Enhanced clearance of Abeta in brain by sustaining the plasmin proteolysis cascade.

The amyloid hypothesis states that a variety of neurotoxic beta-amyloid (Abeta) species contribute to the pathogenesis of Alzheimer's disease. Accordingly, a key determinant of disease onset and progression is the appropriate balance between Abeta production and clearance. Enzymes responsible for the degradation of Abeta are not well understood, and, thus far, it has not been possible to enhance Abeta catabolism by pharmacological manipulation. We provide evidence that Abeta catabolism is increased after inhibition of plasminogen activator inhibitor-1 (PAI-1) and may constitute a viable therapeutic approach for lowering brain Abeta levels. PAI-1 inhibits the activity of tissue plasminogen activator (tPA), an enzyme that cleaves plasminogen to generate plasmin, a protease that degrades Abeta oligomers and monomers. Because tPA, plasminogen and PAI-1 are expressed in the brain, we tested the hypothesis that inhibitors of PAI-1 will enhance the proteolytic clearance of brain Abeta. Our data demonstrate that PAI-1 inhibitors augment the activity of tPA and plasmin in hippocampus, significantly lower plasma and brain Abeta levels, restore long-term potentiation deficits in hippocampal slices from transgenic Abeta-producing mice, and reverse cognitive deficits in these mice.

Detection of ECG effects of (2R,4R)-monatin, a sweet flavored isomer of a component first identified in the root bark of the Sclerochitin ilicifolius plant.

Enzymatically-synthesized (2R,4R)-monatin has, due to its pure sweet taste, been evaluated for potential use in foods. Non-clinical studies have shown that (2R,4R)-monatin is well tolerated at high dietary concentrations, is not genotoxic/mutagenic, carcinogenic, or overtly toxic. In a pharmacokinetic and metabolism study involving 12 healthy males, consumption of a single oral dose (2 mg/kg) of (2R,4R)-monatin resulted in a small reduction of heart rate and prolongation of the QTcF interval of 20-24 ms, corresponding to the time of peak plasma levels (t(max)). These findings were evaluated in a cross-over thorough QT/QTc study with single doses of 150 mg (2R,4R)-monatin, placebo and positive control (moxifloxacin) in 56 healthy males. Peak (2R,4R)-monatin plasma concentration (1720 ± 538 ng/mL) was reached at 3.1 h (mean tmax). The placebo-corrected, change-from-baseline QTcF (ΔΔQTcF) reached 25 ms three hours after dosing, with ΔΔQTcF of 23 ms at two and four hours. Using exposure response (QTc) analysis, a significant slope of the relationship between (2R,4R)-monatin plasma levels and ΔΔQTcF was demonstrated with a predicted mean QT effect of 0.016 ms per ng/mL. While similarly high plasma levels are unlikely to be achieved by consumption of (2R,4R)-monatin in foods, QTc prolongation at this level is a significant finding.

A review and assessment of potential sources of ethnic differences in drug responsiveness.

The International Conference on Harmonization (ICH) E5 guidelines were developed to provide a general framework for evaluating the potential impact of ethnic factors on the acceptability of foreign clinical data, with the underlying objective to facilitate global drug development and registration. It is well recognized that all drugs exhibit significant inter-subject variability in pharmacokinetics and pharmacologic response and that such differences vary considerably among individual drugs and depend on a variety of factors. One such potential factor involves ethnicity. The objective of the present work was to perform an extensive review of the world literature on ethnic differences in drug disposition and responsiveness to determine their general significance in relation to drug development and registration. A few examples of suspected ethnic differences in pharmacokinetics or pharmacodynamics were identified. The available literature, however, was found to be heterologous, including a variety of study designs and research methodologies, and most of the publications were on drugs that were approved a long time ago.

The conduct of in vitro and in vivo drug-drug interaction studies: a PhRMA perspective.

Current regulatory guidances do not address specific study designs for in vitro and in vivo drug-drug interaction studies. There is a common desire by regulatory authorities and by industry sponsors to harmonize approaches to allow for a better assessment of the significance of findings across different studies and drugs. There is also a growing consensus for the standardization of cytochrome P450 (CYP) probe substrates, inhibitors, and inducers and for the development of classification systems to improve the communication of risk to health care providers and patients. While existing guidances cover mainly CYP-mediated drug interactions, the importance of other mechanisms, such as transporters, has been recognized more recently and should also be addressed. This paper was prepared by the Pharmaceutical Research and Manufacturers of America (PhRMA) Drug Metabolism and Clinical Pharmacology Technical Working Groups and represents the current industry position. The intent is to define a minimal best practice for in vitro and in vivo pharmacokinetic drug-drug interaction studies targeted to development (not discovery support) and to define a data package that can be expected by regulatory agencies in compound registration dossiers.

Integrated population pharmacokinetics of etanercept in healthy subjects and in patients with rheumatoid arthritis and ankylosing spondylitis.

Etanercept pharmacokinetics in patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriasis were assessed separately with distinct models using population pharmacokinetics methods of limited precision. The different model structures and associated significant covariates identified by these earlier methods made it difficult to compare etanercept pharmacokinetics among disease groups. This integrated analysis aimed to establish a framework to evaluate previously established population pharmacokinetic models of etanercept, and to identify consistent and important demographic and disease factors that affected etanercept pharmacokinetics in a diverse population of healthy subjects and patients with RA and AS. In this integrated analysis, cumulative rich and sparse etanercept concentration data from 53 healthy volunteers, 212 patients with RA, and 346 patients with AS were examined and compared using nonlinear mixed effect methodology implemented the in NONMEM VI software package. A more precise estimation method (FOCEi) was employed and compared with the first-order method in population pharmacokinetics model building and evaluation. The integrated analysis found that an optimal population pharmacokinetics model with a 2-compartment structure adequately characterized etanercept pharmacokinetics in all subject groups. Health status or disease type did not significantly affect etanercept pharmacokinetics. In adult patients with RA and AS, age and body weight do not significantly affect etanercept pharmacokinetics.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets.

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution.

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss ). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach.

The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach.

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance.

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective.

Current regulatory guidances do not address specific study designs for in vitro and in vivo drug-drug interaction studies. There is a common desire by regulatory authorities and by industry sponsors to harmonize approaches, to allow for a better assessment of the significance of findings across different studies and drugs. There is also a growing consensus for the standardization of cytochrome P450 (P450) probe substrates, inhibitors and inducers and for the development of classification systems to improve the communication of risk to health care providers and to patients. While existing guidances cover mainly P450-mediated drug interactions, the importance of other mechanisms, such as transporters, has been recognized more recently, and should also be addressed. This article was prepared by the Pharmaceutical Research and Manufacturers of America (PhRMA) Drug Metabolism and Clinical Pharmacology Technical Working Groups and represents the current industry position. The intent is to define a minimal best practice for in vitro and in vivo pharmacokinetic drug-drug interaction studies targeted to development (not discovery support) and to define a data package that can be expected by regulatory agencies in compound registration dossiers.