Prediction of human drug clearance and anticipation of clinical drug-drug interaction potential from in vitro drug transport studies

A major concern in drug development is the characterization of new molecular entities (NMEs) with respect to their safety and efficacy. Both factors are determined by the drug’s exposure within the body which itself is affected by drug clearance processes. The major clearance organs are the liver and the kidney, where an interplay of metabolic enzymes and drug transporters mediates the elimination of drugs by metabolism and/or secretion. By that, inhibition of active clearance pathways, as observed from drug-drug interactions (DDIs), can result in alterations in a drug’s exposure. Therefore, the early characterization of the pharmacokinetic profile (PK) of NMEs is a major goal in preclinical drug development. However, due to lacking human in vivo PK data in this early phase of drug development, in vitro-based methods are commonly used to make a first assessment of the PK profile of NMEs. Consequently, the development, validation, and characterization of these methods is of major importance. Therefore, it was the aim of this work to investigate the prediction of human renal and hepatic drug clearances by in vitro-in vivo extrapolation (IVIVE) models and assess their feasibility to predict the DDI potential of drugs in human. To date, only few IVIVE approaches have been described to predict the human renal organ clearance based on filtration, secretion, and reabsorption. In a first study, we measured in LLC-PK1 cells the transport of 20 compounds with various physiochemical and PK properties. These data were incorporated into a novel kidney model to predict all renal clearance processes in human. Compared to reported renal clearances from clinical studies, the prediction accuracy in terms of percentage within three-fold error was 95%. Moreover, our model allowed the assessment of the contribution of filtration, secretion, and reabsorption to the net renal organ clearance in human. In a second study, we investigated the contribution of the organic anion transporting polypeptides (OATP) 1 and OATP1B3 to the net hepatic uptake clearance of statins. For this purpose, the absolute transporter protein abundances were determined by liquid chromatography-tandem mass spectrometry in cryopreserved human hepatocytes and single-transporter expressing HEK293 cells. Subsequently, uptake kinetics of eight statins and OATP1B1 and OATP1B3-specific reference substrates were determined in all expression systems. Transporter activity data generated in recombinant cell lines were extrapolated to hepatocyte values using relative transporter expression factors (REF) or relative activity factors (RAF). We showed that REF and RAF-based predictions were highly similar indicating a direct transporter expression-activity relationship. Moreover, we demonstrated that the REF-scaling method provided a powerful tool to quantitatively assess the transporter-specific contributions to the net uptake clearance of statins in hepatocytes. In a third study, we applied a recently developed IVIVE method to predict the human hepatic clearance and the DDI potential of eight statins. Application of the recently established Extended Clearance Concept Classification System (ECCCS), demonstrated a good predictability of the human hepatic clearance with six out of eight statins projected within a two-fold deviation to reported values. Furthermore, the DDI potential of the statins was assessed with respect to the impact of possible perpetrator drugs on hepatic uptake, metabolism, and biliary secretion and subsequently compared with reported clinical DDI effects. The predicted DDIs for statins showed excellent quantitative correlations with clinical observations. The ECCCS thus represents a powerful tool to anticipate the DDI potential of victim drugs based on in vitro drug metabolism and transport data. In a last study, we assessed the inhibitory potential of telaprevir, a new, direct-acting antiviral drug, on major human renal and hepatic drug transporters. By that, co-incubations of drug-transporter reference substrates and telaprevir in stable, single-transporter transfected HEK293 cells was investigated. Our data showed that telaprevir exhibited significant potential to inhibit major renal and hepatic drug transporters in human. Therefore, clinical co-administration of telaprevir together with drugs that are substrates of renal and hepatic transporters should be carefully monitored. Taken together, with the help of this work the safety profiles of NMEs can now be assessed in preclinical drug development based on in vitro methods. It is therefore expected, that the establishment, validation, and application of novel in vitro based methods, described in this work, will add significant value in the early assessment of the PK profile of NMEs

