INVERSE SURROGATE MODEL DYNAMIC PHARMACOKINETIC PARAMETER ESTIMATION

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include replicating, with patient parameters, a set of patient data of a patient and conditioning said patient parameters with at least one measure from said patient. The operations may include parameterizing a pharmacokinetic model with said patient parameters and sampling said patient parameters with a constrained optimization generative adversarial network. The operations may include calculating dosage data of a pharmaceutical with said patient parameters with said constrained optimization generative adversarial network and communicating said dosage data to a user.

BACKGROUND

The present disclosure relates to pharmacokinetic parameter estimation and, more specifically, to dynamic pharmacokinetic parameter estimation using inverse surrogate models.

Physiologically-based pharmacokinetic models may be used to represent absorption, distribution, metabolism, and/or excretion of pharmaceuticals. Such models may be similar to pharmacokinetic models with the additional inputs of interactions of the pharmaceutical at the physiologically active target tissue or tissues of delivery. The construction of physiologically-based pharmacokinetic models may be highly idiosyncratic to the specific drug and target tissue as well as to the dynamics of the body. Therefore, constructing dynamic physiologically-based pharmacokinetic models may be risky and costly; it may also require testing on clinical trial data. Validation of such models may be extremely difficult because of the wide variety of conditions. Furthermore, the calibration of the pharmacokinetic model in the context of the physiologically-based model may make use of Bayesian frameworks for fitting the model; however, pharmacokinetic models may be deterministic and noninvertible, and therefore in those instances, traditional Bayesian methods do not apply.

SUMMARY

Embodiments of the present disclosure include a system, method, and computer program product for pharmaceutical combination delivery parameters.

A system in accordance with the present disclosure may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include replicating, with patient parameters, a set of patient data of a patient and conditioning said patient parameters with at least one measure from said patient. The operations may include parameterizing a pharmacokinetic model with said patient parameters and sampling said patient parameters with a constrained optimization generative adversarial network. The operations may include calculating dosage data of a pharmaceutical with said patient parameters with said constrained optimization generative adversarial network and communicating said dosage data to a user.

The above summary is not intended to describe each illustrated embodiment or every implementation of the disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to pharmaceutical combination delivery and more specifically to delivery parameters of pharmaceutical combinations.

Pharmaceutical combinations may be desirable in treating patients because combining pharmaceuticals at sub-toxic doses may achieve efficacy levels exceeding that which an isolated pharmaceutical may achieve and/or efficacy levels which would require one or more toxic doses of an individual pharmaceutical to achieve. Isoboles may be used to represent nonlinear manifolds of pharmaceutical combinations over which efficacy is constant. Dosing may aim to maximize a synchronous concentration of the desired pharmaceutical combination at a treatment site, thereby accessing the optimal isobole for a therapeutic target.

Physiologically-based pharmacokinetic (PBPK) models may be used to represent the absorption, distribution, metabolism, and excretion of a pharmaceutical. A PBPK model may be akin to a standard pharmacokinetic (PK) model with additional inputs from a model of the various interactions of the pharmaceutical at the physiologically active target tissue or tissues of delivery (e.g., blood). Such interactions may be dynamic, especially in contexts where a patient is in a critical or intensive care setting. Specific examples of variable conditioners of the PBPK model include volumes in the body, hydration levels of the patient, urination, brain metabolic state, and surgery: each results in nonlinear effects on clearance of a drug. Furthermore, delivery of drugs through the blood and to a target tissue, can therefore be nonlinearly influenced by a large number of variables in the state of a patient in the critical care unit (CCU) or intensive care unit (ICU).

The construction of PBPK models may therefore be highly idiosyncratic to the specific pharmaceutical and target tissue as well as to the dynamics of a body, including expected dynamics of the body during critical and intensive care. Constructing dynamic PBPK models may therefore be risky, costly, and require testing on already collected clinical trial data. Validation of the models may be extremely difficult because of the wide variety of conditions of patients, particularly the variety of conditions of patients found in the CCU or ICU. Furthermore, the calibration of a PK model in the context of the physiologically-based (PB) model often makes use of Bayesian frameworks for fitting the model; however, PK models are deterministic noninvertible, and therefore in those instances, traditional Bayesian methods do not apply.

