Method and System for Optimizing Research and Development Experimentations

The present disclosure relates to a method and system for optimizing research and development experimentations. The method comprises receiving characteristics of reference listed drug (RLD) and Active Pharmaceutical Ingredient (API) associated with the RLD and identifying a manufacturing process for the pharmaceutical product based on the API and characteristics of RLD received. The method further comprises generating notifications to at least one or more users to develop the pharmaceutical product using the API, one or more excipients associated with the API, and the identified manufacturing process. The method also comprises determining acceptance range of one or more values associated with properties of the pharmaceutical product and optimizing research and development experimentations based on the determination.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to Indian Provisional Patent Application Number 201841035671, filed on Mar. 21, 2019, the entire contents of which are hereby incorporated by reference.

TECHNICAL FIELD

Embodiments of the present disclosure are related, in general to resource management systems, and more particularly, but not exclusively to a method and system for optimizing research and development experimentations.

BACKGROUND

Generally, research & development (R&D) operations in an organization involve manual operations carried out on unconnected technology platforms. In R&D operations, number of experimental iterations while conducting research is not under a control of researcher as the observations or inferences from the experimental iterations will only decide the next steps or experimental processes. Also, among factors that seriously affect a process, only few factors would be critical depending on specific product being developed. Currently, identifying the critical factors in the R&D practice bring unwanted economic burden to industries that often result in delay of time bound functioning of the industry. For example, in manufacturing a drug product, selection of proper manufacturing method is a critical stage of the product development which will eventually decide the success of the product development. Currently, selection of the manufacturing method is purely based on the experience and knowledge of the researcher involved or based on knowledge in publicly available literature. Only upon conducting several experiments or upon monitoring stability of the product for a few months, the researcher or developer would be able to predict the suitability of the experiments or select suitable manufacturing method which is one of major challenges in a pharmaceutical drug development. Also, in an R&D environment, different researchers adopt different experimental approaches while developing the product, which leads to diverse working or experimentation culture across organization. In order to avoid diversities in development across an organization, an intelligent system is needed to help the researcher to deliver products, for example pharmaceutical products in an efficient way. Currently, there is no such system and techniques available that control end to end aspects of pharmaceutical product development with a limited number of R&D experiments and which assists in important science-based decision making in all phases of a product development.

Accordingly, there is required an intelligent technique to provide a method and system for optimizing research and development experimentations.

SUMMARY

Embodiments of the present disclosure relate to a method of optimizing research and development experimentations. The method comprises receiving characteristics of reference listed drug (RLD) and Active Pharmaceutical Ingredient (API) associated with the RLD and identifying a manufacturing process for the pharmaceutical product based on the API and characteristics of RLD received. The method further comprises generating notifications to at least one or more users to develop the pharmaceutical product using the API, one or more excipients associated with the API, and the identified manufacturing process. The method also comprises determining acceptance range of one or more values associated with properties of the pharmaceutical product and optimizing research and development experimentations based on the determination.

Another aspect of the present disclosure relates to a system for optimizing research and development experimentations. The system comprises a memory and a processor. The processor is configured to receive characteristics of reference listed drug (RLD) and Active Pharmaceutical Ingredient (API) associated with the RLD and identify a manufacturing process for the pharmaceutical product based on the API and characteristics of RLD received. The processor is further configured to generate notifications to at least one or more users to develop the pharmaceutical product using the API, one or more excipients associated with the API, and the identified manufacturing process. The processor is also configured to determine acceptance range of one or more values associated with properties of the pharmaceutical product and optimize research and development experimentations based on the determination.

The system, and associated method of the present disclosure overcome one or more of the shortcomings of the prior art. Additional features and advantages may be realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

DETAILED DESCRIPTION

The present disclosure relates to a system and method of optimizing research and development experimentations. The method comprises a comprehensive workflow that enables implementation of minimum number of R&D experiments in all phases of the research and development. The reduction in the number of R&D experiments is achieved by using the experimental knowledge and historical experimental results for optimizing the steps of manufacturing process, thereby optimizing research and development experimentations.

