Pharmacogenetics and Model-Informed Optimisation of Hydroxyurea Therapy in Sickle Cell Disease Patients of Nigerian Descent
Conditions: Sickle Cell Disease
Sponsor: Ochuko Orherhe

1 
 
 
 
PhD Proposal 
 
 
Pharmacogenetics and Model-Informed Optimisation of Hydroxyurea Therapy in 
Sickle Cell Disease Patients of Nigerian Descent 
 
 
 
 
Ochuko Maureen ORHERHE 
 
Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria 
Supervisor: Dr. B. A. Adeagbo 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

2 
 
 
Abstract 
The wide interindividual variability in clinical response to hydroxyurea therapy in the 
management of sickle cell disease has limited its use. These variabilities have been linked to 
differences in pharmacodynamics, pharmacokinetics, and pharmacogenetics. This study, 
therefore, aims to enhance understanding of these factors as they relate to hydroxyurea therapy, 
with the overall goal of developing a precision medicine algorithm. 
The study will be a prospective cohort pharmacokinetic study of 100 Nigerian patients with 
sickle cell disease, including current hydroxyurea users and naive patients. Pharmacodynamic 
markers will be collected to evaluate response. PopPK and PK-PD models will be developed 
in Monolix, exposure-response relationships will be analysed in R, and pregnancy, lactation, 
and paediatrics PBPK models will be developed in Simcyp or PK-SIM to inform dose 
optimisation. 
Overall, this study will provide scientific knowledge that can enhance clinical decision-making 
in sickle cell management within the Nigerian population, and the models could serve as a 
template to optimize hydroxyurea use in this population. 
 
Keywords: Modelling, Genetic Polymorphism, Pharmacokinetics, Clinical Outcomes, 
Hydroxyurea, Sickle Cell Disease, Precision Medicine, Population Pharmacokinetics 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Brief Background 
Sickle Cell Disease (SCD) remains a major public health burden in Nigeria, which carries the 
largest global share of affected individuals. Worldwide, approximately eight (8) million people 
are living with the disease, with nearly 80% residing in Sub-Saharan Africa.(Adigwe et al., 
2023, p.3503; Thomson et al., 2023, p.e589-e590) Earlier modelling by Piel et al. (2013) 
projected 404,190 new births with SCD globally by 2050; however, recent analyses indicate 
that this estimate has already been exceeded by 27% as of 2021.(Piel, Frederic B, Hay et al., 
2013, p.e1001484; Thomson et al., 2023, p.594) Nigeria contributes disproportionately, with 
about 150,000 affected births annually and more than 2 – 3% of the population living with the 
disease (Okocha et al., 2022, p.2). This high prevalence is directly linked to persistently 
elevated morbidity and mortality rates despite therapeutic advances.(Adigwe et al., 2023, 
p.3503; Darshana et al., 2021, p.1) Addressing this burden requires more effective clinical 
interventions, among which hydroxyurea has emerged globally as an approved disease-
modifying therapy. 
Pharmacometric modelling provides a powerful tool to unify inter-individual differences in 
drug response and translate them into evidence-based dosing strategies.(Mould & Upton, 2012, 
p.2-3) Population pharmacokinetic-pharmacodynamic (PK/PD) models have been successfully 
used in other regions to optimise hydroxyurea therapy, demonstrating that model-informed 
approaches can reduce the time required to reach the maximum tolerated dose.(Dong et al., 
2016, p.750; Wiczling et al., 2014, p.1021-1022) This is particularly relevant in low- and 
middle-income countries, where prolonged monitoring and high costs pose barriers to 
achieving optimal dosing.(Okocha et al., 2022, p.6)  Furthermore, pharmacometric simulations 
offer an ethical means to explore hydroxyurea use in vulnerable groups such as pregnant and 
lactating women, who are typically excluded from clinical trials, but form a significant 
proportion of the affected population.(Sewaralthahab et al., 2024, p.10-11) By integrating 
pharmacogenetic, pharmacokinetic, and clinical outcome data, a Nigerian-specific 
pharmacometric model could provide a locally validated decision-support tool, reduce clinical 
hesitancy, and improve outcomes.(Mould & Upton, 2012, p.1)  
 
Research Gap 
Hydroxyurea is an approved, disease-modifying therapy for sickle cell disease. However, its 
adoption in Nigeria remains low because prescribers lack confidence in how to tailor treatment 
to their patients, (Akindele et al., 2022, p.6; Okocha et al., 2022, p.5-6; Sanyaolu et al., 2020, 
p.115) and are uncertain about the optimal dose for Nigerian patients. This uncertainty stems 
out of the wide inter-individual variability observed in individual responses to the drug reported 
in the literature, (Aisha L. Walker, Ryan M. Franke, Alex Sparreboom, 2011, p.2,3,6; Estepp et 
al., 2018, p. 1482-1483; Ware et al., 2011, p.4985, 4989)and the absence of national guidelines 
for its prescription. 
 
