Population Pharmacokinetics Modeling
The population pharmacokinetic (PPK) profile of nemonoxacin was
evaluated using plasma concentration data obtained from the patients
with severe renal impairment and healthy controls. A nonlinear
mixed-effects model software (NONMEM 7.4, ICON Development Solutions,
USA), Modeling and Simulation Studio (Mas Studio 1.2.6 stable, BioVoice
& BioGuider Ltd., Shanghai, China) and Perl-speak-NONMEM (PsN, version
5.0.0, Uppsala University, Sweden) were used for PPK analysis and model
validation [18]. R (version 3.6.1) and RStudio (1.2.5001) software
were used for statistical tests and plotting. The
first-order conditional estimation
with interaction approach was adopted for model development. The
modeling strategy included establishment of the base model and full
model development, assessment of final model adequacy, and model
predictive performance and validation.
The structural base model was initially fitted using a compartment
disposition model based on the PK data. The final base model was
selected by the statistical significance between models using
goodness-of-fit plots, the
objective function value (OFV),
twice the negative log-likelihood (-2LL), and
Akaike’s information criterion.
The inter-individual variabilities for PK parameters were assumed to
follow the multiplicative exponential random effects of the formθi = θ × eηi , whereθi is the value of the parameter as predicted for
the individual and θ is the population typical value of the
parameter. The variability of inter-individual random effect η is
a normal distribution with N (0, ω2). The residual
error was tested using the constant
coefficient of variation model and
expressed as Cobs = Cpred× (1 + ε ), where Cobs is the observed
value of an individual, Cpred is the predicted
value, and ε is the intra-individual deviation with N (0,
σ2).
The fixed effects were evaluated for statistical significance in a
stepwise manner using a stepwise
covariate model building procedure. A decrease of 3.84 in the OFV was
considered a significant improvement for the forward inclusion step
based on Chi-square test (α < 0.05). Meanwhile, the full model
was subjected to a backward elimination step with a significance level
of α = 0.01. The potential covariates of PPK parameters were screened.
Age, sex, body weight, BMI, total
body water (TBW), eGFR, creatinine clearance (CrCl),
and albumin were treated as
candidate variables. TBW was obtained using the classic Watson formula
(for males, TBW = 2.447 - 0.09156 × age + 0.1074 × height + 0.3362 ×
weight; for females, TBW = -2.097 + 0.1069 × height + 0.2466 × weight),
where age is in years, height in centimeters, weight in kilograms, and
water in liters [19,20].
The final PPK model was validated by diagnostic plots and visual
predictive check (VPC) techniques comprising 1000 simulations. The
median, upper and lower bounds of the 95% prediction interval for PK
profiles were compared against the observed plasma concentrations. The
nominal 95% confidence intervals (CIs) around the point estimates were
generated from 1000 bootstrap samples.