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Abstract Background Sinusoidal occlusion syndrome (SOS) is a potentially severe complication following hematopoietic stem cell transplantation (HSCT) in pediatric patients. Treatment related risk factors such as intensity of conditioning, hepatotoxic co-medication and patient related factors such as genetic variants predispose individuals to develop SOS. The variant allele for SNP rs17146905 in UDP-glucuronosyl transferase 2B10 (UGT2B10) gene was correlated with the occurrence of SOS in an exome-wide association study. UGT2B10 is a phase II drug metabolizing enzyme involved in the N-glucuronidation of tertiary amine containing drugs. Methods To shed light on the functionality of UGT2B10 enzyme in the metabolism of drugs used in pediatric HSCT setting, we performed in silico screening against custom based library of putative ligands. First, a list of potential substrates for in silico analysis was prepared using a systematic consensus-based strategy. The list comprised of drugs and their metabolites used in pediatric HSCT setting. The three-dimensional structure of UGT2B10 was not available from the Research Collaboratory Structural Bioinformatics - Protein Data Bank (RCSB - PDB) repository and thus we predicted the first human UGT2B10 3D model by using multiple template homology modeling with MODELLER Version 9.2 and molecular docking calculations with AutoDock Vina Version 1.2 were implemented to quantify the estimated binding affinity between selected putative substrates or ligands and UGT2B10. Finally, we performed molecular dynamics simulations using GROMACS Version 5.1.4 to confirm the potential UGT2B10 ligands prioritized after molecular docking (exhibiting negative free binding energy). Results Four potential ligands for UGT2B10 namely acetaminophen, lorazepam, mycophenolic acid and voriconazole n-oxide intermediate were identified. Other metabolites of voriconazole satisfied the criteria of being possible ligands of UGT2B10. Except for bilirubin and 4-Hydroxy Voriconazole, all the ligands (particularly voriconazole and hydroxy voriconazole) are oriented in substrate binding site close to the co-factor UDP (mean ± SD; 0.72 ± 0.33 nm). Further in vitro screening of the putative ligands prioritized by in silico pipeline is warranted to understand the nature of the ligands either as inhibitors or substrates of UGT2B10. Conclusions These results may indicate the clinical and pharmacological relevance UGT2B10 in pediatric HSCT setting. With this systematic computational methodology, we provide a rational-, time-, and cost-effective way to identify and prioritize the interesting putative substrates or inhibitors of UGT2B10 for further testing in in vitro experiments.

Abstract Busulfan (Bu) is a common component of conditioning regimens before hematopoietic stem cell transplantation (HSCT) and is known for high interpatient pharmacokinetic (PK) variability. This study aimed to develop and externally validate a multicentric, population PK (PopPK) model for intravenous Bu in pediatric patients before HSCT to first study the influence of glutathione‐s‐transferase A1 (GSTA1) polymorphisms on Bu's PK in a large multicentric pediatric population while accounting for fludarabine (Flu) coadministration and, second, to establish an individualized, model‐based, first‐dose recommendation for intravenous Bu that can be widely used in pediatric patients. The model was built using data from 302 patients from five transplantation centers who received a Bu‐based conditioning regimen. External model validation used data from 100 patients. The relationship between body weight and Bu clearance (CL) was best described by an age‐dependent allometric scaling of a body weight model. A stepwise covariate analysis identified Day 1 of Bu conditioning, GSTA1 metabolic groups based on GSTA1 polymorphisms, and Flu coadministration as significant covariates influencing Bu CL. The final model adequately predicted Bu first‐dose CL in the external cohort, with 81% of predicted area under the curves within the therapeutic window. The final model showed minimal bias (mean prediction error, −0.5%; 95% confidence interval [CI], −3.1% to 2.0%) and acceptable precision (mean absolute prediction error percentage, 18.7%; 95% CI, 17.0%–20.5%) in Bu CL prediction for dosing. This multicentric PopPK study confirmed the influence of GSTA1 polymorphisms and Flu coadministration on Bu CL. The developed model accurately predicted Bu CL and first doses in an external cohort of pediatric patients.

Abstract GSTA1 encodes a member of a family of enzymes that function to add glutathione to target electrophilic compounds, including carcinogens, therapeutic drugs, environmental toxins, and products of oxidative stress. GSTA1 has several functional SNPs within its promoter region that are responsible for a change in its expression by altering promoter function. This study aims to investigate distributions of GSTA1 promoter haplotypes across different human populations and to assess their impact on the expression of GSTA1. PHASE 2.1.1 was used to infer haplotypes and diplotypes of six GSTA1 promoter SNPs on 2501 individuals from 26 populations classified by the 1000 Genomes Project into five super-populations that included Africa (N = 660), America (N = 347), East Asia (N = 504), Europe (N = 502), and South Asia (N = 488). We used pairwise FST analysis to compare sub-populations and luciferase reporter assay (LRA) to evaluate the impact of each SNP on activation of transcription and interaction with other SNPs. The distributions of GSTA1 promoter haplotypes and diplotypes were significantly different among the different human populations. Three new promoter haplotypes were found in the African super-population. LRA demonstrated that SNPs at -52 and -69 has the most impact on GSTA1 expression, however other SNPs have a significant impact on transcriptional activity. Based on LRA, a new model of cis-elements interaction is presented. Due to the significant differences in GSTA1 diplotype population frequencies, future pharmacogenomics or disease-related studies would benefit from the inclusion of the complete GSTA1 promoter haplotype based on the newly proposed metabolic grouping derived from the LRA results.

Total-body irradiation (TBI) based conditioning prior to allogeneic hematopoietic stem cell transplantation (HSCT) is generally regarded as the gold-standard for children >4 years of age with acute lymphoblastic leukaemia (ALL). Retrospective studies in the 1990's suggested better survival with irradiation, confirmed in a small randomised, prospective study in the early 2000's. Most recently, this was reconfirmed by the early results of the large, randomised, international, phase III FORUM study published in 2020. But we know survivors will suffer a multitude of long-term sequelae after TBI, including second malignancies, neurocognitive, endocrine and cardiometabolic effects. The drive to avoid TBI directs us to continue optimising irradiation-free, myeloablative conditioning. In chemotherapy-based conditioning, the dominant myeloablative effect is provided by the alkylating agents, most commonly busulfan or treosulfan. Busulfan with cyclophosphamide is a long-established alternative to TBI-based conditioning in ALL patients. Substituting fludarabine for cyclophosphamide reduces toxicity, but may not be as effective, prompting the addition of a third agent, such as thiotepa, melphalan, and now clofarabine. For busulfan, it's wide pharmacokinetic (PK) variability and narrow therapeutic window is well-known, with widespread use of therapeutic drug monitoring (TDM) to individualise dosing and control the cumulative busulfan exposure. The development of first-dose selection algorithms has helped achieve early, accurate busulfan levels within the targeted therapeutic window. In the future, predictive genetic variants, associated with differing busulfan exposures and toxicities, could be employed to further tailor individualised busulfan-based conditioning for ALL patients. Treosulfan-based conditioning leads to comparable outcomes to busulfan-based conditioning in paediatric ALL, without the need for TDM to date. Future PK evaluation and modelling may optimise therapy and improve outcome. More recently, the addition of clofarabine to busulfan/fludarabine has shown encouraging results when compared to TBI-based regimens. The combination shows activity in ALL as well as AML and deserves further evaluation. Like busulfan, optimization of chemotherapy conditioning may be enhanced by understanding not just the PK of clofarabine, fludarabine, treosulfan and other agents, but also the pharmacodynamics and pharmacogenetics, ideally in the context of a single disease such as ALL.