DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 DMD ThisFast article Forward. has not been Published copyedited onand formatted.April 21, The 2008 final asversion doi:10.1124/dmd.108.020479 may differ from this version.

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TREND ANALYSIS OF A DATABASE OF INTRAVENOUS PHARMACOKINETIC PARAMETERS IN HUMANS FOR 670 DRUG COMPOUNDS

R. Scott Obach†, Franco Lombardo,*§ Nigel J. Waters#

Pharmacokinetics Dynamics and Metabolism, Pfizer Global Research and Development, Groton Laboratories, Groton, CT 06340 and Metabolism and Pharmacokinetics Groups Novartis Institutes for Biomedical Research, Cambridge, MA 02139 and Horsham, West Sussex RH12 5AB, United Kingdom. Downloaded from

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at ASPET Journals on September 29, 2021

RSO: Pharmacokinetics Dynamics and Metabolism, Pfizer Global Research and Development, Groton Laboratories. FL: Metabolism and Pharmacokinetics Group, Novartis Institutes for Biomedical Research, Cambridge. NJW: Metabolism and Pharmacokinetics Group, Novartis Institutes for Biomedical Research, Horsham.

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Copyright 2008 by the American Society for Pharmacology and Experimental Therapeutics. DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

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TREND ANALYSIS OF PHARMACOKINETIC PARAMETERS IN HUMANS

Address correspondence to: Franco Lombardo, Novartis Institutes for Biomedical Research, Bldg. 600, 1B-123, 250 Massachusetts Avenue, Cambridge MA, 02139. Phone (617) 871-4003; Fax: (617) 871-3078; e-mail: [email protected].

Text pages:21 Tables: 3 Figures: 10 References : 33 Downloaded from Words in Abstract: 183 Words in Introduction: 562 Words in Discussion: 1833

Non-standard abbreviations: dmd.aspetjournals.org

PSA = Polar Surface Area at ASPET Journals on September 29, 2021

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Abstract

We present herein a compilation and trend analysis of human intravenous pharmacokinetic data on 670 drugs representing, to our knowledge, the largest publicly available set of human clinical pharmacokinetic data. This dataset provides the drug metabolism scientist with a robust and accurate resource suitable for a number of applications including: in silico modelling, in vitro – in vivo scaling, and physiologically- Downloaded from based pharmacokinetic approaches. Clearance, volume of distribution at steady state, mean residence time, and terminal phase half-life were obtained or derived from original references exclusively from studies utilizing intravenous administration. Plasma protein dmd.aspetjournals.org binding data were collected from other sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of simple physicochemical descriptors and resultant trends and patterns within the data are at ASPET Journals on September 29, 2021 presented. Our findings with this much expanded dataset were consistent with earlier described notions of trends between physicochemical properties and pharmacokinetic behavior. These observations and analyses, along with the large database of human pharmacokinetic data, should enable future efforts aimed toward developing quantitative structure-pharmacokinetic relationships and improving our understanding of the relationship between fundamental chemical characteristics and drug disposition.

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The prediction of human pharmacokinetics for new compounds has become an important process in drug research. Previous reports have focused on prediction methods that utilize animal pharmacokinetic data (Ward and Smith, 2004a; Ward and Smith,

2004b; Caldwell et al., 2004; Jolivette and Ward, 2005; Evans et al., 2006; Mahmood et al., 2006; Martinez et al., 2006; Tang and Mayersohn, 2006; McGinnity et al., 2007;

Fagerholm, 2007) as well as in vitro data (Obach et al., 1997; Lombardo et al., 2002; Downloaded from

Nestorov et al., 2002; Lombardo et al., 2004; Riley et al., 2005; Grime and Riley, 2006).

Recently, the availability of computational chemistry methodologies has increased and dmd.aspetjournals.org these have been applied to the prediction of human pharmacokinetics and/or general

ADMET properties (Cruciani et al., 2005; Gleeson et al., 2006; Ghafourian et al., 2006;

Lombardo et al., 2006; Gunturi and Narayanan, 2007; Norinder and Bergstroem, 2007; at ASPET Journals on September 29, 2021

Gleeson, 2007). The construction of effective models not only requires sound computational tools but, very importantly, databases that have been carefully assembled.

Human pharmacokinetic databases are challenging to compile because each data point typically derives from a separate report in which experimental approaches differ from report to report. Such variables include the numbers and types of study subjects (e.g. healthy vs. diseased, gender, age, etc), the routes of administration and doses, sample collection times, methods of analysis of the samples, and the types of pharmacokinetic parameters reported.

To develop computational models for the prediction of human clearance (CL), volume of distribution (VD), and absolute oral bioavailability, it is essential that data used in model training sets are obtained from studies in which the dose was administered

4 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 intravenously. It is also important that the methods of calculations for reported pharmacokinetic parameters be done consistently. (For example, values reported generally as VD can vary from report to report and include central VD, terminal phase

VD, or steady-state VD.) The fairly large set of human pharmacokinetic data reported in

Appendix II of the famous textbook “The Pharmacological Basis of Therapeutics”

(Goodman and Gilman, 2006) has been frequently cited as a source of data for computational model construction. However, it is important to note that this dataset was Downloaded from primarily intended for health care professionals and medical students to understand the pharmacokinetic basis for dosing regimens of frequently used drugs, rather than used for dmd.aspetjournals.org the development of structure-pharmacokinetic relationships. Hence, many of the pharmacokinetic parameters reported are derived from oral administration and many of the VD values reported include terminal phase VD. This can confound the performance at ASPET Journals on September 29, 2021 of models.

The objectives of this study were two-fold: (1) Develop an exhaustive database of human pharmacokinetic parameters exclusively from intravenous administration, that can be used by scientists involved in early drug research in the construction of models for predicting pharmacokinetic parameters for new compounds; and (2) From this dataset, gain some initial insight into the relationships between chemical properties derived from structural attributes to human pharmacokinetic parameters. To the first objective, we have carefully and exhaustively mined the scientific literature for the human pharmacokinetic parameters CLp, VDss, MRT, and t1/2 measured after intravenous administration. We successfully obtained human intravenous pharmacokinetic data for

670 compounds, which, to our knowledge, represents the largest database of this type.

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We have also included plasma protein binding data for most of these compounds. This database is reported herein and also provided in tabular format as supplemental information that can be downloaded from the website of this journal. As such, it can be of use to scientists seeking to develop computational models and correlative approaches to the prediction of human pharmacokinetics of new compounds. For the second objective, we have examined the relationships between human intravenous pharmacokinetic parameters (VDss VDss,u, CLp, CLp,u, MRT, t1/2, and fu) and various Downloaded from computed fundamental physico-chemical parameters (e.g. logP, charge, etc.) using this database. The trends observed can be instructive to those engaged in the design of new dmd.aspetjournals.org drugs. The knowledge that can be gained by utilizing this large database in model construction should be of considerable use to scientists involved in the discovery of new drugs. at ASPET Journals on September 29, 2021

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Methods

Collection and Selection of Data for the Database. Pharmacokinetic data are reported in a large array of manners. Various units and symbols are used, some reports utilize compartmental analysis while others use a non-compartmental approach, and the level of detail describing the conduct of the study included in the methods sections can vary considerably. This requires very careful scrutiny when mining these data for fundamental pharmacokinetic parameters. The pharmacokinetic data included in this Downloaded from database are strictly from intravenous administration. There are no data included from oral, intramuscular, or any other dosing routes. Intravenous data were from rapid bolus dmd.aspetjournals.org injection or infusions.

Of the pharmacokinetic parameters that were gathered for this database, the one that varied the most and required the most attention to how it was determined is the at ASPET Journals on September 29, 2021 volume of distribution. For this database, we sought the steady-state volume of distribution (VDss) since this volume term describes the overall distributional behavior most generally. However, many authors of pharmacokinetic studies report only the terminal phase volume of distribution and many others report volume of distribution without denoting whether it was the terminal phase VD, the central VD, or the steady- state VD. Thus, the preference for papers selected for inclusion in this database specifically reported VDss. When the usage of the VD term was vague (i.e. lacking the subscript designating the value as VDss or another VD), but a clear description of the pharmacokinetic calculations were included and indicated that the term was in fact VDss, the value was included. In other papers, micro constants from compartmental analysis were reported without including VDss. In these cases, the rate constants and the reported

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central volume of distribution were used to calculate VDss using one or the other of the following equations depending upon what was reported:

   +•= k12  ss VDVD c 1   k 21 

VDc refers to the volume of the central compartment, and was sometimes called V1, VP, or V0 in the reports. In other compartmental models, more micro rate constants were

derived from the fit of the data, requiring an expansion of the equation: Downloaded from

 k k   12 ++•= 13  ss VDVD c 1   k 21 k 31  dmd.aspetjournals.org In other cases, the compartmental parameters were reported. If VDss was not included in the paper, then the equation:

   +• BA 

Dose at ASPET Journals on September 29, 2021  βα 22  =   VDss 2  BA   +   βα  was used to calculate VDss. In a few papers, pharmacokinetic parameters were not reported, but plasma concentration vs time data were listed. In these cases, VDss was calculated using the standard equation using mean plasma concentration vs time data:

Dose • AUMC VD = -0 ∞ ss ()2 AUC -0 ∞

In some cases, a drug had neither the VDss reported per se nor did it have compartmental parameters reported that could be used to calculate VDss, in any description of its pharmacokinetics. In these instances, plots of plasma concentration vs. time included in the report were digitized and the resulting concentration vs. time data were subjected to non-compartmental analysis. This was only done if there were no other reports on the

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same drug that had reported VDss values. In some cases, the only references containing pharmacokinetic values were review articles in which the cited original research data were not available. If the VD values listed in these articles were clearly stated to derive from intravenous administration and if they were specifically described as steady-state

VD values these values were used. Product labels, in which the data listed have been reviewed and approved by the U.S. Food and Drug Administration, were sometimes the only source of VDss data. In these cases, since these had been subjected to a Downloaded from governmental regulatory review, they were included in this database even though the original methods and results that yielded the data are not included in the product label. dmd.aspetjournals.org

From the sources that possessed adequate VDss data, could have VDss calculated from compartmental parameters, or could have VDss calculated from digitized plots, the values of plasma clearance and half-life were also obtained. Parameters (CL, VDss) that at ASPET Journals on September 29, 2021 were already corrected for body weight were included as is. If they were not reported in this manner, then they were converted using the mean body weight of the subjects listed in the report. If only a range of body weights were included in the report, then the midpoint value was used for weight. In cases where no body weight information was included, a value of 70 kg was assumed. For those reports in which the parameters were

2 corrected for body surface area, a conversion of 1.73 m to 70 kg was generally assumed.