The reported wide variability is believed to reflect differences in the drug's pharmacodynamic, 
pharmacokinetic, and pharmacogenetic properties. (McGann & Ware, 2015, p.6; Pressiat et al., 

4 
 
2021, p.2283). Patients have reported varying degrees of dose tolerance, ranging from 15 
mg/kg/day to 35 mg/kg/day before myelosuppression occurs, indicating the minimum toxic 
dose. However, most of these associations have been explored in non-African or in vitro 
studies.(Gambari et al., 2024, p.4263; Huang et al., 2004, p.3500) The absence of locally 
generated data for Africans, including Nigerian patients, hampers dose optimisation and 
perpetuates clinical hesitancy. This makes exploring pharmacogenetic and pharmacokinetic 
variability in Nigeria a pressing research priority. 
Of particular concern is the varying reports of proteins such as drug metabolising enzymes and 
transporters suspected to be influencing the inter-individual variabilities. Studies in non-
African cohorts have identified genetic polymorphisms in drug-metabolising enzymes and 
transporters – such as CYP2D6, catalase, and SLC14A1- that influence hydroxyurea 
metabolism and the eventual clinical response and toxicity. (Juul et al., 2010, p.21413-21414; 
Yahouédéhou et al., 2020, p.5-6) Other enzymes that have been implicated include CYP2E1, 
UGT1A1, and CYP2C9, based on the available enzymes that the authors could explore. (Nkya 
et al., 2025a, p.5-6; Yahouédéhou et al., 2018, p.736-737). Given the genetic heterogeneity of 
the African population,(Masimirembwa & Matimba, 2012, p.175; Pereira et al., 2021, 
p.292,303) and the rich diversity in the Nigerian population,(Bolaji et al., 2019, p.921) locally 
generated pharmacogenetic data are essential. Linking these genetic factors to hydroxyurea 
pharmacokinetics will be critical for understanding systemic exposure and clinical outcomes. 
Additionally, no pharmacometric model currently exists to guide individualised dosing in the 
Nigerian population.(Dong et al., 2016, p.750; Mould & Upton, 2012, p.1; Wiczling et al., 
2014, p.1021) This lack of population-specific data and tools contributes to clinical hesitancy, 
suboptimal prescribing, and missed opportunities for better SCD health outcomes.  
 
Aims and Objectives 
This study aims to build a pharmacometric model by integrating differences in 
pharmacokinetics, pharmacodynamics, and pharmacogenetics of hydroxyurea, which could aid 
the optimisation of hydroxyurea for sickle cell patients in Nigeria. 
 
Objectives 
The objectives of the study are  
i. 
To determine the prevalence of genetic polymorphisms in metabolic enzymes and 
transporters relevant to the disposition of hydroxyurea in the Nigerian sickle cell 
disease population, 
ii. 
To develop and validate an analytical method for the quantification of hydroxyurea 
using high-performance liquid chromatography. 
iii. 
To evaluate the influence of genetic and other covariates on hydroxyurea disposition 
in the Nigerian sickle cell disease population using population pharmacokinetic 
modelling, 
iv. 
To investigate the relationship between hydroxyurea exposure and clinical 
outcomes (foetal haemoglobin, mean corpuscular volume, reduction in vaso-

5 
 
occlusive crises (VOC), and improved blood count) using pharmacokinetic-
pharmacodynamic modelling,  
v. 
To develop physiologically-based pharmacokinetic (PBPK) models that could 
predict hydroxyurea concentrations in special populations of sickle cell disease 
patients in Nigeria i.e. pregnant women, lactating mothers, breastfed infants, and 
paediatrics. 
vi. 
To develop a dosing guideline for hydroxyurea therapy in Nigerian sickle cell 
patients.  
 
Research Questions 
1. What is the prevalence of genetic polymorphisms in drug-metabolizing enzymes and 
transporters that affect hydroxyurea disposition among Nigerian patients with sickle 
cell disease? 
2. What analytical method can be best suited for the quantification of hydroxyurea in 
human plasma in the light of available resources in Nigeria? 
3. How do genetic variants and other patient covariates influence the pharmacokinetics of 
hydroxyurea in Nigerian sickle cell disease patients? 
4. What is the relationship between hydroxyurea exposure and key clinical outcomes 
(foetal haemoglobin, mean corpuscular volume, frequency of vaso-occlusive crises, and 
blood count) in Nigerian sickle cell disease patients? 
5. Can physiologically based pharmacokinetic (PBPK) models accurately predict 
hydroxyurea concentrations in special sickle cell disease populations in Nigeria, 
including pregnant women, lactating mothers, breastfed infants, geriatric, and 
paediatric patients? 
6. What doses of hydroxyurea are best suited for different phenotypes of the sickle cell 
patients of Nigerian descent? 
 
 
 
 
 
 
 
 
 
 

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Conceptual Framework 
 
This concept map begins with the obvious barrier to the use of hydroxyurea, namely, clinical 
hesitancy, due to the underlying reason of inter-individual variability that has hampered 
prescribers' and patients’ confidence in the drug. This research aims to develop a population-
specific pharmacometric model that integrates three interrelated concepts: pharmacogenetics, 
pharmacokinetics (PK), and pharmacodynamics (PD), as elucidated below. 
 
Pharmacokinetics 
Pharmacokinetics studies on hydroxyurea are non-existent in Nigeria and scarce in Africa. 
Differences in hydroxyurea pharmacokinetics have been reported across populations, 
emphasizing the need for locally relevant data. The NOHARM ancillary trial in Uganda 
demonstrated that African children had lower clearance and higher systemic exposure to 
hydroxyurea compared to their American counterparts, suggesting that both genetic and non-
genetic factors may influence hydroxyurea disposition.(Marahatta et al., 2017, p.1-2) These 
findings suggest that dosing strategies developed in non-African populations may not be 
directly applicable to African populations. However, in Nigeria, no population-specific 
pharmacokinetic studies have been conducted, leaving clinicians without evidence-based 
guidance to tailor doses for safety and effectiveness. 
 