In accordance with the present disclosure, a conditioned regularized generative adversarial network (cr-GAN) may be trained on a standard PK model with conditional variables associated with other measurements (e.g., measurements from a patient in the CCU or ICU). Such measurements may be used to parameterize the PB component of the PBPK model (e.g., patient urination status, surgical status, brain metabolic measures, features from EEG, MRI, etc.). The present disclosure thus enables rapid training, transfer learning, and continuous inference over the cr-GAN for drug PK in the CCU/ICU without costly and context-specific construction and validation of the PB component of a PBPK model under complex and dynamic conditions.

The present disclosure may use the algorithmic core of a cr-GAN inverse PK model. Training the cr-GAN on conditional variables that may otherwise be inputs to a PB model may enable augmentation of the cr-GAN inverse PK model into a physiologically-based surrogate pharmacokinetic (PBsPK) model. The surrogate (s) may represent a PB model component.

In accordance with the present disclosure, a cr-GAN architecture may be used for generating mechanistic model M parameter samples xgthat may produce outputs ygcoherent with a set of observed data y. The cr-GAN generator may be conditioned on auxiliary observed data a that is not directly accessible to the mechanistic model. Such an implicit generative model may be formulated as:

where joint distributions QX,A, QXg,Aand QY,Ahave marginals QX, QXg, and QY, respectively, and D(·∥·) is an f-divergence measure such a Jensen-Shannon divergence (JSD). Eq. 1 may be solved using a GAN by minimizing divergence D(PX∥QXg) between a given prior PXand generated model parameters QXgover network parameters θ in the generator:

where PZis a Gaussian base distribution, PXis the prior distribution of model parameters, and QAis the marginal of QY,Afor auxiliary variable A. Simultaneously, D(QY,A∥QYg,A) over θ may be minimized in the generator:

To approximate D(QY,A∥QYg,A)=0 while minimizing D(PX∥QXg), the two objectives may be incorporated as separate discriminators with a weighted sum loss such that the weight for the generator loss due to discriminator DXis smaller than that for DY. Auxiliary variable A may be incorporated as a conditioning variable in G and DY.

In accordance with the present disclosure, such a GAN architecture may consist of one generator and two discriminators and a reconstruction network that recreates Z from the output of G and a function representing the mechanistic model M. Each of the networks in such a GAN architecture may be a feedforward neural network such as one described by Table 1.

TABLE 1Details of Neural Networks Used in a GAN ArchitectureHiddenNodesDropoutActivationNetworkLayersPer LayerRateFunctionDX8800.0RELUDY81300.01RELUG8800.0RELUR81800.0RELU

Discriminator DYdistinguishes between samples from the joint distribution QY,Aand samples generated by the generator G forwarded through the mechanistic model and augmented with the conditioning variable A. The standard conditional loss LDYof the discriminator DYmay be described as:

The standard conditional loss DYmay be maximized. Discriminator DXdistinguishes between samples from the prior over mechanistic parameters PXand samples generated by G. The standard loss LDXmay be expressed as:

The standard conditional loss DYmay be maximized. The reconstruction network R aims to reproduce the original base distribution Z from samples generated by G. The squared loss LRmay be described as:

The squared loss LRmay be minimized.

The generator network G generates mechanistic parameter sets from the base variable Z, augmented with the auxiliary observed data a. The weighted sum loss LGmay be expressed as:

The weighted sum loss LGmay be minimized where wY=1.0, wx=0.1, and wR=1.0.

For results shown here, the Adam optimizer used had step size of 0.00001 for G and R, 0.00002 for DX, and 0.00001 for DY. The β1and β2parameters of the Adam optimizer were set to default values of 0.9 and 0.999, respectively. Mini-batch size was 100. Training was performed in two stages: first, G, R, and DXwere trained together with wX=1.0 and the LDYterm removed in Eq. 7 for 100 epochs to initialize G by minimizing D(PX∥(QXg); second, the full GAN was trained for 300 epochs on a dataset y, a˜QY,Aof 10,000 samples.

Divergence between distributions may be tested with JSD and approximated using density ratio estimation with a binary classifier to approximate the KL divergence measure from samples. In this approach, JSD may be estimated using a classifier network trained to distinguish samples from the two distributions.