The pharmacokinetic data were preferably obtained from reports in which healthy young adult subjects were studied, or patient populations in which health or physiological condition is not severely compromised such that there would be reason to believe that that pharmacokinetics in the patients would be different from healthy subjects (e.g. elderly subjects, obese subjects, etc). In some instances, data were only available from

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DMD #20479 patient populations and/or populations taking concomitant medications (e.g. cancer drugs) and in these instances, the data were included. In many cases, reports of pharmacokinetic data were made with the intent to compare different populations, especially drug-drug interaction studies and studies comparing healthy individuals to those with either hepatic or renal dysfunction. In these cases, the pharmacokinetic data were only from the study groups that were used as the controls for comparison (the healthy group in hepatic or renal dysfunction studies or the groups not receiving a second Downloaded from drug in drug interaction studies). Another source of intravenous pharmacokinetic data were studies in which the objective was the determination of the absolute oral dmd.aspetjournals.org bioavailability. In these instances, the pharmacokinetic parameters from the intravenous part of the study were used. Values were reported to two significant digits, and data were rounded from those reports in which the values were reported to greater than two digits. at ASPET Journals on September 29, 2021

Finally, when multiple studies within a report and/or different reports were used, the data were weight-averaged by number of subjects.

For compounds possessing adequate intravenous pharmacokinetic data, the corresponding protein binding values in human plasma (or serum) were searched. In all cases, original research reports containing these values were sought but protein binding data were included even in those cases where the original methods and results were not present (e.g. product labels, review articles). We then calculated the logK as the logarithm of the ratio bound/free using the fu data reported. However, in many instances, no published protein binding data could be found.

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The physicochemical parameters calculated for all compounds and described in the Results sections were extracted from the table available for each compound through

SciFinder (2007), generated via version 8.14 of the ACDLabs software. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021

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DMD #20479 Results

Characteristics of the Pharmacokinetic and Physicochemical Values. Through extensive mining of the scientific literature and in some cases reanalysis of concentration vs. time data, the human intravenous pharmacokinetic parameters VDss, CL, MRT, and t1/2, were found or calculated for 670 compounds. In addition, human plasma protein binding values were obtained for 554 of the 670 compounds. These data are listed in Table 1, and a spreadsheet containing these values along with the literature references is included as Downloaded from an attachment in the Supporting Information. The data span over considerable ranges

(Figure 1). Volume of distribution at steady-state ranged from a low of 0.035 L/kg to 700

L/kg, for indocyanine green and hydroxychloroquine, respectively. The vast majority dmd.aspetjournals.org

(90%) reside in a 100-fold range between 0.1 and 10 L/kg, with mean and median values of 4.2 and 0.96 L/kg, respectively. Forty-one percent of the compounds have VDss values less than 0.7 L/kg (Table 2), a value generally accepted as representative of total body at ASPET Journals on September 29, 2021 water. Eight percent of compounds had VDss values greater than 10 L/kg, indicating an extensive level of tissue partitioning.

Plasma clearance values ranged from 0.0037 (7-hydroxystaurosporine) to 1070

(artesunate) mL/min/kg (Figure 1), with mean and median values of 10 and 4.0 mL/min/kg, respectively. Approximately three-fifths of the compounds resided in a range between 1 and 10 mL/min/kg, with 16% possessing clearance values below 1 mL/min/kg (very low clearance). Fifty-six of the compounds had CL values greater than liver blood flow (Table 2), suggesting the possibility of extrahepatic clearance mechanisms in these cases, or blood-to-plasma ratios in excess of unity. Many of the compounds possessing high CL values are agents like anaesthetics, pain medications, or cytotoxic cancer chemotherapeutics, which are drug classes that are frequently

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DMD #20479 administered via the intravenous route to optimize therapy or use, and other high CL compounds are prodrugs or drugs in which pharmacologically active metabolites are responsible for the bulk of the effect (e.g. dolasetron, esmolol, etc).

Terminal phase half-life values ranged from 4 min (indocyanine green) to 50 days

(suramin) (Figure 1), with two thirds residing between 1 and 12 hr (Table 2). (Half-lives for the bisphosphonate class of drugs may be underestimated since they sequester into bone, but may not be detectable in the systemic circulation.) The average t1/2 was 18 hr Downloaded from and the median value was 4.1 hr. Since half-life is derived from CL and VD, it cannot be directly related to physiological properties such as hepatic blood flow or volume of total

body water. Half-life values can be partitioned into zones loosely based on dosing dmd.aspetjournals.org regimen frequency values (provided that there is not a considerable difference between pharmacokinetics and pharmacodynamics), and it is a common practice in

pharmaceutical research to seek drugs that would be amenable to convenient q.d. dosing at ASPET Journals on September 29, 2021 regimens. However, based on this, over three quarters of compounds would likely require dosing greater than once-per-day since they have t1/2 values below 12 hr (Table

2). This same conclusion could be drawn if MRT were used to address this question.

Interestingly, a plot of MRT versus CL (Figure 2) shows a clustering of datapoints which implies that in order to obtain a MRT of at least 5 h for an acidic drug a CL value lower than 0.5 mL/min/kg is required; a consequence of the low VDss for acidic compounds.

The ranking of protein binding values is in Figure 1. The values ranged between no binding (several compounds) and 99.98% bound (amiodarone), with mean and median free fractions of 0.38 and 0.26, respectively. Two-thirds of the compounds in the set are less than 90% bound, and about one-eighth could be considered highly bound (> 99%;

Table 2).

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DMD #20479 The 670 compounds in this dataset span a wide range of fundamental physico- chemical characteristics (Figure 3). The typical drug-like space for molecular weight

(200 to 600 Da) is represented by 80% of the compounds, with median value of 342 Da and a range from 3 (lithium) to 1816 (dalbavancin). The median value for polar surface area (PSA) was 87 Å2. The set encompasses 159 acids, 271 bases, 173 neutral compounds, and 67 zwitterions (Figure 3). These were categorized with a cutoff value of

10% ionized (anionic for acids; cationic for bases, both for zwitterions) at pH 7, using the Downloaded from calculated pKa values from ACD labs. Lipophilicity, as described by clogP and clogD7.0 had median values of 1.92 and 0.42, respectively The median values for numbers of

rotatable bonds, hydrogen bond acceptors, and hydrogen bond donors were 5, 6, and 2, dmd.aspetjournals.org respectively. It can be noted that, even though these data were derived from intravenously dosed compounds, the median values for logP as well as for the number of

hydrogen bond acceptors and hydrogen bond donors were well below the limits set by the at ASPET Journals on September 29, 2021

Lipinski “rule of 5” (Lipinski et al., 1997) as perhaps may be expected for compounds either marketed or in clinical development. Furthermore, the number of rotatable bonds was below the limit reported by Veber (Veber et al., 2002) for permeable compounds.

Trends in the Dataset: VDss vs Physicochemical Properties. The dataset was mined for any trends between the physicochemical values obtained and the human VDss values. In no case was there a relationship such that any single physicochemical property could be considered uniquely predictive of VDss, nevertheless there were trends in the data suggesting a significant contribution of some physicochemical properties to VDss.

Trends could not be easily observed when values were partitioned by means, because of the scatter and overlap in the data (as exemplified in Figure 4B), however trends could be observed for median values. Median VDss values showed trends with clogP, PSA, and

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DMD #20479 number of H-bond acceptors and donors (Figure 4), as well as charge type (Table 3).

These four properties have varying extents of inter-relatedness. The properties of high polar surface area, high numbers of H-bond acceptors/donors, and low lipophilicity tend to reside in similar sets of molecules, and median VDss values trend higher for low PSA, low numbers of H-bond acceptors/donors, and high lipophilicity (Figure 4). Negative charge (acids) have lower median and mean VDss values than bases, with neutral molecules and zwitterions in between (Table 3). The median values of VDss for the acids Downloaded from remain around 0.2 L/kg irrespective of clogP, whereas for bases, neutrals and zwitterions there is an upward trend observed for VDss with increasing lipophilicity (Figure 5).

Data were also examined after correcting VDss for free fraction (i.e. free VDss = dmd.aspetjournals.org

VDss/fu), which is more indicative of the extent of tissue partitioning. As with total VDss, trends for free VDss were observed primarily through examination of median values since there is considerable scatter among the data. The same four physicochemical parameters, at ASPET Journals on September 29, 2021 lipophilicity, PSA, charge type, and sum of hydrogen bond donors and acceptors appear to be the properties that contribute to free VDss (Figure 6). An interesting trend emerged when the relationship between VDss and lipophilicity was stratified by charge type; a considerable improvement was observed when the trend was examined for free VDss than for total VDss (Figure 5). For example, there was no trend in the relationship between

VDss and lipophilicity for acids, but when examined for free VDss, there was a trend that increasing lipophilicity leads to greater free VDss values. This tends to indicate that plasma protein binding dominates in the distribution of negatively charged compounds.

Trends in the Dataset: CL vs Physicochemical Properties. Similar to the observations with VDss, readily discernable trends could be observed between some physicochemical properties and CL, however no relationship was tight enough to suggest that any single

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DMD #20479 property could be quantitatively predictive of CL. Decreases in median CL were observed with increases in PSA or sum of hydrogen bond acceptors and donors (Figure

7). Only a weak trend could be observed between median CL and lipophilicity. Bases had generally greater CL values than acids, neutrals, or zwitterions (Figure 7). The relationship between lipophilicity and median CL was considerably strengthened when

CL values were converted to free CL values (Figures 8 and 9). Interestingly, unlike the median CL values, the difference in median free CL values for acids, bases, and neutrals Downloaded from was not observable (Table 3). When CL values were examined for any relationship to lipophilicity after stratification by charge type, no apparent trends could be observed, but

when corrected for protein binding (i.e. free CL = CL/fu), the relationship showed a weak dmd.aspetjournals.org trend with free CL increasing with increasing lipophilicity (Figure 9).

