytical services logic  122 , and/or any component thereof, may be located within an accelerator, a processor core, an interface, an individual processor die, implemented entirely as a software application (for instance, analytical services application  140 ) and/or the like. 
     Memory unit  130  may include various types of computer-readable storage media and/or systems in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, an array of devices such as Redundant Array of Independent Disks (RAID) drives, solid state memory devices (e.g., USB memory, solid state drives (SSD) and any other type of storage media suitable for storing information. In addition, memory unit  130  may include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD), a magnetic floppy disk drive (FDD), and an optical disk drive to read from or write to a removable optical disk (e.g., a CD-ROM or DVD), a solid state drive (SSD), and/or the like. 
     Memory unit  130  may store an analytical services application  140  that may operate, alone or in combination with analytical services logic  122 , to perform various analytical services according to some embodiments. In various embodiments, analytical services application  140  may interact with analytical devices  115   a - n  and/or components thereof through various drivers (which may include application programming interfaces (APIs) and/or the like), software and/or hardware interfaces, and/or the like. 
     In various embodiments, analytical services logic  122  may be configured to provide and/or implement analytical services for analytical devices  115   a - n . In exemplary, analytical services may operate to generate, receive, and/or process analytical information  132  resulting from analyses performed via analytical devices  115   a - n . In some embodiments, data processing logic  124  may operate to process analytical information  132 . For example, data processing logic  124  may perform processing on raw data received from analytical instruments  115   a - n , such as aggregating, clustering, apex building, and/or the like (see, for example,  FIG. 3 ). In various embodiments, compound identification logic  126  may operate to identify compounds in a sample associated with analytical information  132 . For example, compound identification logic  126  may access compound specification information  134  to determine compounds of interest (for instance, known or expected compounds, CQAs, and/or the like) in a sample. Compound identification logic  126  may perform targeted compound detection (or clustering) processes (see, for example,  FIG. 3 ) according to some embodiments to determine compound identification information  136  based on compound specification information  134 . In some embodiments, compound identification information  136  may include compounds and/or qualities concerning compounds (for instance, CQAs) in a sample based on compound specification information  134 . In some embodiments, some or all of analytical information  132 , compound specification information  134 , and/or compound identification information  136  may be received from and/or stored on data sources  154   a - n  accessible via network  150 . 
     Included herein are one or more logic flows representative of exemplary methodologies for performing novel aspects of the disclosed architecture. While, for purposes of simplicity of explanation, the one or more methodologies shown herein are shown and described as a series of acts, those skilled in the art will understand and appreciate that the methodologies are not limited by the order of acts. Some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel implementation. Blocks designated with dotted lines may be optional blocks of a logic flow. 
     A logic flow may be implemented in software, firmware, hardware, or any combination thereof. In software and firmware embodiments, a logic flow may be implemented by computer executable instructions stored on a non-transitory computer readable medium or machine readable medium, such as an optical, magnetic or semiconductor storage. The embodiments are not limited in this context. 
       FIG. 2  illustrates an embodiment of a logic flow  200 . Logic flow  200  may be representative of some or all of the operations executed by one or more embodiments described herein, such as by computing device  110 . For instance, logic flow  200  may be representative of some or all of the operations of a sample analysis according to some embodiments. 
     At block  202 , logic flow  200  may receive raw data. For example, analytical service logic  122  may receive or otherwise access analytical information  132  in the form of raw data from analytical device  115   a . Although raw data may be used in some examples in this Detailed Description, embodiments are not so limited, as processed and/or semi-processed data may also be used according to some embodiments. In various embodiments, raw data may include, without limitation, raw or unprocessed data. In some embodiments, raw data may include, without limitation, processed data, chromatograms, spectra, peak lists, mass values, retention time values, concentration values, compound identification information, and/or the like. Embodiments are not limited in this context. 
     Logic flow  200  may generate cumulative data at block  204 . For example, analytical services logic  122  may perform alignment, aggregation, clustering, apex building, and/or the like (see, for example,  304 ,  306 ,  308 , and/or  310  of  FIG. 3 ) on analytical information  132  to generate cumulative data. In various embodiments, the cumulative data may be stored as analytical information. In this manner, logic flow  200  may perform sample analysis processes on a cumulative set of information rather than, as in conventional systems, individual sampling data sets (for instance, a cumulative set of injections as compared to individual injections). 
