Pharmacokinetic (PK) data programming in `{admiral}` and the Pharmaverse

R/Pharma 2023

Jeff Dickinson, Navitas Data Sciences

Agenda

What is PK data
Types of PK analysis data
Brief overview of {admiral} and the Pharmaverse
Programming Workflow
Some Additional Pharmaverse tools
Pharmaverse Website Example

Pharmacokinetics Overview

Pharmacokinetics is the interaction of a drug with the body
Samples collected at regular time intervals after dosing
Metrics calculated from concentrations over time

Two Main Types of Modelling

Non-Compartmental Analysis (NCA)
Population PK (PopPK)

Non-Compartmental Analysis (NCA) – `ADNCA/ADPC`

Individual Based
Intensive Sampling
Derive Parameters e.g. AUC, TMAX, CMAX

Population PK (PopPK) – `ADPPK`

Population Based
Less Intensive Sampling
Can Detect Variations based on Covariates

CDISC Standards for Non-Compartmental Analysis

https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0

CDISC Standards for Population PK Analysis

Published October 6, 2023!

https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0

CDISC ADaM PK Standards

ADNCA (NCA) – released November 2021
ADPPK (PopPK) – released October 2023
ADPP (PK Parameters) – coming in 2024

Characteristics of PK Analysis Data

Includes both exposure (EX) and concentration data (PC)
Includes nominal and actual relative time variables
Duplicated Records for Analysis (ADNCA)
Numeric Covariates for Modelling (ADPPK)
May need to expand dosing records

Time Variables (`ADNCA`)

Variable	Variable Label
`NFRLT`	Nom. Rel. Time from Analyte First Dose
`AFRLT`	Act. Rel. Time from Analyte First Dose
`NRRLT`	Nominal Rel. Time from Ref. Dose
`ARRLT`	Actual Rel. Time from Ref. Dose
`MRRLT`	Modified Rel. Time from Ref. Dose

Note: a relative time variable may refer to previous dose or next dose

Nominal vs. Actual Relative Time

Time Variables (`ADPPK`)

Variable	Variable Label
`NFRLT`	Nominal Rel Time from First Dose
`AFRLT`	Actual Rel Time from First Dose
`NPRLT`	Nominal Rel Time from Previous Dose
`APRLT`	Actual Rel Time from Previous Dose

Time Variables Example

`USUBJID`	`EVID`	`NFRLT`	`AFRLT`	`NPRLT`	`APRLT`
001	0	0	-0.05	0	-0.05
001	1	0	0	0	0
001	0	0.5	0.465	0.5	0.465
001	0	3	2.89	3	2.89
001	1	24	24.05	0	0
001	0	25	25.15	1	1.15

`ADPPK` Expected Variables

EVID Event ID
DV Dependent Variable
MDV Missing Dependent Variable
BLQ Below Limit of Quantitation

`ADPPK` Covariates

<COV>BL for baseline covariate, (e.g., WTBL, BMIBL)
<COV>N for numerical version of categorical covariate (e.g., SEXN, RACEN)
<COV>I for any covariates with imputed values (e.g., WTI, BMII)
<COV>GRy for grouping covariates (e.g. AGEGR1)

`{admiral}`

ADaM in R Asset Library
{admiral} is Open Source and Collaborative
{admiral} is a Modular Set of Functions
PK Templates and Vignette Available
Stable 1.0 Release Expected in December

https://github.com/pharmaverse/admiral

`{admiral}` PK Templates and Vignette

https://pharmaverse.github.io/admiral/cran-release/articles/pk_adnca.html

`{admiral}` Date/Time Functions

derive_vars_dtm()
derive_vars_dtm_to_dt()
derive_vars_dtm_to_tm()
derive_vars_dy()
derive_vars_duration()

`{admiral}` Merge and Join Functions

derive_vars_merged()
derive_vars_joined()
derive_vars_transposed()
create_single_dose_dataset()

`{admiral}` Special Purpose Functions

compute_bmi()
compute_bsa()
compute_egfr()