Trends in the Dataset: Protein Binding vs Physicochemical Properties. Of the physico-

chemical properties examined, relationships were only observed between protein binding at ASPET Journals on September 29, 2021 and lipophilicity and charge type (Figure 10). With greater lipophilicity, protein binding tends to increase, and this trend becomes apparent for all charge types when protein binding is expressed as the logarithm of the apparent affinity constant, logK (or log[bound/free]) as shown in Figure 10.

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DMD #20479 Discussion

The utility of human pharmacokinetic databases in the development of a greater understanding of the relationship between chemical structure and pharmacokinetic behavior is unquestionable. Over the years, trends have been noted in the relationship between PK and structure (and physicochemical properties), but these have been for limited compound sets, in many cases within a single class of drugs (Smith, 1997; van de

Waterbeemd et al., 2001). In this report, we have gathered a database of human Downloaded from pharmacokinetic parameters of a size never before assembled. Each value was obtained after careful analysis of original scientific literature (or in a few cases from drug product

labels approved by government regulatory authorities). In many cases, different dmd.aspetjournals.org investigators chose to analyze data and report pharmacokinetic parameters in different manners (e.g. non-compartmental vs compartmental analysis; VDss vs VDβ, intravenous vs oral dosing, etc). Thus, we reviewed each original report to ensure that the parameters at ASPET Journals on September 29, 2021 reported were consistent from compound to compound, and if not, to reanalyze the data to obtain the desired parameters. Unlike other commonly cited pharmacokinetic databases (Goodman and Gilman, 2006), the database values presented in this report are exclusively from studies in which drugs were administered intravenously. Thus, these values are not confounded by effects of slow and incomplete absorption or extensive first-pass extraction. The database is offered as a supplemental file to this report, so that other scientists can easily download it and use it to test various hypotheses and develop

Quantitative Structure PharmacoKinetic Relationships models. However, the following limitations of this database should be appreciated: (1) study designs vary from drug to drug with regard to dose level, number of subjects, blood sampling times, analytical procedures, etc; (2) values reported are means and do not account for inter-subject

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DMD #20479 variability. Nevertheless, the database should be able to be used to provide insight into the relationship between structure and pharmacokinetics and useful in building computational models. Efforts on the latter are underway and a discussion of initial efforts on the former are described below.

The set of compounds reported in this trend analysis encompasses a wide variety of structures and therapeutic areas as well as physicochemical properties as shown in the

Results section, and Table 2 shows the pharmacokinetics parameters grouped according Downloaded from to defined thresholds. For VDss a large proportion of the values (approximately 42 %) falls within total body water (0.7 L/kg). Only a small subset of these drugs was confined

to blood (or plasma) volume, with a value close to the generally accepted distribution dmd.aspetjournals.org volume of albumin, or 0.1 L/kg (McGinnity et al., 2007). (The latter can be approximated to the above value, from the extravascular:intravascular ratio, or RE/I, of 1.4, as in the

Øie-Tozer equation (Øie and Tozer, 1979) assuming a blood volume of 0.07 L/kg.) That at ASPET Journals on September 29, 2021 would indicate that while most of these compounds do not extensively partition into tissues they can distribute throughout bodily fluids and, therefore, may be considered unbound in tissues and potentially capable to reach the intended target. If a volume of 0.7

L/kg is taken as the threshold for total body water, about 60% of the compounds have values exceeding that threshold, with almost one-half of these compounds (29% of total) having volumes in excess of 2 L/kg. These compounds may be expected to partition moderately to extensively into tissues and contribute significantly to the residence time

(or t1/2) of the drug. However, no direct correlation can be drawn between VDss values and the ability of a compound to reach the intended pharmacological target, or to diffuse into a particular organ, since these values are overall “averages” across all tissues and fluids.

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DMD #20479 Overall, nearly 85% of the compounds reside in the moderate to low range (< 15 mL/min/kg, or hepatic extraction ratio EH approximately taken as ≤ 0.7) of clearance which, in conjunction with a moderate to high volume of distribution, might contribute to yielding a fairly large residence or terminal half-life, since a good proportion of compounds reside in ranges of volume of distribution that exceed total body water.

Nevertheless, only a minority of compounds, about 20%, have a t½ profile consistent with a once-a-day dosage regimen, and about one-half show a half-life equal to or lower than 4 Downloaded from hours. A very significant proportion, about one-third, show an intermediate half-life which would still be generally considered unsuitable for a once-a-day dosage (barring

non-direct PK/PD relationships or tolerance to a high Cmax/Ceff ratio). However, dmd.aspetjournals.org depending on the therapeutic target, a rapid onset and short half-life may be desirable and a once-a-day dosing regimen may not always be needed especially for acute therapy.

Conversely, absorption kinetics may be such that t1/2 via the oral route exceeds that at ASPET Journals on September 29, 2021 following iv administration, permitting once-a-day dosage regimens.

Physicochemical properties and their impact on drug metabolism and pharmacokinetics is a broad and complex topic. Unfortunately, there is no single unifying descriptor which is able to explain drug pharmacokinetic behavior. The rule-of-5 concept developed by Lipinski et al. (Lipinski et al., 1997) utilizes thresholds of logP, molecular weight, hydrogen bond acceptors and hydrogen bond donors to classify oral drug absorption. However, with respect to other PK parameters typically two key descriptors, lipophilicity (expressed as logP or logD) and charge/ionization type, have been employed with general trends observed in certain drug classes (Smith, 1997; van de Waterbeemd et al., 2001). Lipophilicity effectively models a number of partitioning and distribution processes including cell membrane permeation, membrane and protein binding, affinity

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DMD #20479 for drug-metabolizing enzymes, while charge type reflects ion-pair interactions with plasma proteins, membrane lipids and specific drug-metabolizing enzymes.

Plasma protein binding typically shows a sigmoidal relationship with lipophilicity as demonstrated by van de Waterbeemd et al. (van de Waterbeemd et al., 2001) on a dataset of ~ 150 acidic, basic and neutral compounds. When expressed as the logarithm of the apparent affinity constant (logK) this sigmoidal trend becomes linear and is exemplified in the dataset of 554 compounds presented here (Figure 10). Increasing lipophilicity Downloaded from typically yields increased protein binding as the interaction with albumin and α1-acid glycoprotein is driven by hydrophobic forces. Acidic compounds show correspondingly

higher binding relative to bases and neutrals due to an ion-pair interaction with a basic dmd.aspetjournals.org residue within albumin (Ghuman et al., 2005). Basic compounds tend to show high affinity for α1-acid glycoprotein due to an electrostatic interaction with acidic residues

(Kremer et al., 1988). Overall, plasma protein binding influences the disposition profile at ASPET Journals on September 29, 2021 in terms of both CL and VDss as only free drug is available for elimination and distribution into peripheral tissues. By converting CL and VDss into their free forms, the confounding impact of plasma protein binding, and the physicochemistry which drives it, was removed. This led to much improved trends between physicochemical properties and free CL and VDss.

In a physiological sense, steady-state volume of distribution can be described by the Gillette or Øie-Tozer equations (Øie and Tozer, 1979), where the weighted mean ratio of plasma binding to tissue binding is the crucial term. Factors driving the unbound fraction in plasma are as described above, while the unbound fraction in tissue is dependent on cell membrane permeability and non-specific hydrophobic interaction with cellular lipids and protein. In addition, bases typically exhibit an ion-pair interaction with

20 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 the charged polar head group of membrane phospholipids contributing to an increased tissue affinity and therefore VDss. Furthermore, basic compounds have the potential for specific interaction with subcellular organelles such as lysosomes and mitochondria through a pH partition mechanism, as illustrated by some of the psychotropic drugs

(Daniel and Wojcikowski, 1997). The lipophilic and charge dependence of tissue affinity has been nicely demonstrated in correlations of free VDss and logD on small compound datasets (Smith, 1997; van de Waterbeemd et al., 2001) corroborating our findings with Downloaded from 670 compounds. Additionally, we observed a trend with polar surface area and numbers of hydrogen bond donors and hydrogen bond acceptors; increasing polarity leads to a

reduction in the median VDss. These polarity descriptors, to some extent inversely related dmd.aspetjournals.org to lipophilicity, demonstrate that VDss cannot be explained by logP and charge type alone and highlight the utility of multivariate in silico approaches to VDss prediction (Gleeson et al., 2006; Lombardo et al., 2006). The magnitude of this dataset may now allow at ASPET Journals on September 29, 2021 exploration of specific functional moieties or substituents that play an integral role in tissue distribution and VDss.

Mammals have evolved a number of clearance mechanisms designed to render lipophilic xenobiotics more water soluble to allow efficient renal or biliary elimination.

Introducing hydrophilicity into xenobiotics is typically a concerted effort of functionalization and conjugative metabolic reactions, mediated by cytochromes P450 and UDP-glucuronosyl transferases or sulfotransferases, respectively. The Km for P450 reactions is inversely related to lipophilicity as the binding of substrate to enzyme relies on hydrophobic interactions (Lewis and Dickins, 2003). Positive correlations of free metabolic clearance and logD support this finding, being irrespective of charge type

(Smith, 1997). We observed a general trend of increasing free CL with logP, supporting

21 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 earlier work with this much expanded dataset. The variation within this trend contains latent information not described solely by lipophilicity. Renal CL, for example, is typically inversely correlated with partition or distribution coefficients, as increasing lipophilicity allows efficient tubular passive reabsorption in the nephron of the kidney.

Trends with polarity descriptors (hydrogen bond donors/acceptors and PSA) were also observed such that median CL decreases as polarity is amplified, in line with established reasoning on metabolic CL SAR. Latent information within the global trend of logP and Downloaded from CL also concerns functionality and substituent effects on CL. One such example is the N- dealkylation of the benzodiazepines; clonazepam, oxazepam, diazepam and temazepam,

where differences in CL are related to the propensity of N-demethylation and not bulk dmd.aspetjournals.org lipophilicity (Smith, 1997). These examples of functionalization effects should be well represented in this 670 compound dataset and provide an opportunity for further

exploration. at ASPET Journals on September 29, 2021

Future efforts are directed at the utilization of this dataset in gaining a better understanding of the relationship between chemical structure and pharmacokinetic behavior. While the analysis in this report focused on gross physicochemical properties such as lipophilicity and charge, further questions can be asked regarding the impact of specific chemical substituents on human pharmacokinetic parameters and, potentially, lead to structure-property relationships. In particular the exploration of the predictability of clearance and half-life using in silico, in vitro-in vivo or in vivo correlative methods could be attempted using this large and accurate dataset of human data. Volume of distribution, as an example, may be more related to overall physicochemical properties which would explain the previous successes in model building for this parameter(Gleeson

22 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 et al., 2006; Lombardo et al., 2006), while clearance may have a greater dependence on specific chemical substituents.

