Patent Application: US-201013380839-A

Abstract:
an electronic system is provided that simulates a glucose - insulin metabolic system of a t2dm or prediabetic subject , wherein the system includes a subsystem that models dynamic glucose concentration in a t2dm or prediabetic subject , including an electronic module that models endogenous glucose production ), or meal glucose rate of appearance ), or renal excretion of glucose ), a subsystem that models dynamic insulin concentration in said t2dm or prediabetic subject , including an electronic module that models insulin secretion ), an electronic database containing a population of virtual t2dm or prediabetic subjects , each virtual subject having a plurality of metabolic parameters , and a processing module that calculates an effect of variation of at least one metabolic parameter value on the glucose insulin metabolic system of a virtual subject by inputting the plurality of metabolic parameter values .

Description:
two key components of the simulator of the glucose - insulin metabolic system in prediabetes and t2dm in accordance with the present invention are : ( 1 ) a physiological model of glucose - insulin metabolism in prediabetes and t2dm , and ( 2 ) a population of virtual subjects with prediabetes ( n = 100 ) and t2dm ( n = 100 ). both model equations and the procedures which were used to identify model parameter distributions from prediabetes and t2dm meal data and to generate the virtual subject population are now described in accordance with an embodiment of the invention . the model structure consists of a glucose subsystem and an insulin subsystem [ 7 ], each characterized by various unit processes , e . g . endogenous glucose production ( egp ), meal glucose rate of appearance ( ra ), glucose utilization ( u ), insulin secretion ( s ), and renal excretion ( e ). the glucose subsystem model is defined by the following group of equations ( 1 ): g p and g t ( mg / kg ) are glucose masses in plasma and rapidly - equilibrating tissues , and in slowly - equilibrating tissues , respectively ; u ii and u id are insulin - independent and dependent glucose utilizations , respectively ( mg / kg / min ); v g is the distribution volume of glucose ( dl / kg ); and k 1 and k 2 ( min − 1 ) are rate parameters . at the basal steady state , endogenous glucose production , egp b , equals glucose disappearance , i . e . the sum of glucose utilization and renal excretion ( which is zero in normal subjects ), u b + e b : the insulin subsystem model is defined by the following group of equations ( 3 ): where i p and i i ( pmol / kg ) are insulin masses in plasma and in the liver , respectively ; v i is the distribution volume of insulin ( l / kg ); and m 1 - m 4 ( min − 1 ) are rate parameters . degradation , d , occurs both in the liver and peripherally . peripheral degradation has been assumed to be linear ( m 4 ). hepatic extraction of insulin , he , i . e . the insulin flux which leaves the liver irreversibly , divided by the total insulin flux leaving the liver , is assumed to be dependent from insulin secretion , s : he ( t )=− m 5 · s ( t )+ m 6 he ( 0 )= he b ( 4 ) moreover , given that the liver is responsible for 60 % of insulin clearance in the steady state , one has : with s b and d b basal secretion and degradation , respectively ( he b was fixed to 0 . 6 ). egp ( t )= k p1 − k p2 · g p ( t )− k p3 · i d ( t )− k p4 · i po ( t ) egp ( 0 )= egp b ( 10 ) where i po is the amount of insulin in the portal vein ( pmol / kg ); i d ( pmol / l ) is a delayed insulin signal realized with a chain of two compartments : k p1 ( mg / kg / min ) is the extrapolated egp at zero glucose and insulin ; k p3 ( mg / kg / min per pmol / l ) is a parameter governing amplitude of insulin action on the liver ; k p4 ( mg / kg / min /( pmol / kg )) is a parameter governing amplitude of portal insulin action on the liver ; and k i ( min − 1 ) is a rate parameter accounting for the delay between an insulin signal and insulin action . k p1 = egp b + k p2 · g pb + k p3 · i b + k p4 · i pob ( 12 ) the glucose rate of appearance ( ra ) is defined by the following group of equations ( 13 ): where q sto ( mg ) is the amount of glucose in the stomach ( solid phase , q sto1 , and liquid phase , q sto2 ); k empt ( q sto ) ( min − 1 ) is a rate constant of gastric emptying which is a nonlinear function of q sto ; k abs ( min − 1 ) is a rate constant of intestinal absorption ; f is a fraction of intestinal absorption which actually appears in the plasma ; glucose utilization is made up of two components : insulin - independent utilization and insulin - dependent utilization . insulin - independent utilization takes place in the first compartment , is constant and represents glucose uptake by the brain and erythrocytes ( f cns ): insulin - dependent utilization takes place in the remote compartment and depends nonlinearly ( michaelis menten ) from glucose in the tissues : where vm ( x ( t )) is assumed to be linearly dependent from a remote insulin , x ( t ): v m ( x ( t ))= v m0 + v mx · x ( t ) ( 16 ) { dot over ( x )}( t )=− p 2u · x ( t )+ p 2u [ i ( t )− i b ] x ( 0 )= 0 ( 17 ) where i is plasma insulin and p 2u ( min − 1 ) is rate constant of insulin action on the peripheral glucose utilization . where γ ( min − 1 ) is the transfer rate constant between the portal vein and the liver ; k ( pmol / kg per mg / dl ) is the pancreatic responsivity to glucose rate of change ; a ( min − 1 ) is the delay between the glucose signal and insulin secretion ; β ( pmol / kg / min per mg / dl ) is the pancreatic responsivity to the glucose level ; and