Welcome to the Pharmaverse

https://pharmaverse.org/

Other Pharmaverse Packages in `ADPPK` Workflow

{pharmaversesdtm} CDISC pilot SDTM data
{metacore} Store metadata
{metatools} Work with metadata and perform checks
{xportr} Perform checks and export transport files (XPT)

End to End Example Site

https://pharmaverse.github.io/e2e_pk/

Will focus on ADPPK example

Coding Highlights

Load Specs with {metacore}
Derive PC Dates
Expand Dosing Records
Find First Dose
Find Previous Dose
Find Previous Nominal Dose
Derive Covariates Using {metacore}
{metacore} Checks
{xportr} Steps

Load Specs with `{metacore}`

# ---- Load Specs for Metacore ----
metacore <- spec_to_metacore("pk_spec.xlsx") %>%
  select_dataset("ADPPK")

pc_dates <- pc %>%
  # Join ADSL with PC (need TRTSDT for ADY derivation)
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = adsl_vars,
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  # Derive analysis date/time
  # Impute missing time to 00:00:00
  derive_vars_dtm(
    new_vars_prefix = "A",
    dtc = PCDTC,
    time_imputation = "00:00:00"
  ) %>%
  # Derive dates and times from date/times
  derive_vars_dtm_to_dt(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ADTM)) %>%
  # Derive event ID and nominal relative time from first dose (NFRLT)
  mutate(
    EVID = 0,
    DRUG = PCTEST,
    NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID
  )

# A tibble: 14 × 6
   USUBJID     VISIT    PCTPT            PCDTC         ADTM                NFRLT
   <chr>       <chr>    <chr>            <chr>         <dttm>              <dbl>
 1 01-701-1028 BASELINE Pre-dose         2013-07-18T2… 2013-07-18 23:30:00  0   
 2 01-701-1028 BASELINE 5 Min Post-dose  2013-07-19T0… 2013-07-19 00:05:00  0.08
 3 01-701-1028 BASELINE 30 Min Post-dose 2013-07-19T0… 2013-07-19 00:30:00  0.5 
 4 01-701-1028 BASELINE 1h Post-dose     2013-07-19T0… 2013-07-19 01:00:00  1   
 5 01-701-1028 BASELINE 1.5h Post-dose   2013-07-19T0… 2013-07-19 01:30:00  1.5 
 6 01-701-1028 BASELINE 2h Post-dose     2013-07-19T0… 2013-07-19 02:00:00  2   
 7 01-701-1028 BASELINE 4h Post-dose     2013-07-19T0… 2013-07-19 04:00:00  4   
 8 01-701-1028 BASELINE 6h Post-dose     2013-07-19T0… 2013-07-19 06:00:00  6   
 9 01-701-1028 BASELINE 8h Post-dose     2013-07-19T0… 2013-07-19 08:00:00  8   
10 01-701-1028 BASELINE 12h Post-dose    2013-07-19T1… 2013-07-19 12:00:00 12   
11 01-701-1028 BASELINE 16h Post-dose    2013-07-19T1… 2013-07-19 16:00:00 16   
12 01-701-1028 BASELINE 24h Post-dose    2013-07-20T0… 2013-07-20 00:00:00 24   
13 01-701-1028 BASELINE 36h Post-dose    2013-07-20T1… 2013-07-20 12:00:00 36   
14 01-701-1028 BASELINE 48h Post-dose    2013-07-21T0… 2013-07-21 00:00:00 48

Expand Dosing Records

Code
Input
Output

ex_exp <- ex_dates %>%
  create_single_dose_dataset(
    dose_freq = EXDOSFRQ,
    start_date = ASTDT,
    start_datetime = ASTDTM,
    end_date = AENDT,
    end_datetime = AENDTM,
    nominal_time = NFRLT,
    lookup_table = dose_freq_lookup,
    lookup_column = CDISC_VALUE,
    keep_source_vars = exprs(
      STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM,
      NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM,
      VISIT, VISITNUM, VISITDY,
      TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars
    )
  ) %>%
  # Derive AVISIT based on nominal relative time
  # Derive AVISITN to nominal time in whole days using integer division
  # Define AVISIT based on nominal day
  mutate(
    AVISITN = NFRLT %/% 24 + 1,
    AVISIT = paste("Day", AVISITN),
    ADTM = ASTDTM,
    DRUG = EXTRT
  ) %>%
  # Derive dates and times from datetimes
  derive_vars_dtm_to_dt(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ASTDTM)) %>%
  derive_vars_dtm_to_tm(exprs(AENDTM))