Pharmacogenetics 
Variants in drug-metabolising enzymes and transporters can influence both the efficacy and 
toxicity of hydroxyurea. For example, polymorphisms in variants of CYP2D6 (rs3892097), 
CAT (rs7943316, rs1001179), and SLC14A1 (rs2298720) have been correlated with improved 

7 
 
hematologic parameters in Brazilian patients with sickle cell disease who are on hydroxyurea. 
(Yahouédéhou et al., 2018, 2020) The role of genetic differences was further supported by a 
Tanzanian study that identified CYP2C9 and CYP2E1 as implicated in HU metabolism.(Nkya 
et al., 2025b). Earlier in-vitro studies also demonstrated catalase-mediated hydroxyurea 
metabolism and its potential toxicity mechanisms.(Huang et al., 2004; Juul et al., 2010) It is 
worth noting that these studies examined a limited number of genes and may not provide a 
comprehensive picture of the extent of the genetic polymorphism effect, and none have been 
conducted in Nigeria. This is particularly important because Nigeria and Africa, as a whole, are 
known for their rich genetic diversity, with unique allelic variations that cannot be extrapolated 
from non-African studies.(Adehin et al., 2017, p.3; Bolaji et al., 2019; Masimirembwa & 
Matimba, 2012; Pereira et al., 2021) 
 
Pharmacometrics 
Pharmacometrics provides a methodological framework to integrate diverse sources of 
variability – pharmacogenetics, pharmacokinetics, and clinical outcomes – into unified models 
that can inform individualised therapy. Population pharmacokinetic-pharmacodynamic 
(PK/PD) modelling uses non-linear mixed-effects (NLME) approaches to quantify drug 
disposition and response across a population, accounting for covariates such as age, sex, 
weight, genetic variants, and co-morbidities(Bonate, 2011, p.303; Darwich et al., 2021, p.228-
229; US FDA, 2022, p.2-7) This approach has been successfully applied in Europe and North 
America to optimise hydroxyurea therapy, demonstrating reductions in the time to reach the 
maximum tolerated dose through model-informed dosing.(Dong et al., 2016; Wiczling et al., 
2014) Such strategies are especially valuable in resource-limited settings, where prolonged 
monitoring is costly and often impractical.(Okocha et al., 2022) Ultimately, this model will 
provide a decision-support tool that enables safer, personalized treatment with hydroxyurea. 
 
 
DATA MANAGEMENT AND ANALYSIS PLAN 
STUDY DESCRIPTION 
The study will be a three-phase prospective cohort study of sickle cell patients of Nigerian 
descent receiving hydroxyurea therapy and hydroxyurea-naive patients who are willing to be 
placed on hydroxyurea therapy for the duration of the study (6 months). The three phases are 
as follows: 
 
Pre-Field Phase – Objective 1&2 
The study will start with the analysis of available pharmacogenetic data from sickle cell 
patients to explore genetic polymorphisms in metabolic enzymes, including CYP2D6, 
CYP2C9, CYP2C19, and CYP3A5, in Nigerian populations. (Data is available from the 
iProtecta -Implementation of Pharmacogenetic Testing for Effective Care and Treatment in 
Africa: Sickle Cell Disease - study portfolio).  A preliminary physiologically based 

8 
 
pharmacokinetic model will be developed using data available in the literature, which will 
guide the determination of a more precise sample size, sampling time, and probable minimum 
dose for the pharmacokinetic study phase.  
 
The analytical method for hydroxyurea will be developed using adapted methods from the 
tables below. 
 
Table 1: HPLC Methods - 1 
Parameters/Variables Legrand et al., 
2017 (Legrand et 
al., 2017) 
Bachir et al., 2007 
(Bachir et al., 2007) 
Bruna et al., 2014 
(Bruna et al., 2014) 
Instrumentation 
HPLC-UV 
HPLC 
UPLC 
Main Analyte (Source) 
Hydroxyurea 
(Sigma-Aldrich) 
Hydroxyurea (Sigma) 
Hydroxyurea (>98%) 
Internal Standard 
(Source) 
Methylurea (Sigma-
Aldrich) 
Methylurea 
Methylurea (97%) 
Matrix 
 
 
Plasma 
Derivatization 
Compound 
Xanhydrol (Sigma-
Aldrich) 
 
 
Chemicals/Reageants 
(Source) 
• 
Acetonitrile  
• 
Isopropanol 
– HPLC 
grade) 
• 
Methanol – 
HPLC grade 
• 
Ammonium 
Acetate 
• 
37% 
Chlorhydric 
acid 
(All from VWR, 
Fontenay-sous-
Bois, France) 
• 
HCl 
• 
Drug Free 
Human 
Plasma 
 
• 
Urea (Sigma) 
• 
Diacetylmonoxime 
• 
Thiosemicarbazid 
• 
FeCl3 
• 
Sufuric acid 
• 
Orthophosphoric 
acid 
• 
Perchloric acid 
• 
Acetonitrile 
(VWR, Darmstadt, 
Germany) 
• 
Plasma 
• 
Urine 
• 
Diacetyl  
monoxime 
• 
Tiosemicarbazide 
• 
Ferric chloride  
(From Sigma) 
• 
Sodium chloride 
(Labsynth) 
• 
Sulfuric acid 
• 
Orthophosphoric 
acid 
• 
Trichloroacetic 
acid 
• 
Acetonitrile 
Chromatographic 
Instrument 
Ultimate* 3000 
(Thermo-Fisher) 
 
A pumping system with 
diode array detector 
(PDA) (Accela Thermo 
Scientific) 
Column 
Kinetex C18 
analytical column 
(4.6 x 150 mm, 
5um) + Security 
guard column C18 
Nucleosil C18, 250-4.6 
column (5um particle 
size; Macherrey-Nagel, 
Hoerdt, France) filled to a 
Nucleosil C18, 250_4.6 
column (5 um particle 
sie) from Phenomenex 
and pre-column C18 
(4x30 mm) 

9 
 
(4.6 mm) both from 
Phenomenex, 
France 
pre-column Nuceosil 8-
4.6. 
Flow rate 
1.0 ml/min 
1.2 ml/min 
1 ml/min 
Mobile phase 
20mM ammonium 
acetate and 
acetonitrile 
Water/Acetonitrile (13/87) 
Acetonitrile/water 
(16.7/83.3) 
Mode 
 
 
 