Due to pharmacokinetics, synchronous concentration of a combination may depend on the specific pharmaceuticals desired, the time of administration of the pharmaceuticals, and the condition being treated. An appropriate model of these pharmacokinetics for each pharmaceutical under each of these conditional variables may be used to identify optimal delivery parameters. A cr-GAN may be used to sample pharmacokinetic (PK) model parameters conditioned by pharmaceutical identity, the ailment type, and the ailment severity to recommend pharmaceutical combination therapies to access the desired isobole with the highest probability. In some embodiments, the present disclosure may use an algorithmic core of a cr-GAN inverse PK model to such an effect.

In accordance with the present disclosure, a cr-GAN may be trained to sample from the parameter space of a PK model for each pharmaceutical in a desired therapy. Conditional variables specific to the condition may be used; for example, in the treatment of cancer, an initial tumor size may be used as one conditional variable and another conditional variable may be an observed baseline change in the tumor size given an identified dose of an identified pharmaceutical. In a critical care example, an initial measurement may be the oxygen levels of the patient, and the observed baseline change may be the change in blood oxygen saturation.

The trained cr-GAN may be used to sample parameters of the PK model, given conditional variables and a therapeutic target, to construct an isobole for a particular pharmaceutical combination. A therapeutic target may be, for example, a specific oxygen level (e.g., achieving a 93% blood oxygen level) or, in another example, a hydration and brain metabolic state targets for a patient. The isobole constructed with the cr-GAN samples, the PK model, and patient response data may be, for example, a 95% isobole such that the isobole identifies doses at which the same or a similar therapeutic target was achieved in 95% of patients with similar characteristics. Similar characteristics may be, for example, patient demographics, health history, ailment type, or other factors which may impact the efficacy of the use of one or more desired pharmaceuticals.

A treatment opportunity window may be identified in the isobole, and a combination dosing therapy may be selected from the treatment opportunity window to maximize dose efficiency while simultaneously minimizing dose toxicity of the pharmaceutical combination.

In some embodiments, an additional cr-GAN may be trained with efficacy data concerning pharmaceutical combinations. Such a pharmaceutical combination cr-GAN may be used to parameterize a nonlinear model of efficacy given an expected concentration of the pharmaceutical at the treatment site. The parameterized nonlinear efficacy model may be used to augment or otherwise modify the isobole construction algorithm developed by the first trained cr-GAN to improve the isobole (e.g., more accurately estimate a 95% isobole).

In some embodiments of the present disclosure, a cr-GAN may be applied to samples from a distribution of parameters. The parameters may replicate a set of patient data including information such as, for example, absorption, distribution, metabolism, and/or excretion of pharmaceuticals; such data may describe pharmaceutical impact on the patient independently (e.g., when only one pharmaceutical is used in the patient) or in combination (e.g., how multiple pharmaceuticals react with a patient when administered together).

The parameters may be used to parameterize a PK model of multiple pharmaceuticals subject to conditioning variables. Conditioning variables may include, for example, a baseline patient state (e.g., a current brain metabolic state), an observed change from the baseline (e.g., the change in the brain metabolic state since the initial measurement), and a therapeutic target (e.g., a desired goal of the brain metabolic state). The trained cr-GAN may determine pharmaceutical dosage data necessary to achieve the therapeutic target.

In some embodiments, the pharmaceutical dosage data may be used to determine an isobole contour plot of pharmaceutical efficacy. Pharmaceutical efficacy information may be plotted on the isobole contour plot such that a user may identify a desired efficacy (e.g., an efficacy on the isobole contour plot within a treatment opportunity window to maximize effectiveness and minimize toxicity) as a contour on the isobole contour plot.

In some embodiments, the isobole contour plot may be refined using a second cr-GAN. The second cr-GAN may be trained to sample from parameters of a model (such as a quantitative systems pharmacology model) capable of mapping pharmaceutical doses to a given distribution of efficacy measures and ailment severities associated with each efficacy measure.

In some embodiments, the pharmaceutical combination may be chosen from a point on the desired efficacy isobole that falls within the treatment opportunity window such that the dosage avoids toxic effects of the pharmaceuticals while maintaining efficacy.

