allow aging - how does it work?

[a2mahara]

Hello Everyone,

I was looking to find information about how the 'allow aging' module works for simulations. Specifically, what is the time-dependence of how a subject's anatomy and physiology changes with aging (in PK-Sim)?

1. If the subject is the an 'average individual' (i.e at the 50th percentile of body weight and height for age), does the subject's anatomy of physiology represent an 'average subject' as they age?

2. If the subject is NOT an 'average subject' (i.e., body weight and height NOT at the 50th percentile for age), how does anatomy and physiology change with aging?

3. Does each subject's anatomy and physiology change based on their own growth curve for population simulations?

4. How often is anatomy and physiology updated in the simulations (every 24 hours?)

If anyone can point me in right direction to answer these questions, that would be much appreciated.

Thanks!

[prvmalik]

Hi Anil,

For #4, the intervals on which the anatomy and physiology are updated can be seen if you export the simulation to Mobi with 'allow aging' checked and then go to the parameter path in the Spatial Structure (e.g. Organism|Kidney|Volume). The parameter is specified as a table as a function of discrete ages.

Paul

[a2mahara]

Thanks, Paul.

I noted these tables, which define changes in parameters by simulation time, are accessible in PK-Sim as well. I was wondering how often are these parameters updated during the actual simulation run....i.e., does the kidney volume (which is interpolated using the table) update every 24 hours? OR is the kidney volume being continuously being updated during the simulation?

[prvmalik]

Thanks, I didn't realize that interpolation was applied.

I ran an observer for kidney volume in a male european default subject from 1 day old to 2 years and it looks like kidney volume updates at every simulation step. Here is the output of the observer KVupdate.xlsx : Also, turns out its linear interpolation with a few sparse points. The premature pop updates a lot more frequently from my experience.

[Yuri05]

@a2mahara PK-Sim stores information about all model parameters (particularly about anatomical and physiological parameters) in one of 3 ways. Each parameter is described by one of the following alternatives:

I. Analytical formula (equation depending on other model quantities) II. Constant (age independent) non-formula III. Age-dependent non-formula

Parameters of the first two kinds are handled in the same way both in simulations with and without aging. Where it comes to differences are parameters of the type III (and the explanation below is for these parameters).

An individual in PK-Sim is given in the first place by the combination of covariates {Population, Gender, [Gestational Age], Age, Weight, Height} (Gestational Age applies only for the Preterms population). For every parameter of the type III and every combination of the covariates {Population, Gender, [Gestational Age]} the information is stored in PK-Sim on the predefined grid of ages (supporting points) based on literature data. For each supporting point information is stored in form of probability distribution {Distribution type; Mean; STD}.

E.g. the information for the heart volume for {"European_ICRP"; "MALE"} looks like this:

Population | Gender | Organ | Parameter | "Age (years)" | Distribution | Mean (l) | Std (l) -- | -- | -- | -- | -- | -- | -- | -- European (ICRP, 2002) | MALE | Heart | Volume | 0 | Normal | 0,03 | 0,01 European (ICRP, 2002) | MALE | Heart | Volume | 1 | Normal | 0,07 | 0,01 European (ICRP, 2002) | MALE | Heart | Volume | 5 | Normal | 0,11 | 0,02 European (ICRP, 2002) | MALE | Heart | Volume | 10 | Normal | 0,18 | 0,03 European (ICRP, 2002) | MALE | Heart | Volume | 15 | Normal | 0,29 | 0,06 European (ICRP, 2002) | MALE | Heart | Volume | 30 | Normal | 0,42 | 0,08 European (ICRP, 2002) | MALE | Heart | Volume | 40 | Normal | 0,43 | 0,11 European (ICRP, 2002) | MALE | Heart | Volume | 50 | Normal | 0,44 | 0,11 European (ICRP, 2002) | MALE | Heart | Volume | 60 | Normal | 0,45 | 0,11 European (ICRP, 2002) | MALE | Heart | Volume | 70 | Normal | 0,46 | 0,10 European (ICRP, 2002) | MALE | Heart | Volume | 80 | Normal | 0,44 | 0,09 European (ICRP, 2002) | MALE | Heart | Volume | 90 | Normal | 0,44 | 0,08 European (ICRP, 2002) | MALE | Heart | Volume | 100 | Normal | 0,43 | 0,08

When creating an individual of age which is not on the predefined age grid, the probability distribution for a given parameter is linearly interpolated based on the information in the supporting points.