Efforts are now underway, by our groups, to use these data to further our understanding of the complex relationships between structure and PK parameters, using several approaches along the lines described above. This large set of carefully compiled human PK data and the basic structure-PK relationships observed thus far should be of use in the design of new medicines. Downloaded from

Acknowledgements. We wish to thank our NIBR Cambridge colleagues, Drs. S.

Harriman and G. Liang, Mr. A. Amaral, Mr. M. Gunduz and Ms. J. Zhan for searching dmd.aspetjournals.org and providing some of the PK data reported.

at ASPET Journals on September 29, 2021

23 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 References

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29 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Footnotes

Supporting Information Available: The complete list of compounds with pharmacokinetic data, CAS numbers, full references and comments by the authors of this manuscript is available as an Excel file. A color version of figures 2, 5, 9 and 10 is also available in a

Word file, as supporting information.

Downloaded from

dmd.aspetjournals.org

at ASPET Journals on September 29, 2021

30 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 FIGURE LEGENDS

FIGURE 1. Span of pharmacokinetic values for the 670 compounds included in this analysis. Panel A: VDss; Panel B: CL; Panel C: t1/2; Panel D: fu.

FIGURE 2. Relationship between MRT and CL for acids (panel A), bases (panel B), neutrals (panel C), and zwitterions (panel D). (Note: A color figure containing all of the compounds in one plot is available in the Supplemental Information.)

FIGURE 3. Spans of physicochemical properties of the 670 compounds included in this Downloaded from analysis. Panel A: molecular weight; Panel B: polar surface area; Panel C: charge; Panel

D: lipophilicity; Panel E: number of rotatable bonds per molecule; and Panel F: number

of hydrogen bond acceptors and donors per molecule. dmd.aspetjournals.org

FIGURE 4. Relationship between median human VDss values and selected physicochemical parameters. Panel A: lipophilicity; Panel B: spans for human VDss values by charge type (horizontal line corresponds to median value); Panel C: polar at ASPET Journals on September 29, 2021 surface area; and Panel D: sum of hydrogen bond acceptors and donors.

FIGURE 5. Relationship between VDss and lipophilicity, separated by charge type.

Panel A: VDss vs clogP for acids; Panel B: VDss vs clogP for bases; Panel C: VDss vs clogP for neutrals; Panel D: VDss vs clogP for zwitterions; Panel E: free VDss vs clogP for acids; Panel F: free VDss vs clogP for bases: Panel G: free VDss vs clogP for neutrals; and Panel H: free VDss vs clogP for zwitterions. DHA-paclitaxel (clog P 15.7) and eritoran (clogP 17.8) were excluded for easier visualization of the data. (Note: Color figures containing all of the compounds in one plot is available in the Supplemental

Information.)

31 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479

FIGURE 6. Relationship between median human free VDss values and selected physicochemical parameters. Panel A: lipophilicity; Panel B: charge; Panel C: polar surface area; and Panel D: sum of hydrogen bond acceptors and donors.

FIGURE 7. Relationship between median human CL values and selected physicochemical parameters. Panel A: lipophilicity; Panel B: spans for human CL values by charge type (horizontal line corresponds to median value); Panel C: polar surface area; and Panel D: sum of hydrogen bond acceptors and donors. Downloaded from FIGURE 8. Relationship between median human free CL values and selected physicochemical parameters. Panel A: lipophilicity; Panel B: charge; Panel C: polar

surface area; and Panel D: sum of hydrogen bond acceptors and donors. dmd.aspetjournals.org

FIGURE 9. Relationship between CL and lipophilicity, separated by charge type. Panel

A: CL vs clogP for acids; Panel B: CL vs clogP for bases; Panel C: CL vs clogP for

neutrals; Panel D: CL vs clogP for zwitterions; Panel E: free CL vs clogP for acids; Panel at ASPET Journals on September 29, 2021

F: free CL vs clogP for bases: Panel G: free CL vs clogP for neutrals; and Panel H: free

CL vs clogP for zwitterions. DHA-paclitaxel (clog P 15.7) and eritoran (clogP 17.8) were excluded for easier visualization of the data. (Note: Color figures containing all of the compounds in one plot is available in the Supplemental Information.)

FIGURE 10. Relationship between protein binding and physicochemical properties.

Panel A: acids; Panel B: bases; Panel C: neutrals, Panel D: zwitterions; Lower Panel: median free fraction vs lipophilicity. (Note: A color figure containing all of the compounds in one plot is available in the Supplemental Information.)

32 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 TABLE 1. Summary of intravenous pharmacokinetic parameters and plasma protein binding values for 670 compounds in humans. Name VDss CL MRT t1/2 (L/kg) (mL/min/kg) fu (h) (h) Abacavir 0.84 13 0.50 1.1 1.0 Abanoquil 6.3 14 NF 7.6 5.9 Acarbose 0.32 2.2 NF 2.4 2.7 Acebutolol 1.7 10 0.74 2.8 3.5 Acecainide (N-Acetylprocainamide) 1.9 4.0 0.90 7.9 6.4 Acetaminophen (Paracetamol) 1.0 5.0 0.52 3.3 2.5 Acetazolamide 0.37 0.65 0.04 9.5 13 N-Acetylcysteine 0.55 3.1 0.17 3.0 5.5 L-Acetylmethadol 8.3 5.4 0.20 26 18 Acetylsalicylic Acid 0.22 12 0.68 0.30 0.26 Downloaded from Acivicin 0.50 0.69 NF 12 9.9 Acyclovir 0.71 4.7 0.85 2.5 2.5 Adefovir 0.42 3.7 0.96 1.9 1.6 Adinazolam 0.98 6.2 0.31 2.6 2.1 dmd.aspetjournals.org Albuterol (Salbutamol) 1.9 7.8 0.92 4.1 2.4 Alcuronium 0.32 1.3 NF 4.1 3.6 Alfentanil 0.45 3.9 0.086 1.9 1.6 Alfuzosin 1.5 5.9 NF 4.2 4.8 Alizapride 1.6 6.6 NF 4.0 2.8

Allopurinol 0.58 11 0.97 0.88 0.80 at ASPET Journals on September 29, 2021 Almotriptan 2.2 8.9 0.60 4.2 3.4 Alosetron 1.1 8.7 0.18 2.1 1.6 Alprazolam 0.80 0.74 0.29 18 12 Alprenolol 3.2 15 0.18 3.6 2.5 Amantadine 6.6 4.8 0.33 23 16 Amdinocillin 0.37 6.3 NF 1.0 1.1 Amikacin 0.16 1.1 0.88 2.4 2.4 9-Aminocamptothecin 2.2 6.5 0.003 5.6 7.0 6-Aminohexanoic Acid 0.39 2.5 0.92 2.6 4.9 5-Aminosalicylic Acid 0.33 9.3 0.39 0.59 0.61 Amiodarone 60 1.9 0.0002 620 820 Amitriptyline 8.7 6.1 0.07 24 17 Amlodipine 17 7.0 0.005 40 34 Amoxicillin 0.25 3.3 0.85 1.3 1.1 Amphetamine 6.1 9.7 0.80 10 7.3 Ampicillin 0.22 2.8 0.85 1.3 1.4 Amrinone 1.3 8.9 0.89 2.4 2.0 Amsacrine 1.6 4.3 0.029 6.2 4.7 Amsalog 0.30 2.6 0.0011 1.9 2.4 Anhydrovinblastine 12 12 NF 17 18 Anidulafungin 0.57 0.21 0.16 45 40 Antipyrine 0.77 0.64 0.93 20 12

33 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 R-Apomorphine 1.6 40 0.06 0.67 0.68 Aprepitant 0.94 1.0 0.05 16 13 Argatroban 0.17 5.0 NF 0.57 0.40 Aripiprazole 4.9 0.83 0.01 98 75 Artesunate 15 1070 0.25 0.32 0.22 Atenolol 0.95 2.5 0.94 6.3 6.1 Atomoxetine 0.85 9.3 0.02 1.5 5.2 Atovaquone 0.60 0.15 0.001 67 63 Atracurium 0.10 5.7 0.52 0.29 0.31 Atropine 3.3 7.6 0.61 7.2 4.1 Azacitidine 0.47 35 NF 0.22 0.36 Azapropazone 0.12 0.14 0.004 14 17 Azelastine 15 9.0 0.17 27 22 Azimilide 13 2.4 0.06 100 79 Downloaded from Azithromycin 33 10 0.88 57 69 Azlocillin 0.26 2.5 0.65 1.7 1.2 Aztreonam 0.18 1.5 0.40 2.1 1.5

Bambuterol 1.6 18 NF 1.4 2.6 dmd.aspetjournals.org BB 83698 1.1 3.1 NF 5.9 9.4 Beclomethasone Dipropionate 0.29 36 0.13 0.10 0.50 Benperidol 3.8 8.3 NF 7.5 5.8 Betamethasone 1.3 2.8 0.36 7.9 5.6 Betamipron 0.28 9.2 0.17 0.51 0.59

Betaxolol 4.8 3.4 0.40 24 17 at ASPET Journals on September 29, 2021 Bevantolol 0.67 5.5 0.015 2.0 1.9 Biapenem 0.20 2.5 0.92 1.3 1.1 Bilobalide 2.5 10 NF 4.0 3.2 Biperiden 12 12 0.097 17 24 Bisaramil 9.0 17 NF 8.8 8.6 Bisoprolol 2.4 3.7 0.66 11 10 BMS-214662 0.84 9.6 NF 1.4 1.6 Bortezomib 10 19 0.17 8.8 18 Bosentan 0.29 2.1 0.037 2.3 4.1 Bromazepam 0.85 0.51 0.48 28 21 Bromfenac 0.11 1.3 0.0011 1.5 2.6 Bromopride 3.1 13 0.60 4.0 2.9 Brotizolam 0.75 2.0 0.092 6.0 4.8 Budesonide 3.9 20 0.13 3.2 2.8 Buflomedil 1.3 5.6 0.40 3.9 3.3 Bufuralol 1.7 8.9 0.19 3.2 2.3 Bumetanide 0.16 2.5 0.031 1.1 1.2 Bunazosin 0.72 4.8 0.06 2.5 2.0 Bupivacaine 0.84 4.3 0.056 3.3 3.1 Buprenorphine 4.9 19 0.04 4.9 3.2 Busulphan 0.55 2.4 1.0 3.8 3.4 Butorphanol 12 41 0.17 5.3 4.8