A system in accordance with the present disclosure may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include replicating, with patient parameters, a set of patient data of a patient and conditioning the patient parameters with at least one measure from the patient. The operations may include parameterizing a pharmacokinetic (PK) model with the patient parameters and sampling the patient parameters with a constrained optimization generative adversarial network (cr-GAN). The operations may include calculating dosage data of a pharmaceutical with the patient parameters with the cr-GAN and communicating the dosage data to a user.

In some embodiments of the present disclosure, the operations may further include streaming the at least one measure to the cr-GAN.

In some embodiments of the present disclosure, the operations may further include selecting the at least one measure from a monitoring stream. In some embodiments, the monitoring stream is a neurocritical care monitoring stream.

In some embodiments of the present disclosure, the operations may further include modeling at least one neurocritical care measure with an associated PK model and assessing an effect of the at least one neurocritical care measure on the PK model.

In some embodiments of the present disclosure, the patient is a critical care unit patient.

In some embodiments of the present disclosure, the dosage data is calculated in real time.

FIG.1illustrates a system100in accordance with some embodiments of the present disclosure. The system100may include a MaaS deployment log102and a flow manager104. The system100may include a research side102and a deployment side152separated by a firewall148. In some embodiments, the firewall148may be a network address translation (NAT) component or other communication interface mechanism.

The system100may include several databases on the research side102of the firewall148including, for example, a model simulation data database132, a GAN graph library database134, an inverse surrogate library database136, a synthetic test data database138, a mechanistic model package database142, a model optimizer library database144, and a forward surrogate library database146.

The system100may include several databases on the deployment side152such as, for example, a mechanical model library database182, a statistical model database184, and a proprietary information database188. The proprietary information database188may contain device and experiment190information such as, for example, mechanism prior data192, pharmaceutical data194, conditioning data196, and target data198. The proprietary information database188may be in communication with the synthetic test data database138.

The research side102of the system100may include a research virtual machine110which houses engine graph data112, GAN graph data114, and a cloud deployment API128. The research virtual machine110may include a model generation process120which may include simulation122, validation124, and parameterization126of a model. The research virtual machine110may include a stateless handler116, a stateful partition processor118, and an analytics event log108.

The analytics event log108in the research virtual machine110may be in communication with an analytics event log158in a deployment virtual machine160. The deployment virtual machine160may also include engine graph data162, GAN graph data164, and a cloud deployment API178. The deployment virtual machine160may further include a model generation process170which may include simulation172, validation174, and parameterization176of a model. The deployment virtual machine160may include a stateless handler166, a stateful partition processor168.

FIG.2depicts a PK cr-GAN use case progression diagram200in accordance with some embodiments of the present disclosure. Data210is used to identify biomarkers and endpoints220which is used to identify physiological confounds230which is used to monitor an evolving patient state240.

The data210may include patient data212and pharmaceutical data214. Direct conditioning variables218may be identified in the data; the direct conditioning variables218may be used in the identification of the biomarkers and endpoints220.

The biomarkers and endpoints220may include molecular pathology222and ailment scores224. The physiologically-based conditioning variables228may be identified in the data; the physiologically-based conditioning variables228may be used for the identification of the physiological confounds230.

The physiological confounds230may include bioequivalence and isoboles232, ailment severity234, and pharmaceutical, pharmaceutical-pharmaceutical, and pharmaceutical-tissue236information. Realtime conditioning variables238may be identified in the physiological confounds230; the real-time conditioning variables238may be used for monitoring an evolving patient state240.

The evolving patient state240may be monitored using progression models242and ailment scores244. Other mechanisms may be used for identifying, monitoring, assessing, and quantifying the evolving patient state240. In some embodiments, the evolving patient state240may be communicated to a user such as a care provider.

FIG.3illustrates a graph set300in accordance with some embodiments of the present disclosure. The graph set300communicates samples from cr-GAN simulations using two distinct, disjointed subsets of a dataset. The graph set300includes a set of features graphs302and a set of parameters graphs304.

The features graphs302include a scatterplot320of a beta (β) feature distribution plotted against an alpha (α) feature distribution. The scatterplot320includes a first set of cr-GAN samples322corresponding to a first set of target data324. The scatterplot320includes a second set of cr-GAN samples326corresponding to a second set of target data328.