FIG. 1illustrates an exemplary architecture of a proposed system, to optimize research and development experimentation, in accordance with some embodiments of the present disclosure.

As illustrated inFIG. 1, the exemplary system100comprises one or more components configured to optimize resources for a pharmaceutical product development. In one embodiment, the exemplary system100comprises a resource optimization system (ROS)102, a data repository104(interchangeably referred to as repository104), and a user device106coupled via a communication network108.

The ROS102is configured to optimize resources for development of the pharmaceutical product by establishing an effective workflow for the R&D processes. In one example embodiment, optimizing resources include but not limited to minimizing the R&D experimentations. In one embodiment, the ROS102comprises one or more mathematical, statistical and scientific tools that aids the researcher or user to quickly make efficient scientific decisions on selection of resources. The ROS102comprises one or more components coupled with each other that may be deployed on a single system or on different systems. In an embodiment, the ROS102comprises a central processing unit (“CPU” or “processor”)120, a memory125, a prediction unit130, a formula generator unit140, a risk assessment unit150, and a control unit160.

The ROS102may be configured as a standalone system. In another embodiment, the ROS102may be configured in cloud environment. In yet another embodiment, the ROS102may include a desktop personal computer, workstation, laptop, PDA, cell phone, or any WAP-enabled device or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. The ROS102typically includes one or more user interface devices, such as a keyboard, a mouse, touch screen, pen or the like, for interacting with the Graphical User Interface (GUI) provided on a display. The ROS102also includes an interface provided therein for interacting with the repository104and the user device106to access data from the repository104for R&D process optimization.

The data repository104stores previously recorded data of one or more earlier experimentations as historical data170. In one example, the historical data170may be an experimental data collected from a plurality of experiments previously conducted on the same or similar pharmaceutical product. The historical data170also stores acceptance criteria and regulatory restrictions for parameters of formulation, such as Inactive Ingredient Guide (IIG) specification. The IIG specification describes allowable limits of one or more excipients for a development of the pharmaceutical products. The ROS102is configured to access the data repository104to retrieve the historical data170and the IIG specification during the resource optimization process. In one embodiment, the data repository104may be integrated within the ROS102. In another embodiment, the data repository104may be a standalone repository communicatively coupled with the ROS102and the user device106. The user device106comprises an integrated application that enables interaction of user with the ROS102via a user-friendly application interface. For example, the integrated application of the user device106enables the user to input one or more values such API values, characteristic values of reference listed drug (RLD) to the ROS102. The integrated application of the user device106wirelessly connects to the ROS102, to receive alerts or notification from the ROS102. In another embodiment, the user device106connects with the ROS102over Transfer Control Protocol and Internet Protocol (TCP/IP) via the communication network108.

The communication network108can be a LAN (local area network), WAN (wide area network), wireless network, point-to-point network, or another configuration. One of the most common types of network in current use is a TCP/IP (Transfer Control Protocol and Internet Protocol) network for communication between database client and database server. Other common Internet protocols used for such communication include HTTPS, FTP, AFS, and WAP and using secure communication protocols etc.

FIG. 2illustrates various components and modules of ROS, for a pharmaceutical product development, in accordance with some embodiments of the present disclosure.

In an implementation, the ROS102may include an I/O interface201, the processor120, the memory125, and modules220. The I/O interface201may be configured to receive inputs from one or more users for optimizing resources for the pharmaceutical product development. Further, the I/O interface201may be configured to communicate with the repository104and the user device106. The processor120may be configured to perform one or more functions of the ROS102for optimizing resources for the pharmaceutical product development. The memory125may be communicatively coupled to the processor120and may store data230.

In one embodiment, the Quality Target Product Profile (QTPP) elements data242may be retrieved from the characteristics of reference listed drug (RLD) using the historical database170. In an exemplary embodiment, the RLD is also known as innovator drug. The QTTP elements data242for example, includes dosage form, dosage design, route of administration, dosage strength, pharmacokinetics, stability, drug product quality attributes, container closure system, and alternative methods of administration. In one embodiment, the drug product quality attributes comprise physical attributes, identification, assay content uniformity, dissolution, degradation products, residual solvents, water content and microbial limits. In one embodiment, the physical attributes comprise appearance, order, size, score configuration and friability.

