Examples simplify understanding. Below is an example of how to use the theophylline dataset to generate NCA parameters.

Load the data

## It is always a good idea to look at the data
knitr::kable(head(datasets::Theoph))

Subject	Wt	Dose	Time	conc
1	79.6	4.02	0.00	0.74
1	79.6	4.02	0.25	2.84
1	79.6	4.02	0.57	6.57
1	79.6	4.02	1.12	10.50
1	79.6	4.02	2.02	9.66
1	79.6	4.02	3.82	8.58

The columns that we will be interested in for our analysis are conc, Time, and Subject in the concentration data set and Dose, Time, and Subject for the dosing data set. Note that Dose in the theophylline dataset is in mg/kg rather than mg; the parameters calculated below do not use the dose amount, but dose-dependent parameters (for example, clearance, volume of distribution, or dose-normalized parameters) would first require converting the dose to mg by multiplying by the subject’s body weight (Wt).

## By default it is groupedData; convert it to a data frame for use
conc_obj <- PKNCAconc(as.data.frame(datasets::Theoph), conc~Time|Subject)

## Dosing data needs to only have one row per dose, so subset for
## that first.
d_dose <- unique(datasets::Theoph[datasets::Theoph$Time == 0,
                                  c("Dose", "Time", "Subject")])
knitr::kable(d_dose,
             caption="Example dosing data extracted from theophylline data set")

Example dosing data extracted from theophylline data set
	Dose	Subject
1	4.02	1
12	4.40	2
23	4.53	3
34	4.40	4
45	5.86	5
56	4.00	6
67	4.95	7
78	4.53	8
89	3.10	9
100	5.50	10
111	4.92	11
122	5.30	12

dose_obj <- PKNCAdose(d_dose, Dose~Time|Subject)

Merge the Concentration and Dose

After loading the data, they must be combined to prepare for parameter calculation. Intervals for calculation will automatically be selected based on the single.dose.aucs setting in PKNCA.options

data_obj_automatic <- PKNCAdata(conc_obj, dose_obj)
knitr::kable(PKNCA.options("single.dose.aucs"))

start	end	auclast	auclast_se	auclast_df	aucall	aucall_se	aucall_df	aumclast	aumclast_se	aumclast_df	aumcall	aumcall_se	aumcall_df	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivlast_se	aucivlast_df	aucivall	aucivall_se	aucivall_df	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivlast_se	aumcivlast_df	aumcivall	aumcivall_se	aumcivall_df	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.obs_se	aucinf.obs_df	aucinf.pred	aumcinf.obs	aumcinf.obs_se	aumcinf.obs_df	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.obs_se	aucivinf.obs_df	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.obs_se	aumcivinf.obs_df	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.pred	thalf.eff.obs	thalf.eff.pred	thalf.eff.iv.obs	thalf.eff.iv.pred	kel.obs	kel.pred	kel.iv.obs	kel.iv.pred	kel.int.inf.obs	kel.int.inf.pred	auclast.dn	aucall.dn	aucinf.obs.dn	aucinf.pred.dn	aumclast.dn	aumcall.dn	aumcinf.obs.dn	aumcinf.pred.dn	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	clr.last.dn	clr.obs.dn	clr.pred.dn
0	24	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
0	Inf	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE

knitr::kable(data_obj_automatic$intervals)

end	auclast	auclast_se	auclast_df	aucall	aucall_se	aucall_df	aumclast	aumclast_se	aumclast_df	aumcall	aumcall_se	aumcall_df	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivlast_se	aucivlast_df	aucivall	aucivall_se	aucivall_df	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivlast_se	aumcivlast_df	aumcivall	aumcivall_se	aumcivall_df	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.obs_se	aucinf.obs_df	aucinf.pred	aumcinf.obs	aumcinf.obs_se	aumcinf.obs_df	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.obs_se	aucivinf.obs_df	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.obs_se	aumcivinf.obs_df	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.pred	thalf.eff.obs	thalf.eff.pred	thalf.eff.iv.obs	thalf.eff.iv.pred	kel.obs	kel.pred	kel.iv.obs	kel.iv.pred	kel.int.inf.obs	kel.int.inf.pred	auclast.dn	aucall.dn	aucinf.obs.dn	aucinf.pred.dn	aumclast.dn	aumcall.dn	aumcinf.obs.dn	aumcinf.pred.dn	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	clr.last.dn	clr.obs.dn	clr.pred.dn	Subject	impute
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	1	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	2	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	3	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	4	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	5	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	6	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	7	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	8	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	9	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	10	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	11	start_predose_conc0
Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	12	start_predose_conc0

Intervals for calculation can also be specified manually. Manual specification requires at least columns for start time, end time, and the parameters requested. The manual specification can also include any grouping factors from the concentration data set. Column order of the intervals is not important. When intervals are manually specified, they are expanded to the full interval set when added to a PKNCAdata object (in other words, a column is created for each parameter). Also, PKNCA automatically calculates parameters required for the NCA, so while lambda.z is required for calculating AUC_0-\(\infty\), you do not have to specify it in the parameters requested.

