Human Volume Of Distribution Research Articles

Predicting the Intravenous Pharmacokinetics of Covalent Drugs in Animals and Humans.

30 covalent drugs were used to assess clearance (CL) prediction reliability in animals and humans. In animals, marked CL underprediction was observed using cryopreserved hepatocytes or liver microsomes (LMs) supplemented for cytochrome P450 activity. Improved quantitative performance was observed by combining metabolic stability data from LMs and liver S9 fractions, the latter supplemented with reduced glutathione for glutathione transferase activity. While human LMs provided reliable human CL predictions, prediction statistics were improved further by incorporating S9 stability data. CL predictions with allometric scaling were less robust compared to in vitro drug metabolism methods; the best results were obtained using the fu-corrected intercept model. Human volume of distribution (Vd) was well predicted using allometric scaling of animal pharmacokinetic data; the most reliable results were achieved using simple allometric scaling of unbound Vd values. These results provide a quantitative framework to guide appropriate method selection for human PK prediction with covalent drugs.

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

PurposeThe 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.MethodA 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.ResultsThe 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.ConclusionsTCM-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.

Using the fish plasma model to evaluate potential effects of pharmaceuticals in effluent from a large urban wastewater treatment plant

Several pharmaceuticals and personal care products (PPCPs) were evaluated using the fish plasma model (FPM) for juvenile Chinook salmon exposed to effluent from a large urban wastewater treatment plant. The FPM compares fish plasma concentrations to therapeutic values determined in human plasma as an indication of potential adverse effects. We used human Cmax values, which are the maximum plasma concentration for a minimum therapeutic dose. Observed and predicted plasma concentrations from juvenile Chinook salmon exposed to a dilution series of whole wastewater effluent were compared to 1%Cmax values to determine Response Ratios (RR) ([plasma]/1%Cmax) for assessment of possible adverse effects. Several PPCPs were found to approach or exceed an RR of 1, indicating potential effects in fish. We also predicted plasma concentrations from measured water concentrations and determined that several of the values were close to or below the analytical reporting limit (RL) indicating potential plasma concentrations for a large number of PPCPs that were below detection. Additionally, the 1%Cmax was less than the RL for several analytes, which could impede predictions of possible effect concentrations. A comparison of observed and predicted plasma concentrations found that observed values were frequently much higher than values predicted with water concentrations, especially for low log10Dow compounds. The observed versus predicted values using the human volume of distribution (Vd), were generally much closer in agreement. These data appear to support the selection of whole-body concentrations to predict plasma values, which relies more on estimating simple partitioning within the fish instead of uptake via water. Overall, these observations highlight the frequently underestimated predicted plasma concentrations and potential to cause adverse effects in fish. Using measured plasma concentrations or predicted values from whole-body concentrations along with improved prediction models and reductions in analytical detection limits will foster more accurate risk assessments of pharmaceutical exposure for fish.

Physiologically-based pharmacokinetic/pharmacodynamic modeling to predict tumor growth inhibition and the efficacious dose of selective estrogen receptor degraders in humans.

GDC-9545 (giredestrant) is a highly potent, nonsteroidal, oral selective estrogen receptor antagonist and degrader that is being developed as a best-in-class drug candidate for early-stage and advanced drug-resistant breast cancer. GDC-9545 was designed to improve the poor absorption and metabolism of its predecessor GDC-0927, for which development was halted due to a high pill burden. This study aimed to develop physiologically-based pharmacokinetic/pharmacodynamic (PBPK-PD) models to characterize the relationships between oral exposure of GDC-9545 and GDC-0927 and tumor regression in HCI-013 tumor-bearing mice, and to translate these PK-PD relationships to a projected human efficacious dose by integrating clinical PK data. PBPK and Simeoni tumor growth inhibition (TGI) models were developed using the animal and human Simcyp V20 Simulator (Certara) and adequately described each compound's systemic drug concentrations and antitumor activity in the dose-ranging xenograft experiments in mice. The established PK-PD relationship was translated to a human efficacious dose by substituting mouse PK for human PK. PBPK input values for human clearance were predicted using allometry and in vitro in vivo extrapolation approaches and human volume of distribution was predicted from simple allometry or tissue composition equations. The integrated human PBPK-PD model was used to simulate TGI at clinically relevant doses. Translating the murine PBPK-PD relationship to a human efficacious dose projected a much lower efficacious dose for GDC-9545 than GDC-0927. Additional sensitivity analysis of key parameters in the PK-PD model demonstrated that the lower efficacious dose of GDC-9545 is a result of improvements in clearance and absorption. The presented PBPK-PD methodology can be applied to support lead optimization and clinical development of many drug candidates in discovery or early development programs.