In one embodiment, the critical quality attribute (CQA) data244includes at least one of physical, chemical, biological, or microbiological property or characteristic of the pharmaceutical product that should be within an appropriate limit, range, or distribution to ensure a desired product quality of the pharmaceutical product.

In one embodiment, the critical process parameters (CPPs) data246includes one or more process parameters whose variability has an impact on the CQA data244and therefore should be monitored or controlled to ensure that the process produces the desired quality.

In one embodiment, the critical material attributes (CMAs) data248includes at least one of physical, chemical, biological, or microbiological property or characteristic of the pharmaceutical product whose variability has an impact on the CQA data244and therefore should be monitored or controlled to produce the desired quality.

In some embodiments, the data230may be stored within the memory125in the form of various data structures. Additionally, the data230may be organized using data models, such as relational or hierarchical data models. The other data249may comprises other temporary data generated by other modules260for performing various functions of the ROS102.

In some embodiments, the ROS102may include the modules220for performing various operations in accordance with embodiments of the present disclosure. The modules220may include, for example, the prediction unit130, the formula generator unit140, the risk assessment unit150, the strategy unit160, and a report generation unit240. The modules220may also comprise other modules260to perform various miscellaneous functionalities of the ROS102. It will be appreciated that such modules260may be represented as a single module or a combination of different modules. The modules220may be implemented in the form of software, hardware, and/or firmware. As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

In an embodiment, the prediction unit130is configured to receive characteristics of RLDs and the Active Pharmaceutical Ingredient's (APIs) inputted by one or more users. Upon receiving the characteristics of RLDs, the prediction unit130identifies CQA data244and QTPP elements data242for the pharmaceutical product to be developed. The prediction unit130also predicts an appropriate manufacturing process, by verifying if one or more APIs meet predefined criteria for the pharmaceutical product based on the historical data170for the same or similar RLDs. In an example, the predefined criteria can be the weight percentage of the one or more APIs in the pharmaceutical product. The prediction unit130determines the manufacturing process as wet granulation if the weight percentage of the one or more APIs data in the pharmaceutical product exceeds 30%. Once the manufacturing process is determined, the prediction unit130determines one or more excipients data and composition of each of the one or more excipients to be used for developing the pharmaceutical product based on the historical data170for API's of the same or similar RLDs. The prediction unit130also generates notifications to the one or more users to develop the pharmaceutical product and determines acceptance range of one or more values associated with properties of the pharmaceutical product based on the historical data170for API's of the same or similar RLDs. The prediction unit130also receives the one or more values associated with properties of the pharmaceutical product to be developed from the one or more users. For example, the properties of the pharmaceutical product may be, but not limited to hardness, friability, weight, and sticking. The prediction unit130modifies the manufacturing process for the pharmaceutical product upon determination that the one or more values exceed the acceptance range. In an example, the predicted unit130modifies the manufacturing process for example, from the wet granulation process to a direct compression method upon determination that the one or more values exceeded the acceptance range.

The formula generator unit140receives the one or more excipients data associated with the API data from the prediction unit130and calculates weight per tablet for each of the one or more excipients data. The formula generator unit140further determines qualification of the one or more excipients to be used in the pharmaceutical product development by comparing composition of each of the one or more excipients data with the preferred range indicated in the IIG specification. If the one or more excipients composition is determined to exceed the preferred range as per the IIG specification, then the formula generator unit140alters the composition of the one or more excipients by recalculating the weight per tablet of the excipients and further continues to compare with the preferred range of the IIG specification to determine the correct set of excipients data that are conforming within the preferred range as per the IIG specification.

