B 기타 비구획분석 소프트웨어

B.1 이 장에서는

이 장에서는 몇가지 NCA 용 소프트웨어(상용 소프웨어, R 패키지)를 비교하고 분석하여 그 결과와 사용법의 공통점과 차이점을 알아볼 것입니다. 특별히 Theoph 데이타셋에서 C_max, AUC_inf가 동일하게 나오는지 초점을 맞추어 실펴보겠습니다.

library(tidyverse)

B.2 Certara Phoenix WinNonLin

https://www.certara.com/software/pkpd-modeling-and-simulation/phoenix-winnonlin/

B.2.1 Pros

• Validated for several years
• Industry standard
• Versatile unit setting
• Easy using by GUI
• Generating plots supported

B.2.2 Cons

• Expansive (~several thousand dollars)
• Not suitable for reproducible research
• CDISC SDTM not compatible (input and output)

B.3 R package: PKNCA

Automation of Noncompartmental Analysis in R https://github.com/billdenney/pknca

B.3.1 ISoP Pharmacometrics Study Group Presentation

• 강의 동영상 https://www.youtube.com/watch?v=WCmFrheYtcc
• 프로젝트 https://github.com/billdenney/pknca
• Package https://cran.r-project.org/web/packages/PKNCA/
  • 예제 R Markdown 파일 : https://github.com/billdenney/pknca/tree/master/vignettes
• PPT 파일
• PKNCA 패키지란 무엇인가? * Pharmacokinetic(PK) data를 위한 모든 noncompartmental analysis (NCA) 계산이 가능한 R용 패키지

library(devtools)
install_github("billdenney/pknca")

B.3.2 오픈소스 NCA - 지금이 적기이다.

• Data standards 가 점점 많아짐
• CDISC/SDTM가 FDA requirement
• CDISC ADaM working group is standardizing NCA data set (ADNCA) |
  • CDISC SDTM pharmacokinetic concentration (PC) and pharmacokinetic parameter (PP) domains have been standardized
• 우리도 R로 NCA?
  • Organizes concentration/time and dose/time data
• Predicts what you most likely need from NCA parameters from the concentration and dosing data.
• Allows user control of all NCA parameter and summary calculations
• Calculates all (standard) NCA parameters (Targeting the SDTM PK 파라메터)
• 한계
  • 그래픽 못그림
  • 파라메터의 statistics 못구함 (곧 기능 추가할듯)
• NCA 파라메터 계산가능 (Cmax, Tmax, AUClast, AUCinf, AUMC, half-life, …)
• NCA-related calculations (Superposition, Concentration interpolation/extrapolation (with AUC methods), Time to steady-state)
• SDTM PP-READY OUTPUT 가능
• 인풋에서 아웃풋까지 TRACK가능하다.
• 800개 넘는 테스트 케이스가 있음.
• Github에서 모두 다운로드 가능
• CRAN에 package올라왔다. (0.7) https://cran.r-project.org/web/packages/PKNCA/
  • wdenney@humanpredictions.com 으로 메일 보내라
• 모든게 오픈이기 때문에 Github에서 기여 환영
• RStudio를 사용한 Hands-on 실습
• Example-theophylline.Rmd
  • Theophylline 농도를 가지고 PK Parameter 구하는 법
  • https://raw.githubusercontent.com/billdenney/pknca/master/vignettes/Example-theophylline.Rmd
  • 이 파일을 RStudio에서 실행해본다.
  • 이후 article에서 분석할 것입니다.
• Superposition.Rmd
  • https://raw.githubusercontent.com/billdenney/pknca/master/vignettes/Superposition.Rmd
  • 이 파일을 RStudio에서 실행해본다.
• PKNCA.options() 모든 옵션을 볼 수 있다.