Human Apparent Volume of Distribution Predicts Bioaccumulation of Ionizable Organic Chemicals in Zebrafish Embryos.

Chemicals with elevated bioaccumulation profiles present potential hazards to public health and the environment. Ionizable organic compounds (IOCs) increasingly represent a large proportion of commercial chemicals; however, historical approaches for bioaccumulation determinations are mainly developed for neutral chemicals, which were not appropriate for IOCs. Herein, we employed the zebrafish embryo, a common vertebrate model in environmental and biomedical studies, to elucidate toxicokinetics and bioconcentration of eight IOCs with diverse physicochemical properties and pharmacokinetic parameters. At an environmentally relevant pH (7.5), most IOCs exhibited rapid uptake and depuration in zebrafish, suggesting the ionized forms of IOCs are readily bioavailable. Bioconcentration factors (BCF) of these IOCs ranged from 0.0530 to 250 L·kg-1 wet weight. The human pharmacokinetic proportionality factor, apparent volume of distribution (VD), better predicted the BCF of selected IOCs than more commonly used hydrophobicity-based parameters (e.g., pH-dependent octanol-water distribution ratio, Dow). Predictive bioaccumulation models for IOCs were constructed and validated using VD alone or with Dow. Significant relationships between fish BCF and human VD, which is readily available for pharmaceuticals, highlighted the utility of biologically based "read-across" approaches for predicting bioaccumulative potential of IOCs. Our novel findings thus provided an understanding of the partitioning behavior and improved predictive bioconcentration modeling for IOCs.

Abstract P228: Preclinical pharmacokinetics and pharmacodynamics of KB-0742, a selective, oral CDK9 inhibitor

Abstract Tumorigenesis is driven by the accumulation of adverse genetic changes resulting in dysregulated transcription promoting altered gene expression and the cancer cell state. Thus, tumors can develop dependencies on the transcriptional regulators that promote the reprogrammed gene expression landscape. One such regulator is cyclin-dependent kinase 9 (CDK9), which regulates transcriptional elongation and promotes activation of transcription factors. We developed a potent, selective, and orally bioavailable CDK9 inhibitor, KB-0742. KB-0742 is highly selective for CDK9 as compared to other CDKs and has shown minimal off-target effects in kinase and receptor panel screens. Here we present the nonclinical pharmacologic characterization of KB-0742. The pharmacokinetics (PK) of KB-0742 in rats and dogs showed rapid absorption and high bioavailability with a %F of ≥84 after oral administration in rats and >100 in dogs. KB-0742 exhibited low turnover in human microsome and hepatocyte preparations and is projected to have low clearance and a high volume of distribution in humans based on allometric scaling from nonclinical species. These properties may facilitate achievement of sustained target engagement in patients using intermittent dosing which may mitigate the toxicity of CDK9 inhibition. To test this hypothesis, mice bearing MV4-11 (acute myeloid leukemia) xenografts were treated with KB-0742 at either 60 mg/kg for 3-days on/4-days off or continuous dosing at 25 mg/kg. Intermittent dosing showed similar tumor growth inhibition (81%) as seen with continuous dosing (74%). Target engagement was assessed in the tumors by measuring the inhibition of RNA polymerase II (pSER2). KB-0742 treatment resulted in an over 50% decrease in pSER2 compared with vehicle-treated controls. To support the phase I clinical trial, 2 pharmacodynamic assays were developed to measure drug target engagement in peripheral blood mononuclear cells (PBMCs). The first used RNA-expression profiling to measure alterations in gene expression with treatment, and the second used MSD protein analysis to measure changes in pSER2 levels. Ex vivo experiments using PBMCs from 3 healthy donors showed an over 80% reduction in pSER2 levels at 4 hours post-exposure to 1 µM of KB-0742. Similar results were observed using the RNA-expression profiling assay, in which decreased gene expression was observed in key genes at 1 µM exposures. KB-0742’s high specificity, oral bioavailability, and favorable PK properties distinguish it from other available CDK9 inhibitors. We are currently testing the hypothesis in the clinic with a phase I trial (NCT04718675) that the ability to dose intermittently will allow for favorable anti-tumor activity while minimizing adverse effects. Citation Format: Melinda A. L. Day, Douglas C. Saffran, Nathalie Rioux, Tom Chen, Christina Lee, David B. Freeman, Crystal MacKenzie, Joseph P. Vacca, Peter B. Rahl, Benjamin Wesley Trotter, Charles Y. Lin, Pavan Kumar, Jorge DiMartino. Preclinical pharmacokinetics and pharmacodynamics of KB-0742, a selective, oral CDK9 inhibitor [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P228.