34 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Caffeine 0.63 1.4 0.64 7.5 4.9 Captopril 0.75 12 0.73 1.0 2.0 Carbenicillin 0.17 1.9 0.52 1.5 1.1 Carboplatin 0.26 1.5 1.0 3.0 2.0 Carmustine (BCNU) 1.2 78 0.23 0.26 0.37 Carumonam 0.18 1.5 0.72 2.0 1.6 Carvedilol 1.3 7.8 0.02 2.8 2.4 Caspofungin 0.13 0.14 0.035 15 27 CB 10-277 0.25 1.3 NF 3.0 2.5 Cefadroxil 0.23 2.5 0.39 1.5 1.1 Cefamandole 0.16 3.6 0.25 0.75 0.75 Cefatrizine 0.22 3.3 0.40 1.1 1.2 Cefazolin 0.12 0.89 0.18 2.2 1.7 Cefcanel 0.13 2.6 NF 0.83 1.0 Downloaded from Cefepime 0.28 2.2 0.78 2.1 1.9 Cefetamet 0.28 1.9 0.78 2.5 2.1 Cefixime 0.24 1.0 0.31 3.9 3.2

Cefmetazole 0.13 1.5 0.15 1.5 1.5 dmd.aspetjournals.org Cefodizime 0.054 0.61 0.18 1.5 4.2 Cefoperazone 0.17 1.3 0.07 2.2 1.8 Ceforanide 0.17 0.66 0.19 4.3 3.0 Cefotaxime 0.19 2.7 0.60 1.2 1.2 Cefotetan 0.13 0.42 0.15 5.2 4.5

Cefoxitin 0.17 3.6 0.73 0.79 0.81 at ASPET Journals on September 29, 2021 Cefpirome 0.24 1.9 0.90 2.1 1.7 Cefprozil 0.21 2.9 0.59 1.2 1.2 Ceftazidime 0.31 2.4 0.79 2.2 1.8 Ceftizoxime 0.20 2.1 0.72 1.6 1.5 Ceftobiprole 0.27 1.5 0.62 3.0 3.3 Ceftriaxone 0.085 0.26 0.052 5.4 8.6 Cefuroxime 0.15 2.2 0.12- 1.1 1.1 Cephalexin 0.21 3.9 0.600.85 0.89 0.57 Cephaloridine 0.46 3.3 0.80 2.3 1.5 Cephalothin 0.070 1.8 0.22 0.65 0.95 Cephapirin 0.13 2.7 0.55 0.80 1.2 Cephradine 0.21 3.3 0.95 1.1 0.85 Cerivastatin 0.33 2.9 0.01 1.8 1.8 Cetrorelix 0.39 1.2 0.14 5.4 12 Chlorambucil 0.26 2.8 0.01 1.5 1.1 Chloramphenicol 0.94 2.4 0.34 6.5 4.6 Chlorazepate 0.20 1.1 NF 3.0 2.4 Chlordiazepoxide 0.25 0.37 0.056 11 8.3 Chlormethiazole 6.6 33 0.36 3.3 4.5 m-Chlorophenylpiperazine 2.5 6.1 NF 7.0 4.7 Chloroquine 140 4.1 0.43 740 570 Chlorpheniramine 3.3 2.5 0.70 22 22

35 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Chlorpromazine 10 16 0.056 10 11 Chlorpropamide 0.19 0.045 0.03 70 46 Chlortetracycline 0.90 2.0 0.52 7.4 7.0 Chlorthalidone 3.9 1.5 0.24 43 36 Cibenzoline 4.1 8.6 0.50 7.8 7.3 Cicaprost 0.08 3.8 NF 0.35 1.1 Cidofovir 0.49 2.5 1.0 3.3 2.6 Cilastatin 0.15 2.3 0.60 1.1 0.86 Cilomilast 0.23 0.50 0.006 8.5 7.9 Cimetidine 1.2 8.1 0.78 2.5 2.2 Ciprofloxacin 2.1 8.3 0.70 4.2 3.8 Cisatracurium 0.14 5.4 0.62 0.43 0.42 Citalopram 12 4.3 0.20 47 33 Cladribine 7.7 14 0.79 9.2 16 Downloaded from Clarithromycin 1.5 7.3 0.23 3.4 2.8 Clavulanic Acid 0.22 3.1 0.91 1.0 0.90 Clevidipine 0.58 142 0.0038 0.07 0.30

Clinafloxacin 1.9 4.7 0.96 6.7 5.4 dmd.aspetjournals.org Clindamycin 0.79 4.5 0.06 2.9 2.1 Clomipramine 13 8.2 0.029 26 26 Clonazepam 2.9 0.88 0.15 55 38 Clonidine 3.3 4.0 0.56 14 7.6 Clozapine 1.6 2.5 0.055 11 10

Cocaethylene 1.6 13 0.64 2.0 1.5 at ASPET Journals on September 29, 2021 Cocaine 2.0 32 0.12 1.0 0.76 Codeine 3.5 15 0.70 3.9 4.0 Colchicine 6.1 2.1 0.61 49 58 Conivaptan 0.76 3.0 0.01 4.2 6.7 Cotinine 1.1 0.89 NF 21 17 Cyclophosphamide 0.73 1.1 0.87 11 8.0 Cyclosporine 3.3 7.5 0.068 7.3 7.3 Dacarbazine 1.2 2.6 1.0 7.8 6.2 Dalbavancin 0.14 0.010 0.02 230 170 Dalfopristin 0.39 16 0.82 0.41 0.74 Dantrolene 0.37 0.43 NF 14 10 Dapsone 0.83 0.48 0.25 29 22 Daptomycin 0.088 0.16 0.083 9.2 8.1 Darifenacin 2.6 12 0.02 3.6 3.6 Decitabine 4.6 130 1.0 0.59 0.58 Delorazepam 2.2 0.17 0.051 220 200 Demethylchlortetracycline(Chlordesmethyldiazepam) 1.3 1.1 0.59 17 12 Denaverine 7.1 5.7 NF 21 34 4-Desacetyl-4-Methylpaclitaxel 6.9 8.6 NF 13 31 Desipraminecarbonate 15 11 0.16 23 22 Desmethyldiazepam 0.45 0.12 0.03 63 46 Desmopressin 0.30 1.4 NF 3.6 3.0

36 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Dexamethasone 0.94 3.3 0.32 4.7 3.7 Dexfenfluramine 11 11 0.66 18 14 Dexloxiglumide 0.18 3.7 0.024 0.79 1.2 Dexmedetomidine 1.6 11 0.06 2.5 2.2 Dexniguldipine 8.4 9.3 NF 15 22 Dexrazoxane 0.55 3.1 1.0 3.0 2.5 DHA Paclitaxel 0.068 0.025 0.0038 45 60 Diatrizoic Acid (Amidotrizoate) 0.26 1.7 1.0 2.6 1.8 Diazepam 1.0 0.38 0.023 44 42 Diazoxide 0.21 0.060 0.06 58 48 Dibekacin 0.13 0.80 NF 2.7 2.4 Dichloroacetic Acid 0.19 5.3 NF 0.60 0.65 Diclofenac 0.22 3.5 0.005 1.0 1.4 Dicloxacillin 0.11 2.0 0.033 0.92 0.88 Downloaded from Didanosine 0.77 11 0.95 1.2 1.4 Diflunisal 0.097 0.10 0.0016 14 10 Digitoxin 0.44 0.043 0.066 170 180

Digoxin 4.1 1.7 0.70 40 38 dmd.aspetjournals.org Dihydroquinidine 2.8 4.2 0.22 11 5.7 Dilevalol 4.8 29 NF 2.8 3.3 Diltiazem 4.1 13 0.18 5.3 5.6 Diphenhydramine 6.5 9.8 0.19 11 9.3 Diprafenone 1.2 11 0.017 1.9 1.5

Diprophylline 0.42 4.8 NF 1.5 1.7 at ASPET Journals on September 29, 2021 Disopyramide 0.52 0.90 0.16 9.6 7.0 Docetaxel 2.1 14 0.04 2.5 11 Dofetilide 3.3 5.2 0.36 11 8.1 Dolasetron 2.0 180 NF 0.19 0.13 Domperidone 3.4 9.5 0.082 6.0 7.5 Doxacurium 0.22 2.7 0.53 1.4 1.7 Doxapram 1.2 5.3 NF 3.7 4.1 Doxazosin 1.3 1.6 0.017 14 10 Doxepin 12 14 0.20 15 15 Doxifluridine 0.28 11 0.61 0.46 0.35 Doxorubicin 22 15 0.28 24 32 Doxycycline 0.69 0.46 0.12 25 14 DP-b 99 1.1 14 NF 1.3 3.5 Drotaverine 1.9 3.5 0.12 9.0 9.3 Edrophonium 1.1 9.6 NF 1.9 1.8 Eletriptan 1.6 5.3 0.15 5.0 4.2 Eltanolone 2.0 22 0.010 1.5 4.7 Enalaprilat 0.38 1.6 0.62 4.0 39 Encainide 2.2 13 0.26 2.8 2.6 Endralazine 2.2 13 NF 2.7 5.6 Eniporide 1.1 8.2 NF 2.3 2.2 Enoxacin 2.0 5.4 0.80 6.2 5.1

