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Pandey et al. J Transl Genet Genom 2021;5:22-36  I  http://dx.doi.org/10.20517/jtgg.2020.45                                         Page 31

               Table 2. Summary of hydroxyurea pharmacodynamic (PD) models for sickle cell patients
                Individual
                patients PD       PD model             Parameters - predictors/covariates  IIV      RV
                model
                Ware et al. [32]  Univariate and multivariate linear   Univariate analysis  Coefficient
                          regression               HbF% at MTD - baseline HbF, baseline total   of variation
                                                   bilirubin, baseline ARC, age, height, cystatin C
                                                   MTD - creatinine, weight, BSA, age, height, BMI
                                                   Multivariate analysis
                                                   %HbF at MTD - baseline HbF, baseline total
                                                   bilirubin, baseline ARC, BMI, MRT ∞
                                                   MTD - baseline creatinine, baseline ARC, baseline
                                                   BMI, half-life, fast PK phenotype
                Paule et al. [33]  NLME model (individual   HbF% production rate, k in  – ΔMCV  Exponential   Proportional
                          parameter estimation),   MCV parameter, β - ΔHbF%            model     model
                          turnover PD models with inhibition
                          of the elimination rate
               IIV: inter-individual variability; RV: residual variability; NLME: nonlinear mixed effect; HbF: fetal hemoglobin; MTD: maximum tolerated
               dose; ARC: absolute reticulocyte count; BSA: body surface area; BMI: body mass index; MRT ∞ : mean residence time; MCV: mean cell
               volume; ΔMCV: change in MCV/day; ΔHbF%: change in HbF%/day

               the drug concentration. The PD modeling approach has focused on relating efficacy with drug exposure.
               A model that can relate both drug toxicity and drug efficacy with drug exposure is needed to obtain
               a dose that maximizes efficacy and minimizes toxicity. Table 2 summarizes the PD models developed
               for SCD patients and reviewed in this section. A PD model able to correlate drug biophase/plasma
               concentration with MCV of RBC and HbF will be useful for predicting individual patient trajectory with
               time and will reduce clinicians’ waiting time in reaching the individual specific dose. Additionally, genetic
               polymorphisms were seen in patients treated with HU in genes associated with HU metabolism, erythroid
                                                     [57]
               progenitor proliferation, and HbF expression . Incorporation of genetic polymorphisms in the population
               PD model will help in better explaining the IIV in response. In PD modeling, an indirect response or
               turnover model is useful to correlate change in HbF and MCV with HU exposure. However, for a detailed
               approach, a mechanistic model needs to be developed. Several studies were conducted to investigate the
               signaling pathway involved in the mechanism of HU-mediated reactivation of HbF.


               Hydroxyurea signaling pathway
               HU increased nitric oxide (NO) and activated soluble guanylyl cyclase (sGC) [43,58-60] . sGC induced γ-globin
               gene expression, mediated by cyclic guanosine monophosphate (cGMP) [44,61] . The role of p38 mitogen-
               activated protein kinase (MAPK) in HbF activation has been studied [45,62-64] . Phosphorylation of p38
               MAPK increased in HU responsive erythroid cells while it was unaffected in HU resistant cells . In some
                                                                                                [65]
               studies, NO stimulated p38 MAPK phosphorylation through cGMP-dependent protein kinase (PKG) [66,67] .
               In separate studies, the transcription factors such as BCL11A, KLF1, and SOX6 silenced γ-globin gene
               expression [68-70] . The HU treatment reduced the expression of repressors, which activated the γ-globin
               gene . Additionally, HU induced immediate reduction in WBCs adhesion to vascular endothelium and
                   [71]
               reduction in WBC-RBC interaction, which are associated with the vaso-occlusive crisis. The studies have
               shown the potential role of the NO-cGMP signaling pathway in HU induced anti-inflammatory effects [72,73] .

               The HU induced HbF synthesis is represented by a proposed signaling pathway, as shown in Figure 1. In
                                                                                                         *
               the figure, HU is metabolized to NO. Then, NO binds to sGC and activates it. The activated sGC (sGC )
               converts GTP to cGMP. The transcription factors that silence the γ-globin gene expression are all combined
                                                        *
               into TF. HU activates p38 MAPK (p38 MAPK ) as well. The HU-dependent cGMP production and p38
                      r
               MAPK activation inhibit the expression of TF. As a result, the reduced expression of TF  promotes γ-globin
                                                                                         r
                                                      r
               gene expression and, consequently, HbF production.
               The signaling pathway plays a significant role in HU-induced HbF stimulation. Hence, a deeper
               understanding of the signaling network is needed. Further studies are needed to establish the role of the
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