Application of partition coefficient methods to predict tissue:plasma affinities in common farm animals: Influence of ionisation state

Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.

The Effect of Inflammatory Bowel Disease and Irritable Bowel Syndrome on Pravastatin Oral Bioavailability: In vivo and in silico evaluation using bottom-up wbPBPK modeling

The common disorders irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) can modify the drugs' pharmacokinetics via their induced pathophysiological changes. This work aimed to investigate the impact of these two diseases on pravastatin oral bioavailability. Rat models for IBS and IBD were used to experimentally test the effects of IBS and IBD on pravastatin pharmacokinetics. Then, the observations made in rats were extrapolated to humans using a mechanistic whole-body physiologically-based pharmacokinetic (wbPBPK) model. The rat in vivo studies done herein showed that IBS and IBD decreased serum albumin (> 11% for both), decreased PRV binding in plasma, and increased pravastatin absolute oral bioavailability (0.17 and 0.53 compared to 0.01) which increased plasma, muscle, and liver exposure. However, the wbPBPK model predicted muscle concentration was much lower than the pravastatin toxicity thresholds for myotoxicity and rhabdomyolysis. Overall, IBS and IBD can significantly increase pravastatin oral bioavailability which can be due to a combination of increased pravastatin intestinal permeability and decreased pravastatin gastric degradation resulting in higher exposure. This is the first study in the literature investigating the effects of IBS and IBD on pravastatin pharmacokinetics. The high interpatient variability in pravastatin concentrations as induced by IBD and IBS can be reduced by oral administration of pravastatin using enteric-coated tablets. Such disease (IBS and IBD)-drug interaction can have more drastic consequences for narrow therapeutic index drugs prone to gastric degradation, especially for drugs with low intestinal permeability.

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.

Systematic Analysis of the Whole-Body Tissue Distribution and Fatty Acid Compositions of Membrane Lipids in CD1 and NMRI Mice and Wistar Rats

Understanding the tissue distribution of phospholipids and glycerolipids in animal models enables promoting the pharmacokinetic study of drugs and related PK predictions. The measurement of lipid compositions in animal models, usually mice and rats, without a standardized approach hindered the accuracy of PBPK investigation. In this work, high resolution mass spectrometry was applied to profile the tissue distribution of phospholipids and glycerolipids in 12 organs/tissues of mice and rats. Using this method, not only the amounts of phospholipids and glycerolipids in each organ/tissue but also the fatty acid compositions were acquired. In order to explore the interspecies specificity of lipid distribution in different organs/tissues, three animal species including CD1 mice, NMRI mice, and Wister rats were used in this systematic study. Globally, more organ specificity was observed. It was found that the brain is the organ containing the most abundant phosphatidylserine lipids (PSs) in all three animal models, leading to brain tissues having the most concentrated acidic phospholipids. Diverse fatty acid compositions in each lipid class were clearly revealed. Certain tissues/organs also had a specific selection of unique fatty acid compositions, for example, unreferenced FA(18 : 2) in the brain. It turned out that the access of free fatty acids affects the incorporation of acyl chain in phospholipids and glycerolipids. In the analysis, ether lipids were also profiled with the observation of dominant ePEs in brain tissues. However, little interspecies difference was found for fatty acid constituents and tissues distribution of phospholipids and glycerolipids.

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Seeking Nonspecific Binding: Assessing the Reliability of Tissue Dilutions for Calculating Fraction Unbound

It has become commonplace (270+ article citations to date) to measure the fraction unbound (FrUn) of drugs in tissue homogenates and diluted plasma and then use a Correction Factor Equation (CFE) to extrapolate to the undiluted state. The CFE is based on assumptions of nonspecific binding with experimental use of very low drug concentrations. There are several possible determinants of apparent nonspecific binding as measured by methods such as equilibrium dialysis: true macromolecule binding and lipid partitioning along with receptor, enzyme, and transporter interactions. Theoretical calculations based on nonlinear protein binding indicate that the CFE will be most reliable to obtain FrUn when added drug concentration is small, binding constants are weak, protein concentrations are relatively high, and tissue dilution is minimal. When lipid partitioning is the sole factor determining apparent tissue binding, the CFE should be perfectly accurate. Use of very low drug concentrations, however, makes it more likely that specific binding to receptors and other targets may occur, and thus FrUn may reflect some binding to such components. Inclusion of trapped blood can clearly cause minor to marked discrepancies from purely tissue binding alone, which can be corrected. Furthermore, assessment of the occurrence of ionization/pH shifts, drug instability, and tissue metabolism may be necessary. Caution is needed in the use and interpretation of results from tissue dilution studies and other assessments of nonspecific binding, particularly for very strongly bound drugs with very small FrUn values and in tissues with metabolic enzymes, receptors, and trapped blood. SIGNIFICANCE STATEMENT: The use of tissue, plasma, and cell preparations to help obtain fraction unbound and tissue-to-plasma partition coefficients in pharmacokinetics has grown commonplace, especially for brain. This report examines theoretical, physiological, and experimental issues that need consideration before trusting such measurements and calculations.

Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors—ketoconazole, itraconazole, and posaconazole—were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.

Heterogeneous drug tissue binding in brain regions of rats, Alzheimer's patients and controls: impact on translational drug development

For preclinical and clinical assessment of therapeutically relevant unbound, free, brain concentrations, the pharmacokinetic parameter fraction of unbound drug in brain (f_u,brain) is commonly used to compensate total drug concentrations for nonspecific brain tissue binding (BTB). As, homogenous BTB is assumed between species and in health and disease, rat BTB is routinely used. The impact of Alzheimer’s disease (AD) on drug BTB in brain regions of interest (ROI), i.e., f_u,brain,ROI, is yet unclear. This study for the first time provides insight into regional drug BTB and the validity of employing rat f_u,brain,ROI as a surrogate of human BTB, by investigating five marketed drugs in post-mortem tissue from AD patients (n = 6) and age-matched controls (n = 6). Heterogeneous drug BTB was observed in all within group comparisons independent of disease and species. The findings oppose the assumption of uniform BTB, highlighting the need of case-by-case evaluation of f_u,brain,ROI in translational CNS research.

Application of the Tissue Composition-Based Model to Minipig for Predicting the Volume of Distribution at Steady State and Dermis-to-Plasma Partition Coefficients of Drugs Used in the Physiologically Based Pharmacokinetics Model in Dermatology

The minipig continues to build a reputation as a viable alternative large animal model to predict humans in dermatology and toxicology studies. Therefore, it is essential to describe and predict the pharmacokinetics in that species to speed up the clinical candidate selection. Essential input parameters in whole-body physiologically based pharmacokinetic models are the tissue-to-plasma partition coefficients and the resulting volume of distribution at steady-state (V_ss). Mechanistic in vitro- and in silico-based models used for predicting these parameters of tissue distribution of drugs refer to the tissue composition-based model (TCM). Robust TCMs were initially developed for some preclinical species (e.g., rat and dog) and human; however, there is currently no model available for the minipig. Therefore, the objective of this present study was to develop a TCM for the minipig and to estimate the corresponding tissue composition data. Drug partitioning into the tissues was predominantly governed by lipid and protein binding effects in addition to drug solubilization and pH gradient effects in the aqueous phase on both sides of the biological membranes; however, some more complex tissue distribution processes such as drug binding to the collagen-laminin material in dermis and a restricted drug partitioning into membranes of tissues for compounds that are amphiphilic and contain sulfur atom(s) were also challenged. The model was validated by predicting V_ss and the dermis-to-plasma partition coefficients (K_p-dermis) of 68 drugs. The prediction of K_p-dermis was extended to humans for comparison with the minipig. The results indicate that the extended TCM provided generally good agreements with observations in the minipig showing that it is also applicable to this preclinical species. In general, up to 86% and 100% of the predicted V_ss values are respectively within 2-fold and 3-fold errors compared to the experimentally determined values, whereas these numbers are 78% and 94% for K_p-dermis when the anticipated outlier compounds are not included. Binding data to dermis are comparable between minipigs and humans. Overall, this study is a first step toward developing a mechanistic TCM for the minipig, with the aim of increasing the use of physiologically based pharmacokinetic models of drugs for that species in addition to rats, dogs, and humans because such models are used in preclinical and clinical transdermal studies.