37 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Enprofylline 0.63 4.0 0.55 2.6 1.8 Entacapone 0.27 12 0.02 0.38 2.4 Epirubicin 45 20 0.25 38 36 Epristeride 0.54 0.33 0.03 27 27 Eprosartan 0.17 1.9 0.017 1.5 2.1 Eptifibatide 0.17 1.2 0.75 2.4 4.2 Ergotamine 3.6 15 0.02 4.0 2.1 Eritoran 0.048 0.013 NF 62 51 Ertapenem 0.12 0.45 0.10 4.4 3.8 Erythromycin 0.95 5.6 0.10 2.8 2.0 Esmolol 1.2 290 0.59 0.070 0.15 Estradiol 1.2 30 0.016 0.7 1.7 Estramustine Phosphate 0.12 0.94 NF 2.1 2.4 Ethacrynic Acid 0.26 8.7 NF 0.50 0.50 Downloaded from Ethambutol 1.7 10 0.18 2.7 3.1 Ethinylestradiol 4.3 7.0 0.014 11 9.7 Ethinylestradiol-17-Sulfate 0.61 1.3 NF 7.8 9.3

Ethinylestradiol-3-Sulfate 2.3 4.7 0.011 8.2 8.4 dmd.aspetjournals.org Etilefrine 2.1 14 0.77 2.5 2.2 Etomidate 2.7 13 0.23 2.9 3.5 Etoposide 0.18 0.50 0.12 6.0 5.7 Etoricoxib 1.5 0.79 0.081 32 26 Exatecan (Acid) 0.44 0.96 NF 7.6 6.7

Famotidine 1.2 6.6 0.84 3.0 2.8 at ASPET Journals on September 29, 2021 Felodipine 4.4 11 0.0036 6.7 10 Fenoterol 1.2 29 0.53 0.69 0.87 Fenoximone 1.8 29 0.30 1.0 1.0 Fenspiride 3.0 2.6 NF 19 14 Fentanyl 0.89 4.7 0.16 3.2 3.0 Finasteride 1.1 2.4 0.095 7.6 6.0 Flavopiridol 1.5 6.1 0.06 4.0 5.2 Flecainide 6.1 4.9 0.52 21 12 Fleroxacin 1.6 2.4 0.73 11 8.6 Flucloxacillin 0.19 2.4 0.043 1.3 1.4 Fluconazole 0.75 0.31 0.89 40 30 Flucytosine 0.68 2.0 1.0 5.7 4.2 Fludarabine 2.2 3.2 0.76 12 11 Flumazenil 0.80 16 0.58 1.0 0.78 Flunitrazepam 1.9 1.4 0.17 23 25 Fluorescein 0.14 1.3 0.11 1.8 1.6 5-Fluorouracil 0.23 26 0.64 0.15 0.12 Flupirtine 1.1 1.6 0.15 11 8.5 Fluticasone Propionate 3.6 17 0.10 3.9 6.0 Fluvastatin 0.42 16 0.0079 0.44 0.70 Folinic Acid 0.25 5.8 0.087 0.80 1.3 Foscarnet 0.50 2.1 0.85 4.0 4.7

38 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Fosfluconazole 0.15 1.3 0.042 2.0 2.1 Fosfomycin 0.32 2.0 1.0 2.7 1.9 Fosinoprilat 0.13 0.32 0.006 6.8 9.4 Fostriecin 0.086 1.2 NF 1.2 1.5 Frovatriptan 3.6 2.5 0.85 22 24 Furosemide 0.12 1.6 0.012 1.3 2.5 Gabapentin 0.71 1.7 0.97 7.0 5.3 Gadoversetamide 0.16 1.2 1.0 2.2 1.7 Galanthamine 2.3 5.6 0.83 6.8 5.3 Ganciclovir 1.0 4.6 0.99 3.7 3.7 Gatifloxacin 1.7 2.8 0.80 11 10 Gefitinib 23 12 0.089 32 34 Gemcitabine 1.5 32 1.0 0.81 1.0 Genaconazole 0.62 0.19 NF 54 49 Downloaded from Gentamicin 0.33 1.0 1.0 5.5 4.7 Gestodene 0.46 0.80 0.023 9.6 10 Ginkgolide A 0.62 2.3 NF 4.6 3.8

Ginkgolide B 0.91 2.3 NF 6.6 5.2 dmd.aspetjournals.org Glimepiride 0.19 0.50 0.005 6.3 10 Glipizide 0.16 0.56 0.02 4.8 3.3 Glyburide 0.080 0.82 0.021 1.6 2.2 Granisetron 3.7 9.1 0.35 6.7 5.2 Guanfacine 5.6 4.5 0.28 22 15

Haloperidol 17 7.8 0.08 36 35 at ASPET Journals on September 29, 2021 Hexobarbital 1.1 3.4 0.53 5.4 4.9 Hydralazine 1.5 85 0.12 0.29 1.0 Hydrocortisone 0.38 5.7 0.20 1.9 1.6 Hydroflumethazide 2.2 9.7 NF 3.8 5.2 Hydromorphone 4.3 28 0.86 2.6 2.3 Hydroxychloroquine 700 11 0.57 1300 850 3’-Hydroxycotinine 0.85 1.8 NF 7.9 5.9 3(S)-Hydroxydihydroquinidine 6.8 12 0.47 9.4 6.7 2-Hydroxyimipramine 6.6 9.2 0.36 12 10 7-Hydroxystaurosporine 0.15 0.0037 0.0022 680 790 Hydroxyurea 0.52 1.5 1.0 4.8 3.4 Hypericin 0.25 0.13 NF 32 42 Ibandronic Acid (Ibandronate) 0.55 1.8 0.15 5.1 14 Ibuprofen 0.15 0.82 0.006 3.0 1.6 Ibutilide 12 26 0.60 7.7 7.0 Idarubicin 38 24 0.076 26 16 Idazoxan 3.3 10 NF 5.5 4.2 Ifetroban 4.4 6.4 NF 11 22 Ifosfamide 0.62 1.1 1.0 9.4 6.6 Iloprost 0.37 16 0.40 0.39 0.57 Imatinib 3.9 3.3 0.05 20 22 Imipenem 0.24 3.0 0.86 1.3 0.95

39 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Imipramine 12 13 0.075 15 16 Imipramine N-oxide 1.9 12 NF 2.6 1.8 Indinavir 0.82 18 0.36 0.76 1.0 Indocyanine Green 0.035 6.8 0.05 0.086 0.065 Indomethacin 0.096 1.3 0.01 1.2 1.4 Indoramin 4.9 20 0.15 4.1 4.3 Intoplicine 11 18 NF 11 19 Iohexol 0.16 2.0 1.0 1.3 1.5 Iopamidol 0.28 1.9 1.0 2.5 2.1 Iopromide 0.22 1.4 0.99 2.6 2.6 Iothalamic Acid (Iothalamate) 0.17 2.4 0.98 1.2 1.3 Irbesartan 0.94 2.3 0.10 6.8 14 Irinotecan 3.5 7.0 0.51 8.3 9.0 Irofulven 3.1 140 NF 0.40 0.30 Downloaded from Isepamicin 0.32 1.3 0.95 4.1 24 Isoniazide 0.82 7.4 1.0 1.8 1.0 Isoproterenol 1.5 56 0.71 0.43 0.41

Isosorbide Dinitrate 1.9 31 0.72 1.0 1.7 dmd.aspetjournals.org Isosorbide-2-Mononitrate 0.86 6.2 NF 2.3 1.9 Isosorbide-5-Mononitrate 0.70 2.0 1.0 5.8 4.1 Isoxicam 0.19 0.07 0.035 44 33 Isradipine 1.5 26 0.04 1.0 3.3 Itraconazole 7.4 5.1 0.002 24 25

IVL745 0.25 11 NF 0.38 1.6 at ASPET Journals on September 29, 2021 Ixabepilone 11 10 NF 18 17 Kanamycin 0.26 1.4 0.99 3.1 2.1 Ketamine 2.9 19 0.47 2.5 2.8 Ketanserin 3.9 6.7 0.055 9.7 12 Ketobemidone 0.48 9.2 NF 0.87 2.3 Ketoprofen 0.13 1.6 0.008 1.4 2.1 Ketorolac 0.11 0.35 0.0068 5.2 5.1 KRN-5500 0.17 2.7 NF 1.1 1.3 KW-2170 12 18 NF 11 32 Labetalol 4.8 23 0.50 3.5 4.4 Lamifiban 0.29 1.9 0.94 2.5 2.1 Lamivudine 1.3 4.8 0.94 4.4 9.1 Lansoprazole 0.28 4.4 0.021 1.1 1.0 Letrozole 1.9 0.57 0.41 59 45 Leuprolide 0.38 2.0 0.54 3.1 2.9 Levodopa 1.7 23 0.76 1.2 1.3 Levofloxacin 1.2 1.9 0.75 11 8.8 Levomepromazine 12 9.9 NF 21 30 Levonorgestrel 1.5 1.8 0.025 14 9.4 Levosimendan 0.24 3.8 0.02 1.1 1.1 Lidocaine 1.8 16 0.33 1.9 1.6 Lincomycin 1.0 2.1 0.14- 7.5 5.6 0.72

40 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Linezolid 0.58 1.8 0.69 5.4 4.5 Lisinopril 0.89 1.2 1.0 12 42 Lithium Carbonate 0.62 1.3 1.0 7.9 7.8 Lorazepam 1.3 1.0 0.09 22 17 Lorcainide 6.6 16 0.15 6.9 6.5 Lormetazepam 1.6 4.0 0.12 6.7 4.9 Losartan 0.37 8.2 0.01 0.75 1.8 Lovastatin (acid) 0.87 7.2 0.043 1.3 1.4 Loxiglumide 0.24 1.0 NF 4.0 5.2 Maprotiline 45 14 0.11 54 51 Maxipost 10 15 0.0038 11 37 Mebendazole 1.2 15 0.086 1.3 1.1 Medroxalol 7.9 16 NF 8.2 11 Melagatran 0.23 1.9 0.93 2.0 1.6 Downloaded from Meloxicam 0.15 0.12 0.003 21 18 Melperone 14 46 NF 5.0 3.9 Melphalan 0.48 7.0 0.14 1.1 1.0

MEN-10755 2.4 2.5 NF 17 21 dmd.aspetjournals.org Meperidine 2.3 4.9 0.42 6.4 7.9 Mepivacaine 0.95 6.8 0.30 2.5 2.0 Meprobamate 0.70 0.60 1.0 19 14 Meptazinol 3.3 28 0.73 2.0 1.7 6-Mercaptopurine 1.0 15 0.85 1.1 1.0

