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

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

Relative Performance of Volume of Distribution Prediction Methods for Lipophilic Drugs with Uncertainty in LogP Value

PURPOSE

The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New.

METHOD

A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values.

RESULTS

The Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss.

CONCLUSIONS

TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (V_ss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of V_ss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Folate-Targeted Curcumin-Loaded Niosomes for Site-Specific Delivery in Breast Cancer Treatment: In Silico and In Vitro Study

As the most common cancer in women, efforts have been made to develop novel nanomedicine-based therapeutics for breast cancer. In the present study, the in silico curcumin (Cur) properties were investigated, and we found some important drawbacks of Cur. To enhance cancer therapeutics of Cur, three different nonionic surfactants (span 20, 60, and 80) were used to prepare various Cur-loaded niosomes (Nio-Cur). Then, fabricated Nio-Cur were decorated with folic acid (FA) and polyethylene glycol (PEG) for breast cancer suppression. For PEG-FA@Nio-Cur, the gene expression levels of Bax and p53 were higher compared to free drug and Nio-Cur. With PEG-FA-decorated Nio-Cur, levels of Bcl2 were lower than the free drug and Nio-Cur. When MCF7 and 4T1 cell uptake tests of PEG-FA@Nio-Cur and Nio-Cur were investigated, the results showed that the PEG-FA-modified niosomes exhibited the most preponderant endocytosis. In vitro experiments demonstrate that PEG-FA@Nio-Cur is a promising strategy for the delivery of Cur in breast cancer therapy. Breast cancer cells absorbed the prepared nanoformulations and exhibited sustained drug release characteristics.

A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles

To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K_p,mem), and PermQ. K_p,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V_ss and t_1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

In vitro enantioselective inhibition of the main human CYP450 enzymes involved in drug metabolism by the chiral pesticide tebuconazole

Tebuconazole (TEB) is a chiral triazole fungicide worldwide employed to control plant pathogens and preserve wood. People can be exposed to TEB either through diet and occupational contamination. This work investigates the in vitro inhibitory potential of rac-TEB, S-(+)-TEB, and R-(-)-TEB over the main cytochrome P450 enzymes (CYP450) using human liver microsomes to predict TEB in vivo inhibition potential. The IC₅₀ values showed that in vitro inhibition was enantioselective for CYP2C9, CYP2C19, and CYP2D6, but not for CYP3A4/5. Despite enantioselectivity, rac-TEB and its single enantiomers were always classified in the same category. The inhibition mechanisms and constants were determined for rac-TEB and it has shown to be a mixed inhibitor of CYP3A4/5 (K_i = 1.3 ± 0.3 μM, αK_i = 3.2 ± 0.5 μM; K_i = 0.6 ± 0.3 μM, αK_i = 1.3 ± 0.3 μM) and CYP2C9 (K_i = 0.7 ± 0.1 μM, αK_i = 2.7 ± 0.5 μM), and a competitive inhibitor of CYP2D6 (K_i = 11.9 ± 0.7 μM) and CYP2C19 (K_i = 0.23 ± 0.02 μM), respectively, suggesting that in some cases, rac-TEB has a higher or comparable inhibitory potential than well-known strong inhibitors of CYP450 enzymes, especially for CYP2C9 and CYP2C19. In vitro-in vivo extrapolations (IVIVE) were conducted based on the results and data available in the literature about TEB absorption and metabolism. R₁ values were estimated based on the Food and Drug Administration guideline and suggested that in a chronic oral exposure scenario considering the acceptable daily intake dose proposed by the European Food and Safety Authority, the hypothesis of rac-TEB to inhibit the activities of CYP3A4/5, CYP2C9, and CYP2C19 in vivo and cause pesticide-drug interactions cannot be disregarded.

Technetium-99m-MAG3 and technetium-99m-DTPA: Renal clearance measured by the constant infusion technique - Old news?

BACKGROUND

Accurate, precise and straightforward methods for measuring glomerular filtration rate (GFR) and/or renal plasma flow (RPF) are still in demand today. The time-consuming constant infusion technique (CIT) is the gold standard and preferred for research, whereas the simple, but less precise, single injection technique (SIT) is used in clinical settings. This study investigated the use of ^99m Tc-DTPA and ^99m Tc-MAG₃ by CIT as a measure of renal function. We developed and evaluated a model to balance the primer dose and infusion rate in an attempt to obtain plasma steady state as quickly as possible.

