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Page 26 Pandey et al. J Transl Genet Genom 2021;5:22-36 I http://dx.doi.org/10.20517/jtgg.2020.45

administered orally is well absorbed and shows good bioavailability of 79% or more [19,47] . Studies showed
that the rapid absorption, distribution, and elimination might be facilitated by solute carrier (SLC)
transporters belonging to OATP families and urea transporters [38,39] . The distribution of HU is rapid, and
the volume of distribution approximates total body water volume [19,48] . The drug concentration in the blood
[49]
achieves rapid equilibrium with that in the tissues and fluids . Hydroxyurea is eliminated through hepatic
[48]
and renal pathways . In the in vitro experiments performed, hydroxyurea was metabolized to urea in
mouse liver and kidney [40,50] . Another study showed the involvement of the mouse liver monooxygenase
system in the metabolism of HU into urea . The other possible metabolites from hydroxyurea were
[41]
[51]
identified as hydroxylamine, nitric oxide, nitrite, and nitrate . From the pharmacokinetic studies, it is
observed that the drug is eliminated both linearly and nonlinearly. One study demonstrated that, when
administered intraperitoneally, the majority of HU was recovered as an unchanged drug and urea from
[50]
the urine of mice . The drug pharmacokinetics is modeled to gain insight into the ADME processes and
make predictions on the amount of drug and metabolites present in the plasma, tissues, and organs and the
changes in them with time. The predictive PK model aid in estimating drug exposure and the effect of drug
exposure on efficacy and toxicity.

Pharmacokinetic modeling
Pharmacokinetic models are formulated using a compartment modeling approach. The whole body is
assumed to be a system that is divided into a series of compartments where each compartment consists
[52]
of organs and tissues with similar drug distribution profiles . The following factors are considered
while constructing a compartment model of the drug pharmacokinetics: (1) elimination (central and/or
peripheral); (2) absorption and elimination rates (linear or nonlinear); and (3) administration (orally or
[52]
intravenously) . The compartment model’s performance in predicting the concentration-time relationship
is evaluated using criteria such as Akaike information criteria (AIC), Bayesian information criteria (BIC),
and likelihood test ratio (LRT) that balances between the goodness of fit and model complexity [19,33,34] .

Population PK studies help to model individual patients and incorporate inter-patient, intra-patient, and
inter-study variability in drug pharmacokinetics. Population PK modeling uses nonlinear mixed effect
(NLME) models. The NLME modeling is a two-stage hierarchical model with individual and population
models, and NLME considers fixed effects and random effects. A structural PK model is constructed
using a compartment modeling approach. The inter-individual variability (IIV) is incorporated by taking
parameters, θ, as a function of an average parameter (fixed effect), θ, from the population, and an error
term (random effect), η, which describes the individual deviation from average parameter value. The
2
random variable, η, is assumed to be normally distributed with mean 0 and variance ω . The parameters
are further expressed as a function of covariates, which are individual-specific clinical, laboratory, or
demographic variables. The covariate selection for a particular parameter is made if it lowers the model’s
[53]
objective function value compared to when the covariate is not selected . Intra-individual variability or
residual variability (RV) is introduced into the model by a residual error, ε, expressed as the difference
between the observed variable, y and the output from the model. The residual error, ε, is assumed to be
normally distributed with mean 0 and variance σ . This random variability can arise due to variability
2
[53]
in assay, error in sample collection, and model misspecification . The following equation provides a
generalized formulation of NLME modeling:

(1)

(2)

th
th
where y is the observed drug concentration of i individual at j time and f denotes the structural model
ij
th
output with t as time and θ and D as PK parameters and dose for i individual. The i subscript denotes
ij
i
i