Meropenem 0.30 3.9 0.87 1.3 1.0 at ASPET Journals on September 29, 2021 Metformin 0.64 7.4 1.0 1.4 1.7 Methadone 4.4 1.7 0.21 43 31 Methamphetamine 4.3 4.4 0.85 16 12 Methicillin 0.32 6.6 0.43 0.80 0.64 Methimazole 0.86 3.7 NF 3.8 2.4 Methohexital 1.1 12 0.27 1.5 1.6 Methotrexate 0.43 2.1 0.37 3.4 3.9 Methyldopa 0.69 3.5 0.85 3.3 5.9 Methylnaltrexone 2.6 22 NF 2.0 2.5 7α-Methyl-19-Nortestosterone 0.80 20 NF 0.67 0.65 Methylphenidate 2.2 0.55 0.85 4.5 4.8 Methylprednisolone 1.2 6.1 0.23 3.5 2.3 7-Methylthiomethylpaclitaxel 11 6.4 NF 29 32 Metoclopramide 3.2 5.7 0.60 9.4 7.2 Metocurine 0.40 1.2 0.65 5.6 5.1 Metolazone 1.6 1.4 0.05 19 20 Metoprolol 3.1 13 0.88 4.0 3.6 Metrizoate 0.17 2.7 1.0 1.0 1.3 Metronidazole 0.40 0.85 0.96 7.8 7.1 Mexiletine 5.9 8.3 0.36 12 9.9 Mezlocillin 0.09 2.1 0.70 0.71 1.2 Mibefradil 3.1 4.0 0.005 13 13

41 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Micafungin 0.21 0.17 0.0023 21 16 Midazolam 1.1 5.3 0.017 3.5 3.1 Miglitol 0.28 1.7 1.0 2.7 2.3 Milrinone 0.25 6.2 0.035 0.67 0.80 Minocycline 1.6 1.2 NF 22 17 Mirtazapine 4.2 8.0 0.15 8.8 15 Mitoxantrone 12 7.9 0.25 25 53 Mivacurium (cis/cis) 0.27 5.2 0.70 0.87 0.83 Moclobemide 1.1 10 0.77 1.8 1.5 Montelukast 0.15 0.68 0.002 3.8 5.0 Morphine 2.3 26 0.65 1.5 2.0 Morphine-6-Glucuronide 0.12 2.2 NF 0.91 1.4 Moxalactam 0.17 0.72 0.39 3.9 2.9 Moxifloxacin 1.4 2.4 0.60 9.7 8.2 Downloaded from Moxonidine 1.8 11 0.93 2.7 2.2 Nadolol 1.9 2.9 0.14 11 9.2 Nafcillin 0.22 3.3 0.13 1.1 0.70

Nalbuphine 4.6 22 0.50 3.5 3.7 dmd.aspetjournals.org Nalmefene 8.2 15 0.65 9.1 8.8 Naloxone 1.7 23 0.54 1.2 1.1 Naltrexone 7.6 57 0.79 2.2 1.9 Napsagatran 0.36 6.4 NF 0.94 1.9 Naratriptan 2.4 6.6 0.70 6.1 6.6

Nateglinide 0.15 1.8 0.029 1.4 1.5 at ASPET Journals on September 29, 2021 Nebivolol 11 14 NF 13 10 Nefazodone 0.51 7.5 0.01 1.1 1.2 Nefopam 5.6 12 NF 8.1 5.0 Nelarabine 4.9 81 0.80 1.0 0.50 Neostigmine 0.74 9.2 NF 1.3 1.3 Netilmicin 0.073 0.55 1.0 2.2 3.2 Nevirapine 1.3 0.30 0.32 81 53 Nicardipine 1.0 11 0.01 1.5 4.1 Nicotine 2.6 18 0.95 2.4 2.0 Nifedipine 0.79 7.3 0.044 1.8 1.9 Nimodipine 1.1 15 0.016 1.2 1.3 Nisoldipine 5.5 15 0.003 6.5 11 Nitrazepam 1.7 0.86 0.13 33 26 Nitrendipine 6.1 25 0.02 4.1 8.2 Nitrofurantoin 0.57 9.7 0.17 0.98 0.97 Nizatidine 1.0 9.8 0.65 1.7 1.5 NK 611 0.30 0.40 0.013 17 14 Nomifensine 6.0 22 0.40 4.5 6.5 Nortilidine 2.5 9.9 NF 4.2 4.4 Nortriptyline 22 10 0.12 37 30 Ofloxacin 1.6 2.5 0.75 11 8.9 Olcegepant 0.31 2.6 NF 1.9 2.5

42 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Olsalazine 0.070 1.2 NF 0.97 0.90 Omeprazole 0.24 8.4 0.05 0.48 0.58 Ondansetron 1.8 5.8 0.27 5.2 3.4 Oseltamivir Acid 0.37 4.8 0.97 1.3 1.8 Oxacillin 0.19 6.3 0.07 0.50 0.70 Oxazepam 0.59 1.1 0.04 8.9 6.7 Oxiracetam 0.55 1.8 NF 4.9 7.5 Oxybutynin 0.85 5.1 0.0034 2.7 7.2 Oxycodone 2.5 6.1 0.55 6.8 5.5 Oxytetracycline 1.7 2.0 0.90 12 10 Paclitaxel 3.0 6.4 0.12 7.8 11 Palonosetron 6.3 2.6 0.38 40 39 Pamidronic Acid (Pamidronate) 1.8 2.5 NF 12 32 Pancuronium 0.20 1.5 0.090 2.2 1.9 Downloaded from Panipenem 0.19 2.6 NF 1.2 0.96 Pantoprazole 0.17 2.2 0.02 1.9 1.9 Papaverine 1.0 11 0.073 1.4 1.8

Paricalcitol 0.41 0.89 0.0016 7.7 5.3 dmd.aspetjournals.org Paroxetine 18 18 0.06 17 13 Pefloxacin 1.5 2.0 0.75 13 11 Penciclovir 1.1 8.4 0.84 2.4 2.1 Penicillin G 0.24 6.9 0.40 0.58 0.70 Pentamidine 53 74 NF 12 25

Pentazocine 3.4 23 0.39 2.5 2.5 at ASPET Journals on September 29, 2021 Pentobarbital 0.91 0.47 0.39 32 22 Pentoxifylline 1.8 39 NF 0.77 1.2 Perindoprilat 0.76 1.7 0.16 7.6 29 Perphenazine 18 27 0.07 11 9.4 Phenacetin 1.4 21 0.47 1.1 0.92 Phencyclidine 6.9 5.1 0.35 23 16 Phenethicillin 0.30 4.2 0.25 1.2 0.91 Phenobarbital 0.54 0.063 0.49 140 99 Phenoxymethylpenicillin 0.41 6.8 0.45 1.0 0.84 Pimobendan 2.0 14 0.024 2.2 1.8 Pindolol 1.2 7.7 0.58 2.6 2.2 Piperacillin 0.27 4.0 0.50 1.1 0.96 Pipercuronium 0.35 3.0 0.98 1.9 1.9 Piretanide 0.17 3.1 0.058 0.91 1.3 Piritramide 4.7 7.8 0.061 10 8.0 Pirmenol 1.4 1.8 0.13 13 8.4 PNU-145156E 0.27 0.0047 NF 960 730 Pravastatin (acid) 0.46 14 0.50 0.59 0.78 Prazosin 0.73 4.7 0.060 2.6 2.0 Prednisolone 0.86 2.9 0.25 4.8 3.4 Prednisone 0.57 2.5 0.27 3.8 2.9 Prilocaine 3.7 29 0.72 2.2 1.8

43 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Primaquine 4.0 5.8 NF 11 7.1 Probenecid 0.13 0.25 0.13 8.7 5.9 Procainamide 2.2 10 0.84 3.7 3.1 Prochlorperazine 22 16 NF 23 9.0 Procyclidine 0.74 0.86 NF 14 12 Promazine 8.1 14 0.11 9.6 7.9 Promethazine 14 14 0.09 17 14 Propafenone 2.2 16 0.038 2.2 2.1 Propofol 4.7 36 0.016 2.2 3.2 Propoxyphene 12 15 0.24 13 18 Propranolol 3.1 12 0.13 4.3 3.4 Propylthiouracil 0.34 3.1 0.18 1.8 1.3 Pseudohypericin 0.56 0.62 NF 15 20 Pyridostigmine 1.1 9.6 1.0 1.8 1.5 Downloaded from Pyrimethamine 0.43 0.052 0.095 140 140 Quercetin 0.12 11 0.009 0.20 0.60 Quinacrine 45 5.1 0.13 150 120

Quinaprilat 0.13 0.93 0.32 2.3 2.3 dmd.aspetjournals.org Quinidine 2.9 4.0 0.26 12 6.6 Quinine 1.8 1.9 0.30 16 11 Quinupristin 0.67 16 0.34 0.70 0.87 Rabeprazole 0.22 4.0 0.037 0.92 1.0 Ranitidine 1.2 9.6 0.95 2.1 2.1

Reboxetine 0.65 0.82 0.019 13 10 at ASPET Journals on September 29, 2021 Recainam 1.4 4.5 0.86 5.2 5.0 Remifentanil 0.40 37 0.30 0.18 0.80 Remoxipride 0.65 1.7 0.16 6.4 5.5 Repaglinide 0.35 7.8 0.015 0.75 0.87 Repinotan 0.21 2.2 NF 1.6 1.2 Ribavirin 14 5.2 1.0 45 45 Ribostamycin 0.25 1.5 NF 2.8 2.5 Rifabutin 9.3 2.4 0.29 65 37 Rifampin 0.97 3.5 0.20 4.6 3.8 Risedronic Acid (Risedronate) 6.3 1.5 0.76 70 200 Risperidone 1.1 5.4 0.10 3.4 3.2 Ritodrine 4.4 31 0.64 2.4 2.6 Rivastigmine 1.3 12 0.60 1.9 1.4 Rizatriptan 1.9 16 0.86 2.0 2.2 Rocuronium 0.21 3.7 0.54 0.95 1.6 Rolitetracycline 0.54 0.97 0.50 9.3 8.8 Romidepsin 1.2 7.4 NF 2.6 11 Ropivacaine 0.75 5.5 0.06 2.3 2.2 Roquinimex 0.21 0.082 NF 43 31 Rosiglitazone 0.20 0.65 0.002 5.1 3.9 Rosuvastatin 1.7 11 0.12 2.6 2.0 RPR 109881A 12 14 NF 14 20