     At block  208 , logic flow  200  may perform targeted compound detection. For example, analytical services logic  122  may access compound specification information  134  to determine which compounds and/or compound information (for instance, CQAs) that the sample analysis processes are looking for (see, for example,  314 ,  316 , and  318  of  FIG. 3 ). Accordingly, analytical services logic  122  may analyze, search, or otherwise process specified locations (for instance, based on m/z, retention time, intensity, drift time, and/or the like) to determine whether a compound of interest is present (as well as expected characteristics, for example, based on corresponding CQA information). 
     Logic flow  200  may determine quantified compound information at block  210 . For example, analytical services logic  122  may determine compounds and associated information (for instance, concentrations, variants, and/or the like) of compounds specified in the compound specification information. In this manner, logic flow  200  may verify the presence of expected compounds (or “hits”) in the raw data. At block  212 , logic flow  200  may determine unknown compound information. For example, analytical services logic  122  may determine which potential compounds (for instance, peaks) are in the raw data that were not indicated in the compound specification information (for instance, “unknowns”) (see, for example,  320 ,  322 ,  324 ,  326 , and  328  of  FIG. 3 ). 
       FIG. 3  illustrates an embodiment of a logic flow  300 . Logic flow  300  may be representative of some or all of the operations executed by one or more embodiments described herein, such as by computing device  110 . For instance, logic flow  300  may be representative of some or all of the operations of a sample analysis operating a MAM processing workflow for CQAs according to some embodiments. 
     At block  302 , logic flow  300  may receive raw data. For example, analytical services logic  122  may receive raw data from a control or data application for analytical device  115   a . Non-limiting examples of control or data applications may include computing systems operating the UNIFI® platform and/or the Empower® platform provided by Waters Corporation of Milford, Mass., United States. 
     Logic flow  300  may perform alignment target selection at block  304  and alignment at block  306 . For example, analytical services logic  122  may determine an alignment target for use in aligning the raw data. Selecting an alignment target may include a manual and/or automatic process for selecting distinct data having a reasonably high signal, including, for instance, prominent features across an injection. 
     In various embodiments, alignment may be or may include correcting for retention time differences for samples run through LC at different times. For example, comparing the features in different injections to determine a time correction. An alignment process may operate to attempt to get each signal at the same retention time across all injections. For instance, in some embodiments, alignment may include moving data back/forward in time to line up with the alignment target (or reference sample). Accordingly, retention times may be consistent or substantially consistent across an experiment. In various embodiments, an alignment process may include finding the alignment target on each injection, then aligning each injection individually to the alignment target. In some embodiments, alignment may be or may include shape matching across multiple injections based on, for example, the alignment target. 
     Logic flow  300  may perform aggregate building at block  308 . In conventional processes, each injection is processed individually. Accordingly, for example, apex building may be performed on each injection individually to pick out ions in the data. However, in some embodiments, aggregate building may allow for, with each injection, applying retention time correction, then building an averaged pseudo-injection. All or substantially all of the injections may be summed together after applying corrections, such as retention time corrections (for instance, alignment). 
     During an analysis, ions may elute over a retention time with a particular m/z. Logic flow  300  may locate all of the ions in the data (for instance, a particular retention time range with an m/z range, another particular retention time range with an m/z range, and so forth) to find matches. Conventional system may perform such processes on each injection, then attempt to match afterwards. However, an aggregate building process according to some embodiments may, on each injection, apply retention time correction, then build an averaged pseudo-injection summing all of the injections together after applying retention time corrections. In some embodiments, corrections may not be a single number for an entire run, for example, corrections may be non-linear and may vary across a run (for instance, data may be moved back 30 seconds at one point, moved back 2 seconds at a different point, and so on). Accordingly, embodiments may generate, through blocks  304 - 308 , an aggregate (“aggregate set” or “super set”) of all ions that exist in all injections of the analysis. 
     At block  310 , logic flow  300  may perform apex building. For example, analytical services logic  122  may perform apex building on the aggregate set. Accordingly, in some embodiments, apex building may be run once on the aggregate set to provide for, inter alia, measurement of the same area on each run. As described above, processes according to some embodiments may be or may include MAM processes. In general, MAM is a comparative workflow in which analyses may operate to determine how a compound (for instance, a peptide) or an area increases or decreases in different samples. Therefore, MAM may be performed according to some embodiments to measure the same area in each sample, and operators are able to obtain more robust statistics and are less likely to get missing data points from some injections. 