# A tibble: 3 × 7
  USUBJID     EXTRT EXDOSFRQ VISIT NFRLT ASTDTM              AENDTM             
  <chr>       <chr> <chr>    <chr> <dbl> <dttm>              <dttm>             
1 01-701-1028 XANO… QD       BASE…     0 2013-07-19 00:00:00 2013-08-01 00:00:00
2 01-701-1028 XANO… QD       WEEK…   312 2013-08-02 00:00:00 2014-01-06 00:00:00
3 01-701-1028 XANO… QD       WEEK…  4008 2014-01-07 00:00:00 2014-01-14 00:00:00

# A tibble: 20 × 6
   USUBJID     EXTRT      EXDOSFRQ AVISIT NFRLT ASTDTM             
   <chr>       <chr>      <chr>    <chr>  <dbl> <dttm>             
 1 01-701-1028 XANOMELINE ONCE     Day 1      0 2013-07-19 00:00:00
 2 01-701-1028 XANOMELINE ONCE     Day 2     24 2013-07-20 00:00:00
 3 01-701-1028 XANOMELINE ONCE     Day 3     48 2013-07-21 00:00:00
 4 01-701-1028 XANOMELINE ONCE     Day 4     72 2013-07-22 00:00:00
 5 01-701-1028 XANOMELINE ONCE     Day 5     96 2013-07-23 00:00:00
 6 01-701-1028 XANOMELINE ONCE     Day 6    120 2013-07-24 00:00:00
 7 01-701-1028 XANOMELINE ONCE     Day 7    144 2013-07-25 00:00:00
 8 01-701-1028 XANOMELINE ONCE     Day 8    168 2013-07-26 00:00:00
 9 01-701-1028 XANOMELINE ONCE     Day 9    192 2013-07-27 00:00:00
10 01-701-1028 XANOMELINE ONCE     Day 10   216 2013-07-28 00:00:00
11 01-701-1028 XANOMELINE ONCE     Day 11   240 2013-07-29 00:00:00
12 01-701-1028 XANOMELINE ONCE     Day 12   264 2013-07-30 00:00:00
13 01-701-1028 XANOMELINE ONCE     Day 13   288 2013-07-31 00:00:00
14 01-701-1028 XANOMELINE ONCE     Day 14   312 2013-08-01 00:00:00
15 01-701-1028 XANOMELINE ONCE     Day 14   312 2013-08-02 00:00:00
16 01-701-1028 XANOMELINE ONCE     Day 15   336 2013-08-03 00:00:00
17 01-701-1028 XANOMELINE ONCE     Day 16   360 2013-08-04 00:00:00
18 01-701-1028 XANOMELINE ONCE     Day 17   384 2013-08-05 00:00:00
19 01-701-1028 XANOMELINE ONCE     Day 18   408 2013-08-06 00:00:00
20 01-701-1028 XANOMELINE ONCE     Day 19   432 2013-08-07 00:00:00

Find First Dose

Code
Output

adppk_first_dose <- pc_dates %>%
  derive_vars_merged(
    dataset_add = ex_exp,
    filter_add = (!is.na(ADTM)),
    new_vars = exprs(FANLDTM = ADTM, EXDOSE_first = EXDOSE),
    order = exprs(ADTM, EXSEQ),
    mode = "first",
    by_vars = exprs(STUDYID, USUBJID, DRUG)
  ) %>%
  filter(!is.na(FANLDTM)) %>%
  # Derive AVISIT based on nominal relative time
  # Derive AVISITN to nominal time in whole days using integer division
  # Define AVISIT based on nominal day
  mutate(
    AVISITN = NFRLT %/% 24 + 1,
    AVISIT = paste("Day", AVISITN),
  )