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 (VD,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict VD,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 VD,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict VD,ss directly and to predict input parameters required for mechanistic and empirical VD,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 VD,ss predictions was also investigated. In predicting VD,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog VD,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 VD,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic VD,ss predictions significantly (81% within 3-fold, r2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the VD,ss but was limited in estimating the compounds with low VD,ss.SIGNIFICANCE STATEMENTThis work advances the in silico prediction of VD,ss directly from structure and with the aid of in vitro data. Rigorous and comprehensive evaluation of various methods using a large set of clinical compounds (n = 956) is presented. The scale of techniques evaluated is far beyond any previously presented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing VD,ss prediction methodologies.

Prediction of volume of distribution in humans: analysis of eight methods and their application in drug discovery

The performance of eight different methods to predict human volume of distribution (VDss) using a large data set (N > 100) was evaluated.The accuracy was assessed by the end points % within two-fold and absolute average fold error (AAFE). The ability to rank order was accessed by the σ and bias was examined using average fold error. Significance of observed differences was established using statistical permutation testing.The Rodgers-Lukova equation, a tissue composition model, for acids and single species scaling based on rat for other ion classes showed the best results in absence of non-rodent data.The semimechanistic Øie-Tozer model based on all thee preclinical species showed the best performance overall (81% within two-fold, AAFE 1.55, σ 0.62). This was not statistically significantly better at the 95% confidence level than the same model based on two preclinical species or single species scaling from monkey. Thus, the use of primates appears difficult to justify when the sole goal is to extrapolate human volume of distribution.

In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors.

We present three in silico volume of distribution at steady state (VDss) models generated on a training set comprising 1096 compounds, which goes well beyond the conventional drug space delineated by the Rule of 5 or similar approaches. We have performed a careful selection of descriptors and kept a homogeneous Molecular Interaction Field-based descriptor set and linear (Partial Least Squares, PLS) and nonlinear (Random Forest, RF) models. We have tested the models, which we deem orthogonal in nature due to different descriptors and statistical approaches, with good results. In particular we tested the RF model, via a leave-class-out approach and by using a set of 34 additional compounds not used for training. We report comparable results against in vivo scaling approaches with geometric mean-fold error at or below 2 (for a set of 60 compounds with animal data available) and discuss the predictive performance based on the ionization states of the compounds. Lastly, we report the findings using a two-tier approach (classification followed by regression) based on VDss ranges, in an attempt to improve the prediction of compounds with very high VDss. We would recommend, overall, the RF model, with 33 descriptors, as the primary choice for VDss prediction in humans.

Usefulness of minipigs for predicting human pharmacokinetics: Prediction of distribution volume and plasma clearance

In this study, advantages of minipigs to use in preclinical study for new drug development were evaluated in terms of prediction of human pharmacokinetic (PK) parameters of various drugs. Fourteen model drugs having diverse physicochemical properties were selected and intravenously administered to mice, rats and minipigs to obtain their PK parameters. The human volume of distribution (Vd) and clearance (CL) of model drugs were predicted from PK parameters in each animal species.When Vd of 14 drugs in each species were directly compared with those in humans, minipigs showed the highest correlation. Correction of Vd with an unbound fraction of drugs in tissues further improved the correlation. Allometric scaling that included minipig data resulted in high accuracy in the prediction of human Vd, clearly indicating an importance of minipig data. Minipigs also showed the high predictability of human CL. The prediction of human CL by allometric scaling showed a high accuracy when the data of minipigs were included.In conclusion, potential advantages of minipigs for predicting human Vd and CL were clearly demonstrated. Reliable prediction of human PK from data of minipigs appears to be possible in preclinical PK study, without relying on PK analysis in other species.