Further, the probability distributions like in example above, are stored for the Mean body weight and Mean Height of a combination {Population, Gender, [Gestational Age]}. If the required weight and/or height differs from the mean value: probability distributions of some parameters are scaled as described in detail in the Create Individual Algorithm.

Finally, after an individual was created, user can optionally modify some of the individual parameters. Based on the probability distribution of a parameter and its current value, a percentile for this parameter can be calculated (which is also shown in PK-Sim).

Now the short answer to your questions 1. and 2.: the percentile of each distributed parameter is kept constant during the whole simulation duration.

In more detail: when a simulation with the aging option is created, for each distributed parameter the following is done:

• The table with the supporting points information for this parameter corresponding to {Population, Gender, [Gestational Age]} of the individual is taken from the PK-Sim database
• For some parameters: each distribution in the table is scaled based on weight/height in the same way as for the original individual (s. Create Individual Algorithm) (weight/height themselves are estimated following the same principle: it's a value corresponding to the percentile of the original individual and the new probability distribution)
• For each supporting point in this modified distributions table a value corresponding to the percentile of the original individual within the probability distribution for this supporting point is calculated
• The table is shifted in the X direction so that the 0 point on the X-Axis corresponds to the age of the individual (e.g. if your table containes supporting points 0, 10, 20, 30 years and the individual is 12 years old, the shifted table will contain supporting points 0, 8, 18 years). Thus if we would like to know the value of a distributed parameter at any time T during the simulation - we just have to get the table value corresponding to this time T.
• Distributed parameter in the simulation is replaced by the table build in the steps above

In an individual simulation with aging you can view such a table if you click on Show Table

For example, if you create a 80 years old European_ICRP MALE individual with mean body weight and mean height and then create a simulation with aging based on this individual, then the table for the heart volume would look like this: Which is the original table shifted by 80 years. (and if you change body weight/height when creating the individual or change the heart volume manually, the values in the table will change according to the new percentile of the heart volume)

Now during the simulation with aging, table values are used all the time to retrieve the values of anatomical and physiological parameters. So the answer to your question 4: parameters are updated continuously.

Regarding the last question:

3. Does each subject's anatomy and physiology change based on their own growth curve for population simulations?

For a population it's exactly the same principle. Each individual in a population has corresponding probability distributions for age-dependent (non-formula) parameters and corresponding percentiles. So for each individual of a population lookup-tables for distributed parameters are created as described above and used during the simulation for continuous updating of parameter values.

P.S. Age grids of the supporting points differ between populations and are not equidistant.