The features graphs302include a ρ-α line graph310of density (ρ) tracked against the alpha (α) feature. The ρ-α line graph310includes a first set of cr-GAN samples312corresponding to a first set of target data314and a second set of cr-GAN samples316corresponding to a second set of target data318.

The features graphs302include a ρ-β line graph330of density (ρ) tracked against the beta (β) feature. The ρ-β line graph330includes a first set of cr-GAN samples332corresponding to a first set of target data334and a second set of cr-GAN samples336corresponding to a second set of target data338.

The parameters graphs304include line graphs and contour graphs tracking parameters against other parameters and/or density. The parameters graphs304include a ρ-k10line graph340of density (ρ) tracked against parameter k10. The ρ-k10line graph340includes a first set of cr-GAN samples342corresponding to a first set of true parameters344and a second set of cr-GAN samples346corresponding to a second set of true parameters348.

The parameters graphs304include a ρ-k12line graph370of density (ρ) tracked against parameter k12. The ρ-k12line graph370includes a first set of cr-GAN samples372corresponding to a first set of true parameters374and a second set of cr-GAN samples376corresponding to a second set of true parameters378.

The parameters graphs304include a ρ-k10line graph390of density (ρ) tracked against parameter k21. The ρ-k21line graph390includes a first set of cr-GAN samples392corresponding to a first set of true parameters394and a second set of cr-GAN samples396corresponding to a second set of true parameters398.

FIG.4depicts a graph set400in accordance with some embodiments of the present disclosure. The graph set400tracks density (ρ), resting sarcomere length (dSL), sarcomere length at maximum contraction (sSL), and time to peak contraction (ttp) for inferred distributions and corresponding target distributions.

The first graph410of the graph set400tracks density (ρ) as it relates to time to peak contraction (ttp). The first graph410includes a first ρ-ttp inferred distribution412and a corresponding first ρ-ttp target distribution414. The first graph410includes a second ρ-ttp inferred distribution416and a corresponding second ρ-ttp target distribution418.

The second graph420of the graph set400tracks density (ρ) as it relates to resting sarcomere length (dSL). The second graph420includes a first ρ-dSL inferred distribution422and a corresponding first ρ-dSL target distribution424. The second graph420includes a second ρ-dSL inferred distribution426and a corresponding second ρ-dSL target distribution428.

The third graph430of the graph set400tracks density (ρ) as it relates to sarcomere length at maximum contraction (sSL). The third graph430includes a first ρ-sSL inferred distribution432and a corresponding first ρ-sSL target distribution434. The third graph430includes a second ρ-sSL inferred distribution436and a corresponding second ρ-sSL target distribution438.

The fourth graph440of the graph set400tracks resting sarcomere length (dSL) as it relates to time to peak contraction (ttp). The fourth graph440includes a first set of dSL-ttp inferred distribution samples444(scatterplot) and a corresponding first dSL-ttp target distribution442(contour lines). The fourth graph440includes a second set of dSL-ttp inferred distribution samples448(scatterplot) and a corresponding second dSL-ttp target distribution446(contour lines).

The fifth graph450of the graph set400tracks sarcomere length at maximum contraction (sSL) as it relates time to peak contraction (ttp). The fifth graph450includes a first set of sSL-ttp inferred distribution samples454(scatterplot) and a corresponding first sSL-ttp target distribution452(contour lines). The fifth graph450includes a second set of sSL-ttp inferred distribution samples458(scatterplot) and a corresponding second sSL-ttp target distribution456(contour lines).

The sixth graph460of the graph set400tracks sarcomere length at maximum contraction (sSL) as it relates to resting sarcomere length (dSL). The sixth graph460includes a first set of sSL-dSL inferred distribution samples464(scatterplot) and a corresponding first sSL-dSL target distribution462(contour lines). The sixth graph460includes a second set of sSL-dSL inferred distribution samples468(scatterplot) and a corresponding second sSL-dSL target distribution466(contour lines).

FIG.5illustrates a graph500in accordance with some embodiments of the present disclosure. The graph500tracks sarcomere length in micrometers over time in milliseconds to identify time to peak contraction (ttp) and the rate of relaxation (k2) after achieving peak contraction for inferred distributions (depicted in graph500as solid lines) and target distributions (depicted in graph500as dashed lines). The graph500includes simulated model projections tracked with corresponding experimental data.