METHODS

14 healthy subjects received ^99m Tc-DTPA and 6 hypertensive patients received ^99m Tc-MAG₃ in a standardized protocol. All participants had an eGFR above 60 ml/min and none had fluid retention. An intravenous primer injection of the relevant tracer was followed by a sustained infusion over 4.5 h with the same radiopharmaceutical. Blood and urine samples were collected at fixed intervals.

RESULTS

^99m Tc-DTPA clearance reached steady state after 210 min (plasma clearance 78 ± 18 ml/min, urine clearance 110 ± 28 ml/min), whereas ^99m Tc-MAG₃  clearance achieved steady state after 150 min (plasma clearance 212 ± 56 ml/min, urine clearance 233 ± 59 ml/min).

CONCLUSION

Constant infusion technique with fixed primer and infusion rate using ^99m Tc-MAG₃ is feasible for research purposes. The longer time for reaching plasma steady state using ^99m Tc-DTPA makes CIT with this tracer less optimal. If the primer/sustained balance can be optimized, for example using a priori SIT information, ^99m Tc-DTPA as tracer for CIT may also be feasible.

Enantioselective inhibition of human CYP2C19 by the chiral pesticide ethofumesate: Prediction of pesticide-drug interactions in humans

Ethofumesate is a chiral herbicide that may display enantioselective behavior in humans. For this reason, the enantioselective potential of ethofumesate and its main metabolite ethofumesate-2-hydroxy to cause pesticide-drug interactions on cytochrome P450 forms (CYPs) has been evaluated by using human liver microsomes. Among the evaluated CYPs, CYP2C19 had its activity decreased by the ethofumesate racemic mixture (rac-ETO), (+)-ethofumesate ((+)-ETO), and (-)-ethofumesate ((-)-ETO). CYP2C19 inhibition was not time-dependent, but a strong inhibition potential was observed for rac-ETO (IC₅₀ = 5 ± 1 μmol L^-1), (+)-ETO (IC₅₀ = 1.6 ± 0.4 μmol L^-1), and (-)-ETO (IC₅₀ = 1.8 ± 0.4 μmol L^-1). The reversible inhibition mechanism was competitive, and the inhibition constant (K_i) values for rac-ETO (2.6 ± 0.4 μmol L^-1), (+)-ETO (1.5 ± 0.2 μmol L^-1), and (-)-ETO (0.7 ± 0.1 μmol L^-1) were comparable to the K_i values of strong CYP2C19 inhibitors. Inhibition of CYP2C19 by ethofumesate was enantioselective, being almost twice higher for (-)-ETO than for (+)-ETO, which indicates that this enantiomer may be a more potent inhibitor of this CYP form. For an in vitro-in vivo correlation, the Food and Drug Administration's (FDA) guideline on the assessment of drug-drug interactions used in the early stages of drug development was used. The FDA's R₁ values were estimated on the basis of the obtained ethofumesate K_i and distribution volume, metabolism, unbound plasma fraction, gastrointestinal and dermal absorption data available in the literature. The correlation revealed that ethofumesate probably inhibits CYP2C19 in vivo for both chronic (oral) and occupational (dermal) exposure scenarios.

A Biophysical Insight of Camptothecin Biodistribution: Towards a Molecular Understanding of Its Pharmacokinetic Issues

Camptothecin (CPT) is a potent anticancer drug, and its putative oral administration is envisioned although difficult due to physiological barriers that must be overcome. A comprehensive biophysical analysis of CPT interaction with biointerface models can be used to predict some pharmacokinetic issues after oral administration of this or other drugs. To that end, different models were used to mimic the phospholipid composition of normal, cancer, and blood–brain barrier endothelial cell membranes. The logD values obtained indicate that the drug is well distributed across membranes. CPT-membrane interaction studies also confirm the drug’s location at the membrane cooperative and interfacial regions. The drug can also permeate membranes at more ordered phases by altering phospholipid packing. The similar logD values obtained in membrane models mimicking cancer or normal cells imply that CPT has limited selectivity to its target. Furthermore, CPT binds strongly to serum albumin, leaving only 8.05% of free drug available to be distributed to the tissues. The strong interaction with plasma proteins, allied to the large distribution (VD_SS = 5.75 ± 0.932 L·Kg⁻¹) and tendency to bioaccumulate in off-target tissues, were predicted to be pharmacokinetic issues of CPT, implying the need to develop drug delivery systems to improve its biodistribution.