Pharmacokinetics of the natural antibiotic negamycin

1. Negamycin exerts its antimicrobial activity by inhibiting bacterial protein synthesis and is efficacious in animal models of infection. In order to optimize negamycin exposure for therapeutic purposes, its pharmacokinetics in pre-clinical species were determined.2. Negamycin has a dipeptide-like structure with logD7.4 < −1, causing low permeation into Caco-2 cells, low-oral bioavailability in rats of 6% and low-plasma protein binding of 10% in mouse, rat, dog and human plasma. Negamycin degradation rates in microsomes and hepatocytes predicted low-hepatic intrinsic clearance in pre-clinical species, which was confirmed in vivo where clearance varied between 3.4 and 11.5 mL/min/kg and virtually all negamycin was cleared unchanged renally. The similar behavior in multiple animal species allowed for the prediction of systemic clearance and volume of distribution in humans using multiple-scaling methods and physiological-based pharmacokinetic modeling and simulation.3. Only 0.05–0.25% (mol/mol) of administered negamycin was recovered as 2-(1-methylhydrazinyl)acetic acid, a potential reactive metabolite, from rat and dog urine, respectively.4. In summary, negamycin is a very polar molecule with low-plasma protein binding and low-oral bioavailability that is slowly and exclusively cleared into the urine. Its physicochemical properties make intravenous or intramuscular administration, or a derivative thereof, for therapeutic purposes most likely.

Prediction of drug distribution in rat and humans using an artificial neural networks ensemble and a PBPK model.

To develop a QSAR model, based on calculated molecular descriptors and an Artificial Neural Networks Ensemble (ANNE), for the estimation of rat tissue-to-blood partition coefficients (Kt:b), as well as the assessment of the applicability domain of the model and its utility in predicting the drug distribution in humans. A total of 1460 individual Kt:b values (75% train and 25% validation), obtained in 13 different rat tissues were collected in the literature. A correlation between simple molecular descriptors for lipophilicity, ionization, size and hydrogen bonding capacity and Kt:b data was attempted by using an ANNE. Similar statistics were observed between the train and validation group of data with correlations, between the observed values and the predicted average ANNE values, of 0.909 and 0.896, respectively. A degradation of the correlations was observed for predicted values with high uncertainty, as judged by the standard deviations of the ANNE outputs. This was further observed when using the ANNE Kt:b values in a Physiologically based pharmacokinetic (PBPK) model for predicting the Human Volume of distribution of another 532 drugs. This model (available as a MS Excel® workbook in the Supporting material of this article) may be a valuable tool for prediction and simulation in early drug development, allowing the in silico estimation of rat Kt:b values for PBPK purposes and also indicating its applicability domain.

Tissue-to-blood distribution coefficients in the rat: Utility for estimation of the volume of distribution in man

A compilation of rat tissue-to-blood partition coefficient data obtained both in vitro and in vivo in thirteen different tissues for a total of 309 different drugs is presented. An evaluation of the relationship between several fundamental physicochemical molecular descriptors and these distribution parameters was made. In addition, the ability to predict the Human Volume of distribution by regression analysis and by a Physiologically-based approach was also tested. Results have shown different trends between the drug classes and tissues, consistent with earlier described relationships between physicochemical properties and pharmacokinetic behavior. It was also possible to conclude for the acceptable ability to predict the volume of distribution in Humans by both regression and mechanistic approaches, which suggests that this type of data represents a convenient tool to describe the drug distribution on a new drug development context. These observations and analyses, along with the large database of rat tissue distribution data, should enable future efforts aimed toward developing a full in silico quantitative structure–pharmacokinetic relationships and improving our understanding of the correlations between fundamental chemical characteristics and drug distribution.