     In some embodiments, logic flow  300  may perform overlap detection at optional block  312 . In some embodiments, overlap detection may be performed to handle co-eluting ions having the same or substantially similar mass and/or m/z. In such situations, areas where two different ions are being measured at the same time may occur. Overlap detection processes may operate to exclude one of them from the measurements. In some embodiments, an overlap detection process may operate to analyze the shapes in the apexes, look for areas where they merge with each other or other characteristics of co-elution. Some embodiments may provide for a tolerance for noise and/or parametric analysis to determine where the peaks compromise each other. 
     At block  314 , logic flow  300  may perform CQA specification. For example, analytical services logic  122  may receive or otherwise access compound specification information  134 . For example, a set of compounds and/or characteristics of interest may be established for a sample as compound specification information  134 . In some embodiments, compound specification information  134  may be or may include a list of compounds of interest (for instance, 10-20 compounds). Compound specification information  134  may operate to specify where the analysis expects to detect certain information, including, without limitation, peaks, retention time, m/z, fragment information, sequence information, translational modifications, versions (for instance, oxidized, sulfide bonds, and/or the like), to identify a compound (for instance, an ion of a compound of interest). 
     Logic flow  300  may perform targeted clustering at block  316 . For instance, because compound specification information  134  (for example, CQA information) indicates what the analysis is (or is expecting) to measure (for example, information to work out what the ion of interest should look like (for example, isotope configuration, spacing, retention time, m/z (i.e., charge 3+ with a particular configuration), then the analysis can go and look for that ion. 
     In conventional systems, processes use a blind discovery method with a compound building task. For example, putting different ions into a cluster, then placing charge states together to get a single peptide. However, in some embodiments, because compound specification information  134  (for example, CQA information) indicates which charge states to look for, sample analysis processes according to some embodiments may work out what the isotope cluster for each charge state should look like, which may then be detected in the detected apexes. For example, sample analysis processes according to various embodiments may analyze the data directly, looking at a specific data location to know what should be there. 
     Accordingly, sample analysis processes according to some embodiments may operate by confirming detected and/or expected data (compared with detecting what is there as with conventional systems). In conventional systems, each apex at each injection would be analyzed, while another process determines how to join them together to put a group of clusters together to make a single peptide. However, such processes are prone to mistakes, such as missing the first peak of an isotope cluster, wrong charge state, and/or the like. For example, conventional processes may look for a peptide cluster with a particular monoisotopic m/z through a “blind” process where the process may not be able to identify as it does not know what it is looking for. In particular, conventional processes may look for an ion with a particular m/z and/or retention time. However, if the process is wrong, then the process will not find it because the process does not know ahead of time what it is looking for. However, sample analysis processes according to some embodiments may use compound specification information  134  (for example, CQA information) to go and look at the exact right place, which provides for more efficient and effective sample analyses. 
     Logic flow  300  may determine quantified CQAs at block  318 . For example, analytical services logic  122  may generate compound identification information  136  indicating compounds located during the sample analysis and any associated information (for instance, concentration, variances, and/or the like). In a MAM process, an operator may desire to determine all CQAs in all of the injections and the amount in each injection. In some embodiments, the compound identification information  136  may be generated from the raw data (analytical information  132 ) an amount of each CQA in each run (for instance, areas under the curves of the clusters built via logic flow  300 ). 
     In some embodiments, blocks  320 ,  322 ,  324 ,  326 , and  328  may be used to determine new or unknown compounds in the sample. For example, unknown compounds may include non-target compounds not specified in CQA specification at block  314 . 
     Logic flow  300  may determine unassigned apexes at block  320 . For example, analytical services logic  122  may determine unassigned apexes via taking the output of block  310  (for instance, data generated as a result of apex building on the aggregate set) and removing data that contributed to the targeted clusters determined in block  316 . At block  322 , logic flow  300  may perform blind clustering. For example, analytical services logic  122  may cluster the unassigned apexes (for instance, determined in block  320 ) into peptide isotope clusters. In some embodiments, the unassigned apexes may be clustered by grouping unassigned apexes with similar retention time profiles and/or m/z gaps that signify that the unassigned apexes belong to different isotope peaks of the same peptide isotope cluster (for instance, a m/z difference of 1 Da implies a charge 1 ion, a m/z difference of 0.5 Da implies a charge 2 ion, etc.). In various embodiments, when the retention time profiles are similar, and the m/z differences are as expected, the unassigned apexes may be clustered to form a peptide isotope cluster. In some embodiments, each peptide isotope cluster may include a series of areas (for instance, rectangular areas) in m/z and retention time, each of which may correspond to a single isotope peak. 