Lipid Nanosystems and Serum Protein as Biomimetic Interfaces: Predicting the Biodistribution of a Caffeic Acid-Based Antioxidant

Purpose

AntiOxCIN₃ is a novel mitochondriotropic antioxidant developed to minimize the effects of oxidative stress on neurodegenerative diseases. Prior to an investment in pre-clinical in vivo studies, it is important to apply in silico and biophysical cell-free in vitro studies to predict AntiOxCIN₃ biodistribution profile, respecting the need to preserve animal health in accordance with the EU principles (Directive 2010/63/EU). Accordingly, we propose an innovative toolbox of biophysical studies and mimetic models of biological interfaces, such as nanosystems with different compositions mimicking distinct membrane barriers and human serum albumin (HSA).

Methods

Intestinal and cell membrane permeation of AntiOxCIN₃ was predicted using derivative spectrophotometry. AntiOxCIN₃ –HSA binding was evaluated by intrinsic fluorescence quenching, synchronous fluorescence, and dynamic/electrophoretic light scattering. Steady-state and time-resolved fluorescence quenching was used to predict AntiOxCIN₃-membrane orientation. Fluorescence anisotropy, synchrotron small- and wide-angle X-ray scattering were used to predict lipid membrane biophysical impairment caused by AntiOxCIN₃ distribution.

Results and Discussion

We found that AntiOxCIN₃ has the potential to permeate the gastrointestinal tract. However, its biodistribution and elimination from the body might be affected by its affinity to HSA (>90%) and by its steady-state volume of distribution (VD_SS=1.89± 0.48 L∙Kg⁻¹). AntiOxCIN₃ is expected to locate parallel to the membrane phospholipids, causing a bilayer stiffness effect. AntiOxCIN₃ is also predicted to permeate through blood-brain barrier and reach its therapeutic target – the brain.

Conclusion

Drug interactions with biological interfaces may be evaluated using membrane model systems and serum proteins. This knowledge is important for the characterization of drug partitioning, positioning and orientation of drugs in membranes, their effect on membrane biophysical properties and the study of serum protein binding. The analysis of these interactions makes it possible to collect valuable knowledge on the transport, distribution, accumulation and, eventually, therapeutic impact of drugs which may aid the drug development process.

Evaluation of Tissue Binding in Three Tissues across Five Species and Prediction of Volume of Distribution from Plasma Protein and Tissue Binding with an Existing Model

Volume of distribution (V_d) is a primary pharmacokinetic parameter used to calculate the half-life and plasma concentration-time profile of drugs. Numerous models have been relatively successful in predicting V_d, but the model developed by Korzekwa and Nagar is of particular interest because it utilizes plasma protein binding and microsomal binding data, both of which are readily available in vitro parameters. Here, Korzekwa and Nagar's model was validated and expanded upon using external and internal data sets. Tissue binding, plasma protein binding, V_d, physiochemical, and physiologic data sets were procured from literature and Genentech's internal data base. First, we investigated the hypothesis that tissue binding is primarily governed by passive processes that depend on the lipid composition of the tissue type. The fraction unbound in tissues (fu_tissue) was very similar across human, rat, and mouse. In addition, we showed that dilution factors could be generated from nonlinear regression so that one fu_tissue value could be used to estimate another one regardless of species. More importantly, results suggested that microsomes could serve as a surrogate for tissue binding. We applied the parameters from Korzekwa and Nagar's V_d model to two distinct liver microsomal data sets and found remarkably close statistical results. Brain and lung data sets also accurately predicted V_d, further validating the model. V_d prediction accuracy for compounds with log D_7.4 > 1 significantly outperformed that of more hydrophilic compounds. Finally, human V_d predictions from Korzekwa and Nagar's model appear to be as accurate as rat allometry and slightly less accurate than dog and cynomolgus allometry. SIGNIFICANCE STATEMENT: This study shows that tissue binding is comparable across five species and can be interconverted with a dilution factor. In addition, we applied internal and external data sets to the volume of distribution model developed by Korzekwa and Nagar and found comparable V_d prediction accuracy between the V_d model and single-species allometry. These findings could potentially accelerate the drug research and development process by reducing the amount of resources associated with in vitro binding and animal experiments.

