Prediction of volume of distribution at steady state in humans: comparison of different approaches

Chemical profiling of volatile compounds of the essential oil of grey-leaved rockrose (Cistus albidus L.) and its antioxidant, anti-inflammatory, antibacterial, antifungal, and anticancer activity in vitro and in silico

Cistus albidus: L., also known as Grey-leaved rockrose and locally addressed as šṭab or tûzzâla lbîḍa, is a plant species with a well-established reputation for its health-promoting properties and traditional use for the treatment of various diseases. This research delves into exploring the essential oil extracted from the aerial components of Cistus albidus (referred to as CAEO), aiming to comprehend its properties concerning antioxidation, anti-inflammation, antimicrobial efficacy, and cytotoxicity. Firstly, a comprehensive analysis of CAEO's chemical composition was performed through Gas Chromatography-Mass Spectrometry (GC-MS). Subsequently, four complementary assays were conducted to assess its antioxidant potential, including DPPH scavenging, β-carotene bleaching, ABTS scavenging, and total antioxidant capacity assays. The investigation delved into the anti-inflammatory properties via the 5-lipoxygenase assay and the antimicrobial effects of CAEO against various bacterial and fungal strains. Additionally, the research investigated the cytotoxic effects of CAEO on two human breast cancer subtypes, namely, MCF-7 and MDA-MB-231. Chemical analysis revealed camphene as the major compound, comprising 39.21% of the composition, followed by α-pinene (19.01%), bornyl acetate (18.32%), tricyclene (6.86%), and melonal (5.44%). Notably, CAEO exhibited robust antioxidant activity, as demonstrated by the low IC50 values in DPPH (153.92 ± 4.30 μg/mL) and β-carotene (95.25 ± 3.75 μg/mL) assays, indicating its ability to counteract oxidative damage. The ABTS assay and the total antioxidant capacity assay also confirmed the potent antioxidant potential with IC50 values of 120.51 ± 3.33 TE μmol/mL and 458.25 ± 3.67 µg AAE/mg, respectively. In terms of anti-inflammatory activity, CAEO displayed a substantial lipoxygenase inhibition at 0.5 mg/mL. Its antimicrobial properties were broad-spectrum, although some resistance was observed in the case of Escherichia coli and Staphylococcus aureus. CAEO exhibited significant dose-dependent inhibitory effects on tumor cell lines in vitro. Additionally, computational analyses were carried out to appraise the physicochemical characteristics, drug-likeness, and pharmacokinetic properties of CAEO's constituent molecules, while the toxicity was assessed using the Protox II web server.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Prospective Prediction of Dapaconazole Clinical Drug-Drug Interactions Using an In Vitro to In Vivo Extrapolation Equation and PBPK Modeling

This study predicted dapaconazole clinical drug−drug interactions (DDIs) over the main Cytochrome P450 (CYP) isoenzymes using static (in vitro to in vivo extrapolation equation, IVIVE) and dynamic (PBPK model) approaches. The in vitro inhibition of main CYP450 isoenzymes by dapaconazole in a human liver microsome incubation medium was evaluated. A dapaconazole PBPK model (Simcyp version 20) in dogs was developed and qualified using observed data and was scaled up for humans. Static and dynamic models to predict DDIs following current FDA guidelines were applied. The in vitro dapaconazole inhibition was observed for all isoforms investigated, including CYP1A2 (IC50 of 3.68 µM), CYP2A6 (20.7 µM), 2C8 (104.1 µM), 2C9 (0.22 µM), 2C19 (0.05 µM), 2D6 (0.87 µM), and 3A4 (0.008−0.03 µM). The dynamic (PBPK) and static DDI mechanistic model-based analyses suggest that dapaconazole is a weak inhibitor (AUCR > 1.25 and <2) of CYP1A2 and CYP2C9, a moderate inhibitor (AUCR > 2 and <5) of CYP2C8 and CYP2D6, and a strong inhibitor (AUCR ≥ 5) of CYP2C19 and CYP3A, considering a clinical scenario. The results presented may be a useful guide for future in vivo and clinical dapaconazole studies.