The extended clearance model and its use for the interpretation of hepatobiliary elimination data

Hepatic elimination is a function of the interplay between different processes such as sinusoidal uptake, intracellular metabolism, canalicular (biliary) secretion, and sinusoidal efflux. In this review, we outline how drugs can be classified according to their in vitro determined clearance mechanisms using the extended clearance model as a reference. The approach enables the determination of the rate-determining hepatic clearance step. Some successful applications will be highlighted, together with a discussion on the major consequences for the pharmacokinetics and the drug-drug interaction potential of drugs. Special emphasize is put on the role of passive permeability and active transport processes in hepatic elimination

Application of the extended clearance concept classification system (ECCCS) to predict the victim drug-drug interaction potential of statins

Background: During drug development, it is an important safety factor to identify the potential of new molecular entities to become a victim of drug-drug interactions (DDIs). In preclinical development, however, anticipation of clinical DDIs remains challenging due to the lack of in vivo human pharmacokinetic data. Methods: We applied a recently developed in vitro-in vivo extrapolation method, including hepatic metabolism and transport processes, herein referred to as the Extended Clearance Concept Classification System (ECCCS). The human hepatic clearances and the victim DDI potentials were predicted for atorvastatin, cerivastatin, fluvastatin, lovastatin acid, pitavastatin, pravastatin, rosuvastatin, and simvastatin acid. Results: Hepatic statin clearances were well-predicted by the ECCCS with six out of eight clearances projected within a two-fold deviation to reported values. In addition, worst-case DDI predictions were projected for each statin. Based on the ECCCS class assignment (4 classes), the mechanistic interplay of metabolic and transport processes, resulting in different DDI risks, was well-reflected by our model. Furthermore, predictions of clinically observed statins DDIs in combination with relevant perpetrator drugs showed good quantitative correlations with clinical observations. Conclusions: The ECCCS represents a powerful tool to anticipate the DDI potential of victim drugs based on in vitro drug metabolism and transport data

The Amsterdam Studies of Acute Psychiatry - II (ASAP-II): a comparative study of psychiatric intensive care units in the Netherlands

Background The number of patients in whom mental illness progresses to stages in which acute, and often forced treatment is warranted, is on the increase across Europe. As a consequence, more patients are involuntarily admitted to Psychiatric Intensive Care Units (PICU). From several studies and reports it has become evident that important dissimilarities exist between PICU's. The current study seeks to describe organisational as well as clinical and patient related factors across ten PICU's in and outside the Amsterdam region, adjusted for or stratified by level of urbanization. Method/Design This paper describes the design of the Amsterdam Studies of Acute Psychiatry II (ASAP-II). This study is a prospective observational cohort study comparing PICU's in and outside the Amsterdam region on various patient characteristics, treatment aspects and recovery related variables. Dissimilarities were measured by means of collecting standardized forms which were filled out in the framework of care as usual, by means of questionnaires filled out by mental health care professionals and by means of extracting data from patient files for every consecutive patient admitted at participating PICU's during a specific time period. Urbanization levels for every PICU were calculated conform procedures as proposed by the Dutch Central Bureau for Statistics (CBS). Discussion The current study may provide a deeper understanding of the differences between psychiatric intensive care units that can be used to promote best practice and benchmarking procedures, and thus improve the standard of care

Application of the extended clearance concept classification system (ECCCS) to predict the victim drug-drug interaction potential of statins

