Prediction of Drug Tissue to Plasma Concentration Ratios Using a Measured Volume of Distribution in Combination With Lipophilicity

Distribution Clearance: Significance and Underlying Mechanisms

PURPOSE

Evaluation of distribution kinetics is a neglected aspect of pharmacokinetics. This study examines the utility of the model-independent parameter whole body distribution clearance (CLD) in this respect.

METHODS

Since mammillary compartmental models are widely used, CLD was calculated in terms of parameters of this model for 15 drugs. The underlying distribution processes were explored by assessment of relationships to pharmacokinetic parameters and covariates.

RESULTS

The model-independence of the definition of the parameter CLD allowed a comparison of distributional properties of different drugs and provided physiological insight. Significant changes in CLD were observed as a result of drug-drug interactions, transporter polymorphisms and a diseased state.

CONCLUSION

Total distribution clearance CLD is a useful parameter to evaluate distribution kinetics of drugs. Its estimation as an adjunct to the model-independent parameters clearance and steady-state volume of distribution is advocated.

Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

Assessment of wastewater-borne pharmaceuticals in tissues and body fluids from riverine fish

Riverine fish in densely populated areas is constantly exposed to wastewater-borne contaminants from effluent discharges. These can enter the organism through the skin, gills or by ingestion. Whereas most studies assessing the contaminant burden in exposed fish have focused either on muscle or a limited set of tissues. Here we set out to generate a more comprehensive overview of the distribution of pollutants across tissues by analyzing a panel of matrices including liver, kidney, skin, brain, muscle, heart, plasma and bile. To achieve a broad analyte coverage with a minimal bias towards a specific contaminant class, sample extracts from four fish species were analyzed by High-Performance Liquid Chromatography (HPLC) - high-resolution mass spectrometry (HRMS) for the presence of 600 wastewater-borne pharmaceutically active compounds (PhACs) with known environmental relevance in river water through a suspect-screening analysis. A total of 30 compounds were detected by suspect screening in at least one of the analyzed tissues with a clear prevalence of antidepressants. Of these, 15 were detected at confidence level 2.a (Schymanski scale), and 15 were detected at confidence level 1 following confirmation with authentic standards, which furthermore enabled their quantification. The detected PhACs confirmed with level 1 of confidence included acridone, acetaminophen, caffeine, clarithromycin, codeine, diazepam, diltiazem, fluoxetine, ketoprofen, loratadine, metoprolol, sertraline, sotalol, trimethoprim, and venlafaxine. Among these substances, sertraline stood out as it displayed the highest detection frequency. The values of tissue partition coefficients for sertraline in the liver, kidney, brain and muscle were correlated with its physicochemical properties. Based on inter-matrix comparison of detection frequencies, liver, kidney, skin and heart should be included in the biomonitoring studies of PhACs in riverine fish.

QSAR model to predict Kp,uu,brain with a small dataset, incorporating predicted values of related parameter

The unbound brain-to-plasma concentration ratio (K_p,uu,brain) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain K_p,uu,brain. However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure-activity relationship (QSAR) model with a small dataset of K_p,uu,brain, we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental K_p,uu,brain from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the K_p,uu,brain of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455, r² = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the K_p,uu,brain QSAR model when the available number of datasets is small.

Discovery of 1'-(1-phenylcyclopropane-carbonyl)-3H-spiro[isobenzofuran-1,3'-pyrrolidin]-3-one as a novel steroid mimetic scaffold for the potent and tissue-specific inhibition of 11β-HSD1 using a scaffold-hopping approach

11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) has been identified as the primary enzyme responsible for the activation of hepatic cortisone to cortisol in specific peripheral tissues resulting in the concomitant antagonism of insulin action within these tissues. Dysregulation of 11β-HSD1, particularly in adipose tissues, has been associated with metabolic syndrome and type 2 diabetes mellitus. Therefore, inhibition of 11β-HSD1 with a small nonsteroidal molecule is therapeutically desirable. Implementation of a scaffold-hopping approach revealed a three-point pharmacophore for 11β-HSD1 that was utilized to design a steroid mimetic scaffold. Reiterative optimization provided valuable insight into the bioactive conformation of our novel scaffold and led to the discovery of INCB13739. Clinical evaluation of INCB13739 confirmed for the first time that tissue-specific inhibition of 11β-HSD1 in patients with type 2 diabetes mellitus was efficacious in controlling glucose levels and reducing cardiovascular risk factors.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate’s volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.

Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight

Rosuvastatin is a frequently used probe to study transporter‐mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time‐concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293‐transfected cells, and developed a semimechanistic model with a bottom‐up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two‐fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (C_max; 76%; AFE: 1.05), and terminal half‐life (t_1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Methods to Predict Volume of Distribution

PURPOSE OF REVIEW

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement.

RECENT FINDINGS

Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input.

SUMMARY

Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Simultaneous Assessment of Hepatic Transport and Metabolism Pathways with a Single Probe Using Individualized PBPK Modeling of 14CO2 Production Rate Data

Erythromycin is a substrate of cytochrome P4503A4 (CYP3A4) and multiple drug transporters. Although clinical evidence suggests that uptake transport is likely to play a dominant role in erythromycin's disposition, the relative contributions of individual pathways are unclear. Phenotypic evaluation of multiple pathways generally requires a probe drug cocktail. This approach can result in ambiguous conclusions due to imprecision stemming from overlapping specificity of multiple drugs. We hypothesized that an individualized physiologically based pharmacokinetic modeling approach incorporating ¹⁴CO₂ production rates (iPBPK-R) of the erythromycin breath test (ERMBT) would enable us to differentiate the contribution of metabolic and transporter pathways to erythromycin disposition. A seven-compartmental physiologically based pharmacokinetic (PBPK) model was built for ¹⁴C-erythromycin administered intravenously. Transporter clearance and CYP3A4 clearance were embedded in hepatic compartments. ¹⁴CO₂ production rates were simulated taking the first derivative of by-product ¹⁴CO₂ concentrations. Parameters related to nonrenal elimination pathways were estimated by model fitting the ERMBT data of 12 healthy subjects individually. Optimized iPBPK-R models fit the individual rate data well. Using one probe, nine PBPK parameters were simultaneously estimated per individual. Maximum velocity of uptake transport, CYP3A4 clearance, total passive diffusion, and others were found to collectively control ¹⁴CO₂ production rates. The median CYP3A4 clearance was 12.2% of the input clearance. Male subjects had lower CYP3A4 activity than female subjects by 11.3%. We applied iPBPK-R to ERMBT data to distinguish and simultaneously estimate the activity of multiple nonrenal elimination pathways in healthy subjects. The iPBPK-R framework is a novel tool for delineating rate-limiting and non-rate-limiting elimination pathways using a single probe. SIGNIFICANCE STATEMENT: Our developed individualized physiologically based pharmacokinetic modeling approach incorporating rate data (iPBPK-R) enabled us to distinguish and simultaneously estimate the activity of multiple nonrenal elimination pathways of erythromycin in healthy subjects. A new interpretation of erythromycin breath test (ERMBT) data was also obtained via iPBPK-R. We found that rate data have rich information allowing estimation of per-person PBPK parameters. This study serves as proof of principle that the iPBPK-R framework is a novel tool for delineating rate-limiting and non-rate-limiting elimination pathways using a single probe. iPBPK-R can be applied to other rate-derived data beyond ERMBT. Potential areas of application include drug-drug interaction, pathophysiological effects on drug disposition, and the role of biomarkers on hemodialysis efficiency utilizing estimated adjustment factors with correlation analysis.

Synthesis and Biological Evaluation of Polyfluoroalkylated Antipyrines and their Isomeric O-Methylpyrazoles

Background:

Formally belonging to the non-steroidal anti-inflammatory drug class pyrazolones have long been used in medical practices.

Objective:

Our goal is to synthesize N-methylated 1-aryl-3-polyfluoroalkylpyrazolones as fluorinated analogs of antipyrine, their isomeric O-methylated derivatives resembling celecoxib structure and evaluate biological activities of obtained compounds.