The risk assessment unit150is configured to determine one or more product challenges anticipated for the pharmaceutical product based on the identified QTPP and CQA data values received from the prediction unit130and one or more physiochemical properties that are received from one or more users. The risk assessment unit150identifies one or more product challenges and the risk associated with each of the one or more product challenges using the historical database170. The historical database170includes one or more product challenges that are encountered during previous product development, root cause for the one or more product challenges, and one or more CQA's that are affected by the product challenges, wherein the root cause is either due to aberrations in physicochemical properties of the molecule or CPPs and/or CMAs. The risk assessment unit150then retrieves a set of parameter values such as CPPs data values and/or CMAs data values and associated values for each of the one or more product challenges identified. The risk assessment unit150then retrieves a probability score, a severity score and a detectability score for each of the CPP and CMA values from the historical database170and calculates the risk score for each of the one or more product challenges based on the probability score, the severity score, and the detectability score corresponding to each of the CPP data values and CMA data values.

The control unit160is configured to identify a functional relationship between the plurality of CMAs data values and CPPs data values and the one or more CQAs data values, identify a range of the values of the plurality of CMAs data and CPPs data that satisfy the identified relationship and desired values of the one or more CQAs by dynamic adjustment of CMAs values and CPPs values. In one embodiment, statistical methods are utilized for the identification of the range of values. In an example, the statistical methods may be one or more of factorial design, response surface design, mixture experiments, Box-Behnken design, Plackett-Burman design, and central composite design methods.

The report generation unit240is configured to generate a report based on the identified ranges of CMAs and CPPs values, and also notifies the adjusted data values of CMAs and CPPs and the corresponding risk score to the repository104for updating the historical database170for developing one or more pharmaceutical products.

In operation, the prediction unit130is configured to receive the characteristics of RLDs and the APIs inputted by the one or more users. In one embodiment, the characteristics of RLDs include but not limited to characterization data such as description, batch no., expiry date, strength (mg), average weight (mg), score, coating, diameter (mm), thickness (mm), volume (mm3), hardness (kP), disintegration time (min), disintegration observation, assay (% w/w of label claim), related compound (RC) (%), highest individual unknown, and so on. Upon receiving the characteristics of RLDs, the prediction unit130identifies CQAs data values and QTTP data values for the pharmaceutical product to be developed. The prediction unit130also predicts the appropriate manufacturing process. In an embodiment, the manufacturing process comprises at least one of wet granulation, dry granulation, direct compression or any other manufacturing process. The prediction unit130further determines one or more excipients and composition of the one or more excipients to be used for developing the pharmaceutical product. In an example, for branded Acetriptan 20 mg, the composition determined by the prediction unit130is as shown in below table 1:

The formula generator unit140coupled with the prediction unit130receives the one or more excipients data associated with the API from the prediction unit130and calculates weight per tablet for each of the one or more excipient values. The formula generator unit140is further configured to determine qualification of the one or more excipients to be used in the pharmaceutical product development by comparing composition of each of the one or more excipient values with an IIG specification. If the one or more excipients composition is determined to exceed the ranges as per the IIG specification or handbook of excipients limit or any other respective limits that are preconfigured, then the formula generator unit140corrects the one or more excipients composition by recalculating the weight per tablet of the corrected excipients and further continues to compare with the IIG specification. For instance, the formula generator140determines whether the excipients composition for Acetriptan 20 mg, as shown in the table 1, are within the IIG specification.

Upon obtaining the corrected excipients, the prediction unit130may alert the one or more users to develop the pharmaceutical product and receives one or more values associated with properties of the pharmaceutical product thus developed. In an example, the properties can be hardness, friability, weight variation, sticking and other related properties of the pharmaceutical product. The prediction unit130receives the properties of the pharmaceutical product as input from the one or more users and determines the acceptance range of the received properties. In one embodiment, the prediction unit130determines whether the drug properties are acceptable based on the historical data170for a similar pharmaceutical product or RLD. If the drug properties are exceeding the acceptance range, the prediction unit130proposes modifications in the manufacturing process steps. In an example, the predicted unit130propose to modify the manufacturing process from the direct compression to the wet granulation. If the drug properties are acceptable, the prediction unit130alerts the one or more users to subject the pharmaceutical product to drug release process. The observations of the drug release process are inputted to the risk assessment unit150. The risk assessment unit150is configured to determine one or more product challenges anticipated for the pharmaceutical product based on the identified QTPP and CQA data values and one or more physiochemical properties that are received from one or more users. The risk assessment unit150determines one or more CPPs and/or CMAs, associated data values for each of the one or more product challenges, and one or more CQA's that are affected by the product challenges.