Population dependent grid of age supporting points

Population | Age (years) -- | -- Asian (Tanaka, 1996) | 0 Asian (Tanaka, 1996) | 0,008219 Asian (Tanaka, 1996) | 0,0192 Asian (Tanaka, 1996) | 0,0383 Asian (Tanaka, 1996) | 0,0416667 Asian (Tanaka, 1996) | 0,0833 Asian (Tanaka, 1996) | 0,1667 Asian (Tanaka, 1996) | 0,2083333 Asian (Tanaka, 1996) | 0,375 Asian (Tanaka, 1996) | 0,5 Asian (Tanaka, 1996) | 0,7083333 Asian (Tanaka, 1996) | 1 Asian (Tanaka, 1996) | 2 Asian (Tanaka, 1996) | 2,1 Asian (Tanaka, 1996) | 3 Asian (Tanaka, 1996) | 4 Asian (Tanaka, 1996) | 5 Asian (Tanaka, 1996) | 6 Asian (Tanaka, 1996) | 6,1 Asian (Tanaka, 1996) | 6,9 Asian (Tanaka, 1996) | 7 Asian (Tanaka, 1996) | 8 Asian (Tanaka, 1996) | 9 Asian (Tanaka, 1996) | 10 Asian (Tanaka, 1996) | 11 Asian (Tanaka, 1996) | 12 Asian (Tanaka, 1996) | 12,1 Asian (Tanaka, 1996) | 13 Asian (Tanaka, 1996) | 14 Asian (Tanaka, 1996) | 15 Asian (Tanaka, 1996) | 16 Asian (Tanaka, 1996) | 17 Asian (Tanaka, 1996) | 18 Asian (Tanaka, 1996) | 19 Asian (Tanaka, 1996) | 20 Asian (Tanaka, 1996) | 21 Asian (Tanaka, 1996) | 22 Asian (Tanaka, 1996) | 23 Asian (Tanaka, 1996) | 24 Asian (Tanaka, 1996) | 27 Asian (Tanaka, 1996) | 30 Asian (Tanaka, 1996) | 34,5 Asian (Tanaka, 1996) | 44,5 Asian (Tanaka, 1996) | 45 Asian (Tanaka, 1996) | 54,5 Asian (Tanaka, 1996) | 60 Asian (Tanaka, 1996) | 74,5 Asian (Tanaka, 1996) | 81 Black American (NHANES, 1997) | 0 Black American (NHANES, 1997) | 1 Black American (NHANES, 1997) | 2 Black American (NHANES, 1997) | 3 Black American (NHANES, 1997) | 4 Black American (NHANES, 1997) | 5 Black American (NHANES, 1997) | 6,9 Black American (NHANES, 1997) | 7 Black American (NHANES, 1997) | 10 Black American (NHANES, 1997) | 13 Black American (NHANES, 1997) | 15 Black American (NHANES, 1997) | 18 Black American (NHANES, 1997) | 20 Black American (NHANES, 1997) | 30 Black American (NHANES, 1997) | 40 Black American (NHANES, 1997) | 50 Black American (NHANES, 1997) | 60 Black American (NHANES, 1997) | 70 Black American (NHANES, 1997) | 81 European (ICRP, 2002) | 0 European (ICRP, 2002) | 0,008219 European (ICRP, 2002) | 0,0192 European (ICRP, 2002) | 0,0383 European (ICRP, 2002) | 0,0833 European (ICRP, 2002) | 0,1667 European (ICRP, 2002) | 0,5 European (ICRP, 2002) | 1 European (ICRP, 2002) | 2 European (ICRP, 2002) | 2,1 European (ICRP, 2002) | 3 European (ICRP, 2002) | 4 European (ICRP, 2002) | 5 European (ICRP, 2002) | 6 European (ICRP, 2002) | 6,1 European (ICRP, 2002) | 6,9 European (ICRP, 2002) | 7 European (ICRP, 2002) | 10 European (ICRP, 2002) | 12 European (ICRP, 2002) | 12,1 European (ICRP, 2002) | 13 European (ICRP, 2002) | 15 European (ICRP, 2002) | 18 European (ICRP, 2002) | 30 European (ICRP, 2002) | 40 European (ICRP, 2002) | 50 European (ICRP, 2002) | 60 European (ICRP, 2002) | 70 European (ICRP, 2002) | 80 European (ICRP, 2002) | 81 European (ICRP, 2002) | 90 European (ICRP, 2002) | 100 Japanese (2015) | 0 Japanese (2015) | 0,008219 Japanese (2015) | 0,0192 Japanese (2015) | 0,0383 Japanese (2015) | 0,0833 Japanese (2015) | 0,1667 Japanese (2015) | 0,25 Japanese (2015) | 0,5 Japanese (2015) | 0,75 Japanese (2015) | 1 Japanese (2015) | 2 Japanese (2015) | 2,1 Japanese (2015) | 3 Japanese (2015) | 4 Japanese (2015) | 5 Japanese (2015) | 6 Japanese (2015) | 6,1 Japanese (2015) | 6,9 Japanese (2015) | 7 Japanese (2015) | 8 Japanese (2015) | 9 Japanese (2015) | 10 Japanese (2015) | 11 Japanese (2015) | 12 Japanese (2015) | 12,1 Japanese (2015) | 13 Japanese (2015) | 14 Japanese (2015) | 15 Japanese (2015) | 16 Japanese (2015) | 17 Japanese (2015) | 18 Japanese (2015) | 19 Japanese (2015) | 20 Japanese (2015) | 21 Japanese (2015) | 22 Japanese (2015) | 23 Japanese (2015) | 24 Japanese (2015) | 25 Japanese (2015) | 26 Japanese (2015) | 27 Japanese (2015) | 28 Japanese (2015) | 29 Japanese (2015) | 30 Japanese (2015) | 81 Mexican American - White (NHANES, 1997) | 0 Mexican American - White (NHANES, 1997) | 1 Mexican American - White (NHANES, 1997) | 2 Mexican American - White (NHANES, 1997) | 3 Mexican American - White (NHANES, 1997) | 4 Mexican American - White (NHANES, 1997) | 5 Mexican American - White (NHANES, 1997) | 6,9 Mexican American - White (NHANES, 1997) | 7 Mexican American - White (NHANES, 1997) | 10 Mexican American - White (NHANES, 1997) | 13 Mexican American - White (NHANES, 1997) | 15 Mexican American - White (NHANES, 1997) | 18 Mexican American - White (NHANES, 1997) | 20 Mexican American - White (NHANES, 1997) | 30 Mexican American - White (NHANES, 1997) | 40 Mexican American - White (NHANES, 1997) | 50 Mexican American - White (NHANES, 1997) | 60 Mexican American - White (NHANES, 1997) | 70 Mexican American - White (NHANES, 1997) | 81 Pregnant (Dallmann et al. 2017) | 30 Pregnant (Dallmann et al. 2017) | 30,002737907007 Pregnant (Dallmann et al. 2017) | 30,005475814014 Pregnant (Dallmann et al. 2017) | 30,008213721021 Pregnant (Dallmann et al. 2017) | 30,010951628028 Pregnant (Dallmann et al. 2017) | 30,0136895350349 Pregnant (Dallmann et al. 2017) | 30,0164274420419 Pregnant (Dallmann et al. 2017) | 30,0191653490489 Pregnant (Dallmann et al. 2017) | 30,0219032560559 Pregnant (Dallmann et al. 2017) | 30,0246411630629 Pregnant (Dallmann et al. 2017) | 30,0273790700699 Pregnant (Dallmann et al. 2017) | 30,0301169770769 Pregnant (Dallmann et al. 2017) | 30,0328548840839 Pregnant (Dallmann et al. 2017) | 30,0355927910908 Pregnant (Dallmann et al. 2017) | 30,0383306980978 Pregnant (Dallmann et al. 2017) | 30,0410686051048 Pregnant (Dallmann et al. 2017) | 30,0438065121118 Pregnant (Dallmann et al. 2017) | 30,0465444191188 Pregnant (Dallmann et al. 2017) | 30,0492823261258 Pregnant (Dallmann et al. 2017) | 30,0520202331328 Pregnant (Dallmann et al. 2017) | 30,0547581401398 Pregnant (Dallmann et al. 2017) | 30,0574960471468 Pregnant (Dallmann et al. 2017) | 30,0602339541537 Pregnant (Dallmann et al. 2017) | 30,0629718611607 Pregnant (Dallmann et al. 2017) | 30,0657097681677 Pregnant (Dallmann et al. 2017) | 30,0684476751747 Pregnant (Dallmann et al. 2017) | 30,0711855821817 Pregnant (Dallmann et al. 2017) | 30,0739234891887 Pregnant (Dallmann et al. 2017) | 30,0766613961957 Pregnant (Dallmann et al. 2017) | 30,0793993032027 Pregnant (Dallmann et al. 2017) | 30,0821372102097 Pregnant (Dallmann et al. 2017) | 30,0848751172166 Pregnant (Dallmann et al. 2017) | 30,0876130242236 Pregnant (Dallmann et al. 2017) | 30,0903509312306 Pregnant (Dallmann et al. 2017) | 30,0930888382376 Pregnant (Dallmann et al. 2017) | 30,0958267452446 Pregnant (Dallmann et al. 2017) | 30,0985646522516 Pregnant (Dallmann et al. 2017) | 30,1013025592586 Pregnant (Dallmann et al. 2017) | 30,1040404662656 Pregnant (Dallmann et al. 2017) | 30,1067783732726 Pregnant (Dallmann et al. 2017) | 30,1095162802795 Pregnant (Dallmann et al. 2017) | 30,1122541872865 Pregnant (Dallmann et