intervals_manual <- data.frame(start=0,
                               end=Inf,
                               cmax=TRUE,
                               tmax=TRUE,
                               aucinf.obs=TRUE,
                               auclast=TRUE)
data_obj_manual <- PKNCAdata(conc_obj, dose_obj,
                             intervals=intervals_manual)
knitr::kable(data_obj_manual$intervals)

start	end	auclast	auclast_se	auclast_df	aucall	aucall_se	aucall_df	aumclast	aumclast_se	aumclast_df	aumcall	aumcall_se	aumcall_df	aucint.last	aucint.all	aumcint.last	aumcint.all	c0	cmax	cmin	tmax	tmin	tlast	tfirst	clast.obs	cl.last	cl.all	cl.int.all	cl.int.last	mrt.last	mrt.all	mrt.int.all	mrt.int.last	mrt.iv.last	vss.last	vss.iv.last	vss.all	vss.int.all	vss.int.last	cav	cav.int.last	cav.int.all	ctrough	cstart	ptr	tlag	deg.fluc	swing	ceoi	aucabove.predose.all	aucabove.trough.all	count_conc	count_conc_measured	totdose	volpk	ae	clr.last	clr.obs	clr.pred	fe	ertlst	ermax	ertmax	erint	erlst	ratio.cmax	ratio.auclast	ratio.aucint.last	ratio.aucint.all	sparse_auclast	sparse_auc_se	sparse_auc_df	sparse_aumclast	sparse_aumc_se	sparse_aumc_df	time_above	aucivlast	aucivlast_se	aucivlast_df	aucivall	aucivall_se	aucivall_df	aucivint.last	aucivint.all	aucivpbextlast	aucivpbextall	aucivpbextint.last	aucivpbextint.all	aumcivlast	aumcivlast_se	aumcivlast_df	aumcivall	aumcivall_se	aumcivall_df	aumcivint.last	aumcivint.all	half.life	r.squared	adj.r.squared	lambda.z.corrxy	lambda.z	lambda.z.time.first	lambda.z.time.last	lambda.z.n.points	clast.pred	span.ratio	tobit_residual	adj_tobit_residual	lambda.z.n.points_blq	thalf.eff.last	thalf.eff.iv.last	kel.last	kel.iv.last	kel.all	kel.int.all	kel.int.last	cl.iv.all	cl.iv.last	cl.ivint.all	cl.ivint.last	cl.sparse.last	f.last	f.int.last	f.int.all	mrt.sparse.last	mrt.iv.all	mrt.ivint.all	mrt.ivint.last	vz.all	vz.int.all	vz.int.last	vz.iv.all	vz.iv.last	vz.ivint.all	vz.ivint.last	vz.last	vss.iv.all	vss.ivint.all	vss.ivint.last	vss.sparse.last	aucinf.obs	aucinf.obs_se	aucinf.obs_df	aucinf.pred	aumcinf.obs	aumcinf.obs_se	aumcinf.obs_df	aumcinf.pred	aucint.inf.obs	aucint.inf.pred	aumcint.inf.obs	aumcint.inf.pred	aucivinf.obs	aucivinf.obs_se	aucivinf.obs_df	aucivinf.pred	aucivpbextinf.obs	aucivpbextinf.pred	aumcivinf.obs	aumcivinf.obs_se	aumcivinf.obs_df	aumcivinf.pred	aucpext.obs	aucpext.pred	kel.iv.all	kel.ivint.all	kel.ivint.last	kel.sparse.last	cl.obs	cl.pred	cl.int.inf.obs	cl.int.inf.pred	cl.iv.obs	cl.iv.pred	f.obs	f.pred	f.int.obs	f.int.pred	mrt.obs	mrt.pred	mrt.int.inf.obs	mrt.int.inf.pred	mrt.iv.obs	mrt.iv.pred	mrt.md.obs	mrt.md.pred	mrt.ivmd.obs	mrt.ivmd.pred	vz.obs	vz.pred	vz.int.inf.obs	vz.int.inf.pred	vz.iv.obs	vz.iv.pred	vz.sparse.last	vss.obs	vss.pred	vss.iv.obs	vss.iv.pred	vss.md.obs	vss.md.pred	vss.ivmd.obs	vss.ivmd.pred	vss.int.inf.obs	vss.int.inf.pred	cav.int.inf.obs	cav.int.inf.pred	ratio.aucinf.obs	ratio.aucinf.pred	thalf.eff.obs	thalf.eff.pred	thalf.eff.iv.obs	thalf.eff.iv.pred	kel.obs	kel.pred	kel.iv.obs	kel.iv.pred	kel.int.inf.obs	kel.int.inf.pred	auclast.dn	aucall.dn	aucinf.obs.dn	aucinf.pred.dn	aumclast.dn	aumcall.dn	aumcinf.obs.dn	aumcinf.pred.dn	cmax.dn	cmin.dn	clast.obs.dn	clast.pred.dn	cav.dn	ctrough.dn	clr.last.dn	clr.obs.dn	clr.pred.dn
0	Inf	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE

Compute the parameters

Parameter calculation will automatically split the data by the grouping factor(s), subset by the interval, calculate all required parameters, record all options used for the calculations, and include data provenance to show that the calculation was performed as described. For all this, just call the pk.nca function with your PKNCAdata object.

results_obj_automatic <- pk.nca(data_obj_automatic)
knitr::kable(head(as.data.frame(results_obj_automatic)))

Subject	end	PPTESTCD	PPORRES	PPANMETH	exclude
1	Inf	auclast	147.2347	Imputation: start_predose_conc0. AUC: lin up/log down	NA
1	Inf	cmax	10.5000	Imputation: start_predose_conc0	NA
1	Inf	tmax	1.1200	Imputation: start_predose_conc0	NA
1	Inf	tlast	24.3700	Imputation: start_predose_conc0	NA
1	Inf	clast.obs	3.2800	Imputation: start_predose_conc0	NA
1	Inf	tlag	0.0000	Imputation: start_predose_conc0	NA

summary(results_obj_automatic)

start	end	N	auclast	cmax	tmax	tlag	count_conc	half.life	aucinf.obs	aucpext.obs	cl.obs
0	Inf	12	98.7 [22.5]	8.65 [17.0]	1.14 [0.630, 3.55]	0.000 [0.000, 0.000]	11.0 [11.0, 11.0]	8.18 [2.12]	115 [28.4]	13.8 [6.34]	0.0398 [29.4]

results_obj_manual <- pk.nca(data_obj_manual)
knitr::kable(head(as.data.frame(results_obj_manual)))

Subject	end	PPTESTCD	PPORRES	PPANMETH	exclude
6	Inf	auclast	71.6970150	AUC: lin up/log down	NA
6	Inf	cmax	6.4400000		NA
6	Inf	tmax	1.1500000		NA
6	Inf	tlast	23.8500000		NA
6	Inf	clast.obs	0.9200000		NA
6	Inf	lambda.z	0.0877957		NA

summary(results_obj_manual)

start	end	N	auclast	cmax	tmax	aucinf.obs
0	Inf	12	98.7 [22.5]	8.65 [17.0]	1.14 [0.630, 3.55]	115 [28.4]

Multiple Dose Example

Assessing multiple dose pharmacokinetics is conceptually the same as single-dose in PKNCA.

To assess multiple dose PK, the theophylline data will be extended from single to multiple doses using superposition (see the superposition vignette for more information).

conc_obj <- PKNCAconc(as.data.frame(datasets::Theoph), conc~Time|Subject)
conc_obj_multi <-
  PKNCAconc(
    superposition(conc_obj,
                  tau=168,
                  dose.times=seq(0, 144, by=24),
                  n.tau=1,
                  check.blq=FALSE),
    conc~time|Subject)
conc_obj_multi

## Formula for concentration:
##  conc ~ time | Subject
## Data are dense PK.
## With 12 subjects defined in the 'Subject' column.
## Nominal time column is not specified.
## 
## First 6 rows of concentration data:
##  Subject     conc time exclude
##        1  0.74000 0.00    <NA>
##        1  2.84000 0.25    <NA>
##        1  4.23875 0.37    <NA>
##        1  6.57000 0.57    <NA>
##        1 10.50000 1.12    <NA>
##        1  9.66000 2.02    <NA>

dose_obj_multi <- PKNCAdose(expand.grid(Subject=unique(as.data.frame(conc_obj_multi)$Subject),
                                      time=seq(0, 144, by=24)),
                          ~time|Subject)
dose_obj_multi

## Formula for dosing:
##  ~time | Subject
## Nominal time column is not specified.
## 
## First 6 rows of dosing data:
##  Subject time exclude         route duration
##        1    0    <NA> extravascular        0
##        2    0    <NA> extravascular        0
##        3    0    <NA> extravascular        0
##        4    0    <NA> extravascular        0
##        5    0    <NA> extravascular        0
##        6    0    <NA> extravascular        0

The superposition-simulated scenario is not especially realistic as it includes dense sampling on every day. With this scenario, the intervals automatically selected have an interval for every subject on every day.

data_obj <- PKNCAdata(conc_obj_multi, dose_obj_multi)
data_obj$intervals[,c("Subject", "start", "end")]

## # A tibble: 84 × 3
##    Subject start   end
##    <ord>   <dbl> <dbl>
##  1 1           0    24
##  2 1          24    48
##  3 1          48    72
##  4 1          72    96
##  5 1          96   120
##  6 1         120   144
##  7 1         144   168
##  8 2           0    24
##  9 2          24    48
## 10 2          48    72
## # ℹ 74 more rows

In a more realistic scenario, dense PK sampling occurs for every subject on the first and last days. To select those intervals manually, specify the intervals of interest in the intervals argument to the PKNCAdata function call. The intervals are automatically expanded not to calculate anything that was not requested.