44 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Saquinavir 3.6 13 0.028 4.6 13 SarCNU 0.76 9.0 NF 1.4 1.0 Sch34343 0.30 7.5 0.35 0.67 0.80 Scopolamine 3.1 16 NF 3.2 4.5 Selegiline 1.9 20 0.13 1.6 1.3 Sematilide 0.82 3.7 0.96 3.7 3.8 Sildenafil 1.4 9.1 0.04 2.6 3.9 Sisomicin 0.19 1.0 0.15 3.2 2.4 Sitafloxacin 1.5 3.7 0.51 6.7 6.6 Sitagliptin 2.8 6.0 0.62 7.8 12 Solifenacin 8.2 2.1 0.02 65 52 Sotalol 1.3 2.0 0.62 11 6.3 Sparfloxacin 3.9 2.7 0.55 24 20 Spectinomycin 0.13 0.99 NF 2.2 1.8 Downloaded from Squalamine 0.23 1.2 NF 3.2 9.5 Stavudine 0.67 8.2 1.0 1.4 1.4 Streptomycin 0.34 0.78 0.65 7.3 4.3

Sufentanil 9.4 15 0.075 10 14 dmd.aspetjournals.org Sulbactam 0.32 5.1 0.62 1.0 1.1 Sulbenicillin 0.15 1.6 0.50 1.6 1.2 Sulfadiazine 0.29 0.55 0.44 8.8 7.0 Sulfamethoxazole 0.30 0.36 0.23 14 9.8 Sulfinpyrazone 0.12 0.34 0.017 5.9 6.2

Sulfisoxazole 0.17 0.30 0.079 9.4 7.4 at ASPET Journals on September 29, 2021 Sumatriptan 1.7 19 0.83 1.5 1.7 Suprofen 0.040 0.76 0.006 0.88 2.1 Suramin 0.54 0.0057 0.003 1400 1200 Tacrine 11 56 0.25 3.3 3.3 Tacrolimus 1.2 0.70 0.01 27 26 Talinolol 3.3 4.9 0.39 11 11 Tamsulosin 0.21 0.62 0.01 5.6 6.8 Tegaserod 5.3 18 0.02 4.9 11 Teicoplanin A2-1 1.5 0.20 0.11 140 150 Telavancin 0.11 0.20 0.07 9.2 6.7 Telithromycin 3.0 14 0.41 3.6 12 Telmisartan 5.3 8.4 0.004 11 20 Temozolomide 0.50 3.6 NF 2.3 1.5 Temsirolimus 3.0 7.7 NF 10 18 Teniposide 0.41 0.19 0.0044 37 16 Tenofovir 0.83 3.1 0.93 3.8 5.8 Tenoxicam 0.19 0.03 0.0085 92 67 Terazosin 0.98 1.1 0.08 15 9.0 Terbutaline 1.5 3.4 0.75 7.4 15 Terodiline 5.1 1.1 0.08 81 56 Tesaglitazar 0.13 0.039 0.0011 56 45 Tetracycline 1.2 1.5 0.78 12 9.4

45 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 ∆9-Tetrahydrocannabinol 8.9 3.2 0.05 46 33 Tezosentan 0.28 8.1 0.0085 0.58 3.8 Thalidomide 0.95 3.4 0.40 4.3 4.7 Theophylline 0.51 0.86 0.61 9.9 7.2 Thionylan (Methapyrilene) 3.3 28 NF 2.0 1.6 Thiopental 1.2 8.2 0.14 2.4 2.0 Thiotepa 1.6 6.7 0.90 4.0 2.7 Tiagabine 1.1 1.6 0.04 12 10 Tiazofurin 1.1 2.4 NF 7.6 5.9 Ticarcillin 0.16 1.8 0.55 1.5 1.0 Tigecycline 12 3.8 0.20 53 48 Tilidine 4.0 16 0.21 4.2 5.0 Timolol 1.5 8.5 0.90 3.0 2.2 Tinidazole 0.59 0.60 0.80 16 13 Downloaded from Tirapazamine 0.56 8.9 0.81 1.0 0.78 Tirofiban 0.99 4.3 0.36 1.8 1.6 Tizanidine 2.4 11 0.70 3.6 2.4

Tobramycin 0.23 1.6 1.0 2.4 2.0 dmd.aspetjournals.org Tocainide 1.8 2.2 0.87 14 12 Tolamolol 4.0 14 0.089 4.8 2.5 Tolbutamide 0.12 0.21 0.05 9.5 7.0 Tolcapone 0.12 1.9 0.0012 1.1 1.1 Toloxatone 1.5 11 0.49 2.3 1.6

Tolterodine 1.5 8.4 0.037 3.0 2.4 at ASPET Journals on September 29, 2021 Tomopenem 0.23 1.9 0.91 2.0 1.7 Topixantrone 57 27 NF 39 45 Topotecan 1.8 13 0.65 2.3 2.3 Torsemide 0.21 0.53 0.01 6.6 5.1 Trabectedin 25 12 NF 35 44 Tramadol 2.8 6.5 0.80 7.2 5.8 Tranexamic Acid 0.38 2.4 NF 2.6 2.3 Traxoprodil 4.4 27 0.63 2.6 3.7 Trazodone 0.52 1.4 NF 6.2 7.3 Triamcinolone Acetonide 1.4 9.4 0.20 2.5 2.4 Triamterene 13 63 0.42 3.4 4.3 Triazolam 0.58 3.0 0.10 3.2 2.7 Trimazosin 0.18 0.96 NF 3.1 3.1 Trimethoprim 1.5 2.1 0.50 12 9.6 Trimetrexate 0.77 0.77 0.05 17 17 Trimipramine 16 16 0.051 17 23 Tropisetron 9.7 26 NF 6.3 5.6 Trospectomycin 0.70 1.7 NF 6.9 11 Trovafloxacin 1.3 1.4 0.24 15 11 Troxacitabine 1.0 2.4 NF 6.9 82 Tubocurarine 0.45 3.4 0.58 2.2 2.0 UK-240,455 0.80 6.0 0.13 2.2 1.4

46 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 Urapidil 0.75 3.1 NF 4.0 3.5 Valproic Acd 0.14 0.16 0.08 15 12 Valsartan 0.22 0.49 0.04 7.5 9.5 Valspodar 1.8 2.6 0.022 12 10 Vancomycin 0.54 1.3 0.70 6.9 6.5 Vardenafil 3.0 13 0.05 3.8 4.5 Vecuronium Bromide 0.30 4.5 0.25 1.1 1.4 Venlafaxine 4.4 14 0.73 5.2 5.0 Verapamil 3.7 18 0.093 3.4 2.8 Verlukast 0.11 0.68 0.0004 2.7 2.3 Viloxazine 0.73 2.1 NF 5.8 4.1 Vinblastine 28 3.1 0.14 150 67 Vincristine 2.4 2.0 0.40 20 23 Vindesine 5.0 2.2 NF 38 35 Downloaded from Vinorelbine 23 20 0.87 18 26 Voriconazole 2.2 8.3 0.42 4.4 5.6 Vorinostat 0.50 28 0.29 0.30 0.76

Warfarin 0.13 0.055 0.015 39 29 dmd.aspetjournals.org Xamoterol 0.64 3.0 0.97 3.6 7.7 Zalcitabine 0.54 5.6 NF 1.6 1.2 Zaleplon 1.3 16 0.40 1.4 1.1 Zanamivir 0.23 1.6 0.86 2.4 1.7 Zidovudine 1.8 25 0.80 1.2 1.3

Ziprasidone 1.0 5.1 0.0012 3.3 3.1 at ASPET Journals on September 29, 2021 Zoledronic Acid 0.60 2.2 0.78 4.5 11 Zolmitriptan 1.8 6.7 0.75 4.6 3.6 Zolpidem 0.54 4.3 0.08 2.1 1.7 Zonampanel 0.19 5.3 NF 0.60 0.78 Zopiclone 1.3 3.3 0.20 6.6 5.2 Zotarolimus 1.3 0.67 NF 33 35 NF, data not found

47 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479 TABLE 2. Characteristics of the human intravenous pharmacokinetic parameters and plasma protein binding for 670 compounds.

Parameter N %

Steady-State Volume of Distribution (VDss) compounds less than 0.1 L/kg 17 2.5 compounds between 0.1 and 0.7 L/kg 260 39 compounds between 0.7 and 2 L/kg 197 29 compounds between 2 and 10 L/kg 143 21 compounds 10 L/kg or greater 53 7.9

Downloaded from Clearance (CL) compounds less than 1 mL/min/kg 104 16 compounds between 1 and 5 mL/min/kg 262 39 compounds between 5 and 15 mL/min/kg 200 30

compounds between 15 and 21 mL/min/kg 48 7.2 dmd.aspetjournals.org compounds greater than 21 mL/min/kg 56 8.4

Half-Life (t½) compounds less than 1 hr 64 10 compounds between 1 and 4 hr 266 40 compounds between 4 and 12 hr 185 28 at ASPET Journals on September 29, 2021 compounds between 12 and 24 hr 73 11 compounds greater than 24 hr 82 12

Plasma Protein Binding compounds greater than 99.9% bound 41 7.4 compounds between 99 and 99.9% bound 28 5.1 compounds between 90 and 99% bound 116 21 compounds between 50 and 90% bound 167 30 compounds less than 50% bound 202 36

48 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version.

DMD #20479

TABLE 3. Comparison of median values for total and free VDss and CL values relative to charge type.

median median median median Charge Type (N) VDss free VDss CL free CL

Acids (159) 0.22 2.2 2.1 17 Bases(267) 2.3 9.2 7.6 23 Neutrals (173) 1.1 5.5 4.0 21 Zwitterions (68) 0.83 2.0 2.4 3.8

Gatoversetamide, gentamycin and lithium carbonate are not included in the charge classes. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021

49 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021 DMD Fast Forward. Published on April 21, 2008 as DOI: 10.1124/dmd.108.020479 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from dmd.aspetjournals.org at ASPET Journals on September 29, 2021