A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs.

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Chronological Review and Rational and Future Prospects of Cannabis-Based Drug Development.

Despite the surge in cannabis chemistry research and its biological and medical activity, only a few cannabis-based pharmaceutical-grade drugs have been developed and marketed to date. Not many of these drugs are Food and Drug Administration (FDA)-approved, and some are still going through regulation processes. Active compounds including cannabinergic compounds (i.e., molecules targeted to modulate the endocannabinoid system) or phytocannabinoid analogues (cannabinoids produced by the plant) may be developed into single-molecule drugs. However, since in many cases treatment with whole-plant extract (whether as a solvent extraction, galenic preparation, or crude oil) is preferred over treatment with a single purified molecule, some more recently developed cannabis-derived drugs contain several molecules. Different combinations of active plant ingredients (API) from cannabis with proven synergies may be identified and developed as drugs to treat different medical conditions. However, possible negative effects between cannabis compounds should also be considered, as well as the effect of the cannabis treatment on the endocannabinoid system. FDA registration of single, few, or multiple molecules as drugs is a challenging process, and certain considerations that should be reviewed in this process, including issues of drug-drug interactions, are also discussed here.

Integrated Management of Residential Indoor Air Quality: A Call for Stakeholders in a Changing Climate.

A paradigm change in the management of environmental health issues has been observed in recent years: instead of managing specific risks individually, a holistic vision of environmental problems would assure sustainable solutions. However, concrete actions that could help translate these recommendations into interventions are lacking. This review presents the relevance of using an integrated indoor air quality management approach to ensure occupant health and comfort. At the nexus of three basic concepts (reducing contaminants at the source, improving ventilation, and, when relevant, purifying the indoor air), this approach can help maintain and improve indoor air quality and limit exposure to several contaminants. Its application is particularly relevant in a climate change context since the evolving outdoor conditions have to be taken into account during building construction and renovation. The measures presented through this approach target public health players, building managers, owners, occupants, and professionals involved in building design, construction, renovation, and maintenance. The findings of this review will help the various stakeholders initiate a strategic reflection on the importance of indoor air quality and climate change issues for existing and future buildings. Several new avenues and recommendations are presented to set the path for future research activities.

An approach to define potential radon emission level maps using indoor radon concentration measurements and radiogeochemical data positive proportion relationships.

The aim of this paper is to present the first step of a new approach to make a map of radonprone areas showing different potential radon emission levels in the Quebec province. This map is a tool intended to assist the Quebec government in identifying populations with a higher risk of indoor radon gas exposure. This map of radon-prone areas used available radiogeochemical information for the province of Quebec: (1) Equivalent uranium (eU) concentration from airborne surface gamma-ray surveys; (2) uranium concentration measurements in sediments; and (3) bedrock and surficial geology. Positive proportion relationships (PPR) between each individual criterion and the 1417 available basement radon concentrations were demonstrated. It was also shown that those criteria were reliable indicators of radon-prone areas. The three criteria were discretized into 3, 2 and 2 statistically significant different classes respectively. For each class, statistical heterogeneity was validated by Kruskal-Wallis one way analyses of variance on ranks. Maps of radon-prone areas were traced down for each criterion. Based on this statistical study and on the maps of radon-prone areas in Quebec, 18% of the dwellings located in areas with an equivalent uranium (eU) concentration from airborne surface gamma-ray surveys under 0.75 ppm showed indoor radon concentrations above 150 Bq/m3. This percentage increases to 33% when eU concentrations are between 0.75 ppm and 1.25 ppm and exceeds 40% when eU concentrations are above 1.25 ppm. A uranium concentration in sediments above 20 ppm showed an indoor radon concentration geometric mean of 215 Bq/m3 with more than 69% of the dwellings exceeding 150 Bq/m3 or more than 50% of dwellings exceeding the Canadian radon guideline of 200 Bq/m3. It is also shown that the radon emission potential is higher where a uranium-rich bedrock unit is not covered by a low permeability (silt/clay) surficial deposit.

A single-species approach considering additional physiological information for prediction of hepatic clearance of glycoprotein derivate therapeutics.

BACKGROUND AND OBJECTIVES: Existing methods for the prediction of human clearance of therapeutic proteins involve the use of allometry approaches. In general, these approaches have concentrated on the role of body weight, with only occasional attention given to more specific physiological parameters. The objective of this study was to develop a mechanism-based model of hepatic clearance (CL(H)), which combines a single-species scaling approach with liver physiology, for predicting CL(H) of selected glycoprotein derivate therapeutics, and to compare the outcome of this novel method with those of two empirical methods obtained from the literature - namely, the single-exponent theory and multiple-species allometry. Thus, this study was designed as an explanatory study to verify if the addition of physiological information is of benefit for extrapolating clearance of selected therapeutic proteins from one species to another. METHODS: Five glycoprotein derivate therapeutics that are known to be principally eliminated by asialoglycoprotein receptors (ASGPRs) under in vivo conditions were selected. It was assumed that the interspecies differences in CL(H) reported for these compounds are reflected by the interspecies differences in the abundance of these receptors. Therefore, key scaling factors related to these differences were integrated into one model. Fourteen extrapolation (prediction) scenarios across species were used in this study while comparing the single-species model, based on physiology, with the single-exponent theory. In addition, the physiological model was compared with multiple-species allometry for three proteins. RESULTS: In general, the novel physiological model is superior to the derived allometric methods. Overall, the physiological model produced a predicted CL(H) value with levels of accuracy of 100% within 3-fold, 100% within 2-fold and about 82% within 1.5-fold, compared with the observed values, whereas the levels of accuracy decreased to 93%, 77% and 53%, respectively, for allometry. The proposed physiological model is also superior to allometry on the basis of the root mean square error and absolute average fold error values. CONCLUSIONS: It has been demonstrated that interspecies differences in the abundance of ASGPRs principally govern interspecies variations in CL(H) of compounds that are principally eliminated by ASGPRs. Overall, the proposed physiological model is an additional tool, which should facilitate investigation and prediction of human CL(H) of specific glycoproteins solely on the basis of clearance data determined in a single preclinical species.

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.

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.

Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection.

The present paper proposes a modeling and simulation strategy for the prediction of pharmacokinetics (PK) of drug candidates by using currently available in silico and in vitro based prediction tools for absorption, distribution, metabolism and excretion (ADME). These methods can be used to estimate specific ADME parameters (such as rate and extent of absorption into portal vein, volume of distribution, metabolic clearance in the liver). They can also be part of a physiologically based pharmacokinetic (PBPK) model to simulate concentration-time profiles in tissues and plasma resulting from the overall PK after intravenous or oral administration. Since the ADME prediction tools are built only on commonly generated in silico and in vitro data, they can be applied already in early drug discovery, prior to any in vivo study. With the suggested methodology, the following advantages of the mechanistic PBPK modeling framework can now be utilized to explore potential clinical candidates already in drug discovery: (i) prediction of plasma (blood) and tissue PK of drug candidates prior to in vivo experiments, (ii) supporting a better mechanistic understanding of PK properties, as well as helping the development of more rationale PK-PD relationships from tissue kinetic data predicted, and hence facilitating a more rational decision during clinical candidate selection, and (iii) the extrapolation across species, routes of administration and dose levels.