Quantification mode 
 
 
 
m/z for HU 
 
 
 
m/z for Internal 
standard 
 
 
 
Detection wavelength 
240 nm 
449 nm 
440 nm 
Retention time for 
Hydroxyurea 
Xan-HU = 6.0 min 
 
6.763 min 
Retention time for I.S 
Xan-MeU = 10.6 
min 
 
11.472 min 
Run time 
 
 
 
PK Sampling times 
 
 
 
 
 
Table 2: HPLC Methods 2 
Parameters/Variables Jong et al., 2003 
(Jong et al., 2003) 
Pujari et al., 1997 
Yan et al., 2005 
Instrumentation 
HPLC – 
Electrochemical 
detection 
HPLC – 
Electrochemical 
detection (LC-4C 
amperometric 
detector, a CC-4 
cell system 
consisting of a 
glassy-carbon dual 
electrode cell and 
an Ag/AgCl 
reference cell 
HPLC 
HPLC-
Electrochemical 
detection (ESA 
coulochem, guard 
cell 0.65 V 
Main Analyte (Source) 
Hydroxyurea 
(Sigma) 
Hydroxyurea 
Hydroxyurea 
Internal Standard 
(Source) 
2-thiouracil 
(Sigma) 
 
 
Matrix 
Plasma 
 
 
Derivatization 
Compound 
 
 
 
Chemicals/Reagents 
(Source) 
• 
Sodium 
acetate 
• 
Trihydrate  
• 
Acetic acid 
• 
Perchloric 
acid 
• 
Methanol 
 

10 
 
(Merck, 
Darmstatdt, 
Germany) 
• 
Milli-Q 
water 
• 
Acetonitrile 
(HPLC 
grade) 
• 
Acetonitrile 
(HPLC 
grade) 
Chromatographic 
Instrument 
 
 
 
Column 
 
Econosil C18, 
250x4.6 mm, 
10um particle size 
(from Altech) 
Beckman Ultrasphere 
ODS, 5u, 250 x 4.6 
mm; guard column: 
Brownlee RP-18, 7u, 
15 x 3 mm 
Flow rate 
 
 
1.0 ml/min 
Mobile phase 
 
0.2M sodium 
perchlorate-
methanol (95:5, 
v/v) adjusted to pH 
5.0 using 
perchloric acid. 
5% acetonitrile in 50 
mP sodium acetate, 
5mM 
tetrabutylammonium-
hydrogen sulfate 
(TBAHS), pH 6.77 
Run time 
 
 
5 minutes 
 
 
Table 3 - HPLC Methods 3 
Parameters/Variables Marahatta & 
Ware, 2017 
Instrumentation 
HPLC-UV 
Main Analyte (Source) 
Hydroxyurea 
Internal Standard 
(Source) 
Methylurea 
Matrix 
Plasma 
Chemicals/Reagents 
(Source) 
• 
Acetonitrile 
Column 
Zorbax Eclipse 
XDB C18 (250 mm 
x 4.6 mm, 5uM 
particle size) 
Flow rate 
1 ml/min 
Mobile phase 
870 ml MILLIQ 
water and 130 ml 
Acetonitrile (13% 
of Acetonitrile) 
Detection wavelength 
449 nm 
 
 

11 
 
Table 4: LC-MS/MS Methods 
Parameters/Variables 
Hai et al, 2017 
(Hai et al., 2017) 
Marahatta et al., 
2016 ((Anu 
Marahatta, 
Vandana Megaraj, 
Patrick T. 
McGann, Russell 
E. Ware, 2016) 
Instrumentation 
LC-MS/MS 
LC-MS/MS 
Main Analyte 
(Source) 
Hydroxyurea (Qilu 
Tianhe 
Pharmaceutica, 
China) 
Hydroxyurea 
(Sigma) 
Internal Standard 
(Source) 
Hydroxyurea-
13C1,15N2 – 
Isotope labelled 
(Toronto Research 
Chemicals) 
Hydroxyurea-
13C1,15N2 – 
Isotope labelled 
(Toronto Research 
Chemicals) 
Matrix 
Plasma 
Whole blood - DBS 
Chemicals/Reagents 
(Source) 
• 
Acetonitrile 
-HPLC 
grade 
(Thermo 
Fisher) 
• 
Ammonium 
Formate – 
HPLC 
grade 
(Sigma-
Aldrich) 
• 
Drug Free 
Human 
Plasma 
(Healthy 
donors) 
• 
Ammonium 
formate 
(Sigma) 
• 
Water 
(HPLC-MS 
grade) 
• 
Formic 
acid 
(HPLC-MS 
grade) 
• 
Acetonitrile 
(HPLC-MS 
grade) 
From Fischer 
Scientific 
• 
DMPK-C 
cards 
• 
Whatman 
903 
specimen 
collection 
paper 
• 
FTA classic 
cards 
From GE health 
• 
VAMS 
devices 
(Neoteryx, 
Torrance, 
USA) 

12 
 
• 
Nitrogen 
gas 
Chromatographic 
Instrument 
Shimadzu ultra fast 
liquid 
chromatography 
20A system 
Shimadzu LC-2-AD 
coupled to an AB 
Sciex 5500 Q-Trap 
MS 
Column 
Phenomenex Luna 
Silica Column (100 
x 2 mm, 3um) 
Atlantis HILIC 
Silica 3um column 
(2.1 x 100 mm) 
Flow rate 
0.35 ml/min 
0.4 ml/min 
Mobile phase 
Acetonitrile-water 
containing 1.5mM 
ammonium 
formate (90:10, 
V:V) 
10 mmol/l 
ammonium 
formate and 
acetonitrile with 
0.1% formic acid 
Mode 
Electrospray 
ionization (ESI) in 
positive ion mode 
 
Quantification mode 
Multiple reaction 
monitoring (MRM) 
mode 
 
m/z for HU 
77.2 – 44.1 
44 
m/z for Internal 
standard 
80.2 – 46.1 
46 
Run time 
 
5 min 
PK Sampling times 
0, 0.25h, 0.5 h, 1h, 
2h, 4h, 6h, 8h 
 
 
 
Pharmacokinetic/Pharmacodynamic Field Phase 
The pharmacokinetic/pharmacodynamic study phase will involve adult sickle cell patients of 
Nigerian descent including both current hydroxyurea users and naive patients, stratified by 
metabolizing enzyme phenotypes, with CYP2D6 serving as the reference enzyme 
(Yahoudehou et al., 2020, p. 5-6).  
 
