Whole Body and CNS Biodistribution of rhHNS in Cynomolgus Monkeys after Intrathecal Lumbar Administration: Treatment Implications for Patients with MPS IIIA.

Mucopolysaccharidosis III type A (MPS IIIA; Sanfilippo syndrome), a genetic lysosomal disorder causing a deficiency of heparan N-sulfatase (HNS), leads to progressive cognitive decline from an early age. An effective enzyme replacement therapy (ERT) for MPS IIIA requires central nervous system (CNS) biodistribution. Recombinant human heparan N-sulfatase (rhHNS), an investigatory ERT for MPS IIIA, has been formulated for intrathecal (IT) administration since intravenous (IV) administration cannot cross the blood brain barrier (BBB) in sufficient amounts to have a therapeutic effect. In this study, systemic and CNS distribution of rhHNS in cynomolgus monkeys following IV and IT administration was evaluated by quantitation of rhHNS in serum, cerebral spinal fluid (CSF) and various tissues, and positron emission tomography (PET) imaging of live animals. Following IV administration, rhHNS levels were low to non-detectable in the CSF, and systemic clearance was rapid (≤2 h). With IT administration, rhHNS was observable in CNS tissues in ≤1 h, with varying Tmax (1-24 h). Appreciable systemic distribution was observed up to 7 days. This provides evidence that in this animal model, intrathecal administration of rhHNS delivers the replacement enzyme to therapeutically relevant tissues for the treatment of Sanfilippo Syndrome type A. Penetration into grey matter and cortex was 3-4 times greater than concentrations in white matter and deeper parenchymal regions, suggesting some limitations of this ERT strategy.

Domperidone interacts with pioglitazone but not with ondansetron via common CYP metabolism in vitro.

Domperidone (prokinetic agent) is frequently co-administered with pioglitazone (anitidiabetic) or ondansetron (antiemetic) in gastroparesis management. These drugs are metabolized via cytochome P-450 (CYP) 3A4, raising the possibility of interaction and adverse reactions. The concentration-dependent inhibitory effect of pioglitazone and ondansetron on domperidone hydroxylation was monitored in pooled human liver microsomes (HLM). Pioglitazone was further assessed as a mechanism-based inhibitor. Microsomal binding was evaluated in our assessment. In HLM, Vmax/Km estimates for monohydroxy domperidone formation decreased in presence of pioglitazone. Diagnostic plots indicated that pioglitazone inhibited domperidone in a partial mixed-type manner. The in vitro Ki was 1.52 µM. Predicted in vivo AUCi/AUC ratio was 1.98. Pioglitazone also exerted time-dependent inhibition on the metabolism of domperidone and the average remaining enzymatic activity decreased significantly upon preincubation with pioglitazone over 0-40 min. Diagnostic plots showed no inhibitory effect of ondansetron on domperidone hydroxylation. 6. In conclusion, pioglitazone inhibited domperidone metabolism in vitro through different complex mechanisms. Our in vitro data predict that the co-administration of these drugs can potentially trigger an in vivo drug-drug interaction.

Population Pharmacokinetics of Bepirovirsen in Healthy Participants and Participants with Chronic Hepatitis B Virus Infection: Results from Phase 1, 2a, and 2b Studies.

INTRODUCTION: Bepirovirsen is a novel antisense oligonucleotide in development for chronic hepatitis B virus (HBV) infection therapy. Understanding the impact that clinical characteristics may have on bepirovirsen exposure is important for determining efficacious and well-tolerated dosing regimens. This analysis evaluated demographics and clinical characteristics associated with bepirovirsen exposure using a population pharmacokinetic (PK) analysis. METHODS: Population PK analyses were conducted using pooled data from three phase 1/2 clinical studies (NCT03020745/NCT02981602/NCT04449029) to construct a structural PK model for bepirovirsen that adequately described plasma concentration-time profiles and identify covariates that affect systemic exposure. The final population PK model was used to simulate bepirovirsen exposure measures to inform exposures at different dose levels and within different subpopulations. RESULTS: Bepirovirsen PK data were well-described by a linear, three-compartment model with first-order absorption and absorption delay. Chronic HBV infection status, body weight, and Asian versus non-Asian race were key covariates included in the final model. Visual inspection of correlation scatter plots confirmed general agreement between observed and predicted data from the studies. In simulations, bepirovirsen systemic exposure was dosed proportionally and predicted to be almost completely washed out by 12 weeks following the final 300-mg dose. Differences in body weight, Asian race, or disease status did not result in clinically relevant differences in exposure. CONCLUSIONS: This analysis demonstrated that the linear three-compartmental model accurately described bepirovirsen PK data. The lack of clinically relevant differences seen in exposure indicate that dose adjustments are not recommended for bepirovirsen based on demographics or clinical characteristics.

Identification of domperidone metabolites in plasma and urine of gastroparesis patients with LC-ESI-MS/MS.

Domperidone is a prokinetic agent used to treat gastroparesis. Previous studies reported oxidative metabolites of domperidone, detected by radiometric high-performance liquid chromatography or single quadrupole mass spectrometric techniques. Our aim was to identify domperidone Phase I and Phase II metabolites using liquid chromatography combined with electrospray ionization-enabled tandem mass spectrometry. Domperidone metabolites were identified in the plasma and urine of 11 gastroparesis patients currently being treated with domperidone. In addition, oxidative and conjugative metabolites of domperidone were characterized in human liver subcellular fractions. Seven metabolites were detected in vivo. Domperidone was metabolized to two mono-hydroxylated metabolites (M1 and M2), a de-alkylated metabolite (M5) and a di-hydroxylated metabolite (M7). The mono-hydroxylated metabolites were further glucuronidated to M8, M9 and sulfated to M11. To the best of our knowledge, M7, M8, M9 and M11 have not been reported previously. Five additional metabolites were identified in vitro in human subcellular fractions which comprise two additional mono-hydroxylated metabolites (M3 and M4), an alcohol metabolite (M6) possibly formed from an aldehyde intermediate, and other conjugative metabolites (M10 and M12). M6, M10 and M12 have not been characterized previously. In total, 12 domperidone metabolites including 7 new metabolites were identified in the present study. These results allow a better understanding of domperidone disposition in humans.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.