Drug Disposition in Subjects with Obesity: The Research Work of Darrell R. Abernethy

In 1979, the late Dr. Darrell R. Abernethy and colleagues began a series of clinical studies aimed at understanding the pertinent determinants of drug distribution, elimination, and clearance in obesity, and how those variables are interconnected. The studies confirmed that volume of distribution (Vd) and clearance are the principal independent biological variables, which conjointly determine elimination half-life as a dependent variable. For drugs distributed by passive diffusion, their pharmacokinetic Vd - after correcting for plasma protein binding - was increased in obesity, depending in part on the physicochemical lipophilicity of the individual drugs, and the quantitative extent of obesity in overweight individuals. Across all studies, the ratio of mean clearance in obese divided by control groups had an overall median value of 1.21 (range: 0.75 to 3.11), indicating a small and variable effect of obesity on clearance, without clear directionality. Since drug clearance was not clearly related to lipophilicity or degree of obesity, the prolonged half-life of lipophilic drugs in obese patients was largely explained by the increased Vd. Dr. Abernethy further identified delayed attainment of steady-state after initiation of multiple-dose treatment, and delayed washout after termination of dosage, as potential clinical consequences of the extended half-life in obese persons. These consequences for specific drugs have been recently emphasized in contemporary studies of chronic dosage in subjects with obesity. Without data identifying an obesity-related change in clearance for a specific drug, maintenance doses (in milligrams) should be based on ideal weight rather than adjusted upward based on total weight. This article is protected by copyright. All rights reserved.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods

Volume of distribution at steady state (V_ss) is an important pharmacokinetic parameter of a drug candidate. In this study, V_ss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (f_u,t) method with adipose and muscle also provided high V_ss prediction accuracy. Overall, the high performing methods for human V_ss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The f_u,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful V_ss prediction involves strategic integration of in silico, in vitro and in vivo approaches.

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V_D,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V_D,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V_D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V_D,ss directly and to predict input parameters required for mechanistic and empirical V_D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V_D,ss predictions was also investigated. In predicting V_D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V_D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r² = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V_D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V_D,ss predictions significantly (81% within 3-fold, r² = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V_D,ss but was limited in estimating the compounds with low V_D,ss.

Applicability of the Øie-Tozer model to predict three types of distribution volume (Vd) in humans: Vd in central compartment, Vd at steady state, and Vd at beta phase

This study aimed to investigate the applicability of the Øie-Tozer model to predict human distribution volume (Vd) in the central compartment (V₁ ), Vd at steady state (Vd_ss ), and Vd at beta phase (Vd_β ) based on animal Vd. Twenty compounds that have a human V₁ /Vd_ss of 0.053-0.66 were selected from the literature. After intravenous administration of the compounds at 0.1 mg/kg to rats, dogs, and monkeys, plasma concentrations were determined, and pharmacokinetic parameters were obtained by one/two-compartmental analyses. The human V₁ , Vd_ss , and Vd_β were predicted from animal Vd using the Øie-Tozer model, and the predictability was compared with that using proportionality and simple allometry. The Øie-Tozer model was the most reliable method for the overall prediction of Vd and applicable for accurately predicting human V₁ , Vd_ss , and Vd_β (89%, 85%, and 68% of the compounds within a 3-fold error, respectively) when data of monkey for V₁ and data of three animal species for Vd_ss and Vd_β were used. Additionally, the predicted human Vd with the two-compartment model was applicable for predicting pharmacokinetic profiles/parameters in humans after intravenous administration of 18 compounds [except for valproic acid (monophasic elimination profile) and chlorpromazine (deviation: Vd_ss < V₁ )]. The prediction was more accurate than that using the predicted Vd_ss with the one-compartment model (e.g., underestimation of maximum plasma concentrations: 2 vs 8 compounds within a 3-fold error, respectively). In summary, the Øie-Tozer model was applicable for predicting human V₁ , Vd_ss , and Vd_β , and their predicted Vd with the two-compartment model can lead to accurate pharmacokinetic prediction of compounds that show biphasic elimination.