Sample Size 
100 patients will be recruited to participate in this phase of the study, as this sample size can 
estimate the 95% confidence interval with a power of 0.8 to 0.9 (Ogungbenro & Aarons, 2008; 
Sukarnjanaset & Wattanavijitkul, 2018), as depicted in the figure below. 

13 
 
 
Power plots and tables for optimal sample size calculations sourced from Ogungbenro & 
Aarons, 2008. 
 
The U.S. Department of Health and Human Services, Food and Drug Administration (FDA) 
Population Pharmacokinetics Guidance for Industry recommends a sample size that can 
estimate a covariate effect with high precision and statistical power (US FDA, 2022).The power 
plots as simulated by Ogungbenro & Aarons, 2008 (and referenced by the FDA guideline) 
shows that a sample size of 30  for each covariate population and rich sampling can give a 
statistical power of 0.8 to 0.95 (Ogungbenro & Aarons, 2008). 
 
Sample size per covariate group = 30 
The major covariate groups is the metabolizing enzyme phenotypic group i.e. Fast, Normal, 
and Slow metabolizer 
Hence, Cohort 1 = Patients that are fast metabolizers = 30  
 
Cohort 2 = Patients that are normal metabolizers = 30 
Cohort 3 = Patients that are slow metabolizers = 30 
An additional 10 study participants will be recruited to make up for voluntary 
withdrawal from the study.  
Therefore, Sample size = 100 
 
Blood samples will be collected from all recruited patients at a minimum of 6 time points from 
the pool of the stated time points; 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, and 24 hours 
(Ogungbenro & Aarons, 2008; Paule et al., 2011; Wiczling et al., 2014). Corresponding 
pharmacodynamic data including HbF level and blood counts which will be used as the 
response biomarkers, will also be collected. The blood will be centrifuged, the plasma collected 
and stored at -80 ºC until analysis. The validated HPLC method in plasma will be used to 
quantify HU and used for the analysis. The patients will be followed up for at least 6 months , 
as follows (Hebert et al., 2020). 

14 
 
 
Pharmacodynamic Data to be collected 
Parameters 
Baseline 
3 months 
6 months 
Foetal Haemoglobin 
√ 
X 
√ 
Total Haemoglobin 
√ 
√ 
√ 
Red Blood Cell Count 
√ 
√ 
√ 
Mean Corposcular 
Volume 
√ 
√ 
√ 
Mean Corposcular 
Haemoglobin 
√ 
√ 
√ 
Neutrophils Count 
√ 
√ 
√ 
Platelet Count 
√ 
√ 
√ 
Reticulocyte Count 
√ 
√ 
√ 
Serum Creatinine 
√ 
√ 
√ 
Lactate Dehydrogenase √ 
√ 
√ 
Urea 
√ 
√ 
√ 
Direct Bilirubin 
√ 
√ 
√ 
Total Bilirubin 
√ 
√ 
√ 
Aspartate 
aminotransferase 
√ 
√ 
√ 
Alanine 
aminotransferase 
√ 
√ 
√ 
Number of blood 
transfusions (last 6 
months) 
√ 
√ 
√ 
Number of vaso-
occlusive crises (last 6 
months) 
√ 
√ 
√ 
Number of 
hospitalisations (last 6 
months) 
√ 
√ 
√ 
 

15 
 
The determination of foetal haemoglobin will be performed using a validated cation-exchange 
HPLC method with customised Bio-Rad HPLC equipment (Chumchuen et al., 2023; Leonova 
et al., 1996a). The quantitation of blood counts, i.e., erythrocytes, leucocytes, and platelets, will 
be performed using the Mindray BC-5000 haematology analyser, which has proven to be useful 
for high precision and accuracy and is comparable with conventional methods for determining 
blood counts (Xiang et al., 2015a). An alternative blood counter is the haematological 
autoanalyzer (SFRI blood count counter, H18 light, Saint Jean d’Ilac, France) (Asafa, Ahmed, 
Umar, et al., 2024). The severity of vaso-occlusive pain will be reported using the visual 
analogue scale (VAS), in which patients will be asked to rate their pain on a scale of 0 to 100, 
with 0 indicating ‘no pain’ and 100 indicating ‘worst possible pain’ (Chibueze & Chima, 2021a; 
Lopez et al., 2007a). The number of transfusions and hospitalisations over the last six months 
will be obtained from the participant’s case files. 
 
Other variables that will be collected include Weight, Height, Gender, Tribe, and Age; other 
disease conditions; date of birth; blood pressure readings; pulse; history of surgical procedures; 
other concomitant medications; smoking history; alcohol history; adherence; and other related 
covariates. Overall, the exposure variable for this study is hydroxyurea, and the outcome 
variable is sickle cell patient health outcomes i.e. pharmacodynamic data and clinical outcomes 
e.g. foetal haemoglobin and the frequency and severity of vaso-occlusive crises. 
 