     Logic flow  300  may determine quantified peptide ions, peptide isotopes, peptide isotope clusters, and/or peptide charge states at block  324 . For example, analytical services logic  322  may transpose the peptide isotope clusters (for instance, generated at block  322 ) back to each individual injection. In some embodiments, the transposition may include using the inverse of the transform determined during the alignment process (for instance, at block  306 ) to find the location of the areas (for instance, rectangles) on the original injections. In various embodiments, the transposed rectangles may be used as an area to quantify, and an area under the curve (for instance, in 2D for MS data, 3D for ion mobility or SONAR data, and/or the like) may be calculated for each rectangle. In exemplary embodiments, areas for the rectangles may be summed for each injection to give a peptide isotope cluster abundance measurement for each injection. 
     Logic flow  300  may perform an optional statistical significance test at block  326  to determine whether any of the suspected ions meet certain statistical thresholds (for example, to alleviate noise, false positives, and/or the like). For example, for one or more samples under analysis, the measured peptide isotope cluster abundances for that sample may be compared to those of a reference sample to determine peptide isotope clusters which have changed significantly in abundance relative to the reference. If multiple injections are present for each sample, a statistical significance test can be used to calculate the likelihood that a change of abundance represents a real difference in the sample and is not, for instance, noise recorded during the instrument acquisition process. Additionally, even if only a single injection per sample exists, metrics such as fold change, abundance, isotope profile, charge state distribution and mass defect can be used to judge if an apparent new peak is likely to be real. 
     At block  328 , logic flow  300  may determine suspected new peaks. For example, peptide ion clusters which pass the tests performed in block  326  may be considered suspected new peaks. In some embodiments, the suspected new peaks may be presented to the user for review as they may represent a new peptide or significantly changing existing peptide which could, for example, potentially have an effect on the efficacy or safety of the product. 
       FIG. 4  illustrates an embodiment of an exemplary computing architecture  400  suitable for implementing various embodiments as previously described. In various embodiments, the computing architecture  400  may comprise or be implemented as part of an electronic device. In some embodiments, the computing architecture  400  may be representative, for example, of computing device  110 . The embodiments are not limited in this context. 
     As used in this application, the terms “system” and “component” and “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture  400 . For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. Further, components may be communicatively coupled to each other by various types of communications media to coordinate operations. The coordination may involve the uni-directional or bi-directional exchange of information. For instance, the components may communicate information in the form of signals communicated over the communications media. The information can be implemented as signals allocated to various signal lines. In such allocations, each message is a signal. Further embodiments, however, may alternatively employ data messages. Such data messages may be sent across various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces. 
     The computing architecture  400  includes various common computing elements, such as one or more processors, multi-core processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input/output (I/O) components, power supplies, and so forth. The embodiments, however, are not limited to implementation by the computing architecture  400 . 
     As shown in  FIG. 4 , the computing architecture  400  comprises a processing unit  404 , a system memory  406  and a system bus  408 . The processing unit  404  can be any of various commercially available processors, including without limitation an AMD® Athlon®, Duron® and Opteron® processors; ARM® application, embedded and secure processors; IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony® Cell processors; Intel® Celeron®, Core (2) Duo®, Itanium®, Pentium®, Xeon®, and XScale® processors; and similar processors. Dual microprocessors, multi-core processors, and other multi-processor architectures may also be employed as the processing unit  404 . 
     The system bus  408  provides an interface for system components including, but not limited to, the system memory  406  to the processing unit  404 . The system bus  408  can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters may connect to the system bus  408  via a slot architecture. Example slot architectures may include without limitation Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, Personal Computer Memory Card International Association (PCMCIA), and the like. 
     The system memory  406  may include various types of computer-readable storage media in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, an array of devices such as Redundant Array of Independent Disks (RAID) drives, solid state memory devices (e.g., USB memory, solid state drives (SSD) and any other type of storage media suitable for storing information. In the illustrated embodiment shown in  FIG. 4 , the system memory  406  can include non-volatile memory  410  and/or volatile memory  412 . A basic input/output system (BIOS) can be stored in the non-volatile memory  410 . 