Optimization of a Parallel Artificial Membrane Permeability Assay for the Fast and Simultaneous Prediction of Human Intestinal Absorption and Plasma Protein Binding of Drug Candidates: Application to Dibenz[b,f]azepine-5-Carboxamide Derivatives

Parallel artificial membrane permeability assay (PAMPA) has been successfully applied by pharmaceutical industries as high-throughput technique capable of screening compounds for passive oral absorption. Herein, the possible applicability of this assay to predict simultaneously biodistribution parameters such as plasma protein binding (PPB) and apparent volume of distribution (VD) was investigated for the first time. Apparent permeability (P(app) ) of 18 reference drugs was determined by nine PAMPA conditions and compared with the corresponding intestinal absorption fraction (Fa), PPB, and VD in humans. In all the models, P(app) was not correlated with VD; however, it was correlated with Fa and PPB. In these cases, the best correlations (r ≥ 0.8953) were found when using a membrane of 2% of l-α-phosphatidylcholine, at pH 6.5/7.4 for donor/acceptor compartments. Under these conditions, good correlation with traditional PAMPA (r = 0.9633) and Caco-2 models (r = 0.9246) were also achieved. This method correctly predicted compounds' permeability and was robust enough to distinguish compounds with high Fa (P(app) ≥ 1.1 × 10(-6) cm/s) and PPB (P(app) ≥ 1.0 × 10(-5) cm/s), with no false positives or negatives recorded. In addition, ultrafiltration method was used to determine the PPB of 10 tested derivatives of dibenz[b,f]azepine-5-carboxamide, which were also assessed through the new PAMPA model developed herein, and equal classifications were achieved.

Estimation of the 5-Methyltetrahydrofolate Apparent Volume of Distribution in Humans 3

AbstractThe fractional absorption of a stable isotope-labeled folate dose can be estimated from the subsequent short-term temporal changes in the concentration of labeled L-5-methyltetrahydrofolate (L-5-methyl-THF) in plasma using mathematical modeling. However, the model is dependent on the use of an accurate value for the apparent volume of distribution of L-5-methyl-THF. Previous studies that estimated the apparent volume of distribution of L-5-methyl-THF used large (nonphysiological) doses of unlabeled folates that are not found to any great extent in the circulatory system. The current study estimates the apparent volume of distribution at steady state in 16 healthy humans aged 18–65 y after an i.v. dose (440 nmol) of a stable isotope-labeled version of the naturally circulating plasma folate, L-5-methyl-THF. Blood was collected from 2 min to 2 h postinjection and plasma assayed by specific and sensitive liquid chromatography-tandem MS. The apparent volume of distribution for L-5-methyl-THF was 32.0 ± 11.6 L (mean ± SD; 392 ± 110 mL/kg bodyweight). There was a positive association with volunteer body weight (r = 0.64; P = 0.010), which allowed a simple linear equation to be developed relating apparent volume of distribution to body weight. This has important implications for predicting apparent absorption of labeled folates in future bioavailability studies.

Pharmacokinetics of TH-302: a hypoxically activated prodrug of bromo-isophosphoramide mustard in mice, rats, dogs and monkeys

To characterize the pharmacokinetics of the prodrug, TH-302, and its active metabolite, bromo-IPM (Br-IPM), in nonclinical species. TH-302 was administered in single oral, intraperitoneal and intravenous bolus doses to mice, rats, dogs and monkeys as well as in acute and chronic safety studies in rats and dogs as a 30-min intravenous infusion given once a week for 3 weeks. Assessments were made using liquid chromatography-tandem mass spectrometry. TH-302 was extensively distributed with high systemic clearance exceeding hepatic plasma flow in all species studied, resulting in half-lives ranging between 8 min (mice) and over 4 h (rats). In rats, TH-302 exhibited linear kinetics following intravenous administration and good oral bioavailability. In acute and chronic safety studies, there was no accumulation of TH-302 following once weekly dosing for 3 weeks in the rat and dog. Br-IPM plasma concentrations were a small fraction of the TH-302 plasma concentrations with significantly smaller percentages present in dogs than in rats. Allometric scaling predicted that the systemic clearance and steady-state volume of distribution in humans would be 38.8 l/h/m(2) and 34.3 l/m(2), respectively, resulting in a terminal elimination half-life of about 36 min. These values were similar to those observed in patients with solid tumors (27.1 l/h/m(2), 23.5 l/m(2) and 47 min). TH-302 exhibited good safety, efficacy and pharmacokinetic properties in nonclinical species, translating into favorable properties in humans.