During drug development, it is an important safety factor to identify the potential of new molecular entities to become a victim of drug-drug interactions (DDIs). In preclinical development, however, anticipation of clinical DDIs remains challenging due to the lack of in vivo human pharmacokinetic data. We applied a recently developed in vitro-in vivo extrapolation method, including hepatic metabolism and transport processes, herein referred to as the Extended Clearance Concept Classification System (ECCCS). The human hepatic clearances and the victim DDI potentials were predicted for atorvastatin, cerivastatin, fluvastatin, lovastatin acid, pitavastatin, pravastatin, rosuvastatin, and simvastatin acid. Hepatic statin clearances were well-predicted by the ECCCS with six out of eight clearances projected within a two-fold deviation to reported values. In addition, worst-case DDI predictions were projected for each statin. Based on the ECCCS class assignment (4 classes), the mechanistic interplay of metabolic and transport processes, resulting in different DDI risks, was well-reflected by our model. Furthermore, predictions of clinically observed statins DDIs in combination with relevant perpetrator drugs showed good quantitative correlations with clinical observations. The ECCCS represents a powerful tool to anticipate the DDI potential of victim drugs based on in vitro drug metabolism and transport data

Interaction of the antiviral drug telaprevir with renal and hepatic drug transporters

Telaprevir is a new, direct-acting antiviral drug that has been approved for the treatment of chronic hepatitis C viral infection. First data on drug-drug interactions with co-medications such as cyclosporine, tacrolimus and atorvastatin have been reported recently. Drug transporting proteins have been shown to play an important role in clinically observed drug-drug interactions. The aim of this study was therefore to systematically investigate the potential of telaprevir to inhibit drug transporting proteins. The effect of telaprevir on substrate uptake mediated by drug transporters located in human kidney and liver was investigated on a functional level in HEK293 cell lines that over-express single transporter. Telaprevir was shown to exhibit significant inhibition of the human renal drug transporters OCT2 and MATE1 with IC50 values of 6.4 µM and 23.0 µM, respectively, whereas no inhibitory effect on OAT1 and OAT3 mediated transport by telaprevir was demonstrated. Liver drug transporters were inhibited with IC50 of 2.2 µM for OATP1B1, 6.8 µM for OATP1B3 and 20.7 µM for OCT1. Our data show that telaprevir exhibited significant potential to inhibit human drug transporters. In view of the inhibitory potential of telaprevir, clinical co-administration of telaprevir together with drugs that are substrates of renal or hepatic transporters should be carefully monitored

In Vitro- in Vivo Extrapolation (IVIVE) Method to Predict Human Renal Clearance of Drugs

Renal clearance is a key determinant of the elimination of drugs. To date, only few in vitro – in vi v o extrapolation (IVIVE) approaches have been described to predict the renal organ clearance as the net result of glomerular filtration, tubular secretion, and tubular reabsorption. In this study, we measured in LLC-PK1 cells the transport of 20 compounds that cover all four classes of the Biopharmaceutical Drug Disposition System. These data were incorporated into a novel kidney model to predict all renal clearance processes in human. We showed that filtration and secretion were main contributors to the renal organ clearance for all compounds, whereas reabsorption was predominant for compounds assigned to classes 1 and 2. Our results suggest that anionic drugs were not significantly secreted in LLC-PK1 cells, resulting in under-predicted clearances.When all study compounds were included a high overall correlation between the reported and predicted renal organ clearances was obtained (R2 = 0.83). The prediction accuracy in terms of percentage within twofold and threefold error was 70% and 95%, respectively. In conclusion, our novel IVIVE method allowed to predict the human renal organ clearance and the contribution of each underlying process

Fabrication of four-level hierarchical topographies through the combination of LIPSS and direct laser interference pattering on near-beta titanium alloy

Complex repetitive periodic surface patterns were produced on a near-beta Ti-13Nb-13Zr alloy, using two-beam Direct Laser Interference Patterning (DLIP) employing a picosecond-pulsed laser source with wavelengths of 355 nm, 532 nm and 1064 nm. Different types of Laser-induced periodic surface structures (LIPSS) are produced, including low and high spatial frequency LIPSS, which are observed frequently on top of the line-like DLIP microstructures, as well as quasi-periodic microstructures with periods greater than the laser wavelength. The feature size of the fabricated LIPSS features could be tuned as function of the utilized laser process parameters