The graph500includes a first inferred distribution522, a first target distribution524, and a first peak contraction point526. The first inferred distribution522closely models the first target distribution524. The graph500includes a second inferred distribution532, a second target distribution534, and a second peak contraction point536. The second inferred distribution532closely models the second target distribution534.

FIG.6depicts a data graph set600in accordance with some embodiments of the present disclosure. The graph set600includes model simulation data graphs610and corresponding experimental data graphs620. Each graph in the data graph set600tracks sarcomere length in micrometers over time in milliseconds.

The model simulation data graphs610include a control model simulation graph612predicted based on control data. The control data corresponds to a control dataset captured in the control experimental dataset graph622of the experimental data graphs620.

The model simulation data graphs610include a model simulation graph614predicted based on Omecamtiv Mecarbil (OM) data. The experimental data corresponds to an uncontrolled experimental OM dataset captured in the experimental dataset graph624of the experimental data graphs620.

A computer-implemented method in accordance with the present disclosure may include replicating, with patient parameters, a set of patient data of a patient and conditioning the patient parameters with at least one measure from the patient. The method may include parameterizing a PK model with the patient parameters and sampling the patient parameters with a cr-GAN. The method may include calculating dosage data of a pharmaceutical with the patient parameters with the cr-GAN and communicating the dosage data to a user.

In some embodiments of the present disclosure, the method may further include streaming the at least one measure to the cr-GAN.

In some embodiments of the present disclosure, the method may further include selecting the at least one measure from a monitoring stream. In some embodiments, the monitoring stream is a neurocritical care monitoring stream.

In some embodiments of the present disclosure, the method may further include modeling at least one neurocritical care measure with an associated PK model and assessing an effect of the at least one neurocritical care measure on the PK model.

In some embodiments of the present disclosure, the patient is a critical care unit patient.

In some embodiments of the present disclosure, the dosage data is calculated in real time.

FIG.7illustrates a method700in accordance with some embodiments of the present disclosure. The method700includes replicating710a set of patient data of a patient; patient parameters may be used to replicate the set of patient data. The method700includes conditioning720the patient parameters with one or more measures from the patient. The method700includes parameterizing730a PK model with the patient parameters. The method700includes sampling740the patient parameters with a cr-GAN. The method700includes calculating750dosage data of a pharmaceutical for the patient; the dosage data may be calculated using the patient parameters and the cr-GAN. The method700includes communicating760the dosage data to a user.

In accordance with the present disclosure, a computer program product may be used to obtain a pharmaceutical combination parameter estimation via model surrogate. A computer program product in accordance with the present disclosure may include a computer readable storage medium having program instructions embodied therewith. The program instructions may be executable by a processor to cause the processor to perform a function. The function may include replicating, with patient parameters, a set of patient data of a patient and conditioning the patient parameters with at least one measure from the patient. The function may include parameterizing a pharmacokinetic model with the patient parameters and sampling the patient parameters with a constrained optimization generative adversarial network. The function may include calculating dosage data of a pharmaceutical with the patient parameters with the constrained optimization generative adversarial network and communicating the dosage data to a user.

In some embodiments of the present disclosure, the function may further include streaming the at least one measure to the constrained optimization generative adversarial network.

In some embodiments of the present disclosure, the function may further include selecting the at least one measure from a monitoring stream. In some embodiments, the monitoring stream is a neurocritical care monitoring stream.

In some embodiments of the present disclosure, the function may further include modeling at least one neurocritical care measure with an associated pharmacokinetic model and assessing an effect of the at least one neurocritical care measure on the pharmacokinetic model.

In some embodiments of the present disclosure, the patient is a critical care unit patient.

In some embodiments of the present disclosure, the dosage data is calculated in real time.

Characteristics are as follows:

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of portion independence in that the consumer generally has no control or knowledge over the exact portion of the provided resources but may be able to specify portion at a higher level of abstraction (e.g., country, state, or datacenter).

Service models are as follows:

Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software which may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, and deployed applications, and the consumer possibly has limited control of select networking components (e.g., host firewalls).

Deployment models are as follows:

Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and/or compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.