# A tibble: 14 × 7
   USUBJID   DRUG  FANLDTM             AVISIT PCTPT ADTM                PCSTRESC
   <chr>     <chr> <dttm>              <chr>  <chr> <dttm>              <chr>   
 1 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  Pre-… 2013-07-18 23:30:00 <BLQ    
 2 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  5 Mi… 2013-07-19 00:05:00 0.10156…
 3 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  30 M… 2013-07-19 00:30:00 0.54690…
 4 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  1h P… 2013-07-19 01:00:00 0.92546…
 5 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  1.5h… 2013-07-19 01:30:00 1.18750…
 6 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  2h P… 2013-07-19 02:00:00 1.36888…
 7 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  4h P… 2013-07-19 04:00:00 1.68314…
 8 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  6h P… 2013-07-19 06:00:00 1.75529…
 9 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  8h P… 2013-07-19 08:00:00 1.77185…
10 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  12h … 2013-07-19 12:00:00 0.49503…
11 01-701-1… XANO… 2013-07-19 00:00:00 Day 1  16h … 2013-07-19 16:00:00 0.13792…
12 01-701-1… XANO… 2013-07-19 00:00:00 Day 2  24h … 2013-07-20 00:00:00 0.01070…
13 01-701-1… XANO… 2013-07-19 00:00:00 Day 2  36h … 2013-07-20 12:00:00 <BLQ    
14 01-701-1… XANO… 2013-07-19 00:00:00 Day 3  48h … 2013-07-21 00:00:00 <BLQ

Find Previous Dose

Code
Output

adppk_prev <- adppk_first_dose %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(ADTM),
    new_vars = exprs(
      ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,
      AENDTM_prev = AENDTM
    ),
    join_vars = exprs(ADTM),
    filter_add = NULL,
    filter_join = ADTM > ADTM.join,
    mode = "last",
    check_type = "none"
  )

# A tibble: 14 × 6
   USUBJID     ADTM                AVISIT PCTPT  ADTM_prev           AVISIT_prev
   <chr>       <dttm>              <chr>  <chr>  <dttm>              <chr>      
 1 01-701-1028 2013-07-18 23:30:00 Day 1  Pre-d… NA                  <NA>       
 2 01-701-1028 2013-07-19 00:05:00 Day 1  5 Min… 2013-07-19 00:00:00 Day 1      
 3 01-701-1028 2013-07-19 00:30:00 Day 1  30 Mi… 2013-07-19 00:00:00 Day 1      
 4 01-701-1028 2013-07-19 01:00:00 Day 1  1h Po… 2013-07-19 00:00:00 Day 1      
 5 01-701-1028 2013-07-19 01:30:00 Day 1  1.5h … 2013-07-19 00:00:00 Day 1      
 6 01-701-1028 2013-07-19 02:00:00 Day 1  2h Po… 2013-07-19 00:00:00 Day 1      
 7 01-701-1028 2013-07-19 04:00:00 Day 1  4h Po… 2013-07-19 00:00:00 Day 1      
 8 01-701-1028 2013-07-19 06:00:00 Day 1  6h Po… 2013-07-19 00:00:00 Day 1      
 9 01-701-1028 2013-07-19 08:00:00 Day 1  8h Po… 2013-07-19 00:00:00 Day 1      
10 01-701-1028 2013-07-19 12:00:00 Day 1  12h P… 2013-07-19 00:00:00 Day 1      
11 01-701-1028 2013-07-19 16:00:00 Day 1  16h P… 2013-07-19 00:00:00 Day 1      
12 01-701-1028 2013-07-20 00:00:00 Day 2  24h P… 2013-07-19 00:00:00 Day 1      
13 01-701-1028 2013-07-20 12:00:00 Day 2  36h P… 2013-07-20 00:00:00 Day 2      
14 01-701-1028 2013-07-21 00:00:00 Day 3  48h P… 2013-07-20 00:00:00 Day 2

