NCA.jl

NCA.jl performs Non-Compartmental Analysis (NCA) of pharmacokinetic and pharmacodynamic data. It supports both plasma and urine analyses, single-dose and steady-state profiles, and provides a comprehensive set of NCA endpoints.

Workflow

A typical NCA workflow consists of these steps:

Read data with read_nca or read_nca_pumas
Transform (optional) with transform_nca for BLQ handling, sparse sampling, or LLQ truncation
Run analysis with run_nca to compute NCA endpoints
Summarize with summarize to compute summary statistics across subjects

Additional analyses:

Dose linearity assessment with DoseLinearityPowerModel or DoseLinearityRatioTest
Superposition for predicting steady-state profiles from single-dose data

Quick Start

using NCA, PharmaDatasets, DataFrames

df = dataset("ncakeppra")
rename!(df, :conc => :concentration)
df.route .= "ev"

nca_data = read_nca(df)
results = run_nca(nca_data)
summary = summarize(results)

41×10 DataFrame

Row	parameters	numsamples	nonmissing	minimum	maximum	mean	std	geomean	geostd	geomeanCV
	String	Int64	Int64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	auc_extrap_percent_obs	300	300	0.00706545	15.7227	2.38515	2.37085	1.30532	3.65251	208.695
2	auc_extrap_percent_pred	300	300	0.00567207	15.6322	2.31265	2.32856	1.26777	3.62928	206.586
3	aucinf_dn_obs	300	300	0.42427	1.30889	0.710368	0.130884	0.699089	1.1943	17.8967
4	aucinf_dn_pred	300	300	0.424935	1.30732	0.709792	0.130603	0.698562	1.19386	17.8596
5	aucinf_obs	300	300	115.721	880.963	358.642	171.121	317.582	1.66053	54.1558
6	aucinf_pred	300	300	115.772	876.162	358.316	170.887	317.343	1.65995	54.1134
7	auclast	300	300	113.1	851.251	349.096	164.126	309.913	1.65257	53.5749
8	auclast_dn	300	300	0.413885	1.15837	0.691845	0.118552	0.682208	1.18133	16.7809
9	aumc_extrap_percent_obs	300	300	0.0753906	47.6452	10.4298	8.43656	6.80804	2.93837	148.175
10	aumc_extrap_percent_pred	300	300	0.0605309	47.4744	10.1357	8.28026	6.63016	2.92152	146.844
11	aumcinf_obs	300	300	412.171	8404.79	2218.65	1430.18	1823.1	1.89078	70.7412
12	aumcinf_pred	300	300	412.516	8861.8	2208.7	1424.38	1816.78	1.88712	70.48
13	aumclast	300	300	410.388	5951.2	1914.45	1094.04	1625.08	1.79655	63.9931
14	cl_f_obs	300	300	0.764003	2.35699	1.45269	0.253333	1.43043	1.1943	17.8967
15	cl_f_pred	300	300	0.764921	2.3533	1.45368	0.252892	1.43151	1.19386	17.8596
16	clast	300	300	0.01	6.97	1.294	1.33291	0.724117	3.41382	187.503
17	clast_pred	300	300	0.00802778	8.04286	1.24415	1.28958	0.702757	3.37959	184.548
18	cmax	300	300	18.91	196.52	69.1436	34.1329	60.6221	1.70052	57.0646
19	cmax_dn	300	300	0.07384	0.262027	0.137483	0.0349463	0.133447	1.27322	24.5112
20	cmin	300	300	0.01	6.97	1.28713	1.32343	0.722181	3.40547	186.781
21	cmin_dn	300	300	1.33333e-5	0.01112	0.00249504	0.00210269	0.00158973	3.03703	156.049
22	half_life	300	300	1.70807	9.84021	4.15702	1.30476	3.96817	1.35603	31.1759
23	lambdaz	300	300	0.0704403	0.405807	0.182871	0.056119	0.174677	1.35603	31.1759
24	lambdazadjr2	300	300	0.913544	0.999998	0.986538	0.014383	0.986431	1.01494	1.48313
25	lambdazintercept	300	300	1.59923	5.68188	4.03615	0.647865	3.98042	1.18695	17.2649
26	lambdaznpoints	300	300	3.0	15.0	7.67667	3.69924	6.74466	1.69517	56.6761
27	lambdazr2	300	300	0.920749	0.999999	0.988801	0.0126919	0.988718	1.01313	1.30429
28	lambdaztimefirst	300	300	0.25	16.0	6.245	5.64979	3.61224	3.25265	173.766
29	lambdaztimelast	300	300	24.0	24.0	24.0	0.0	24.0	1.0	1.33449e-13
30	mrtinf_obs	300	300	2.48017	12.6047	5.97119	1.67544	5.74055	1.32798	28.9463
31	mrtinf_pred	300	300	2.47984	12.5772	5.95325	1.66648	5.72499	1.32635	28.8157
32	mrtlast	300	300	2.47848	7.83028	5.3642	1.10646	5.24367	1.24389	22.0866
33	n_samples	300	300	16.0	16.0	16.0	0.0	16.0	1.0	0.0
34	span	300	300	0.852602	13.6119	4.69781	2.12638	4.18378	1.66741	54.6575
35	tfinal	300	300	24.0	24.0	24.0	0.0	24.0	1.0	1.33449e-13
36	tlag	300	300	0.0	0.0	0.0	0.0	0.0	NaN	NaN
37	tlast	300	300	24.0	24.0	24.0	0.0	24.0	1.0	1.33449e-13
38	tmax	300	300	0.0	2.0	0.364167	0.390212	0.0	NaN	NaN
39	tmin	300	300	20.0	24.0	23.9333	0.512932	23.9272	1.02366	2.33828
40	vz_f_obs	300	300	3.9339	18.4613	8.4898	2.39128	8.18903	1.30424	27.0373
41	vz_f_pred	300	300	3.93347	18.4417	8.49643	2.39281	8.1952	1.30444	27.054