A physiologically based pharmacokinetic model to predict pegylated liposomal doxorubicin disposition in rats and human.

The use of nanoparticles (NPs) can support an enhancement of drug distribution, resulting in increased drug penetration into key tissues. Experimental in vitro data can be integrated into computational approaches to simulate NP absorption, distribution, metabolism and elimination (ADME) processes and provide quantitative pharmacokinetic predictions. The aim of this study is to develop a novel mechanistic and physiologically based pharmacokinetic (m-PBPK) model to predict the biodistribution of NPs focusing on Doxil. The main processes underpinning NPs ADME were represented considering molecular and cellular mechanisms such as stability in biological fluids, passive permeability and uptake activity by macrophages. A whole-body m-PBPK rat and human models were designed in Simbiology v. 9.6.0 (MATLAB R2019a). The m-PBPK models were successfully qualified across doxorubicin and Doxil® in both rat and human since all PK parameters AUC0-inf, Cmax, t1/2, Vd and Cl were within twofold, with an AUC0-inf absolute average-fold error (AAFE) value of 1.23 and 1.16 and 1.76 and 1.05 for Doxorubicin and Doxil® in rat and human, respectively. The time to maximum concentration in tissues for doxorubicin in both rat and human models was before 30 min of administration, while for Doxil®, the tmax was after 24 h of administration. The organs that accumulate most NP are the spleen, liver and lungs, in both models. The m-PBPK represents a predictive platform for the integration of in vitro and formulation parameters in a physiological context to quantitatively predict the NP biodistribution. Schematic diagram of the whole-body m-PBPK models developed for Doxil® in rat and human physiology.

Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling.

OBJECTIVES: The aim of this study was to simulate the drug-drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. METHODS: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL. RESULTS: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off. CONCLUSIONS: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients.

The Current Landscape of Novel Formulations and the Role of Mathematical Modeling in Their Development.

Drug delivery is an integral part of the drug development process, influencing safety and efficacy of active pharmaceutical ingredients. The application of nanotechnology has enabled the discovery of novel formulations for numerous therapeutic purposes across multiple disease areas. However, evaluation of novel formulations in clinical scenarios is slow and hampered due to various ethical and logistical barriers. Computational models have the ability to integrate existing domain knowledge and mathematical correlations, to rationalize the feasibility of using novel formulations for safely enhancing drug delivery, identifying suitable candidates, and reducing the burden on preclinical and clinical studies. In this review, types of novel formulations and their application through several routes of administration and the use of modeling approaches that can find application in different stages of the novel formulation development process are discussed.

The role of proteasome beta subunits in gastrin-mediated transcription of plasminogen activator inhibitor-2 and regenerating protein1.

The hormone gastrin physiologically regulates gastric acid secretion and also contributes to maintaining gastric epithelial architecture by regulating expression of genes such as plasminogen activator inhibitor 2 (PAI-2) and regenerating protein 1 (Reg1). Here we examine the role of proteasome subunit PSMB1 in the transcriptional regulation of PAI-2 and Reg1 by gastrin, and its subcellular distribution during gastrin stimulation. We used the gastric cancer cell line AGS, permanently transfected with the CCK2 receptor (AGS-GR) to study gastrin stimulated expression of PAI-2 and Reg1 reporter constructs when PSMB1 was knocked down by siRNA. Binding of PSMB1 to the PAI-2 and Reg1 promoters was assessed by chromatin immunoprecipitation (ChIP) assay. Subcellular distribution of PSMB1 was determined by immunocytochemistry and Western Blot. Gastrin robustly increased expression of PAI-2 and Reg1 in AGS-GR cells, but when PSMB1 was knocked down the responses were dramatically reduced. In ChIP assays, following immunoprecipitation of chromatin with a PSMB1 antibody there was a substantial enrichment of DNA from the gastrin responsive regions of the PAI-2 and Reg1 promoters compared with chromatin precipitated with control IgG. In AGS-GR cells stimulated with gastrin there was a significant increase in the ratio of nuclear:cytoplasmic PSMB1 over the same timescale as recruitment of PSMB1 to the PAI-2 and Reg1 promoters seen in ChIP assays. We conclude that PSMB1 is part of the transcriptional machinery required for gastrin stimulated expression of PAI-2 and Reg1, and that its change in subcellular distribution in response to gastrin is consistent with this role.

Detection of drug bioactivation in vivo: mechanism of nevirapine-albumin conjugate formation in patients.

The non-nucleoside reverse transcriptase inhibitor nevirapine (NVP) is widely used for the treatment of human immunodeficiency virus type 1 (HIV-1), particularly in developing countries. Despite its therapeutic benefits, NVP has been associated with skin and liver injury in exposed patients. Although the mechanism of the tissue injury is not yet clear, it has been suggested that reactive metabolites of NVP may be involved. The detection of NVP mercapturate in the urine of patients undergoing standard antiretroviral chemotherapy indicates that NVP undergoes bioactivation in vivo. However, covalent binding of drug to protein in patients remains to be determined. In this study, we investigate the chemical basis of NVP protein adduct formation by using human serum albumin (HSA) and glutathione S-transferase pi (GSTP) as model proteins in vitro. In addition, HSA was isolated from serum samples of HIV-1 patients undergoing NVP therapy to measure NVP haptenation. Mass spectrometric analysis of 12-sulfoxyl-NVP-treated HSA revealed that the drug bound selectively to histidine (His146, His242, and His338) and a cysteine residue (Cys34). The reaction proceeds most likely by a concerted elimination-addition mechanism. This pathway was further confirmed by the observation of NVP-modified Cys47 in GSTP. Importantly, the same adduct (His146) was detected in HSA isolated from the blood of patients receiving NVP, providing direct evidence that NVP modifies protein in vivo, via the formation of a reactive metabolite.