Lipid, water, and protein composition to facilitate kinetic modeling of the auditory pathway

Environments combining JP-8 jet fuel exposure with heightened ambient noise may accelerate hearing loss induced by noise. To reduce animal use and facilitate kinetic modeling of this military aviation fuel, tissue-specific parameters are required, including water, protein, and lipid content. However, tissues involved in hearing, including cochlea, brainstem, frontal, and temporal lobe, have not been characterized before. Therefore, water content was determined by lyophilization of rat auditory tissues and the protein of the freeze dried remainder was quantified using a bicinchoninic acid assay. Lipids were extracted from fresh-frozen rat auditory tissues and separated into neutral lipids, free fatty acids, neutral phospholipids, and acidic phospholipids using solid phase extraction. Phospholipid fractions were confirmed by ³¹P nuclear magnetic resonance analysis showing distinct phospholipid profiles. Lipid content in reference tissues, such as kidney and adipose, confirmed literature values. For the first time, lipid content in the rat auditory pathway was determined showing that total lipid content was lowest in cochlea and highest in brainstem compared with frontal and temporal lobes. Auditory tissues displayed distinct lipid fraction profiles. The information on water, protein, and lipid composition is necessary to validate algorithms used in mathematical models and predict partitioning of chemicals of future interest into these tissues. This research may reduce the use of animals to measure partition coefficients for prospective physiological models.

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.

Development of Physiologically Based Organ Models to Evaluate the Pharmacokinetics of Drugs in the Testes and the Thyroid Gland

We extended a generic whole-body physiologically based pharmacokinetic (PBPK) model for rats and humans for organs of the reproductive and endocrine systems (i.e., the testes and the thyroid gland). An extensive literature search was performed, first, to determine the most generic organ model structures for testes and thyroid across species, and, second, to identify the corresponding anatomic and physiological parameters in rats and humans. The testes and thyroid organ models were implemented in the PBPK modeling software PK-Sim and MoBi. The capability of the PBPK approach to simulate the testes and thyroid tissue concentration data was demonstrated using a series of test compounds. The presented organ model structures and parameterization yielded a close agreement between observed and simulated tissue concentrations over time. The organ models are ready to be used to predict the pharmacokinetics of passively entering drugs in the testes and thyroid tissue in a generic PBPK modeling framework.

Drug Distribution to Human Tissues: Prediction and Examination of the Basic Assumption in In Vivo Pharmacokinetics-Pharmacodynamics (PK/PD) Research

The tissue:plasma partition coefficients (Kp ) are good indicators of the extent of tissue distribution. Therefore, advanced tissue composition-based models were used to predict the Kp values of drugs under in vivo conditions on the basis of in vitro and physiological input data. These models, however, focus on animal tissues and do not challenge the predictions with human tissues for drugs. The first objective of this study was to predict the experimentally determined Kp values of seven human tissues for 26 drugs. In all, 95% of the predicted Kp values are within 2.5-fold error of the observed values in humans. Accordingly, these results suggest that the tissue composition-based model used in this study is able to provide accurate estimates of drug partitioning in the studied human tissues. Furthermore, as the Kp equals to the ratio of total concentration between tissue and plasma, or the ratio of unbound fraction between plasma (fup ) and tissue (fut ), this parameter Kp would deviate from the unity. Therefore, the second objective was to examine the corresponding relationships between fup and fut values experimentally determined in humans for several drugs. The results also indicate that fup may significantly deviate to fut ; the discrepancies are governed by the dissimilarities in the binding and ionization on both sides of the membrane, which were captured by the tissue composition-based model. Hence, this violated the basic assumption in in vivo pharmacokinetics-pharmacodynamics (PK/PD) research, since the free drug concentration in tissue and plasma was not equal particularly for the ionizable drugs due to the pH gradient effect on the fraction of unionized drug in plasma (fuip ) and tissue (fuit ) (i.e., fup × fuip × total plasma concentration = fut × fuit × total tissue concentration, and, hence, the free drug concentration in plasma and tissue differed by fuip/fuit). Therefore, this assumption should be adjusted for the ionized drugs, and, hence, a mathematical correction to the basic assumption of similar free drug concentration in plasma and tissues can be derived from the tissue composition-based model. Note that this assumption will be further challenged in a dynamic in vivo system in a companion manuscript. Overall, this study was a first attempt to predict the in vivo Kp values for specific human tissues by considering separately the effect of fup and fut , with the aim of facilitating the use of physiologically-based PK (PBPK) model in PK/PD studies.

A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development

Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drug-disposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, half-life, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.