Find Previous Nominal Dose

Code
Output

adppk_nom_prev <- adppk_prev %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(NFRLT),
    new_vars = exprs(NFRLT_prev = NFRLT),
    join_vars = exprs(NFRLT),
    filter_add = NULL,
    filter_join = NFRLT > NFRLT.join,
    mode = "last",
    check_type = "none"
  )

# A tibble: 15 × 4
   USUBJID     AVISIT NFRLT NFRLT_prev
   <chr>       <chr>  <dbl>      <dbl>
 1 01-701-1028 Day 1   0            NA
 2 01-701-1028 Day 1   0.08          0
 3 01-701-1028 Day 1   0.5           0
 4 01-701-1028 Day 1   1             0
 5 01-701-1028 Day 1   1.5           0
 6 01-701-1028 Day 1   2             0
 7 01-701-1028 Day 1   4             0
 8 01-701-1028 Day 1   6             0
 9 01-701-1028 Day 1   8             0
10 01-701-1028 Day 1  12             0
11 01-701-1028 Day 1  16             0
12 01-701-1028 Day 2  24             0
13 01-701-1028 Day 2  36            24
14 01-701-1028 Day 3  48            24
15 01-701-1033 Day 1   0            NA

Derive Covariates Using Metacore

Code
Output

#---- Derive Covariates ----
covar <- adsl %>%
  create_var_from_codelist(metacore, input_var = STUDYID, out_var = STUDYIDN) %>%
  create_var_from_codelist(metacore, input_var = SEX, out_var = SEXN) %>%
  create_var_from_codelist(metacore, input_var = RACE, out_var = RACEN) %>%
  create_var_from_codelist(metacore, input_var = ETHNIC, out_var = AETHNIC) %>%
  create_var_from_codelist(metacore, input_var = AETHNIC, out_var = AETHNICN) %>%
  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORT) %>%
  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORTC) %>%
  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYN) %>%
  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYL) %>%
  mutate(
    STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed("-"))),
    SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed("-"))),
    USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed("-"))),
    SUBJIDN = as.numeric(SUBJID),
    ROUTE = unique(ex$EXROUTE),
    FORM = unique(ex$EXDOSFRM),
    REGION1 = COUNTRY,
    REGION1N = COUNTRYN,
    SUBJTYPC = "Volunteer",
  ) %>%
  create_var_from_codelist(metacore, input_var = FORM, out_var = FORMN) %>%
  create_var_from_codelist(metacore, input_var = ROUTE, out_var = ROUTEN) %>%
  create_var_from_codelist(metacore, input_var = SUBJTYPC, out_var = SUBJTYP)

# A tibble: 15 × 8
   STUDYID      STUDYIDN USUBJID     USUBJIDN SEX    SEXN RACE  RACEN
   <chr>           <dbl> <chr>          <dbl> <chr> <dbl> <chr> <dbl>
 1 CDISCPILOT01        1 01-701-1015     1015 F         2 WHITE     1
 2 CDISCPILOT01        1 01-701-1023     1023 M         1 WHITE     1
 3 CDISCPILOT01        1 01-701-1028     1028 M         1 WHITE     1
 4 CDISCPILOT01        1 01-701-1033     1033 M         1 WHITE     1
 5 CDISCPILOT01        1 01-701-1034     1034 F         2 WHITE     1
 6 CDISCPILOT01        1 01-701-1047     1047 F         2 WHITE     1
 7 CDISCPILOT01        1 01-701-1057     1057 F         2 WHITE     1
 8 CDISCPILOT01        1 01-701-1097     1097 M         1 WHITE     1
 9 CDISCPILOT01        1 01-701-1111     1111 F         2 WHITE     1
10 CDISCPILOT01        1 01-701-1115     1115 M         1 WHITE     1
11 CDISCPILOT01        1 01-701-1118     1118 M         1 WHITE     1
12 CDISCPILOT01        1 01-701-1130     1130 M         1 WHITE     1
13 CDISCPILOT01        1 01-701-1133     1133 F         2 WHITE     1
14 CDISCPILOT01        1 01-701-1145     1145 F         2 WHITE     1
15 CDISCPILOT01        1 01-701-1146     1146 F         2 WHITE     1