     The computer  402  may include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD)  414 , a magnetic floppy disk drive (FDD)  416  to read from or write to a removable magnetic disk  418 , and an optical disk drive  420  to read from or write to a removable optical disk  422  (e.g., a CD-ROM or DVD). The HDD  414 , FDD  416  and optical disk drive  420  can be connected to the system bus  408  by a HDD interface  424 , an FDD interface  426  and an optical drive interface  428 , respectively. The HDD interface  424  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 1384 interface technologies. 
     The drives and associated computer-readable media provide volatile and/or nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For example, a number of program modules can be stored in the drives and memory units  410 ,  412 , including an operating system  430 , one or more application programs  432 , other program modules  434 , and program data  436 . In one embodiment, the one or more application programs  432 , other program modules  434 , and program data  436  can include, for example, the various applications and/or components of computing device  110   
     A user can enter commands and information into the computer  402  through one or more wire/wireless input devices, for example, a keyboard  438  and a pointing device, such as a mouse  440 . Other input devices may include microphones, infra-red (IR) remote controls, radio-frequency (RF) remote controls, game pads, stylus pens, card readers, dongles, finger print readers, gloves, graphics tablets, joysticks, keyboards, retina readers, touch screens (e.g., capacitive, resistive, etc.), trackballs, trackpads, sensors, styluses, and the like. These and other input devices are often connected to the processing unit  404  through an input device interface  442  that is coupled to the system bus  408  but can be connected by other interfaces such as a parallel port, IEEE 1394 serial port, a game port, a USB port, an IR interface, and so forth. 
     A monitor  444  or other type of display device is also connected to the system bus  408  via an interface, such as a video adaptor  446 . The monitor  444  may be internal or external to the computer  402 . In addition to the monitor  444 , a computer typically includes other peripheral output devices, such as speakers, printers, and so forth. 
     The computer  402  may operate in a networked environment using logical connections via wire and/or wireless communications to one or more remote computers, such as a remote computer  448 . The remote computer  448  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  402 , although, for purposes of brevity, only a memory/storage device  450  is illustrated. The logical connections depicted include wire/wireless connectivity to a local area network (LAN)  452  and/or larger networks, for example, a wide area network (WAN)  454 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, for example, the Internet. 
     When used in a LAN networking environment, the computer  402  is connected to the LAN  452  through a wire and/or wireless communication network interface or adaptor  456 . The adaptor  456  can facilitate wire and/or wireless communications to the LAN  452 , which may also include a wireless access point disposed thereon for communicating with the wireless functionality of the adaptor  456 . 
     When used in a WAN networking environment, the computer  402  can include a modem  458 , or is connected to a communications server on the WAN  454  or has other means for establishing communications over the WAN  454 , such as by way of the Internet. The modem  458 , which can be internal or external and a wire and/or wireless device, connects to the system bus  408  via the input device interface  442 . In a networked environment, program modules depicted relative to the computer  402 , or portions thereof, can be stored in the remote memory/storage device  450 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used. 
     The computer  402  is operable to communicate with wire and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively disposed in wireless communication (e.g., IEEE 802.16 over-the-air modulation techniques). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wireless technologies, among others. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.11x (a, b, g, n, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wire networks (which use IEEE 802.3-related media and functions). 
     Numerous specific details have been set forth herein to provide a thorough understanding of the embodiments. It will be understood by those skilled in the art, however, that the embodiments may be practiced without these specific details. In other instances, well-known operations, components, and circuits have not been described in detail so as not to obscure the embodiments. It can be appreciated that the specific structural and functional details disclosed herein may be representative and do not necessarily limit the scope of the embodiments. 
     Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. These terms are not intended as synonyms for each other. For example, some embodiments may be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. 
     Unless specifically stated otherwise, it may be appreciated that terms such as “processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical quantities (e.g., electronic) within the computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within the computing system&#39;s memories, registers or other such information storage, transmission or display devices. The embodiments are not limited in this context. 
     It should be noted that the methods described herein do not have to be executed in the order described, or in any particular order. Moreover, various activities described with respect to the methods identified herein can be executed in serial or parallel fashion. 
     Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. It is to be understood that the above description has been made in an illustrative fashion, and not a restrictive one. Combinations of the above embodiments, and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description. Thus, the scope of various embodiments includes any other applications in which the above compositions, structures, and methods are used. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.