intervals_manual <- data.frame(start=c(0, 144),
                               end=c(24, 168),
                               cmax=TRUE,
                               auclast=TRUE)
data_obj <- PKNCAdata(conc_obj_multi, dose_obj_multi,
                      intervals=intervals_manual)
data_obj$intervals

##   start end auclast auclast_se auclast_df aucall aucall_se aucall_df aumclast
## 1     0  24    TRUE      FALSE      FALSE  FALSE     FALSE     FALSE    FALSE
## 2   144 168    TRUE      FALSE      FALSE  FALSE     FALSE     FALSE    FALSE
##   aumclast_se aumclast_df aumcall aumcall_se aumcall_df aucint.last aucint.all
## 1       FALSE       FALSE   FALSE      FALSE      FALSE       FALSE      FALSE
## 2       FALSE       FALSE   FALSE      FALSE      FALSE       FALSE      FALSE
##   aumcint.last aumcint.all    c0 cmax  cmin  tmax  tmin tlast tfirst clast.obs
## 1        FALSE       FALSE FALSE TRUE FALSE FALSE FALSE FALSE  FALSE     FALSE
## 2        FALSE       FALSE FALSE TRUE FALSE FALSE FALSE FALSE  FALSE     FALSE
##   cl.last cl.all cl.int.all cl.int.last mrt.last mrt.all mrt.int.all
## 1   FALSE  FALSE      FALSE       FALSE    FALSE   FALSE       FALSE
## 2   FALSE  FALSE      FALSE       FALSE    FALSE   FALSE       FALSE
##   mrt.int.last mrt.iv.last vss.last vss.iv.last vss.all vss.int.all
## 1        FALSE       FALSE    FALSE       FALSE   FALSE       FALSE
## 2        FALSE       FALSE    FALSE       FALSE   FALSE       FALSE
##   vss.int.last   cav cav.int.last cav.int.all ctrough cstart   ptr  tlag
## 1        FALSE FALSE        FALSE       FALSE   FALSE  FALSE FALSE FALSE
## 2        FALSE FALSE        FALSE       FALSE   FALSE  FALSE FALSE FALSE
##   deg.fluc swing  ceoi aucabove.predose.all aucabove.trough.all count_conc
## 1    FALSE FALSE FALSE                FALSE               FALSE      FALSE
## 2    FALSE FALSE FALSE                FALSE               FALSE      FALSE
##   count_conc_measured totdose volpk    ae clr.last clr.obs clr.pred    fe
## 1               FALSE   FALSE FALSE FALSE    FALSE   FALSE    FALSE FALSE
## 2               FALSE   FALSE FALSE FALSE    FALSE   FALSE    FALSE FALSE
##   ertlst ermax ertmax erint erlst ratio.cmax ratio.auclast ratio.aucint.last
## 1  FALSE FALSE  FALSE FALSE FALSE      FALSE         FALSE             FALSE
## 2  FALSE FALSE  FALSE FALSE FALSE      FALSE         FALSE             FALSE
##   ratio.aucint.all sparse_auclast sparse_auc_se sparse_auc_df sparse_aumclast
## 1            FALSE          FALSE         FALSE         FALSE           FALSE
## 2            FALSE          FALSE         FALSE         FALSE           FALSE
##   sparse_aumc_se sparse_aumc_df time_above aucivlast aucivlast_se aucivlast_df
## 1          FALSE          FALSE      FALSE     FALSE        FALSE        FALSE
## 2          FALSE          FALSE      FALSE     FALSE        FALSE        FALSE
##   aucivall aucivall_se aucivall_df aucivint.last aucivint.all aucivpbextlast
## 1    FALSE       FALSE       FALSE         FALSE        FALSE          FALSE
## 2    FALSE       FALSE       FALSE         FALSE        FALSE          FALSE
##   aucivpbextall aucivpbextint.last aucivpbextint.all aumcivlast aumcivlast_se
## 1         FALSE              FALSE             FALSE      FALSE         FALSE
## 2         FALSE              FALSE             FALSE      FALSE         FALSE
##   aumcivlast_df aumcivall aumcivall_se aumcivall_df aumcivint.last
## 1         FALSE     FALSE        FALSE        FALSE          FALSE
## 2         FALSE     FALSE        FALSE        FALSE          FALSE
##   aumcivint.all half.life r.squared adj.r.squared lambda.z.corrxy lambda.z
## 1         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
## 2         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
##   lambda.z.time.first lambda.z.time.last lambda.z.n.points clast.pred
## 1               FALSE              FALSE             FALSE      FALSE
## 2               FALSE              FALSE             FALSE      FALSE
##   span.ratio tobit_residual adj_tobit_residual lambda.z.n.points_blq
## 1      FALSE          FALSE              FALSE                 FALSE
## 2      FALSE          FALSE              FALSE                 FALSE
##   thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last kel.all kel.int.all
## 1          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
## 2          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
##   kel.int.last cl.iv.all cl.iv.last cl.ivint.all cl.ivint.last cl.sparse.last
## 1        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
## 2        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