Post-Field Modelling Phase 
Population pharmacokinetic and pharmacokinetic/pharmacodynamic models will be developed 
using the latest version of the Monolix Suite at the time of analysis. Exposure-response 
relationships, model diagnostics, and statistical summaries will be conducted in R. The 
previously developed PBPK model will subsequently be validated using clinical data, and 
subsequently, the model will be adapted for use in pregnant women and lactating mothers 
(Dong et al., 2023, p.426). The pregnancy and lactation PBPK models will be developed in 
Simcyp or PK-SIM to inform dose optimisation in special populations. 
 
Setting and Target Population 
The population will consist of sickle cell disease patients whose genetic profiles of the 
considered enzymes are available in the iProtecta (Implementation of Pharmacogenetic Testing 
for Effective Care and Treatment in Africa: Sickle Cell Disease) study portfolio. The study sites 
will be the Obafemi Awolowo University Teaching Hospital Complex in Ile-Ife and the Sickle 
Cell Foundation in Ibadan, Nigeria.  
 
Data Sources 
Objective 1: Data will be obtained from the iProtecta (Implementation of Pharmacogenetic 
Testing for Effective Care and Treatment in Africa: Sickle Cell Disease) study portfolio.  
Objective 2: Data for method development and validation will be obtained from literature. 
Objective 3 & 4: Data from Pharmacokinetic/Pharmacodynamic Field Phase will be used. 

16 
 
Objective 5: Data from the Pharmacokinetic/Pharmacodynamic field phase and literature 
(physiological data, and physicochemical properties of hydroxyurea). 
Objective 6: Data from Objectives 2, 4, & 5. 
 
DATA COLLECTION METHODS 
RedCap was used to capture the clinical data for the iProtecta study, and in the process to 
synchronize the data collection, it will be used to collect all clinical data in this study. Two 
research assistants will collect data in English; hence, there will be no need to translate the 
tools. Data, i.e., Clinical data, will be collected at baseline (start of field data collection), at 3 
months, and after 6 months, using the REDCap interface, over an 8-month period. The quality 
of the data collected will be ensured through daily quality checks by me, the Principal 
Investigator. Where discrepancies are observed, a review of the data will be conducted with the 
research assistant, and, if necessary, a re-collection of the data will be conducted within 7 days 
of the first data collection. Prior to the onset of clinical data collection, the two research 
assistants will be trained by me on obtaining consent, extracting data from participants’ case 
files, and documentation. A trained phlebotomist will be hired to collect blood samples and 
perform haematological analyses. 
 
DATA TYPES AND FORMATS 
All data collected during this study will be quantitative. As detailed in Table 2 above. Data will 
be saved as CSV files for importation into the analysis software. 
 
 
 
 
 
DATA STORAGE AND BACKUP 
Data will be extracted weekly from the REDCap account, and each version will be saved on 
my personal laptop, OneDrive account, Google Drive account, and an external hard drive as 
backups. These transfers will be done every Friday on or before 12 midnight.  
 
DATA SECURITY AND CONFIDENTIALITY 
Data entered will be transformed after collection to remove the primary personal identifiers, 
which will only be available to me. At any point where the data will be relayed to my 
supervisors or analysed with any software, identifiers will have been extracted to ensure 
confidentiality and anonymity. The folders containing the results on my personal laptop, cloud 
drives, and the external hard drive will be protected by passwords available only to me. 
All hard-copy entries (in case of internet failure or power outage during data collection) will 
be stored in an enclosed file in my physical office at Obafemi Awolowo University, Ile-Ife, 
Nigeria, after being entered into the REDCap account. 
 

17 
 
DATA CLEANING AND PROCESSING 
The data collected per day will be assessed for missing data and outliers. If any data is missing, 
efforts will be made the following day to capture it. If an outlier, attempts will be made to 
recapture the data. If data recapturing is impossible, the analysis will be attempted with and 
without the outlier, and the decision to keep or remove the outlier will be made based on the 
effects of the change. General cleaning of the dataset will be done each week to remove 
duplicates and resolve any other inconsistencies. 
 
DATA ANALYSIS PLAN 
Inclusion Criteria 
For a patient's data to be deemed fit for inclusion in the pop PK analysis, the patient must have 
received at least one dose of HU and have at least six measurable HU concentrations using the 
validated quantitative analytical method. The criteria for including patient data in the exposure-
response/PK-PD data are similar to the Pop PK criteria. However, additional criteria include a 
measurable Cmax concentration and sufficient time points for AUC calculation. 
 
Handling of Missing, Outlier, and Erroneous Data 
The different methods of handling missing data and concentrations below the limit of 
quantification (BLQs), as described by Beal et al, would be explored, i.e., the M1 to M6 method 
(Beal, 2001a). 
 
Exploratory Data Analysis will be done before model building, and in the process, outliers will 
be identified. These outliers will be flagged and may be excluded from the analysis if they 
significantly affect model and covariate model building. In accordance with accepted 
population PK reporting guidelines, the final population PK model will be rerun using the entire 
dataset (i.e., including the outliers).  A list of the excluded individuals and/or data points, along 
with the reasons for exclusion, will be provided in the report. 
 
Non-Compartmental Analysis 
Individual PK parameters will be estimated using non-compartmental analysis methods. 
Parameters to be estimated with the non-compartmental analysis include the AUC and the Cmax, 
which would be used for the exposure-response modelling. 
 
Software 
The Monolix software, a software package for nonlinear mixed-effect analysis (Lixoft, France) 
will be used for population PK modelling. Simulx (also a part of the Monolix Suite) and/or R 
will be used for simulations to derive exposure metrics for the subsequent exposure-response 
analysis. R will be used for graphical analysis, model diagnostics, and statistical summaries.  
Exposure-response (E-R) analyses will be performed using R, and software version numbers 
will be documented in the report. 
 