h ( mg / dl ) is the threshold level of glucose above which the β - cells initiate to produce new insulin ( h was set to the basal glucose concentration g b to guarantee system steady state in basal condition ). renal glucose excretion , e , is defined by the following equation ( 25 ): where k e1 ( min − 1 ) is the glomerular filtration rate ; and the data base used to identify the model consisted of 35 subjects with either ifg or igt , or both ( prediabetes ), and 23 t2dm patients who underwent a triple tracer meal protocol , thus allowing us to obtain in a virtually model - independent fashion the time course of all of the relevant glucose and insulin fluxes during a meal [ 6 , 11 ]. subject characteristics are reported in table 1 . average plasma glucose and insulin concentration , ra , egp , u and sr in prediabetes and t2dm are shown in fig1 together with the profile obtained in a matched healthy population [ 6 ]. in fig1 , plasma glucose and insulin concentrations are shown in the upper panels , meal glucose rate of appearance and endogenous glucose production are shown in the middle panels , and glucose utilization and insulin secretion rate are shown in the lower panels , in prediabetes , t2dm , and matched healthy subjects respectively . the system model described in section ( a ) has been identified in each subject by using a subsystem decomposition and forcing function strategy , as shown in fig2 . in fig2 , a unit process model for endogenous glucose production is shown in the top left panel ; unit process model for glucose rate of appearance is shown in the top right panel ; unit process model for glucose utilization is shown in the bottom left panel ; an unit process model for insulin secretion is shown in the bottom right panel . entering arrows represent forcing function variables , and outgoing arrows are model output . for example , to estimate glucose utilization parameters ( equations 14 - 20 ), we use as known inputs endogenous glucose production , egp , glucose rate of appearance , ra , and insulin concentration , i , and as the model output glucose utilization , u , and plasma glucose concentration , g . model parameters were thus identified in each subject and log - transformed . the average parameter vector and the covariance matrix have been thus calculated for both prediabetic and t2dm populations . assuming that the parameter vector is a log - normally distributed random vector , the average of log - transformed parameters and the covariance matrix univocally define the joint parameter distribution . in order to prove that the generated populations reflect the observed variability , the range of simulated plasma glucose concentrations in both populations ( prediabetes and t2dm ) is shown in fig3 , superimposed with the observed ( measured ) range of variability . a potential application of the simulator is the in silico study of the effect of a drug on glucose metabolism . fig4 shows plasma glucose and insulin concentrations in untreated prediabetics versus a profile obtained with the administration of a drug x , which increases insulin sensitivity , and a drug y , which enhance beta - cell responsivity to glucose . as noted above , the key to successful simulation is the availability of a comprehensive population of simulated “ subjects ” that encompasses the distribution of key metabolic parameters observed in t2dm in vivo . from the joint parameter distributions described in the previous section we have generated n = 200 virtual subjects : n = 100 with prediabetes and n = 100 with t2dm . each virtual subject is uniquely identified by a set of 26 parameters : b = percentage of the dose for which k empt decreases at ( k max − k min )/ 2 c = percentage of the dose for which k empt is back to ( k max − k min )/ 2 k i = rate parameter accounting for delay between insulin signal and insulin action on the liver k p3 = parameter governing amplitude of insulin action on the liver k p4 = parameter governing amplitude of portal insulin action on the liver v mx = parameter governing amplitude of insulin action on glucose utilization k 2 = rate parameter accounting for glucose transit from tissue to plasma k 1 = rate parameter accounting for glucose transit from plasma to tissue p 2u = rate parameter accounting for delay between insulin signal and insulin action on glucose utilization α = rate parameter accounting for delay between glucose signal and insulin secretion provided below in tables 2 and 3 , are sample lists of model parameters for 10 pre - diabetes virtual subjects and 10 t2dm virtual subjects . fig5 is a functional block diagram for a computer system 500 for implementation of an exemplary embodiment or portion of an embodiment of the present invention . for example , a method or system of an embodiment of the present invention may be implemented using hardware , software or a combination thereof and may be implemented in one or more computer systems or other processing systems , such as personal digit assistants ( pdas ) equipped with adequate memory and processing capabilities . in an example embodiment , the invention was implemented in software running on a general purpose computer 50 as illustrated in fig5 . the computer system 500 may includes one or more processors , such as processor 504 . the processor 504 is connected to a communication infrastructure 506 ( e . g ., a communications bus , cross - over bar , or network ). the computer system 500 may include a display interface 502 that forwards graphics , text , and / or other data from the communication infrastructure 506 ( or from a frame buffer not shown ) for display