al. 2017) | 30,1149920942935 Pregnant (Dallmann et al. 2017) | 30,1177300013005 Pregnant (Dallmann et al. 2017) | 30,1204679083075 Pregnant (Dallmann et al. 2017) | 30,1232058153145 Pregnant (Dallmann et al. 2017) | 30,1259437223215 Pregnant (Dallmann et al. 2017) | 30,1286816293285 Pregnant (Dallmann et al. 2017) | 30,1314195363355 Pregnant (Dallmann et al. 2017) | 30,1341574433424 Pregnant (Dallmann et al. 2017) | 30,1368953503494 Pregnant (Dallmann et al. 2017) | 30,1396332573564 Pregnant (Dallmann et al. 2017) | 30,1423711643634 Pregnant (Dallmann et al. 2017) | 30,1451090713704 Pregnant (Dallmann et al. 2017) | 30,1478469783774 Pregnant (Dallmann et al. 2017) | 30,1505848853844 Pregnant (Dallmann et al. 2017) | 30,1533227923914 Pregnant (Dallmann et al. 2017) | 30,1560606993983 Pregnant (Dallmann et al. 2017) | 30,1587986064053 Pregnant (Dallmann et al. 2017) | 30,1615365134123 Pregnant (Dallmann et al. 2017) | 30,1642744204193 Pregnant (Dallmann et al. 2017) | 30,1670123274263 Pregnant (Dallmann et al. 2017) | 30,1697502344333 Pregnant (Dallmann et al. 2017) | 30,1724881414403 Pregnant (Dallmann et al. 2017) | 30,1752260484473 Pregnant (Dallmann et al. 2017) | 30,1779639554543 Pregnant (Dallmann et al. 2017) | 30,1807018624612 Pregnant (Dallmann et al. 2017) | 30,1834397694682 Pregnant (Dallmann et al. 2017) | 30,1861776764752 Pregnant (Dallmann et al. 2017) | 30,1889155834822 Pregnant (Dallmann et al. 2017) | 30,1916534904892 Pregnant (Dallmann et al. 2017) | 30,1943913974962 Pregnant (Dallmann et al. 2017) | 30,1971293045032 Pregnant (Dallmann et al. 2017) | 30,1998672115102 Pregnant (Dallmann et al. 2017) | 30,2026051185171 Pregnant (Dallmann et al. 2017) | 30,2053430255241 Pregnant (Dallmann et al. 2017) | 30,2080809325311 Pregnant (Dallmann et al. 2017) | 30,2108188395381 Pregnant (Dallmann et al. 2017) | 30,2135567465451 Pregnant (Dallmann et al. 2017) | 30,2162946535521 Pregnant (Dallmann et al. 2017) | 30,2190325605591 Pregnant (Dallmann et al. 2017) | 30,2217704675661 Pregnant (Dallmann et al. 2017) | 30,2245083745731 Pregnant (Dallmann et al. 2017) | 30,22724628158 Pregnant (Dallmann et al. 2017) | 30,229984188587 Pregnant (Dallmann et al. 2017) | 30,232722095594 Pregnant (Dallmann et al. 2017) | 30,235460002601 Pregnant (Dallmann et al. 2017) | 30,238197909608 Pregnant (Dallmann et al. 2017) | 30,240935816615 Pregnant (Dallmann et al. 2017) | 30,243673723622 Pregnant (Dallmann et al. 2017) | 30,246411630629 Pregnant (Dallmann et al. 2017) | 30,249149537636 Pregnant (Dallmann et al. 2017) | 30,2518874446429 Pregnant (Dallmann et al. 2017) | 30,2546253516499 Pregnant (Dallmann et al. 2017) | 30,2573632586569 Pregnant (Dallmann et al. 2017) | 30,2601011656639 Pregnant (Dallmann et al. 2017) | 30,2628390726709 Pregnant (Dallmann et al. 2017) | 30,2655769796779 Pregnant (Dallmann et al. 2017) | 30,2683148866849 Pregnant (Dallmann et al. 2017) | 30,2710527936919 Pregnant (Dallmann et al. 2017) | 30,2737907006989 Pregnant (Dallmann et al. 2017) | 30,2765286077058 Pregnant (Dallmann et al. 2017) | 30,2792665147128 Pregnant (Dallmann et al. 2017) | 30,2820044217198 Pregnant (Dallmann et al. 2017) | 30,2847423287268 Pregnant (Dallmann et al. 2017) | 30,2874802357338 Pregnant (Dallmann et al. 2017) | 30,2902181427408 Pregnant (Dallmann et al. 2017) | 30,2929560497478 Pregnant (Dallmann et al. 2017) | 30,2956939567548 Pregnant (Dallmann et al. 2017) | 30,2984318637617 