 

18 
 
Model Development 
The dataset will be run with model files for one-, two-, and three-compartment models written 
in Mlxtran to determine the structural model. Different absorption models, such as those that 
include lag time and transit compartments, will also be explored. Improvements in objective 
function values, likelihood ratio tests, and inspection of diagnostic plots will guide the choice 
of the structural model. An exploration of the different error models (constant, exponential, 
proportional, and combined) will also be conducted to determine which model best fits the 
structural model. Basic goodness-of-fit plots (arithmetic/log scale) of individual fits, 
observations versus predictions, scatter plots of residuals, distribution of residuals, visual 
predictive check, numerical predictive check, and prediction distribution will be used to 
diagnose the structural and statistical models. The Student t-test, Pearson correlation test, 
and/or ANOVA will be used to analyse the statistical models.  
 
 
 
Covariate Selection for the Population PK Model  
The covariates to be tested in the model are ethnicity, weight, age, BMI, and genetic 
polymorphism, as they are clinically relevant in pharmacokinetics. A covariate will be included 
in the final model if it is available for at least 80% of the study population and if it can be 
explained biologically and clinically. Significant covariates in the developed model with an α 
(significance level) of 0.10, adjudged from a combination of relevant statistics (Wald’s test, 
Likelihood ratio test, etc.), will be selected using a forward selection/backward deletion 
approach.  
 
Correlations between Random Effects 
Correlation coefficients between random effects will be explored in a stepwise process. A 
significant reduction in the objective function value will be used as the criterion for adding the 
correlation to the model. The correlations will be biologically and statistically justified. 
 
 
Final Population PK Model 
The final pop PK model, after optimising the base model and developing the covariate model, 
should meet the following criteria. 
• No estimates close to a boundary. 
• Relative errors of the estimates should preferably be < 30% for fixed effect parameters 
and <50% for random effect parameters. 
• No acceptable trends in goodness of fit (GOF) plots. 
 
Evaluation of the Final Population PK Model 
A nonparametric bootstrap analysis will be conducted to evaluate the stability of the final model 
and to estimate confidence intervals (CIs) for the model parameters. The bootstrap analysis 
will be performed with 1000 replicates of the dataset, generated by random resampling of 
subjects from the original dataset with replacement. Visual predictive checks (VPCs) will be 

19 
 
used to evaluate the predictive ability of the final model. If required, VPCs will be performed 
with prediction correction. Plots of observed data distributions will be compared to simulated 
distributions to demonstrate the model’s ability to predict the data on which the model is based 
adequately. VPCs may be based on 500 simulations and may be stratified by covariates of 
potential interest. Other goodness-of-fit plots that will be used include observations versus 
prediction plots, scatter plots of residuals, distribution of the residuals, visual predictive checks, 
numerical predictive checks, and prediction distribution. 
 
Objective 3: EXPOSURE-RESPONSE ANALYSIS 
The final population PK model of HU will be used to predict rich concentration-time profiles 
based on Posterior Bayesian estimates. Exposure metrics, such as the area under the curve over 
24 hours (AUC0-24), the AUC extrapolated to infinity (AUC0-∞), and the maximum 
concentration (Cmax), will be derived from the non-compartmental analysis. The relationship 
between response data (Crisis versus No Crisis in 6 months) and exposure metrics (AUC0-∞, 
Cmax) will be established using nonlinear regression. Various structural models, such as linear 
and saturable structures, will be applied to the data and assessed for their capacity to 
characterise the exposure-response relationship sufficiently. Simulations will be performed to 
evaluate the efficacy of different treatment dose options. 
 
An assumption that there is a direct non-linear relationship between the exposure (AUC0-∞) and 
the response (crisis or no crisis): 
𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑛𝑜 𝑐𝑟𝑖𝑠𝑖𝑠= 𝐸𝑚𝑎𝑥× 𝐴𝑈𝐶0−∞
𝛾
𝐸50
𝛾+ 𝐴𝑈𝐶0−∞
𝛾
 
Where, 
Emax = maximum effect 
AUC0-∞= Exposure  
E50 = Exposure that will produce 50% of the maximum effect 
γ = slope factor 
 
Objective 4: PBPK Analysis 
The method of developing the pregnancy physiologically-based pharmacokinetic model 
(PBPK) model will be used as detailed by Atoyebi et al. (2024) (Atoyebi et al., 2024a). 
Summarily, the PBPK model will be developed by incorporating mathematical equations that 
describe the human biological system and HU-specific parameters. The anatomical, 
physiological, and demographic data available in the literature and obtained from the 
pharmacokinetic analysis performed in ‘objective 1’ above would be used to define the 
biological system parameters. The drug-specific parameters will comprise data on the 
physicochemical properties of the drug (e.g. pKa and lipophilicity) and in vitro data available 
(e.g. fraction of the unbound drug in plasma and intrinsic enzymatic clearance of the drug).  
 

20 
 
 
Schematic description of pregnancy physiologically based pharmacokinetic (p-PBPK) model 
Source: Gaohua et al., 2012 (Gaohua et al., 2012a). 
 
The full-body adult PBPK and paediatric model will be developed in Simcyp®, a product of 
Certara, U.S.A. The modelling process will start with the simulation of a virtual cohort of 100 
healthy adult individuals and a healthy paediatric population, which will be used to validate 
the non-pregnant adult PBPK model and paediatric population. The ratio of males to females 
in this virtual cohort will be set at 50:50 to reflect the mixed-gender composition of clinical 
studies in non-pregnant and paediatric populations. Another cohort of 200 healthy volunteers, 
comprising 100 females and 100 paediatric patients, will also be simulated using the pregnancy 
PBPK model and the paediatric PBK model, respectively, to represent the pregnant and 
paediatric populations.  
 