In an example, the risk assessment unit150determines CPPs impacting one or more CQAs such as not limited to impeller speed, tip speed, granulating liquid temperature, wet massing time, pre and post granulation mix time, bowl temperature, powder feed rate, screw speed, binder addition rate, barrel temperature, and so on for different manufacturing processes. The risk assessment unit150also determines CMAs impacting one or more CQAs such as particle size, solid form, degree of crystallization, shape and morphology, surface area, moisture content, solubility, contact angle, binder type and grade, diluent type and grade, disintegrant type and grade, granulating fluid viscosity, surface tension, and so on. Upon determining the CPPs and/or CMAs impacting the CQAs, the risk assessment unit150determines the risk score for each of the product challenge by multiplying probability (P), severity (S) and detectability (D) scores corresponding to each of the CPP values and CMA values. Upon determining the risk score, the control unit160identifies a range of the CPPs values and CMAs values within which desired CQAs can be achieved. The report generation unit240then generates a report based on the identified ranges of CMAs and CPPs, and also notifies the adjusted values of CMAs and CPPs and the corresponding risk score to the repository104for updating the historical database170.

FIG. 3illustrates an exemplary flowchart for smart product development in accordance with some embodiments of the present disclosure;

As illustrated inFIG. 3, the method300comprises one or more blocks implemented by the ROS102. The method300may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The method300indicates end to end processes involved in development of the pharmaceutical product with one or more processes being enhanced to optimize resources for the pharmaceutical product development. In one example,FIG. 300shows the flowchart for development of pharmaceutical product. In the initial step of development at block310, the prediction unit130receives one or more characteristics of RLDs and the APIs inputted by the user. Upon receiving the one or more characteristics of RLDs, one or more CQAs data and QTPP elements data for the pharmaceutical product are identified at block315. In one embodiment, an appropriate manufacturing process is identified, by verifying if the APIs meet predefined criteria for the pharmaceutical product based on the historical data170for the same or similar RLDs at block320. Once the manufacturing process is determined, one or more suitable excipients data and the composition of the one or more excipients are selected for developing the pharmaceutical product at block325. The formula generator unit140then calculates weight per tablet for each of the one or more excipients data at block335and compares the calculated weight with inactive ingredient guide (IIG) specifications to determine qualification of the one or more excipients to be used in the pharmaceuticals product development at block345. If the formula generator unit140determines that the composition of each of the one or more excipients is out of the IIG specification at block350, then the composition of each of the one or more excipients data that are out of the IIG specification values are corrected at block355, and the weight of the corrected excipients is recalculated at block335and further continues to compare with IIG specifications at block345to satisfy the IIG specification values. Upon obtaining the corrected one or more excipient data values, the prediction unit130may generate one or more alerts and/or notifications to the one or more users to develop the pharmaceutical product at block360. The one or more users then develops the pharmaceutical product and determines one or more values associated with properties of the pharmaceutical product. The determined properties of the pharmaceutical product are input to the prediction unit130and are compared with acceptance range of one or more values associated with properties of the pharmaceutical product at block365. If the one or more values associated with the properties exceeds in the acceptable range, then the one or more user is notified to modify the manufacturing process at block375. If the one or more values associated with the properties are in the acceptable range, then the prediction unit130alerts the one or more users to subject the pharmaceutical product to drug release process at block370. The observations of the drug release process are inputted to the risk assessment unit150at block380to access risk associated with product challenges by determining one or more product challenges anticipated for the pharmaceutical product based on the identified QTPP and CQA data values and one or more physiochemical properties are received from one or more users using the historical database170, wherein the historical database170includes one or more product challenges that are encountered during previous product development, the root cause identified for the one or more challenges, and one or more CQA's that are affected by the product challenges, and wherein the root cause is either due to physicochemical properties of the molecule or CPPs and/or CMAs. In one embodiment, accessing risk associated with product challenges includes retrieving one or more CPPs and/or CMAs and associated data values for each of the one or more product challenges identified, retrieving a score for each probability, severity and detectability for each of the CPP and CMA values from the historical database and calculating the risk score for each of the one or more product challenges based on the probability score, the severity score, and the detectability score corresponding to each of the CPP data values and CMA data values. The risk score associated with each of the product challenges is provided to the control unit160at block385to control the risk associated by identifying a functional relationship between the plurality of CMAs data values and CPPs data values and the one or more CQAs data values, and identifying a range of the data values of the plurality of CMAs and CPPs that satisfy the identified relationship and desired data values of the one or more CQAs by dynamical adjustment of CPPs and CMAs. In one embodiment, statistical methods are utilized for the identification of the range of values. In an exemplary embodiment, the statistical methods include factorial design, response surface design, mixture experiments, Box-Behnken design, Plackett-Burman design, central composite design. The report generation unit240generates a report based on the identified ranges of CMAs and CPPs data values, and also updates the adjusted data values of CMAs and CPPs and the corresponding risk score in the historical database at block390. In one embodiment, the ROS102also facilitates analytical research and development by developing methods of analysis for the pharmaceutical product. Thus, the one or more R&D processes are enhanced to optimize resources for the pharmaceutical product development with a minimum number of R&D experiments by incorporating a series of steps in a structured manner in all phases of the pharmaceutical product development, thereby reducing unwanted economical burden and also reducing delay in development of the pharmaceutical product. Further, incorporating the series of steps in the structured manner in all phases of the pharmaceutical product development will bring unification, consistency, optimal utilization of resources available in an ordered and timely manner in organization for the development of pharmaceutical product. Also, by maintaining the experimental knowledge and data collected from the plurality of experiments previously conducted in the historical database, reduces fatal human errors thereby enhancing the performance of R&D experimentations.