##   f.last f.int.last f.int.all mrt.sparse.last mrt.iv.all mrt.ivint.all
## 1  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
## 2  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
##   mrt.ivint.last vz.all vz.int.all vz.int.last vz.iv.all vz.iv.last
## 1          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
## 2          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
##   vz.ivint.all vz.ivint.last vz.last vss.iv.all vss.ivint.all vss.ivint.last
## 1        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
## 2        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
##   vss.sparse.last aucinf.obs aucinf.obs_se aucinf.obs_df aucinf.pred
## 1           FALSE      FALSE         FALSE         FALSE       FALSE
## 2           FALSE      FALSE         FALSE         FALSE       FALSE
##   aumcinf.obs aumcinf.obs_se aumcinf.obs_df aumcinf.pred aucint.inf.obs
## 1       FALSE          FALSE          FALSE        FALSE          FALSE
## 2       FALSE          FALSE          FALSE        FALSE          FALSE
##   aucint.inf.pred aumcint.inf.obs aumcint.inf.pred aucivinf.obs aucivinf.obs_se
## 1           FALSE           FALSE            FALSE        FALSE           FALSE
## 2           FALSE           FALSE            FALSE        FALSE           FALSE
##   aucivinf.obs_df aucivinf.pred aucivpbextinf.obs aucivpbextinf.pred
## 1           FALSE         FALSE             FALSE              FALSE
## 2           FALSE         FALSE             FALSE              FALSE
##   aumcivinf.obs aumcivinf.obs_se aumcivinf.obs_df aumcivinf.pred aucpext.obs
## 1         FALSE            FALSE            FALSE          FALSE       FALSE
## 2         FALSE            FALSE            FALSE          FALSE       FALSE
##   aucpext.pred kel.iv.all kel.ivint.all kel.ivint.last kel.sparse.last cl.obs
## 1        FALSE      FALSE         FALSE          FALSE           FALSE  FALSE
## 2        FALSE      FALSE         FALSE          FALSE           FALSE  FALSE
##   cl.pred cl.int.inf.obs cl.int.inf.pred cl.iv.obs cl.iv.pred f.obs f.pred
## 1   FALSE          FALSE           FALSE     FALSE      FALSE FALSE  FALSE
## 2   FALSE          FALSE           FALSE     FALSE      FALSE FALSE  FALSE
##   f.int.obs f.int.pred mrt.obs mrt.pred mrt.int.inf.obs mrt.int.inf.pred
## 1     FALSE      FALSE   FALSE    FALSE           FALSE            FALSE
## 2     FALSE      FALSE   FALSE    FALSE           FALSE            FALSE
##   mrt.iv.obs mrt.iv.pred mrt.md.obs mrt.md.pred mrt.ivmd.obs mrt.ivmd.pred
## 1      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
## 2      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
##   vz.obs vz.pred vz.int.inf.obs vz.int.inf.pred vz.iv.obs vz.iv.pred
## 1  FALSE   FALSE          FALSE           FALSE     FALSE      FALSE
## 2  FALSE   FALSE          FALSE           FALSE     FALSE      FALSE
##   vz.sparse.last vss.obs vss.pred vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred
## 1          FALSE   FALSE    FALSE      FALSE       FALSE      FALSE       FALSE
## 2          FALSE   FALSE    FALSE      FALSE       FALSE      FALSE       FALSE
##   vss.ivmd.obs vss.ivmd.pred vss.int.inf.obs vss.int.inf.pred cav.int.inf.obs
## 1        FALSE         FALSE           FALSE            FALSE           FALSE
## 2        FALSE         FALSE           FALSE            FALSE           FALSE
##   cav.int.inf.pred ratio.aucinf.obs ratio.aucinf.pred thalf.eff.obs
## 1            FALSE            FALSE             FALSE         FALSE
## 2            FALSE            FALSE             FALSE         FALSE
##   thalf.eff.pred thalf.eff.iv.obs thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs
## 1          FALSE            FALSE             FALSE   FALSE    FALSE      FALSE
## 2          FALSE            FALSE             FALSE   FALSE    FALSE      FALSE
##   kel.iv.pred kel.int.inf.obs kel.int.inf.pred auclast.dn aucall.dn
## 1       FALSE           FALSE            FALSE      FALSE     FALSE
## 2       FALSE           FALSE            FALSE      FALSE     FALSE
##   aucinf.obs.dn aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn
## 1         FALSE          FALSE       FALSE      FALSE          FALSE
## 2         FALSE          FALSE       FALSE      FALSE          FALSE
##   aumcinf.pred.dn cmax.dn cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn
## 1           FALSE   FALSE   FALSE        FALSE         FALSE  FALSE      FALSE
## 2           FALSE   FALSE   FALSE        FALSE         FALSE  FALSE      FALSE
##   clr.last.dn clr.obs.dn clr.pred.dn
## 1       FALSE      FALSE       FALSE
## 2       FALSE      FALSE       FALSE