Calculate Additional Covariates from Vital Signs and Labs

Code
Output

covar_vslb <- covar %>%
  derive_vars_merged(
    dataset_add = vs,
    filter_add = VSTESTCD == "HEIGHT",
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(HTBL = VSSTRESN)
  ) %>%
  derive_vars_merged(
    dataset_add = vs,
    filter_add = VSTESTCD == "WEIGHT" & VSBLFL == "Y",
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(WTBL = VSSTRESN)
  ) %>%
  derive_vars_transposed(
    dataset_merge = labsbl,
    by_vars = exprs(STUDYID, USUBJID),
    key_var = LBTESTCDB,
    value_var = LBSTRESN
  ) %>%
  mutate(
    BMIBL = compute_bmi(height = HTBL, weight = WTBL),
    BSABL = compute_bsa(
      height = HTBL,
      weight = HTBL,
      method = "Mosteller"
    ),
    CRCLBL = compute_egfr(
      creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
      method = "CRCL"
    ),
    EGFRBL = compute_egfr(
      creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
      method = "CKD-EPI"
    )
  ) %>%
  rename(TBILBL = BILIBL)

# A tibble: 15 × 9
   USUBJID     USUBJIDN  WTBL  HTBL BMIBL BSABL CREATBL CRCLBL EGFRBL
   <chr>          <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>  <dbl>  <dbl>
 1 01-701-1015     1015  54.4  147.  25.1  2.46    79.6   54.8   71.9
 2 01-701-1023     1023  80.3  163.  30.4  2.71   124.    60.6   56.1
 3 01-701-1028     1028  99.3  178.  31.4  2.96   124.    68.1   53.7
 4 01-701-1033     1033  88.4  175.  28.8  2.92   133.    54.2   48.6
 5 01-701-1034     1034  62.6  155.  26.1  2.58    88.4   46.4   58.0
 6 01-701-1047     1047  67.1  149.  30.4  2.48    88.4   43.4   55.2
 7 01-701-1057     1057  NA     NA   NA   NA       NA     NA     NA  
 8 01-701-1097     1097  78.0  169.  27.3  2.82   124.    55.8   54.8
 9 01-701-1111     1111  59.9  158.  23.9  2.64    79.6   46.2   64.2
10 01-701-1115     1115  78.9  182.  23.9  3.03   115.    47.3   54.2
11 01-701-1118     1118  71.2  180.  21.9  3.01    88.4   87.2   90.6
12 01-701-1130     1130  79.4  170.  27.6  2.83   124.    44.2   49.6
13 01-701-1133     1133  74.4  163.  28.2  2.71    97.2   46.9   50.5
14 01-701-1145     1145  NA     NA   NA   NA       NA     NA     NA  
15 01-701-1146     1146  54.0  147.  24.9  2.46    79.6   45.9   66.7

`{metacore}` Checks

adppk <- adppk_prefinal %>%
  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
  check_variables(metacore) %>% # Check all variables specified are present and no more
  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
  order_cols(metacore) %>% # Orders the columns according to the spec
  sort_by_key(metacore) # Sorts the rows by the sort keys

`{xportr}` Steps

Code
Output

adppk_xpt <- adppk %>%
  xportr_type(metacore) %>% # Coerce variable type to match spec
  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
  xportr_write(file.path(dir, "adppk.xpt")) # Write xpt v5 transport file