FIG. 4illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

In an embodiment, the computer system402may be resource management system (ROS)102, which is used for enhancing R&D experimentation. The computer system402may include a central processing unit (“CPU” or “processor”)404. The processor404may comprise at least one data processor for executing program components for executing user or system-generated business processes. The processor404may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

Using the I/O interface410, the computer system402may communicate with one or more I/O devices. For example, the input device406may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, visors, etc. Output device408may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, or the like), audio speaker, etc. In some embodiments, a transceiver409may be disposed in connection with the processor404. The transceiver may facilitate various types of wireless transmission or reception. For example, the transceiver may include an antenna operatively connected to a transceiver chip (e.g., Texas Instruments WiLink WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some implementations, the processor404may be disposed in communication with a communication network106via a network interface414. The network interface414may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface414and the communication network108, the computer system402may be connected to the data repository104and the user device106.

The communication network412can be implemented as one of the several types of networks, such as intranet or any such wireless network interfaces. The communication network412may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network412may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

In some embodiments, the processor404may be disposed in communication with a memory416e.g., RAM418, and ROM420, etc. as shown inFIG. 4, via a storage interface422. The storage interface422may connect to memory416including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory416may store a collection of program or database components, including, without limitation, user/application424, an operating system426, a web browser428, a mail client430, a mail server432, a user interface434, and the like. In some embodiments, computer system402may store user/application data424, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

The operating system426may facilitate resource management and operation of the computer system402. Examples of operating systems include, without limitation, Apple Macintosh™ OS X™, UNIX™, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD™, Net BSD™, Open BSD™, etc.), Linux distributions (e.g., Red Hat™, Ubuntu™, K-Ubuntu™, etc.), International Business Machines (IBM™) OS/2™, Microsoft Windows™ (XP™, Vista/7/8, etc.), Apple iOS™, Google Android™, Blackberry™ Operating System (OS), or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system402, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple™ Macintosh™ operating systems' Aqua™, IBM™ OS/2™, Microsoft™ Windows™ (e.g., Aero, Metro, etc.), Unix X-Windows™, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the embodiments of the disclosure is intended to be illustrative, but not limiting, of the scope of the disclosure.