Pregnant (Dallmann et al. 2017) | 30,3011697707687 Pregnant (Dallmann et al. 2017) | 30,3039076777757 Pregnant (Dallmann et al. 2017) | 30,3066455847827 Pregnant (Dallmann et al. 2017) | 30,3093834917897 Pregnant (Dallmann et al. 2017) | 30,3121213987967 Pregnant (Dallmann et al. 2017) | 30,3148593058037 Pregnant (Dallmann et al. 2017) | 30,3175972128107 Pregnant (Dallmann et al. 2017) | 30,3203351198177 Pregnant (Dallmann et al. 2017) | 30,3230730268246 Pregnant (Dallmann et al. 2017) | 30,3258109338316 Pregnant (Dallmann et al. 2017) | 30,3285488408386 Pregnant (Dallmann et al. 2017) | 30,3312867478456 Pregnant (Dallmann et al. 2017) | 30,3340246548526 Pregnant (Dallmann et al. 2017) | 30,3367625618596 Pregnant (Dallmann et al. 2017) | 30,3395004688666 Pregnant (Dallmann et al. 2017) | 30,3422383758736 Pregnant (Dallmann et al. 2017) | 30,3449762828806 Pregnant (Dallmann et al. 2017) | 30,3477141898875 Pregnant (Dallmann et al. 2017) | 30,3504520968945 Pregnant (Dallmann et al. 2017) | 30,3531900039015 Pregnant (Dallmann et al. 2017) | 30,3559279109085 Pregnant (Dallmann et al. 2017) | 30,3586658179155 Pregnant (Dallmann et al. 2017) | 30,3614037249225 Pregnant (Dallmann et al. 2017) | 30,3641416319295 Pregnant (Dallmann et al. 2017) | 30,3668795389365 Pregnant (Dallmann et al. 2017) | 30,3696174459434 Pregnant (Dallmann et al. 2017) | 30,3723553529504 Pregnant (Dallmann et al. 2017) | 30,3750932599574 Pregnant (Dallmann et al. 2017) | 30,3778311669644 Pregnant (Dallmann et al. 2017) | 30,3805690739714 Pregnant (Dallmann et al. 2017) | 30,3833069809784 Pregnant (Dallmann et al. 2017) | 30,3860448879854 Pregnant (Dallmann et al. 2017) | 30,3887827949924 Pregnant (Dallmann et al. 2017) | 30,3915207019994 Pregnant (Dallmann et al. 2017) | 30,3942586090063 Pregnant (Dallmann et al. 2017) | 30,3969965160133 Pregnant (Dallmann et al. 2017) | 30,3997344230203 Pregnant (Dallmann et al. 2017) | 30,4024723300273 Pregnant (Dallmann et al. 2017) | 30,4052102370343 Pregnant (Dallmann et al. 2017) | 30,4079481440413 Pregnant (Dallmann et al. 2017) | 30,4106860510483 Pregnant (Dallmann et al. 2017) | 30,4134239580553 Pregnant (Dallmann et al. 2017) | 30,4161618650623 Pregnant (Dallmann et al. 2017) | 30,4188997720692 Pregnant (Dallmann et al. 2017) | 30,4216376790762 Pregnant (Dallmann et al. 2017) | 30,4243755860832 Pregnant (Dallmann et al. 2017) | 30,4271134930902 Pregnant (Dallmann et al. 2017) | 30,4298514000972 Pregnant (Dallmann et al. 2017) | 30,4325893071042 Pregnant (Dallmann et al. 2017) | 30,4353272141112 Pregnant (Dallmann et al. 2017) | 30,4380651211182 Pregnant (Dallmann et al. 2017) | 30,4408030281251 Pregnant (Dallmann et al. 2017) | 30,4435409351321 Pregnant (Dallmann et al. 2017) | 30,4462788421391 Pregnant (Dallmann et al. 2017) | 30,4490167491461 Pregnant (Dallmann et al. 2017) | 30,4517546561531 Pregnant (Dallmann et al. 2017) | 30,4544925631601 Pregnant (Dallmann et al. 2017) | 30,4572304701671 Pregnant (Dallmann et al. 2017) | 30,4599683771741 Pregnant (Dallmann et al. 2017) | 30,4627062841811 Pregnant (Dallmann et al. 2017) | 30,465444191188 Pregnant (Dallmann et al. 2017) | 30,468182098195 Pregnant (Dallmann et al. 2017) | 30,470920005202 Pregnant (Dallmann et al. 2017) | 30,473657912209 Pregnant (Dallmann et al. 2017) | 30,476395819216 Pregnant (Dallmann et al. 2017) | 30,479133726223 Pregnant (Dallmann et al. 2017) | 30,48187163323 Pregnant (Dallmann et al. 