After the data is ready, the calculations and summary can progress.

results_obj <- pk.nca(data_obj)
print(results_obj)

## $result
## # A tibble: 48 × 7
##    Subject start   end PPTESTCD PPORRES PPANMETH               exclude
##    <ord>   <dbl> <dbl> <chr>      <dbl> <chr>                  <chr>  
##  1 6           0    24 auclast    71.8  "AUC: lin up/log down" <NA>   
##  2 6           0    24 cmax        6.44 ""                     <NA>   
##  3 6         144   168 auclast    82.2  "AUC: lin up/log down" <NA>   
##  4 6         144   168 cmax        7.37 ""                     <NA>   
##  5 7           0    24 auclast    89.0  "AUC: lin up/log down" <NA>   
##  6 7           0    24 cmax        7.09 ""                     <NA>   
##  7 7         144   168 auclast   101.   "AUC: lin up/log down" <NA>   
##  8 7         144   168 cmax        8.07 ""                     <NA>   
##  9 8           0    24 auclast    86.7  "AUC: lin up/log down" <NA>   
## 10 8           0    24 cmax        7.56 ""                     <NA>   
## # ℹ 38 more rows
## 
## $data
## Formula for concentration:
##  conc ~ time | Subject
## Data are dense PK.
## With 12 subjects defined in the 'Subject' column.
## Nominal time column is not specified.
## 
## First 6 rows of concentration data:
##  Subject     conc time exclude
##        1  0.74000 0.00    <NA>
##        1  2.84000 0.25    <NA>
##        1  4.23875 0.37    <NA>
##        1  6.57000 0.57    <NA>
##        1 10.50000 1.12    <NA>
##        1  9.66000 2.02    <NA>
## Formula for dosing:
##  ~time | Subject
## Nominal time column is not specified.
## 
## First 6 rows of dosing data:
##  Subject time exclude         route duration
##        1    0    <NA> extravascular        0
##        2    0    <NA> extravascular        0
##        3    0    <NA> extravascular        0
##        4    0    <NA> extravascular        0
##        5    0    <NA> extravascular        0
##        6    0    <NA> extravascular        0
## 
## With 2 rows of interval specifications.
## Options changed from default are:
## $adj.r.squared.factor
## [1] 1e-04
## 
## $r.squared.factor
## [1] NA
## 
## $max.missing
## [1] 0.5
## 
## $auc.method
## [1] "lin up/log down"
## 
## $conc.na
## [1] "drop"
## 
## $conc.blq
## $conc.blq$first
## [1] "keep"
## 
## $conc.blq$middle
## [1] "drop"
## 
## $conc.blq$last
## [1] "keep"
## 
## 
## $debug
## NULL
## 
## $first.tmax
## [1] TRUE
## 
## $first.tmin
## [1] TRUE
## 
## $allow.tmax.in.half.life
## [1] FALSE
## 
## $keep_interval_cols
## NULL
## 
## $min.hl.points
## [1] 3
## 
## $max.hl.points
## [1] Inf
## 
## $min.hl.start.time
## [1] 0
## 
## $min.span.ratio
## [1] 2
## 
## $max.aucinf.pext
## [1] 20
## 
## $min.hl.r.squared
## [1] 0.9
## 
## $progress
## [1] TRUE
## 
## $tau.choices
## [1] NA
## 
## $auto.interval.method
## [1] "builder"
## 
## $auto.interval.tolerance
## [1] 0.05
## 
## $single.dose.aucs
##   start end auclast auclast_se auclast_df aucall aucall_se aucall_df aumclast
## 1     0  24    TRUE      FALSE      FALSE  FALSE     FALSE     FALSE    FALSE
## 2     0 Inf   FALSE      FALSE      FALSE  FALSE     FALSE     FALSE    FALSE
##   aumclast_se aumclast_df aumcall aumcall_se aumcall_df aucint.last aucint.all
## 1       FALSE       FALSE   FALSE      FALSE      FALSE       FALSE      FALSE
## 2       FALSE       FALSE   FALSE      FALSE      FALSE       FALSE      FALSE
##   aumcint.last aumcint.all    c0  cmax  cmin  tmax  tmin tlast tfirst clast.obs
## 1        FALSE       FALSE FALSE FALSE FALSE FALSE FALSE FALSE  FALSE     FALSE
## 2        FALSE       FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  FALSE     FALSE
##   cl.last cl.all cl.int.all cl.int.last mrt.last mrt.all mrt.int.all
## 1   FALSE  FALSE      FALSE       FALSE    FALSE   FALSE       FALSE
## 2   FALSE  FALSE      FALSE       FALSE    FALSE   FALSE       FALSE
##   mrt.int.last mrt.iv.last vss.last vss.iv.last vss.all vss.int.all
## 1        FALSE       FALSE    FALSE       FALSE   FALSE       FALSE
## 2        FALSE       FALSE    FALSE       FALSE   FALSE       FALSE
##   vss.int.last   cav cav.int.last cav.int.all ctrough cstart   ptr  tlag
## 1        FALSE FALSE        FALSE       FALSE   FALSE  FALSE FALSE FALSE
## 2        FALSE FALSE        FALSE       FALSE   FALSE  