Methods:

In vitro (permeability) and in vivo (anti-inflammatory and analgesic activities, acute toxicity, hyperalgesia, antipyretic activity, “open field” test) experiments. To suggest the mechanism of biological activity, molecular docking of the synthesized compounds was carried out into the tyrosine site of COX-1/2.

Conclusion:

The trifluoromethyl antipyrine represents a valuable starting point in design of the lead series for discovery new antipyretic analgesics with anti-inflammatory properties.

Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice

BACKGROUND

Majority of reported studies so far developed correlation regression equations using the rat muscle-to-plasma drug concentration ratio (Kp-muscle) to predict tissue-to-plasma drug concentration ratios (Kp-tissues). Use of regression equations derived from rat Kp-muscle may not be ideal to predict the mice tissue-Kps as there are species differences.

OBJECTIVES

(i) To develop the linear regression equations using mouse tissue-Kps; (ii) to assess the correlation between organ blood flow and/or organ weight with tissue-Kps and (iii) compare the observed tissue-Kps from mice with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations.

METHOD

Disposition of 12 small molecules were investigated extensively in mouse plasma and tissues after a single oral dose administration. Linear correlation was assessed for each of the tissue with rest of the other tissues, separately for weak and strong bases.

RESULT

Newly developed regression equations using mice tissue-Kps, predicted 79% data points within twofold. As observed correlation r ² range was 0.75-0.98 between Kp-muscle and Kp-brain, -spleen, -skin, -liver, -lung, suggesting superior correlation between the tissue-Kps. Order of tissue-Kps, showed that tissue concentrations were directly proportional to the organ blood flow and inversely to the organ weight. Further, the observed tissue-Kps from mice were compared with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. Overall, 46, 54 and 63% data points were under predicted (<0.5-fold) for liver, spleen and lung, respectively. Whereas 63 and 75% data points were over predicted (>twofold) for skin and brain, respectively. These findings suggest that cross species extrapolation predictability is poor.

CONCLUSION

All these findings together suggest that mouse specific regression equations developed under controlled experimental conditions could be most appropriate for predicting mouse tissue-Kps for compounds with wide range of volume of distribution.

Prediction of tissue concentrations of monoclonal antibodies in mice from plasma concentrations

The objectives of this study were to develop and evaluate allometric methods for predicting tissue-to-plasma partition coefficients (Kp) in mice from experimentally determined in-vivo volume of distribution at steady state (Vss) for monoclonal antibodies (mAbs). The Vss was allometrically predicted (using a fixed exponent 1.0 or 0.9) in a given tissue of the mice. The Kp was predicted using Vss and tissue specific physiological parameters. In total, Kp values were predicted for 20 mAbs, 121 tissues, and 665 tissue concentrations. The predicted Kp values and tissue concentrations were compared with the experimental results as well as an empirically predicted antibody biodistribution coefficient (ABC). Comparison of the predicted Kp values by the two proposed methods with experimentally determined Kp values indicated that 64-75% of the predicted Kp values were within two-fold prediction error. For 665 tissue concentrations, 63%, 74%, and 48% tissue concentration ratio were within 0.5-2 fold prediction error by exponent 1.0, exponent 0.9, and ABC, respectively. The proposed allometric methods are better than ABC method for the prediction of tissue Kp values and tissue concentrations. The proposed methods can reasonably predict tissue concentrations of mAbs using plasma concentration gathered at early stage of biologics development.

Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models

BACKGROUND

Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models.

METHODS

Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method.

RESULT

Both slow- and fast-growing tumor-Kps showed good correlation (r ² ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts.

CONCLUSION

Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.

Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics

Mavoglurant (MVG) is an antagonist at the metabotropic glutamate receptor-5 currently under clinical development at Novartis Pharma AG for the treatment of central nervous system diseases. The aim of this study was to develop and optimise a population whole-body physiologically-based pharmacokinetic (WBPBPK) model for MVG, to predict the impact of drug-drug interaction (DDI) and age on its pharmacokinetics. In a first step, the model was fitted to intravenous (IV) data from a clinical study in adults using a Bayesian approach. In a second step, the optimised model was used together with a mechanistic absorption model for exploratory Monte Carlo simulations. The ability of the model to predict MVG pharmacokinetics when orally co-administered with ketoconazole in adults or administered alone in 3-11 year-old children was evaluated using data from three other clinical studies. The population model provided a good description of both the median trend and variability in MVG plasma pharmacokinetics following IV administration in adults. The Bayesian approach offered a continuous flow of information from pre-clinical to clinical studies. Prediction of the DDI with ketoconazole was consistent with the results of a non-compartmental analysis of the clinical data (threefold increase in systemic exposure). Scaling of the WBPBPK model allowed reasonable extrapolation of MVG pharmacokinetics from adults to children. The model can be used to predict plasma and brain (target site) concentration-time profiles following oral administration of various immediate-release formulations of MVG alone or when co-administered with other drugs, in adults as well as in children.

cIEF for rapid pKa determination of small molecules: a proof of concept

A capillary isoelectric focusing (cIEF) method was developed for the determination of the ionization constants (pKa) of small molecules. Two approaches used to decrease the electroosmotic flow (EOF) were compared: (i) a hydroxypropylcellulose (HPC) coated capillary in aqueous medium and (ii) the addition of glycerol to act as a viscosifying agent. The cIEF method with the glycerol medium was selected, and the ionization constants of 22 basic and 21 acidic compounds, including 15 pharmaceutical drugs, were determined, resulting in pKa values from 3.5 to 7.4 and 6.4 to 9.3, respectively. cIEF offers an interesting alternative to other techniques for pKa determination with low sample consumption, high throughput and low cost.

Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.

Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics

Accurate prediction of pharmacokinetics (PK) in humans has been a vital part of drug discovery. The aims of this study are to verify the usefulness of scaling factors for clearance (CL) and apparent volume of distribution at the steady state (Vss ) estimated from the difference between observed and predicted PK profiles in rats for human PK prediction, and to develop a novel hybrid physiologically based pharmacokinetic (PBPK) model with the two scaling factors. The human prediction accuracies for CL with in vitro-in vivo extrapolation and Vss with a tissue composition model were improved by using rat-scaling factors. This improvement was explainable by data that the scaling factors for CL and Vss in rats were correlated with those in humans. The predictability of plasma concentration-time profiles by the hybrid PBPK model incorporating two scaling factors was compared mainly with that by the conventional PBPK model. The hybrid PBPK model yielded higher prediction accuracy for plasma concentrations than the conventional method. Furthermore, we proposed a tiered approach using the three prediction methods, including the hybrid Dedrick approach, that were previously reported (Sayama H, Komura H, Kogayu M. 2013. Drug Metab Dispos 41:498-507), taking the available information in the individual stages of drug discovery and development into consideration.

Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters

1.

RATIONALE

Tissue-to-plasma partition coefficients (Kp) that characterize the tissue distribution of a drug are important input parameters in physiologically based pharmacokinetic (PBPK) models. The aim of this study was to develop an empirically derived Kp prediction algorithm using input parameters that are available early in the investigation of a compound. 2.

METHODS

The algorithm development dataset (n = 97 compounds) was divided according to acidic/basic properties. Using multiple stepwise regression, the experimentally derived Kp values were correlated with the rat volume of distribution at steady state (Vss) and one or more physicochemical parameters (e.g. lipophilicity, degree of ionization and protein binding) to account for inter-organ variability of tissue distribution. 3.

RESULTS

Prediction equations for the value of Kp were developed for 11 tissues. Validation of this model using a test dataset (n = 20 compounds) demonstrated that 65% of the predicted Kp values were within a two-fold error deviation from the experimental values. The developed algorithms had greater prediction accuracy compared to an existing empirically derived and a mechanistic tissue-composition algorithm. 4.

CONCLUSIONS

This innovative method uses readily available input parameters with reasonable prediction accuracy and will thus enhance both the usability and the confidence in the outputs of PBPK models.

Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat

OBJECTIVES

To use methods from the literature to predict rat tissue:plasma partition coefficients (Kps) and volume of distribution values. Determine which model provides the most accurate predictions to increase confidence in the use of predicted pharmacokinetic parameters in physiologically based pharmacokinetic modelling.

METHODS

Six models were used to predict Kps and four to predict V(ss) for a dataset of 81 compounds in 11 rat tissues, and the predictions were compared with experimentally derived values.

KEY FINDINGS

Kp predictions made by the Rodgers et al. model were the most accurate, with 77% within threefold of experimental values. The Poulin & Theil model was the most accurate for the prediction of V(ss) , with 87% of predictions within threefold.

CONCLUSIONS

This study has shown that in-silico models available in the literature can be used to accurately predict Kp and V(ss) in rat. The Rodgers et al. model has been shown to provide the most accurate Kp predictions, with consistent accuracy across all drug classes and tissues. It was also the most accurate V(ss) predictor when no in-vivo data were used as input. However, transporter systems and other mechanisms that are not yet fully understood need to be incorporated into these types of models in the future to further increase their applicability.

In silico prediction of efavirenz and rifampicin drug-drug interaction considering weight and CYP2B6 phenotype

AIMS

This study aimed to test whether a pharmacokinetic simulation model could extrapolate nonclinical drug data to predict human efavirenz exposure after single and continuous dosing as well as the effects of concomitant rifampicin and further to evaluate the weight-based dosage recommendations used to counteract the rifampicin-efavirenz interaction.

METHODS

Efavirenz pharmacokinetics were simulated using a physiologically based pharmacokinetic model implemented in the Simcyp™ population-based simulator. Physicochemical and metabolism data obtained from the literature were used as input for prediction of pharmacokinetic parameters. The model was used to simulate the effects of rifampicin on efavirenz pharmacokinetics in 400 virtual patients, taking into account bodyweight and CYP2B6 phenotype.

RESULTS

Apart from the absorption phase, the simulation model predicted efavirenz concentration-time profiles reasonably well, with close agreement with clinical data. The simulated effects of rifampicin co-administration on efavirenz treatment showed only a minor decrease of 16% (95% confidence interval 13-19) in efavirenz area under the concentration-time curve, of the same magnitude as what has been clinically observed (22%). Efavirenz exposure depended on CYP2B6 phenotype and bodyweight. Increasing the efavirenz dose during concomitant rifampicin was predicted to be most successful in patients over 50 kg regardless of CYP2B6 status.

CONCLUSIONS

Our findings, although based on a simulation approach using limited in vitro data, support the current recommendations for using a 50 kg bodyweight cut-off for efavirenz dose increment when co-treating with rifampicin.

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.

Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods

The parameters characterizing tissue distribution refer to the tissue/plasma partition coefficients (Kp), which can be used to derive volume of distribution at steady-state (V(ss)). The effort for predicting drug distribution in human has been further expanded to calculation methods using in vitro-based algorithms. The objective of the present study was to develop a novel prediction method to estimate human V(ss) for moderate-to-strong bases. The predictive performance of the novel method was compared with other well established in vitro-based methods available in the literature. Relevant information collected from previous prediction studies of V(ss) facilitated the development of the novel method. This was based on the calculation of V(ss) from data on Kp, which were estimated by correlating the unbound tissue/plasma ratio in vivo (Kpu) with the unbound red blood cells partitioning (RBCu) determined in vitro. The comparative assessment of the novel correlation method with existing prediction methods of human V(ss) was done using a literature dataset of 61 basic drugs (at least one pK(a) > or = 7). The five existing V(ss) prediction methods published in the literature are comprised of four versions of tissue composition-based models along with the model of Lombardo using the principle of Oie-Tozer. The statistical analysis of the prediction performance indicated that the novel method demonstrated a greater degree of accuracy compared to all other published methods. The maximum percentage of predicted values that fall within a twofold-error range is 77% for the basic drugs tested. Overall, the present study describes the development and the assessment of the predictive performance of the novel prediction method of human V(ss) based upon in vitro data, which appears to be superior based on the current dataset studied for basic drugs.