2017) | 30,484609540237 Pregnant (Dallmann et al. 2017) | 30,487347447244 Pregnant (Dallmann et al. 2017) | 30,4900853542509 Pregnant (Dallmann et al. 2017) | 30,4928232612579 Pregnant (Dallmann et al. 2017) | 30,4955611682649 Pregnant (Dallmann et al. 2017) | 30,4982990752719 Pregnant (Dallmann et al. 2017) | 30,5010369822789 Pregnant (Dallmann et al. 2017) | 30,5037748892859 Pregnant (Dallmann et al. 2017) | 30,5065127962929 Pregnant (Dallmann et al. 2017) | 30,5092507032999 Pregnant (Dallmann et al. 2017) | 30,5119886103069 Pregnant (Dallmann et al. 2017) | 30,5147265173138 Pregnant (Dallmann et al. 2017) | 30,5174644243208 Pregnant (Dallmann et al. 2017) | 30,5202023313278 Pregnant (Dallmann et al. 2017) | 30,5229402383348 Pregnant (Dallmann et al. 2017) | 30,5256781453418 Pregnant (Dallmann et al. 2017) | 30,5284160523488 Pregnant (Dallmann et al. 2017) | 30,5311539593558 Pregnant (Dallmann et al. 2017) | 30,5338918663628 Pregnant (Dallmann et al. 2017) | 30,5366297733697 Pregnant (Dallmann et al. 2017) | 30,5393676803767 Pregnant (Dallmann et al. 2017) | 30,5421055873837 Pregnant (Dallmann et al. 2017) | 30,5448434943907 Pregnant (Dallmann et al. 2017) | 30,5475814013977 Pregnant (Dallmann et al. 2017) | 30,5503193084047 Pregnant (Dallmann et al. 2017) | 30,5530572154117 Pregnant (Dallmann et al. 2017) | 30,5557951224187 Pregnant (Dallmann et al. 2017) | 30,5585330294257 Pregnant (Dallmann et al. 2017) | 30,5612709364326 Pregnant (Dallmann et al. 2017) | 30,5640088434396 Pregnant (Dallmann et al. 2017) | 30,5667467504466 Pregnant (Dallmann et al. 2017) | 30,5694846574536 Pregnant (Dallmann et al. 2017) | 30,5722225644606 Pregnant (Dallmann et al. 2017) | 30,5749604714676 Pregnant (Dallmann et al. 2017) | 30,5776983784746 Pregnant (Dallmann et al. 2017) | 30,5804362854816 Pregnant (Dallmann et al. 2017) | 30,5831741924886 Pregnant (Dallmann et al. 2017) | 30,5859120994955 Pregnant (Dallmann et al. 2017) | 30,5886500065025 Pregnant (Dallmann et al. 2017) | 30,5913879135095 Pregnant (Dallmann et al. 2017) | 30,5941258205165 Pregnant (Dallmann et al. 2017) | 30,5968637275235 Pregnant (Dallmann et al. 2017) | 30,5996016345305 Pregnant (Dallmann et al. 2017) | 30,6023395415375 Pregnant (Dallmann et al. 2017) | 30,6050774485445 Pregnant (Dallmann et al. 2017) | 30,6078153555514 Pregnant (Dallmann et al. 2017) | 30,6105532625584 Pregnant (Dallmann et al. 2017) | 30,6132911695654 Pregnant (Dallmann et al. 2017) | 30,6160290765724 Pregnant (Dallmann et al. 2017) | 30,6187669835794 Pregnant (Dallmann et al. 2017) | 30,6215048905864 Pregnant (Dallmann et al. 2017) | 30,6242427975934 Pregnant (Dallmann et al. 2017) | 30,6269807046004 Pregnant (Dallmann et al. 2017) | 30,6297186116074 Pregnant (Dallmann et al. 2017) | 30,6324565186143 Pregnant (Dallmann et al. 2017) | 30,6351944256213 Pregnant (Dallmann et al. 2017) | 30,6379323326283 Pregnant (Dallmann et al. 2017) | 30,6406702396353 Pregnant (Dallmann et al. 2017) | 30,6434081466423 Pregnant (Dallmann et al. 2017) | 30,6461460536493 Pregnant (Dallmann et al. 2017) | 30,6488839606563 Pregnant (Dallmann et al. 2017) | 30,6516218676633 Pregnant (Dallmann et al. 2017) | 30,6543597746703 Pregnant (Dallmann et al. 2017) | 30,6570976816772 Pregnant (Dallmann et al. 2017) | 30,6598355886842 Pregnant (Dallmann et al. 2017) | 30,6625734956912 Pregnant (Dallmann et al. 2017) | 30,6653114026982 Pregnant (Dallmann et al. 2017) | 30,6680493097052 