Use of the Øie-Tozer Model in Understanding Mechanisms and Determinants of Drug Distribution

Volume of distribution (VD) is a key pharmacokinetic property that together with clearance determines the half-life or residence time of drug in the body. It is commonly expressed as steady-state volume of distribution VD(ss) with a physiological basis for its understanding developed by Øie and Tozer in 1979. The Øie-Tozer equation uses terms for plasma protein binding (f(up)), tissue binding (f(ut)), and the extravascular/intravascular ratio of albumin as well as constants for the volumes of plasma, extracellular fluid, and tissue. We explored this model using a data set of 553 drugs for which VD(ss) and plasma protein binding were available in humans. Eighteen percent of cases (102 compounds) did not obey the Øie-Tozer model, with the rearranged equation giving an aberrant f(ut) value (f(ut) < 0 or f(ut) > 1), in particular for compounds with VD(ss) < 0.6 l/kg and f(up) > 0.1. Further analysis of this group of compounds revealed patterns in physicochemical attributes with a high proportion exemplified by logP less than 0 (i.e., very hydrophilic), polar surface area >150 A(2), and a difference between logP and logD >2.5. In addition there was a high representation of certain drug classes including anti-infectives as well as neuromuscular blockers and contrast agents. The majority of compounds were also found to have literature evidence, implicating active transport processes in their disposition. This analysis provides some important insights for pharmacokinetic optimization in this particular chemical space, as well as in the application of the Øie-Tozer model for predicting volume of distribution in humans.

Species Scaling and Extrapolation

The various scaling methodologies and molecular features analysis were applied to new dataset to predict human pharmacokinetics studies. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from species continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Also, extrapolation is the method for constructing new data points given a set of discrete data points. Methods estimate is reasonably reliable for short times, but for longer times, the estimate is liable to become less accurate. Species Scaling and Extrapolation are useful for acquiring toxicological data- epidemiological and experimental study. Animal studies help us to understand toxicity characteristics of a chemical before human exposure is allowed, whereas the epidemiological method generally does not. Species scaling and extrapolation from animals is necessary in many cases which helps in dealing with the so-called human risks more properly.

Estimation of Volume of Distribution in Humans from High Throughput HPLC-Based Measurements of Human Serum Albumin Binding and Immobilized Artificial Membrane Partitioning

The volume of distribution (VD) in humans of 179 known drug molecules (acids, bases, and neutrals) has been modeled using two biomimetic-binding measurements. The phospholipid binding (log K (IAM)) and the plasma protein binding (log K (HSA)) have been calculated from gradient HPLC retention times on immobilized artificial membrane (IAM) and on human serum albumin (HSA) columns, respectively. The log VD values showed good correlation with the compounds' relative binding to IAM and HSA as follows: log VD=0.44 log K (IAM)-0.22 log K (HSA)-0.66; n=179, r2=0.76, s=0.33, and F=272. It was also observed that positively charged molecules bind relatively more to IAM, while negatively charged ones bind more to HSA, in line with the empirical observation that bases tend to have a larger volume of distribution than acids. These results suggest that with the help of these two simple high throughput HPLC-based biomimetic binding measurements an important in vivo drug disposition property can be estimated for use in early drug discovery.

EXTRAPOLATION OF PRECLINICAL PHARMACOKINETICS AND MOLECULAR FEATURE ANALYSIS OF “DISCOVERY-LIKE” MOLECULES TO PREDICT HUMAN PHARMACOKINETICS

The prediction of human pharmacokinetics from preclinical species is an integral component of drug discovery. Recent studies with a 103-compound dataset suggested that scaling from monkey pharmacokinetic data tended to be the most accurate method for predicting human clearance. Additionally, interrogation of the two-dimensional molecular properties of these molecules produced a set of associations which predict the likely extrapolative outcome (success or failure) of preclinical data to project human pharmacokinetics. However, a limitation of the previous analyses was the relative paucity of data for typical "discovery-like" molecules (molecular weight >300 and/or clogP >3). The objective of this investigation was to generate preclinical data required for extension of this dataset for additional discovery-like molecules and determine whether the aforementioned findings continue to apply for these molecules. In vivo nonrodent intravenous pharmacokinetic data were generated for 13 molecules, and data for 8 additional molecules were obtained from the literature. Additionally, the various scaling methodologies and molecular features analysis were applied to this new dataset to predict human pharmacokinetics. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from monkey liver blood flow continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Lastly, application of the molecular feature associations, particularly data-dependent associations, afforded an improved predictivity compared with the liver blood flow scaling approaches, and provides insight into the extrapolation of high clearance compounds in the preclinical species to human.