B.3.3 Pros

• Open source and free of charge
• CDISC SDTM semi compatible (output)
• Calculate partial(interval) AUC with ‘linear’ or ‘log’ interpolation method but in a cumbersome way

B.3.4 Cons

• CDISC SDTM not compatible (input)
• More tests required
• Experience with R language required
• Generating plots not supported for now (To be supported soon)

library(PKNCA)

my.conc <- PKNCAconc(as.data.frame(Theoph), conc~Time|Subject)
d.dose <- unique(datasets::Theoph[datasets::Theoph$Time == 0,
                                  c("Dose", "Time", "Subject")])
my.dose <- PKNCAdose(d.dose, Dose~Time|Subject)
my.data.automatic <- PKNCAdata(my.conc, my.dose)
my.results.automatic <- pk.nca(my.data.automatic)
my.results.automatic$result %>% filter(grepl(pattern = "cmax|aucinf", PPTESTCD)) %>% 
    arrange(PPTESTCD)

##    start end Subject   PPTESTCD   PPORRES exclude
## 1      0 Inf       1 aucinf.obs 214.92363    <NA>
## 2      0 Inf       2 aucinf.obs  97.37793    <NA>
## 3      0 Inf       3 aucinf.obs 106.12767    <NA>
## 4      0 Inf       4 aucinf.obs 114.21620    <NA>
## 5      0 Inf       5 aucinf.obs 136.30473    <NA>
## 6      0 Inf       6 aucinf.obs  82.17588    <NA>
## 7      0 Inf       7 aucinf.obs 100.98763    <NA>
## 8      0 Inf       8 aucinf.obs 102.15330    <NA>
## 9      0 Inf       9 aucinf.obs  97.52000    <NA>
## 10     0 Inf      10 aucinf.obs 167.86003    <NA>
## 11     0 Inf      11 aucinf.obs  86.90262    <NA>
## 12     0 Inf      12 aucinf.obs 125.83154    <NA>
## 13     0 Inf       1       cmax  10.50000    <NA>
## 14     0 Inf       2       cmax   8.33000    <NA>
## 15     0 Inf       3       cmax   8.20000    <NA>
## 16     0 Inf       4       cmax   8.60000    <NA>
## 17     0 Inf       5       cmax  11.40000    <NA>
## 18     0 Inf       6       cmax   6.44000    <NA>
## 19     0 Inf       7       cmax   7.09000    <NA>
## 20     0 Inf       8       cmax   7.56000    <NA>
## 21     0 Inf       9       cmax   9.03000    <NA>
## 22     0 Inf      10       cmax  10.21000    <NA>
## 23     0 Inf      11       cmax   8.00000    <NA>
## 24     0 Inf      12       cmax   9.75000    <NA>

summary(my.results.automatic)

##  start end  N     auclast        cmax               tmax   half.life aucinf.obs
##      0  24 12 74.6 [24.3]           .                  .           .          .
##      0 Inf 12           . 8.65 [17.0] 1.14 [0.630, 3.55] 8.18 [2.12] 115 [28.4]
## 
## Caption: auclast, cmax, aucinf.obs: geometric mean and geometric coefficient of variation; tmax: median and range; half.life: arithmetic mean and standard deviation

B.4 R package: ncappc

NCA Calculation and Population PK Model Diagnosis (Acharya et al. 2016)

https://cran.r-project.org/web/packages/ncappc/index.html https://www.ncbi.nlm.nih.gov/pubmed/27000291

#install.packages("ncappc")
library(ncappc)

Theoph %>% 
  rename(ID = Subject,
         TIME = Time,
         DV = conc) %>% 
  write_csv('Theoph_ncappc.csv')

Theoph_ncappc_results <- ncappc(obsFile = 'Theoph_ncappc.csv',
       onlyNCA = TRUE,
       extrapolate = TRUE,
       printOut = FALSE,
       evid = FALSE,
       psnOut=FALSE)

B.5 R package: PK

Basic Non-Compartmental Pharmacokinetics

https://cran.r-project.org/web/packages/PK/index.html

#install.packages("PK")
library(PK)

B.6 Kinetica

B.7 Scientist

B.8 PKSolver

B.9 Summary

참고문헌

Acharya, Chayan, Andrew C. Hooker, Gülbeyaz Yıldız Türkyılmaz, Siv Jönsson, and Mats O. Karlsson. 2016. “A Diagnostic Tool for Population Models Using Non-Compartmental Analysis: The Ncappc Package for R.” Computer Methods and Programs in Biomedicine 127: 83–93. https://doi.org/http://dx.doi.org/10.1016/j.cmpb.2016.01.013.