Simulating Intestinal Transporter and Enzyme Activity in a Physiologically Based Pharmacokinetic Model for Tenofovir Disoproxil Fumarate

Enzymatic prodrug degradation in the fasted and fed small intestine: In vitro studies and interindividual variability in human aspirates

The aim of the current study was (1) to develop an automation-based protocol for in vitro assessment of enzymatic drug stability at fasted- and fed-state intestinal conditions, (2) to characterize the inter-individual variability of drug degradation in fasted- and fed-state human intestinal fluids, and (3) to compare the obtained in vitro results to drug degradation in human intestinal fluids by taking variability into account. In human intestinal fluids, drug degradation displayed large inter-individual variability, with coefficients of variance generally ranging between 30 and 70 %. The effect of food on the inter-individual variability was highly dependent on the type of drug. The increase of pH in the range between 5.0 and 7.0 significantly accelerated the degradation rate of the studied drugs both in the in vitro and ex vivo experiments. In contrast, the increase of bile salt and phospholipid concentrations in the in vitro screen decreased strongly the degradation rate of the hydrophobic drugs. The developed automated in vitro screen mimicked relatively well the ex vivo degradation of all drugs in the fasted state, whereas in the fed state the degradation of only one of the drugs was adequately reproduced.

Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.

Increased tenofovir monoester concentrations in patients receiving tenofovir disoproxil fumarate with ledipasvir/sofosbuvir

BACKGROUND

Intracellular tenofovir diphosphate concentrations are markedly increased in HIV/HCV coinfected individuals receiving tenofovir disoproxil fumarate (TDF) with sofosbuvir-containing treatment. Sofosbuvir may inhibit the hydrolysis of TDF to tenofovir, resulting in increased concentrations of the disoproxil or monoester forms, which may augment cell loading. We sought to quantify tenofovir disoproxil and monoester concentrations in individuals receiving TDF with and without ledipasvir/sofosbuvir.

METHODS

HIV/HCV coinfected participants receiving TDF-based therapy were sampled pre-dose and 1 and 4 h post-dose prior to and 4 weeks after initiating ledipasvir/sofosbuvir. Tenofovir disoproxil was not detectable. Tenofovir monoester in plasma and tenofovir diphosphate in PBMC and dried blood spots (DBS) were quantified using LC-MS/MS. Geometric mean ratios (week 4 versus baseline) and 95% CIs were generated for the pharmacokinetic parameters. P values reflect paired t-tests.

RESULTS

Ten participants had complete data. At baseline, geometric mean (95% CI) tenofovir monoester plasma concentrations at 1 and 4 h post-dose were 97.4 ng/mL (33.0-287.5) and 0.74 ng/mL (0.27-2.06), respectively. With ledipasvir/sofosbuvir, tenofovir monoester concentrations at 4 h post-dose were 5.02-fold higher (95% CI 1.40-18.05; P = 0.019), but did not significantly differ at 1 h post-dose (1.72-fold higher, 95% CI 0.25-11.78; P = 0.54), possibly due to absorption variability. Tenofovir diphosphate in PBMC and DBS were increased 2.80-fold (95% CI 1.71-4.57; P = 0.001) and 7.31-fold (95% CI 4.47-11.95; P < 0.0001), respectively, after 4 weeks of ledipasvir/sofosbuvir.

CONCLUSIONS

Tenofovir monoester concentrations were increased in individuals receiving TDF with ledipasvir/sofosbuvir, consistent with inhibition of TDF hydrolysis. Additional studies are needed to determine the clinical relevance of this interaction.

Pharmacokinetics of tenofovir monoester and association with intracellular tenofovir diphosphate following single-dose tenofovir disoproxil fumarate

BACKGROUND

Tenofovir monoester is a relatively lipophilic intermediate formed during the hydrolysis of tenofovir disoproxil to tenofovir. Its clinical pharmacokinetic profile and influence on the cellular pharmacology of tenofovir diphosphate have not been reported.

METHODS

Plasma, PBMC and dried blood spots (DBS) were obtained from HIV-uninfected adults participating in a randomized, cross-over bioequivalence study of single-dose tenofovir disoproxil fumarate (TDF)/emtricitabine unencapsulated or encapsulated with a Proteus® ingestible sensor. Plasma pharmacokinetics of tenofovir monoester and tenofovir were characterized using non-compartmental methods. Relationships with tenofovir diphosphate in DBS and PBMC were examined using mixed-effects models.

RESULTS

Samples were available from 24 participants (13 female; 19 white, 3 black, 2 Hispanic). Tenofovir monoester appeared rapidly with a median (range) Tmax of 0.5 h (0.25-2) followed by a rapid monophasic decline with a geometric mean (coefficient of variation) t½ of 26 min (31.0%). Tenofovir monoester Cmax was 131.6 ng/mL (69.8%) and AUC0-4 was 93.3 ng·h/mL (47.9%). The corresponding values for plasma tenofovir were 222.2 ng/mL (37.1%) and 448.1 ng·h/mL (30.0%). Tenofovir monoester AUC0-∞ (but not tenofovir AUC0-∞) was a significant predictor of tenofovir diphosphate in both PBMC (P = 0.015) and DBS (P = 0.005), increasing by 3.8% (95% CI 0.8%-6.8%) and 4.3% (95% CI 1.5%-7.2%), respectively, for every 10 ng·h/mL increase in tenofovir monoester.

CONCLUSIONS

Tenofovir monoester Cmax and AUC0-4 were 59.2% and 20.6% of corresponding plasma tenofovir concentrations. Tenofovir monoester was significantly associated with intracellular tenofovir diphosphate concentrations in PBMC and DBS, whereas tenofovir concentrations were not. Tenofovir monoester likely facilitates cell loading, thereby increasing tenofovir diphosphate exposures in vivo.

The emerging role of physiologically based pharmacokinetic modelling in solid drug nanoparticle translation

The use of solid drug nanoparticles (SDN) has become an established approach to improve drug delivery, supporting enhancement of oral absorption and long-acting administration strategies. A broad range of SDNs have been successfully utilised for multiple products and several development programmes are currently underway across different therapeutic areas. With some approaches, a large range of material space is available with diversity in physical characteristics, excipient choice and pharmacological behaviour. The selection of SDN lead candidates is a complex process including a broad range of in vitro and in vivo data, and a better understanding of how physical characteristics relate to performance is required. Physiologically-based pharmacokinetic (PBPK) modelling is based upon a comprehensive integration of experimental data into a mathematical description of drug distribution, allowing simulation of SDN pharmacokinetics that can be qualified in vivo prior to human prediction. This review aims to provide a description of how PBPK can find application into the development of SDN. Integration of predictive PBPK modelling into SDN development allows a better understanding of the SDN dose-response relationship, supporting a framework for rational optimisation while reducing the risk of failure in developing safe and effective nanomedicines.