A mechanism-based pharmacokinetic model of remdesivir leveraging interspecies scaling to simulate COVID-19 treatment in humans

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak initiated the global coronavirus disease 2019 (COVID-19) pandemic resulting in 42.9 million confirmed infections and > 1.1 million deaths worldwide as of October 26, 2020. Remdesivir is a broad-spectrum nucleotide prodrug shown to be effective against enzootic coronaviruses. The pharmacokinetics (PKs) of remdesivir in plasma have recently been described. However, the distribution of its active metabolite nucleoside triphosphate (NTP) to the site of pulmonary infection is unknown in humans. Our objective was to use existing in vivo mouse PK data for remdesivir and its metabolites to develop a mechanism-based model to allometrically scale and simulate the human PK of remdesivir in plasma and NTP in lung homogenate. Remdesivir and GS-441524 concentrations in plasma and total phosphorylated nucleoside concentrations in lung homogenate from Ces1c-/- mice administered 25 or 50 mg/kg of remdesivir subcutaneously were simultaneously fit to estimate PK parameters. The mouse PK model was allometrically scaled to predict human PK parameters to simulate the clinically recommended 200 mg loading dose followed by 100 mg daily maintenance doses administered as 30-minute intravenous infusions. Simulations of unbound remdesivir concentrations in human plasma were below 2.48 μM, the 90% maximal inhibitory concentration for SARS-CoV-2 inhibition in vitro. Simulations of NTP in the lungs were below high efficacy in vitro thresholds. We have identified a need for alternative dosing strategies to achieve more efficacious concentrations of NTP in human lungs, perhaps by reformulating remdesivir for direct pulmonary delivery.

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.

Prediction of paclitaxel pharmacokinetic based on in vitro studies: Interaction with membrane models and human serum albumin

Interactions of paclitaxel (PTX) with models mimicking biological interfaces (lipid membranes and serum albumin, HSA) were investigated to test the hypothesis that the set of in vitro assays proposed can be used to predict some aspects of drug pharmacokinetics (PK). PTX membrane partitioning was studied by derivative spectrophotometry; PTX effect on membrane biophysics was evaluated by dynamic light scattering, fluorescence anisotropy, atomic force microscopy and synchrotron small/wide-angle X-ray scattering; PTX distribution/molecular orientation in membranes was assessed by steady-state/time-resolved fluorescence and computer simulations. PTX binding to HSA was studied by fluorescence quenching, derivative spectrophotometry and dynamic/electrophoretic light scattering. PTX high membrane partitioning is consistent with its efficacy crossing cellular membranes and its off-target distribution. PTX is closely located in the membrane phospholipids headgroups, also interacting with the hydrophobic chains, and causes a major distortion of the alignment of the membrane phospholipids, which, together with its fluidizing effect, justifies some of its cellular toxic effects. PTX binds strongly to HSA, which is consistent with its reduced distribution in target tissues and toxicity by bioaccumulation. In conclusion, the described set of biomimetic models and techniques has the potential for early prediction of PK issues, alerting for the required drug optimizations, potentially minimizing the number of animal tests used in the drug development process.

Biological barriers, and the influence of protein binding on the passage of drugs across them

Drug-protein binding plays a key role in determining the pharmacokinetics of a drug. The distribution and protein binding ability of a drug changes over a lifetime, and are important considerations during pregnancy and lactation. Although proteins are a significant fraction in plasma composition, they also exist beyond the bloodstream and bind with drugs in the skin, tissues or organs. Protein binding influences the bioavailability and distribution of active compounds, and is a limiting factor in the passage of drugs across biological membranes and barriers: drugs are often unable to cross membranes mainly due to the high molecular mass of the drug-protein complex, thus resulting in the accumulation of the active compounds and a significant reduction of their pharmacological activity. This review describes the consequences of drug-protein binding on drug transport across physiological barriers, whose role is to allow the passage of essential substances-such as nutrients or oxygen, but not of xenobiotics. The placental barrier regulates passage of xenobiotics into a fetus and protects the unborn organism. The blood-brain barrier is the most important barrier in the entire organism and the skin separates the human body from the environment.

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.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (K_p) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat K_p measured by in vivo experiments for 143 compounds. Initially, predicted K_p were significantly larger than measured K_p for many lipophilic compounds (log₁₀ octanol:water partition coefficient > 3). Hence the approach for predicting K_p was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 K_p across 23 compounds with only 3 K_p worsening by more than a factor of 10. The vast majority (92%) of K_p were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log₁₀-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (V_ss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R² of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.