FIG.8illustrates a cloud computing environment810in accordance with embodiments of the present disclosure. As shown, cloud computing environment810includes one or more cloud computing nodes800with which local computing devices used by cloud consumers such as, for example, personal digital assistant (PDA) or cellular telephone800A, desktop computer800B, laptop computer800C, and/or automobile computer system800N may communicate. Nodes800may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof.

This allows cloud computing environment810to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices800A-N shown inFIG.8are intended to be illustrative only and that computing nodes800and cloud computing environment810can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

FIG.9illustrates abstraction model layers900provided by cloud computing environment810(FIG.8) in accordance with embodiments of the present disclosure. It should be understood in advance that the components, layers, and functions shown inFIG.9are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted below, the following layers and corresponding functions are provided.

Hardware and software layer915includes hardware and software components. Examples of hardware components include: mainframes902; RISC (Reduced Instruction Set Computer) architecture-based servers904; servers906; blade servers908; storage devices911; and networks and networking components912. In some embodiments, software components include network application server software914and database software916.

Virtualization layer920provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers922; virtual storage924; virtual networks926, including virtual private networks; virtual applications and operating systems928; and virtual clients930.

In one example, management layer940may provide the functions described below. Resource provisioning942provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing944provide cost tracking as resources and are utilized within the cloud computing environment as well as billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks as well as protection for data and other resources. User portal946provides access to the cloud computing environment for consumers and system administrators. Service level management948provides cloud computing resource allocation and management such that required service levels are met. Service level agreement (SLA) planning and fulfillment950provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

Workloads layer960provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation962; software development and lifecycle management964; virtual classroom education delivery966; data analytics processing968; transaction processing970; and pharmaceutical combination parameter estimation via model surrogate972.

FIG.10illustrates a high-level block diagram of an example computer system1001that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer) in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system1001may comprise a processor1002with one or more central processing units (CPUs)1002A,1002B,1002C, and1002D, a memory subsystem1004, a terminal interface1012, a storage interface1016, an I/O (Input/Output) device interface1014, and a network interface1018, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus1003, an I/O bus1008, and an I/O bus interface unit1010.

The computer system1001may contain one or more general-purpose programmable CPUs1002A,1002B,1002C, and1002D, herein generically referred to as the CPU1002. In some embodiments, the computer system1001may contain multiple processors typical of a relatively large system; however, in other embodiments, the computer system1001may alternatively be a single CPU system. Each CPU1002may execute instructions stored in the memory subsystem1004and may include one or more levels of on-board cache.

System memory1004may include computer system readable media in the form of volatile memory, such as random access memory (RAM)1022or cache memory1024. Computer system1001may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system1026can be provided for reading from and writing to a non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive for reading from or writing to a removable, non-volatile optical disc such as a CD-ROM, DVD-ROM, or other optical media can be provided. In addition, memory1004can include flash memory, e.g., a flash memory stick drive or a flash drive. Memory devices can be connected to memory bus1003by one or more data media interfaces. The memory1004may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments.

One or more programs/utilities1028, each having at least one set of program modules1030, may be stored in memory1004. The programs/utilities1028may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data, or some combination thereof, may include an implementation of a networking environment. Programs1028and/or program modules1030generally perform the functions or methodologies of various embodiments.

Although the memory bus1003is shown inFIG.10as a single bus structure providing a direct communication path among the CPUs1002, the memory subsystem1004, and the I/O bus interface1010, the memory bus1003may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star, or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface1010and the I/O bus1008are shown as single respective units, the computer system1001may, in some embodiments, contain multiple I/O bus interface units1010, multiple I/O buses1008, or both. Further, while multiple I/O interface units1010are shown, which separate the I/O bus1008from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses1008.

It is noted thatFIG.10is intended to depict the representative major components of an exemplary computer system1001. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG.10, components other than or in addition to those shown inFIG.10may be present, and the number, type, and configuration of such components may vary.

Although the present disclosure has been described in terms of specific embodiments, it is anticipated that alterations and modifications thereof will become apparent to the skilled in the art. The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application, or the technical improvement over technologies found in the marketplace or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. Therefore, it is intended that the following claims be interpreted as covering all such alterations and modifications as fall within the true spirit and scope of the disclosure.