Organ weights will be determined using an anthropometric equation (Bosgra et al., 2012a). 
Organ volumes will be calculated from the organ weights and the respective organ densities, 
and regional blood flows to organs and tissues will also be calculated as fractions of the cardiac 
output (Brown et al., 1997a). A foetal component will be included in the pregnancy PBPK 
model to accommodate the estimation of the drug concentration in the foetus. Similarly, a 
lactation PBPK will be built to estimate the disposition of HU in lactating mothers and their 
infants. 

21 
 
 
 
Ethical Considerations 
Ethical Approval: Ethical Approval will be obtained from the Research Ethics 
Committee of the Obafemi Awolowo University Teaching Hospital Committee.  
 
Informed Consent: The study protocol will be explained to the study participants. Upon 
acceptance to participate in the study, participants will be given an informed consent 
form to review and sign. Questions related to the study protocol will be addressed, and 
participants will then provide written informed consent. Participants will not be subject 
to any form of coercion to participate in the study and may withdraw at any time. 
 
Benefits to Patients: The study will guide tailoring hydroxyurea use for each patient, and 
its results will be published for the benefit of society. 
Harm to Study Participants: Study Participants will be exposed to minimal risks, 
including slight pain and discomfort, during blood sample collection. There is also a 
minimal risk of infection, as all protocols will be followed to protect all parties involved 
from cross-infection. 
Confidentiality and Data Security: Study participants will be assigned personal 
identifiers, with the code linking the data to the participant’s personal information only 
available to me, the principal investigator.  
 
Conclusions 
 
Expected Outcomes 
This study will therefore provide scientific knowledge on the varying responses of sickle cell 
disease patients to hydroxyurea, as well as the effects of genetic polymorphism and other 
covariates on the pharmacokinetics and pharmacodynamics of hydroxyurea in the Nigerian 
population. Using a physiologically based pharmacokinetic model developed for pregnant 
women, lactating mothers, and neonates, the safety and efficacy of hydroxyurea could be 
assessed, and possible dose adjustments recommended for these special populations.  Using 
pharmacometrics tools, this study will demonstrate how technology-driven analysis can be 
instrumental in clinical decision-making, particularly in low-resource settings such as Africa, 
where conducting full-scale clinical trials is challenging due to resource constraints and cultural 
factors.  
 
Proposed Research Outputs 
Objective 1: 
Paper 1 – Genetic Polymorphisms of Metabolic Enzymes and Transporters Associated with 
Medications in Sickle Cell Disease Management in Nigeria 
 

22 
 
Paper 2: Pharmacogenetics in the Disposition of Hydroxyurea in Sickle Cell Disease Patients: 
A Scoping Review 
 
Objective 2: 
Paper 3: Development and Validation of an Analytical Method for the Quantification of 
Hydroxyurea in Human Matrices 
 
Objective 3: 
Paper 4: Population Pharmacokinetics Modelling of Hydroxyurea in Sickle Cell Disease 
Patients of Nigerian Descent 
 
Objective 4: 
Paper 5: Pharmacokinetic-Pharmacodynamic Modelling of Hydroxyurea in Sickle Cell 
Disease Patients of Nigerian Descent. 
 
Objective 5 
Paper 6: Development of a Physiologically Based Pharmacokinetic Modelling of Hydroxyurea 
in Pregnant Women of African Descent. 
 
Scope and Limitations 
This study is limited to the Southwestern part of Nigeria; however, the data collected will still 
be representative of the Nigerian population because the teaching hospital, the central data 
collection site, welcomes people from different parts of the country. Also, the study participants 
will be the HbSS patients only, as they represent the major haemoglobinopathy group. 
Development of a method for routine quantification of foetal haemoglobin might be the most 
challenging, as there are no clearly documented, reproducible methods in the literature for low 
and middle-income economies. Hence, the option of the mean corpuscular volume as an 
alternative to foetal haemoglobin. 
 
Overall, this study will provide scientific knowledge that can enhance clinical decision-making 
in sickle cell management for the Nigerian population. The models could serve as a template 
to optimise hydroxyurea use in this population, and this approach can be extrapolated to the 
broader African population. 
 
 
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Appendices 
Appendix 1: Glossary of Acronyms/Abbreviations 
1. SCD – Sickle Cell Disease 
2. SCA – Sickle Cell Anaemia 
3. VOC – Vaso-occlusive Crisis 
4. HU – Hydroxyurea 
5. PK – Pharmacokinetics 
6. PD – Pharmacodynamics 
7. PGx – Pharmacogenetics 
8. PBPK – Physiologically Based Pharmacokinetic Modelling 
9. HbF – Foetal Haemoglobin 
10. MTD – Minimum Toxic Dose / Maximum Tolerable Dose 
11. MCV – Mean Corpuscular Volume 
12. IIV – Inter-individual Variability 
13. Pop PK – Population Pharmacokinetics 
14. CYP – Cytochrome 
15. HPLC – High Power Liquid Chromatography 
16. LC-MS/MS – Liquid Chromatography – Mass Spectrometry/Mass spectrometry 
17. Hb – Haemoglobin 
18. NLME – Non-Linear Mixed Effects 
 
Appendix 2: Brief Explanation of Technical Terms 
1. Pharmacokinetics is the study of how the body affects drugs i.e. it describes how a drug 
is absorbed, distributed, metabolised and eliminated. The concept of pharmacokinetics 
originated from the work of Paracelsus (medieval alchemist), whose philosophy was 
‘only the dose makes the poison’, such that therapeutic effects are achieved when the 
drug fall within a particular window, when it is below the range, there is no therapeutic 
effect, and when it is above it, there are harmful effects. 
2. Pharmacodynamics is the effect of the drug on the body. 
3. Pharmacogenetics is the study of the influence of differing genetic variability on drug 
response, either on the pharmacodynamics or the pharmacokinetics. 
 
 
 
 
 
 
 
 
 

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Appendix 3: Figure showing the comparison of SCD Pathophysiologic and management 
paths