on the display unit 530 . display unit 530 may be digital and / or analog . the computer system 500 may also include a main memory 508 , preferably random access memory ( ram ), and may also include a secondary memory 510 . the secondary memory 510 may include , for example , a hard disk drive 512 and / or a removable storage drive 514 , representing a floppy disk drive , a magnetic tape drive , an optical disk drive , a flash memory , etc . the removable storage drive 514 reads from and / or writes to a removable storage unit 518 in a well known manner . removable storage unit 518 , represents a floppy disk , magnetic tape , optical disk , etc . which is read by and written to by removable storage drive 514 . as will be appreciated , the removable storage unit 518 includes a computer usable storage medium having stored therein computer software and / or data . in alternative embodiments , secondary memory 510 may include other means for allowing computer programs or other instructions to be loaded into computer system 500 . such means may include , for example , a removable storage unit 522 and an interface 520 . examples of such removable storage units / interfaces include a program cartridge and cartridge interface ( such as that found in video game devices ), a removable memory chip ( such as a rom , prom , eprom or eeprom ) and associated socket , and other removable storage units 522 and interfaces 520 which allow software and data to be transferred from the removable storage unit 522 to computer system 500 . the computer system 500 may also include a communications interface 524 . communications interface 124 allows software and data to be transferred between computer system 500 and external devices . examples of communications interface 524 may include a modem , a network interface ( such as an ethernet card ), a communications port ( e . g ., serial or parallel , etc . ), a pcmcia slot and card , a modem , etc . software and data transferred via communications interface 524 are in the form of signals 528 which may be electronic , electromagnetic , optical or other signals capable of being received by communications interface 524 . signals 528 are provided to communications interface 524 via a communications path ( i . e ., channel ) 526 . channel 526 ( or any other communication means or channel disclosed herein ) carries signals 528 and may be implemented using wire or cable , fiber optics , blue tooth , a phone line , a cellular phone link , an rf link , an infrared link , wireless link or connection and other communications channels . in this document , the terms “ computer program medium ” and “ computer usable medium ” are used to generally refer to media or medium such as various software , firmware , disks , drives , removable storage drive 514 , a hard disk installed in hard disk drive 512 , and signals 528 . these computer program products (“ computer program medium ” and “ computer usable medium ”) are means for providing software to computer system 500 . the computer program product may comprise a computer useable medium having computer program logic thereon . the invention includes such computer program products . the “ computer program product ” and “ computer useable medium ” may be any computer readable medium having computer logic thereon . computer programs ( also called computer control logic or computer program logic ) are may be stored in main memory 508 and / or secondary memory 510 . computer programs may also be received via communications interface 524 . such computer programs , when executed , enable computer system 500 to perform the features of the present invention as discussed herein . in particular , the computer programs , when executed , enable processor 504 to perform the functions of the present invention . accordingly , such computer programs represent controllers of computer system 500 . in an embodiment where the invention is implemented using software , the software may be stored in a computer program product and loaded into computer system 500 using removable storage drive 514 , hard drive 512 or communications interface 524 . the control logic ( software or computer program logic ), when executed by the processor 504 , causes the processor 504 to perform the functions of the invention as described herein . in another embodiment , the invention is implemented primarily in hardware using , for example , hardware components such as application specific integrated circuits ( asics ). implementation of the hardware state machine to perform the functions described herein will be apparent to persons skilled in the relevant art ( s ). in yet another embodiment , the invention is implemented using a combination of both hardware and software . in an example software embodiment of the invention , the methods described above may be implemented in spss control language or c ++ programming language , but could be implemented in other various programs , computer simulation and computer - aided design , computer simulation environment , matlab , or any other software platform or program , windows interface or operating system ( or other operating system ) or other programs known or available to those skilled in the art . the following patents , applications and publications as listed below and throughout this document are hereby incorporated by reference in their entirety herein . the devices , systems , compositions , computer program products , and methods of various embodiments of the invention disclosed herein may utilize aspects disclosed in the following references , 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