Physiologically Based Pharmacokinetic Modeling for Chlorinated Paraffins in Rats and Humans: Importance of Biliary Excretion

Chlorinated paraffins (CPs) are chemicals with high production volumes that can accumulate at high levels in general populations. The pharmacokinetics of CPs as pollutants is unknown, and there is no evidence that the medium chain chlorinated paraffins (MCCPs) and long chain chlorinated paraffins (LCCPs) are safe replacements for short chain chlorinated paraffins (SCCPs). In this study, SCCPs, MCCPs, and LCCPs were first in vivo and in vitro exposed to rat and liver microsomes, respectively. Toxicokinetics of these compounds were assessed and used to establish the corresponding physiologically based pharmacokinetic (PBPK) models in rats. More than 90% of ingested CPs were deposited in the liver and fat, and the compounds were extremely resistant to metabolism and mostly eliminated via biliary excretion. Then, humans' external and internal exposures to CPs were investigated for one year in Shenzhen, South China. The results were used to calibrate the key parameters for the establishment of a PBPK model in humans. In the PBPK models of rats and humans, the rate of biliary excretion had the greatest influence on the accumulated levels and half-lives of CPs. The body half-lives of human were estimated to be 5.1, 1.2, and 0.6 years for SCCPs, MCCPs, and LCCPs, respectively, suggesting the high accumulation of SCCPs in humans compared to other CPs.

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.

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 K_p,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.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

Drug-development concepts as guides for optimizing clinical trials of supplemental zinc for populations at risk of deficiency or diarrhea

Studies on the efficacy of zinc supplementation for treatment or prevention of diarrhea have shown an inconsistent effect in populations at risk for zinc deficiency. Unlike drugs, which have no preexisting presence in the body, endogenous zinc must be assessed pharmacokinetically by isotope tracer studies. Although such methods have produced much data, very few studies have estimated the dose and the timing of dosing of zinc supplementation. This review examines drug kinetics used to establish the best dose, the timing of such doses, and the mechanism of action through pharmacodynamic markers and applies them, where possible, to zinc supplements. The findings reveal that little is known, especially in children at highest risk of zinc deficiency. Key data missing to inform proper dosing, whether for treatment of disease or for preventive nutrient supplementation, are noted. Addressing these uncertainties could improve study design, leading to future studies of zinc supplements that might be of greater benefit.

Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning

PURPOSE

Volume of distribution is an important pharmacokinetic parameter in the distribution and half-life of a drug. Protein binding and lipid partitioning together determine drug distribution.

METHODS

Here we present a simple relationship that estimates the volume of distribution with the fraction of drug unbound in both plasma and microsomes. Model equations are based upon a two-compartment system and the experimental fractions unbound in plasma and microsomes represent binding to plasma proteins and cellular lipids, respectively.

RESULTS

The protein and lipid binding components were parameterized using a dataset containing human in vitro and in vivo parameters for 63 drugs. The resulting equation explains ~84% of the variance in the log of the volume of distribution with an average fold-error of 1.6, with 3 outliers.

CONCLUSIONS

These results suggest that V_ss can be predicted for most drugs from plasma protein binding and microsomal partitioning.

Steady-state volume of distribution of two-compartment models with simultaneous linear and saturated elimination

The model-independent estimation of physiological steady-state volume of distribution ([Formula: see text]), often referred to non-compartmental analysis (NCA), is historically based on the linear compartment model structure with central elimination. However the NCA-based steady-state volume of distribution ([Formula: see text]) cannot be generalized to more complex models. In the current paper, two-compartment models with simultaneous first-order and Michaelis-Menten elimination are considered. In particular, two indistinguishable models [Formula: see text] and [Formula: see text], both having central Michaelis-Menten elimination, while first-order elimination exclusively either from central or peripheral compartment, are studied. The model-based expressions of the steady-state volumes of distribution [Formula: see text] and their relationships to NCA-based [Formula: see text] are derived. The impact of non-linearity and peripheral elimination is explicitly delineated in the formulas. Being concerned with model identifiability and indistinguishability issues, an interval estimate of [Formula: see text] is suggested.

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.