Pregnant (Dallmann et al. 2017) | 30,6707872167122 Pregnant (Dallmann et al. 2017) | 30,6735251237192 Pregnant (Dallmann et al. 2017) | 30,6762630307262 Pregnant (Dallmann et al. 2017) | 30,6790009377332 Pregnant (Dallmann et al. 2017) | 30,6817388447401 Pregnant (Dallmann et al. 2017) | 30,6844767517471 Pregnant (Dallmann et al. 2017) | 30,6872146587541 Pregnant (Dallmann et al. 2017) | 30,6899525657611 Pregnant (Dallmann et al. 2017) | 30,6926904727681 Pregnant (Dallmann et al. 2017) | 30,6954283797751 Pregnant (Dallmann et al. 2017) | 30,6981662867821 Pregnant (Dallmann et al. 2017) | 30,7009041937891 Pregnant (Dallmann et al. 2017) | 30,703642100796 Pregnant (Dallmann et al. 2017) | 30,706380007803 Pregnant (Dallmann et al. 2017) | 30,70911791481 Pregnant (Dallmann et al. 2017) | 30,711855821817 Pregnant (Dallmann et al. 2017) | 30,714593728824 Pregnant (Dallmann et al. 2017) | 30,717331635831 Pregnant (Dallmann et al. 2017) | 30,720069542838 Pregnant (Dallmann et al. 2017) | 30,722807449845 Pregnant (Dallmann et al. 2017) | 30,725545356852 Pregnant (Dallmann et al. 2017) | 30,7282832638589 Preterm | 0 Preterm | 0,0027379 Preterm | 0,0054757 Preterm | 0,0082136 Preterm | 0,0109514 Preterm | 0,0136893 Preterm | 0,0164271 Preterm | 0,019165 Preterm | 0,0219028 Preterm | 0,0246407 Preterm | 0,0273785 Preterm | 0,0301164 Preterm | 0,0328542 Preterm | 0,0355921 Preterm | 0,0383299 Preterm | 0,0410678 Preterm | 0,0438056 Preterm | 0,0465435 Preterm | 0,0492813 Preterm | 0,0520192 Preterm | 0,054757 Preterm | 0,0574949 Preterm | 0,0602327 Preterm | 0,0629706 Preterm | 0,0657084 Preterm | 0,0684463 Preterm | 0,0711841 Preterm | 0,073922 Preterm | 0,0766598 Preterm | 0,0793977 Preterm | 0,0821355 Preterm | 0,1667 Preterm | 0,5 Preterm | 1 Preterm | 2 Preterm | 2,1 Preterm | 3 Preterm | 4 Preterm | 5 Preterm | 6 Preterm | 6,1 Preterm | 10 Preterm | 12 Preterm | 12,1 Preterm | 13 Preterm | 15 Preterm | 18 Preterm | 30 Preterm | 45 Preterm | 60 Preterm | 81 White American (NHANES, 1997) | 0 White American (NHANES, 1997) | 1 White American (NHANES, 1997) | 2 White American (NHANES, 1997) | 3 White American (NHANES, 1997) | 4 White American (NHANES, 1997) | 5 White American (NHANES, 1997) | 6,9 White American (NHANES, 1997) | 7 White American (NHANES, 1997) | 10 White American (NHANES, 1997) | 13 White American (NHANES, 1997) | 15 White American (NHANES, 1997) | 18 White American (NHANES, 1997) | 20 White American (NHANES, 1997) | 30 White American (NHANES, 1997) | 40 White American (NHANES, 1997) | 50 White American (NHANES, 1997) | 60 White American (NHANES, 1997) | 70 White American (NHANES, 1997) | 80 White American (NHANES, 1997) | 81

[msevestre]

Like @Yuri05 said

[a2mahara]

Great!!! Thanks @prvmalik. Also Thanks @Yuri05 for the comprehensive explanation. That pretty much covers everything I asked from #1-4

However, one last follow-up. I noted that models edited in MoBi and exported back to PK-Sim to conduct population simulations will NOT permit for aging during the simulation (even if the original simulation that was first exported to MoBi from PK-Sim included the 'allow aging' option). Is this something that is being considered for future releases?

[Yuri05]

Is this something that is being considered for future releases?

You might give a like here: https://github.com/Open-Systems-Pharmacology/PK-Sim/issues/1458 😉

[a2mahara]

Thanks again for the info, @Yuri05!!