       USUBJID EVID NFRLT NPRLT       AFRLT       APRLT         DV
1  01-701-1028    0  0.00  0.00 -0.50000000 -0.50000000 0.00000000
2  01-701-1028    1  0.00  0.00  0.00000000  0.00000000         NA
3  01-701-1028    0  0.08  0.08  0.08333333  0.08333333 0.10156622
4  01-701-1028    0  0.50  0.50  0.50000000  0.50000000 0.54690177
5  01-701-1028    0  1.00  1.00  1.00000000  1.00000000 0.92546539
6  01-701-1028    0  1.50  1.50  1.50000000  1.50000000 1.18750588
7  01-701-1028    0  2.00  2.00  2.00000000  2.00000000 1.36888945
8  01-701-1028    0  4.00  4.00  4.00000000  4.00000000 1.68314758
9  01-701-1028    0  6.00  6.00  6.00000000  6.00000000 1.75529232
10 01-701-1028    0  8.00  8.00  8.00000000  8.00000000 1.77185470
11 01-701-1028    0 12.00 12.00 12.00000000 12.00000000 0.49503585
12 01-701-1028    0 16.00 16.00 16.00000000 16.00000000 0.13792316
13 01-701-1028    0 24.00 24.00 24.00000000 24.00000000 0.01070627
14 01-701-1028    1 24.00  0.00 24.00000000  0.00000000         NA
15 01-701-1028    0 36.00 12.00 36.00000000 12.00000000         NA
16 01-701-1028    0 48.00 24.00 48.00000000 24.00000000         NA
17 01-701-1028    1 48.00  0.00 48.00000000  0.00000000         NA
18 01-701-1033    0  0.00  0.00 -0.50000000 -0.50000000 0.00000000
19 01-701-1033    1  0.00  0.00  0.00000000  0.00000000         NA
20 01-701-1033    0  0.08  0.08  0.08333333  0.08333333 0.10052550

Summary

CDISC ADaM Implementation guides for ADNCA and ADPPK available
{admiral} template programs exist for these
Pharmaverse tools can streamline the workflow
PK Examples Website https://pharmaverse.github.io/e2e_pk/
Coming Soon! Full Examples Website https://pharmaverse.github.io/examples/

Thank You!

{admiral} Core Team
Slack Pharmaverse PK Working Group
Luke Reinbolt
Ben Straub

Questions

Contact

Jeff Dickinson
email: jeff.dickinson@navitaslifesciences.com
GitHub: jeffreyad
Slack RinPharmaConf or Pharmaverse Channels

Pharmacokinetic (PK) data programming in {admiral} and the Pharmaverse

Agenda

Pharmacokinetics Overview

Two Main Types of Modelling

Non-Compartmental Analysis (NCA) – ADNCA/ADPC

Population PK (PopPK) – ADPPK

CDISC Standards for Non-Compartmental Analysis

CDISC Standards for Population PK Analysis

CDISC ADaM PK Standards

Characteristics of PK Analysis Data

Time Variables (ADNCA)

Nominal vs. Actual Relative Time

Time Variables (ADPPK)

Time Variables Example

ADPPK Expected Variables

ADPPK Covariates

{admiral}

{admiral} PK Templates and Vignette

{admiral} Date/Time Functions

{admiral} Merge and Join Functions

{admiral} Special Purpose Functions

Welcome to the Pharmaverse

Other Pharmaverse Packages in ADPPK Workflow

End to End Example Site

Coding Highlights

Load Specs with {metacore}

Derive PC Dates

Expand Dosing Records

Find First Dose

Find Previous Dose

Find Previous Nominal Dose

Derive Covariates Using Metacore

Calculate Additional Covariates from Vital Signs and Labs

{metacore} Checks

{xportr} Steps

Summary

Thank You!

Questions

Contact

Pharmacokinetic (PK) data programming in `{admiral}` and the Pharmaverse

Non-Compartmental Analysis (NCA) – `ADNCA/ADPC`

Population PK (PopPK) – `ADPPK`

Time Variables (`ADNCA`)

Time Variables (`ADPPK`)

`ADPPK` Expected Variables

`ADPPK` Covariates

`{admiral}`

`{admiral}` PK Templates and Vignette

`{admiral}` Date/Time Functions

`{admiral}` Merge and Join Functions

`{admiral}` Special Purpose Functions

Other Pharmaverse Packages in `ADPPK` Workflow

Load Specs with `{metacore}`

`{metacore}` Checks

`{xportr}` Steps