FALSE FALSE FALSE
##   deg.fluc swing  ceoi aucabove.predose.all aucabove.trough.all count_conc
## 1    FALSE FALSE FALSE                FALSE               FALSE      FALSE
## 2    FALSE FALSE FALSE                FALSE               FALSE      FALSE
##   count_conc_measured totdose volpk    ae clr.last clr.obs clr.pred    fe
## 1               FALSE   FALSE FALSE FALSE    FALSE   FALSE    FALSE FALSE
## 2               FALSE   FALSE FALSE FALSE    FALSE   FALSE    FALSE FALSE
##   ertlst ermax ertmax erint erlst ratio.cmax ratio.auclast ratio.aucint.last
## 1  FALSE FALSE  FALSE FALSE FALSE      FALSE         FALSE             FALSE
## 2  FALSE FALSE  FALSE FALSE FALSE      FALSE         FALSE             FALSE
##   ratio.aucint.all sparse_auclast sparse_auc_se sparse_auc_df sparse_aumclast
## 1            FALSE          FALSE         FALSE         FALSE           FALSE
## 2            FALSE          FALSE         FALSE         FALSE           FALSE
##   sparse_aumc_se sparse_aumc_df time_above aucivlast aucivlast_se aucivlast_df
## 1          FALSE          FALSE      FALSE     FALSE        FALSE        FALSE
## 2          FALSE          FALSE      FALSE     FALSE        FALSE        FALSE
##   aucivall aucivall_se aucivall_df aucivint.last aucivint.all aucivpbextlast
## 1    FALSE       FALSE       FALSE         FALSE        FALSE          FALSE
## 2    FALSE       FALSE       FALSE         FALSE        FALSE          FALSE
##   aucivpbextall aucivpbextint.last aucivpbextint.all aumcivlast aumcivlast_se
## 1         FALSE              FALSE             FALSE      FALSE         FALSE
## 2         FALSE              FALSE             FALSE      FALSE         FALSE
##   aumcivlast_df aumcivall aumcivall_se aumcivall_df aumcivint.last
## 1         FALSE     FALSE        FALSE        FALSE          FALSE
## 2         FALSE     FALSE        FALSE        FALSE          FALSE
##   aumcivint.all half.life r.squared adj.r.squared lambda.z.corrxy lambda.z
## 1         FALSE     FALSE     FALSE         FALSE           FALSE    FALSE
## 2         FALSE      TRUE     FALSE         FALSE           FALSE    FALSE
##   lambda.z.time.first lambda.z.time.last lambda.z.n.points clast.pred
## 1               FALSE              FALSE             FALSE      FALSE
## 2               FALSE              FALSE             FALSE      FALSE
##   span.ratio tobit_residual adj_tobit_residual lambda.z.n.points_blq
## 1      FALSE          FALSE              FALSE                 FALSE
## 2      FALSE          FALSE              FALSE                 FALSE
##   thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last kel.all kel.int.all
## 1          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
## 2          FALSE             FALSE    FALSE       FALSE   FALSE       FALSE
##   kel.int.last cl.iv.all cl.iv.last cl.ivint.all cl.ivint.last cl.sparse.last
## 1        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
## 2        FALSE     FALSE      FALSE        FALSE         FALSE          FALSE
##   f.last f.int.last f.int.all mrt.sparse.last mrt.iv.all mrt.ivint.all
## 1  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
## 2  FALSE      FALSE     FALSE           FALSE      FALSE         FALSE
##   mrt.ivint.last vz.all vz.int.all vz.int.last vz.iv.all vz.iv.last
## 1          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
## 2          FALSE  FALSE      FALSE       FALSE     FALSE      FALSE
##   vz.ivint.all vz.ivint.last vz.last vss.iv.all vss.ivint.all vss.ivint.last
## 1        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
## 2        FALSE         FALSE   FALSE      FALSE         FALSE          FALSE
##   vss.sparse.last aucinf.obs aucinf.obs_se aucinf.obs_df aucinf.pred
## 1           FALSE      FALSE         FALSE         FALSE       FALSE
## 2           FALSE       TRUE         FALSE         FALSE       FALSE
##   aumcinf.obs aumcinf.obs_se aumcinf.obs_df aumcinf.pred aucint.inf.obs
## 1       FALSE          FALSE          FALSE        FALSE          FALSE
## 2       FALSE          FALSE          FALSE        FALSE          FALSE
##   aucint.inf.pred aumcint.inf.obs aumcint.inf.pred aucivinf.obs aucivinf.obs_se
## 1           FALSE           FALSE            FALSE        FALSE           FALSE
## 2           FALSE           FALSE            FALSE        FALSE           FALSE
##   aucivinf.obs_df aucivinf.pred aucivpbextinf.obs aucivpbextinf.pred
## 1           FALSE         FALSE             FALSE              FALSE
## 2           FALSE         FALSE             FALSE              FALSE
##   aumcivinf.obs aumcivinf.obs_se aumcivinf.obs_df aumcivinf.pred aucpext.obs
## 1         FALSE            FALSE            FALSE          FALSE       FALSE
## 2         FALSE            FALSE            FALSE          FALSE       FALSE
##   aucpext.pred kel.iv.all kel.ivint.all kel.ivint.last kel.sparse.last cl.obs
## 1        FALSE      FALSE         FALSE          FALSE           FALSE  FALSE
## 2        FALSE      FALSE         FALSE          FALSE           FALSE  FALSE
##   cl.pred cl.int.inf.obs cl.int.inf.pred cl.iv.obs cl.iv.pred f.obs f.pred
## 1   FALSE          FALSE           FALSE     FALSE      FALSE FALSE  FALSE
## 2   FALSE          FALSE           FALSE     FALSE      FALSE FALSE  FALSE
##   f.int.obs f.int.pred mrt.obs mrt.pred mrt.int.inf.obs mrt.int.inf.pred
## 1     FALSE      FALSE   FALSE    FALSE           FALSE            FALSE
## 2     FALSE      FALSE   FALSE    FALSE           FALSE            FALSE
##   mrt.iv.obs mrt.iv.pred mrt.md.obs mrt.md.pred mrt.ivmd.obs mrt.ivmd.pred
## 1      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
## 2      FALSE       FALSE      FALSE       FALSE        FALSE         FALSE
##   vz.obs vz.pred vz.int.inf.obs vz.int.inf.pred vz.iv.obs vz.iv.pred
## 1  FALSE   FALSE          FALSE           FALSE     FALSE      FALSE
## 2  FALSE   FALSE          FALSE           FALSE     FALSE      FALSE
##   vz.sparse.last vss.obs vss.pred vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred
## 1          FALSE   FALSE    FALSE      FALSE       FALSE      FALSE       FALSE
## 2          FALSE   FALSE    FALSE      FALSE       FALSE      FALSE       FALSE
##   vss.ivmd.obs vss.ivmd.pred vss.int.inf.obs vss.int.inf.pred cav.int.inf.obs
## 1        FALSE         FALSE           FALSE            FALSE           FALSE
## 2        FALSE         FALSE           FALSE            FALSE           FALSE
##   cav.int.inf.pred ratio.aucinf.obs ratio.aucinf.pred thalf.eff.obs
## 1            FALSE            FALSE             FALSE         FALSE
## 2            FALSE            FALSE             FALSE         FALSE
##   thalf.eff.pred thalf.eff.iv.obs thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs
## 1          FALSE            FALSE             FALSE   FALSE    FALSE      FALSE
## 2          FALSE            FALSE             FALSE   FALSE    FALSE      FALSE
##   kel.iv.pred kel.int.inf.obs kel.int.inf.pred auclast.dn aucall.dn
## 1       FALSE           FALSE            FALSE      FALSE     FALSE
## 2       FALSE           FALSE            FALSE      FALSE     FALSE
##   aucinf.obs.dn aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn
## 1         FALSE          FALSE       FALSE      FALSE          FALSE
## 2         FALSE          FALSE       FALSE      FALSE          FALSE
##   aumcinf.pred.dn cmax.dn cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn
## 1           FALSE   FALSE   FALSE        FALSE         FALSE  FALSE      FALSE
## 2           FALSE   FALSE   FALSE        FALSE         FALSE  FALSE      FALSE
##   clr.last.dn clr.obs.dn clr.pred.dn
## 1       FALSE      FALSE       FALSE
## 2       FALSE      FALSE       FALSE
## 
## $allow_partial_missing_units
## [1] FALSE
## 
## $hl_method
## [1] "log-linear"
## 
## $sparse_lambda_z_se
## [1] "delta"
## 
## $tobit_n_points_penalty
## [1] 0
## 
## $tobit_optim_control
## list()
## 
## 
## $columns
## $columns$exclude
## [1] "exclude"
## 
## 
## attr(,"class")
## [1] "PKNCAresults" "list"        
## attr(,"provenance")
## Provenance hash 1010dd656fa2060bf5d457ba131dbe6d generated on 2026-10-09 16:32:15.663052 with R version 4.6.1 (2026-06-24).

summary(results_obj)

##  start end  N     auclast        cmax
##      0  24 12 98.8 [23.0] 8.65 [17.0]
##    144 168 12  115 [28.4] 10.0 [21.0]
## 
## Caption: auclast, cmax: geometric mean and geometric coefficient of variation; N: number of subjects

Computing NCA Parameters for Theophylline

Load the data

Merge the Concentration and Dose

Compute the parameters

Multiple Dose Example